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How to use safe_getattr method in Pytest

Best Python code snippet using pytest

transformer_lm.py

Source:transformer_lm.py

...228    def build_model(cls, args, task):229        """Build a new model instance."""230        if args.decoder_layers_to_keep:231            args.decoder_layers = len(args.decoder_layers_to_keep.split(","))232        if safe_getattr(args, "max_target_positions", None) is None:233            args.max_target_positions = safe_getattr(234                args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS235            )236        if args.character_embeddings:237            embed_tokens = CharacterTokenEmbedder(238                task.source_dictionary,239                eval(args.character_filters),240                args.character_embedding_dim,241                args.decoder_embed_dim,242                args.char_embedder_highway_layers,243            )244        elif args.adaptive_input:245            embed_tokens = AdaptiveInput(246                len(task.source_dictionary),247                task.source_dictionary.pad(),248                args.decoder_input_dim,249                args.adaptive_input_factor,250                args.decoder_embed_dim,251                options.eval_str_list(args.adaptive_input_cutoff, type=int),252                args.quant_noise_pq,253                args.quant_noise_pq_block_size,254            )255        else:256            embed_tokens = cls.build_embedding(257                args, task.source_dictionary, args.decoder_input_dim258            )259        if args.tie_adaptive_weights:260            assert args.adaptive_input261            assert args.adaptive_input_factor == args.adaptive_softmax_factor262            assert (263                args.adaptive_softmax_cutoff == args.adaptive_input_cutoff264            ), "{} != {}".format(265                args.adaptive_softmax_cutoff, args.adaptive_input_cutoff266            )267            assert args.decoder_input_dim == args.decoder_output_dim268        decoder = TransformerDecoder(269            args, task.target_dictionary, embed_tokens, no_encoder_attn=True270        )271        return cls(decoder)272    @classmethod273    def build_embedding(cls, args, dictionary, embed_dim, path=None):274        embed_tokens = Embedding(len(dictionary), embed_dim, dictionary.pad())275        return embed_tokens276def base_lm_architecture(args):277    # backward compatibility for older model checkpoints278    if safe_hasattr(args, "no_tie_adaptive_proj"):279        # previous models defined --no-tie-adaptive-proj, so use the existence of280        # that option to determine if this is an "old" model checkpoint281        args.no_decoder_final_norm = True  # old models always set this to True282        if args.no_tie_adaptive_proj is False:283            args.tie_adaptive_proj = True284    if safe_hasattr(args, "decoder_final_norm"):285        args.no_decoder_final_norm = not args.decoder_final_norm286    args.dropout = safe_getattr(args, "dropout", 0.1)287    args.attention_dropout = safe_getattr(args, "attention_dropout", 0.0)288    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 512)289    args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 2048)290    args.decoder_layers = safe_getattr(args, "decoder_layers", 6)291    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 8)292    args.adaptive_softmax_cutoff = safe_getattr(args, "adaptive_softmax_cutoff", None)293    args.adaptive_softmax_dropout = safe_getattr(args, "adaptive_softmax_dropout", 0)294    args.adaptive_softmax_factor = safe_getattr(args, "adaptive_softmax_factor", 4)295    args.decoder_learned_pos = safe_getattr(args, "decoder_learned_pos", False)296    args.activation_fn = safe_getattr(args, "activation_fn", "relu")297    args.decoder_layerdrop = safe_getattr(args, "decoder_layerdrop", 0)298    args.decoder_layers_to_keep = safe_getattr(args, "decoder_layers_to_keep", None)299    args.quant_noise_pq = safe_getattr(args, "quant_noise_pq", 0)300    args.quant_noise_pq_block_size = safe_getattr(args, "quant_noise_pq_block_size", 8)301    args.quant_noise_scalar = safe_getattr(args, "quant_noise_scalar", 0)302    args.base_layers = safe_getattr(args, "base_layers", 0)303    args.base_sublayers = safe_getattr(args, "base_sublayers", 1)304    args.base_shuffle = safe_getattr(args, "base_shuffle", False)305    args.add_bos_token = safe_getattr(args, "add_bos_token", False)306    args.no_token_positional_embeddings = safe_getattr(307        args, "no_token_positional_embeddings", False308    )309    args.share_decoder_input_output_embed = safe_getattr(310        args, "share_decoder_input_output_embed", False311    )312    args.character_embeddings = safe_getattr(args, "character_embeddings", False)313    args.decoder_output_dim = safe_getattr(314        args, "decoder_output_dim", args.decoder_embed_dim315    )316    args.decoder_input_dim = safe_getattr(args, "decoder_input_dim", args.decoder_embed_dim)317    # Model training is not stable without this318    args.decoder_normalize_before = True319    args.no_decoder_final_norm = safe_getattr(args, "no_decoder_final_norm", False)320    args.adaptive_input = safe_getattr(args, "adaptive_input", False)321    args.adaptive_input_factor = safe_getattr(args, "adaptive_input_factor", 4)322    args.adaptive_input_cutoff = safe_getattr(args, "adaptive_input_cutoff", None)323    args.tie_adaptive_weights = safe_getattr(args, "tie_adaptive_weights", False)324    args.tie_adaptive_proj = safe_getattr(args, "tie_adaptive_proj", False)325    args.no_scale_embedding = safe_getattr(args, "no_scale_embedding", False)326    args.layernorm_embedding = safe_getattr(args, "layernorm_embedding", False)327    args.checkpoint_activations = safe_getattr(args, "checkpoint_activations", False)328    args.offload_activations = safe_getattr(args, "offload_activations", False)329    if args.offload_activations:330        args.checkpoint_activations = True331@register_model_architecture("transformer_lm", "transformer_lm_big")332def transformer_lm_big(args):333    args.decoder_layers = safe_getattr(args, "decoder_layers", 12)334    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1024)335    args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 4096)336    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 16)337    base_lm_architecture(args)338@register_model_architecture("transformer_lm", "transformer_lm_wiki103")339@register_model_architecture("transformer_lm", "transformer_lm_baevski_wiki103")340def transformer_lm_baevski_wiki103(args):341    args.decoder_layers = safe_getattr(args, "decoder_layers", 16)342    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 8)343    args.dropout = safe_getattr(args, "dropout", 0.3)344    args.adaptive_input = safe_getattr(args, "adaptive_input", True)345    args.tie_adaptive_weights = safe_getattr(args, "tie_adaptive_weights", True)346    args.adaptive_input_cutoff = safe_getattr(args, "adaptive_input_cutoff", "20000,60000")347    args.adaptive_softmax_cutoff = safe_getattr(348        args, "adaptive_softmax_cutoff", "20000,60000"349    )350    args.adaptive_softmax_dropout = safe_getattr(args, "adaptive_softmax_dropout", 0.2)351    args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)352    args.activation_dropout = safe_getattr(args, "activation_dropout", 0.1)353    args.no_decoder_final_norm = safe_getattr(args, "no_decoder_final_norm", True)354    args.tie_adaptive_proj = safe_getattr(args, "tie_adaptive_proj", True)355    transformer_lm_big(args)356@register_model_architecture("transformer_lm", "transformer_lm_gbw")357@register_model_architecture("transformer_lm", "transformer_lm_baevski_gbw")358def transformer_lm_baevski_gbw(args):359    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 512)360    args.dropout = safe_getattr(args, "dropout", 0.1)361    args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)362    args.no_decoder_final_norm = safe_getattr(args, "no_decoder_final_norm", True)363    transformer_lm_big(args)364@register_model_architecture("transformer_lm", "transformer_lm_gpt")365def transformer_lm_gpt(args):366    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 768)367    args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 3072)368    args.decoder_layers = safe_getattr(args, "decoder_layers", 12)369    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 12)370    args.dropout = safe_getattr(args, "dropout", 0.1)371    args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)372    args.activation_fn = safe_getattr(args, "activation_fn", "gelu")373    base_lm_architecture(args)374@register_model_architecture("transformer_lm", "transformer_lm_gpt2_small")375def transformer_lm_gpt2_small(args):376    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1024)377    args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 4096)378    args.decoder_layers = safe_getattr(args, "decoder_layers", 24)379    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 16)380    args.dropout = safe_getattr(args, "dropout", 0.1)381    args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)382    args.activation_fn = safe_getattr(args, "activation_fn", "gelu")383    base_lm_architecture(args)384@register_model_architecture("transformer_lm", "transformer_lm_gpt2_tiny")385def transformer_lm_gpt2_tiny(args):386    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 64)387    args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 64)388    args.decoder_layers = safe_getattr(args, "decoder_layers", 2)389    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 1)390    args.dropout = safe_getattr(args, "dropout", 0.1)391    args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)392    args.activation_fn = safe_getattr(args, "activation_fn", "gelu")393    base_lm_architecture(args)394@register_model_architecture("transformer_lm", "transformer_lm_gpt2_medium")395def transformer_lm_gpt2_medium(args):396    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1280)397    args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 5120)398    args.decoder_layers = safe_getattr(args, "decoder_layers", 36)399    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 20)400    args.dropout = safe_getattr(args, "dropout", 0.1)401    args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)402    args.activation_fn = safe_getattr(args, "activation_fn", "gelu")403    base_lm_architecture(args)404@register_model_architecture("transformer_lm", "transformer_lm_gpt2_big")405def transformer_lm_gpt2_big(args):406    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1600)407    args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 6400)408    args.decoder_layers = safe_getattr(args, "decoder_layers", 48)409    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 25)410    args.dropout = safe_getattr(args, "dropout", 0.1)411    args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)412    args.activation_fn = safe_getattr(args, "activation_fn", "gelu")413    base_lm_architecture(args)414def base_gpt3_architecture(args):415    args.decoder_input_dim = args.decoder_embed_dim416    args.decoder_output_dim = args.decoder_embed_dim417    args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", args.decoder_embed_dim * 4)418    # GPT-3 used learned positional embeddings, rather than sinusoidal419    args.decoder_learned_pos = safe_getattr(args, "decoder_learned_pos", True)420    args.dropout = safe_getattr(args, "dropout", 0.0)421    args.attention_dropout = safe_getattr(args, "attention_dropout", 0.0)422    args.activation_fn = safe_getattr(args, "activation_fn", "gelu")423    args.share_decoder_input_output_embed = True424    base_lm_architecture(args)425@register_model_architecture("transformer_lm", "transformer_lm_gpt3_small")426def transformer_lm_gpt3_small(args):427    # 125M params428    args.decoder_layers = safe_getattr(args, "decoder_layers", 12)429    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 768)430    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 12)431    base_gpt3_architecture(args)432@register_model_architecture("transformer_lm", "transformer_lm_gpt3_medium")433def transformer_lm_gpt3_medium(args):434    # 350M params435    args.decoder_layers = safe_getattr(args, "decoder_layers", 24)436    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1024)437    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 16)438    base_gpt3_architecture(args)439@register_model_architecture("transformer_lm", "transformer_lm_gpt3_large")440def transformer_lm_gpt3_large(args):441    # 760M params442    args.decoder_layers = safe_getattr(args, "decoder_layers", 24)443    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1536)444    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 16)445    base_gpt3_architecture(args)446@register_model_architecture("transformer_lm", "transformer_lm_gpt3_xl")447def transformer_lm_gpt3_xl(args):448    # 1.3B params449    args.decoder_layers = safe_getattr(args, "decoder_layers", 24)450    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 2048)451    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 32)452    base_gpt3_architecture(args)453@register_model_architecture("transformer_lm", "transformer_lm_gpt3_2_7")454def transformer_lm_gpt3_2_7(args):455    # 2.7B params456    args.decoder_layers = safe_getattr(args, "decoder_layers", 32)457    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 2560)458    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 32)459    base_gpt3_architecture(args)460@register_model_architecture("transformer_lm", "transformer_lm_gpt3_6_7")461def transformer_lm_gpt3_6_7(args):462    # 6.7B params463    args.decoder_layers = safe_getattr(args, "decoder_layers", 32)464    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 4096)465    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 32)466    base_gpt3_architecture(args)467@register_model_architecture("transformer_lm", "transformer_lm_gpt3_13")468def transformer_lm_gpt3_13(args):469    # 13B params470    args.decoder_layers = safe_getattr(args, "decoder_layers", 40)471    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 5120)472    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 40)473    base_gpt3_architecture(args)474@register_model_architecture("transformer_lm", "transformer_lm_gpt3_175")475def transformer_lm_gpt3_175(args):476    # 175B params477    args.decoder_layers = safe_getattr(args, "decoder_layers", 96)478    args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 12288)479    args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 96)...

model.py

Source:model.py

...454        return self.lm_head(features, masked_tokens)455    def max_positions(self):456        """Maximum output length supported by the encoder."""457        return self.args.max_positions458def safe_getattr(obj, k, default=None):459    from omegaconf import OmegaConf460    if OmegaConf.is_config(obj):461        return obj[k] if k in obj and obj[k] is not None else default462    return getattr(obj, k, default)463@register_model_architecture("roberta", "roberta")464def base_architecture(args):465    args.encoder_layers = safe_getattr(args, "encoder_layers", 12)466    args.encoder_embed_dim = safe_getattr(args, "encoder_embed_dim", 768)467    args.encoder_ffn_embed_dim = safe_getattr(args, "encoder_ffn_embed_dim", 3072)468    args.encoder_attention_heads = safe_getattr(args, "encoder_attention_heads", 12)469    args.dropout = safe_getattr(args, "dropout", 0.1)470    args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1)471    args.activation_dropout = safe_getattr(args, "activation_dropout", 0.0)472    args.pooler_dropout = safe_getattr(args, "pooler_dropout", 0.0)473    args.max_source_positions = safe_getattr(args, "max_positions", 512)474    args.no_token_positional_embeddings = safe_getattr(475        args, "no_token_positional_embeddings", False476    )477    # BERT has a few structural differences compared to the original Transformer478    args.encoder_learned_pos = safe_getattr(args, "encoder_learned_pos", True)479    args.layernorm_embedding = safe_getattr(args, "layernorm_embedding", True)480    args.no_scale_embedding = safe_getattr(args, "no_scale_embedding", True)481    args.activation_fn = safe_getattr(args, "activation_fn", "gelu")482    args.encoder_normalize_before = safe_getattr(args, "encoder_normalize_before", False)483    args.pooler_activation_fn = safe_getattr(args, "pooler_activation_fn", "tanh")484    args.untie_weights_roberta = safe_getattr(args, "untie_weights_roberta", False)485    # Adaptive input config486    args.adaptive_input = safe_getattr(args, "adaptive_input", False)487    # LayerDrop config488    args.encoder_layerdrop = safe_getattr(args, "encoder_layerdrop", 0.0)489    args.encoder_layers_to_keep = safe_getattr(args, "encoder_layers_to_keep", None)490    # Quantization noise config491    args.quant_noise_pq = safe_getattr(args, "quant_noise_pq", 0)492    args.quant_noise_pq_block_size = safe_getattr(args, "quant_noise_pq_block_size", 8)493    args.quant_noise_scalar = safe_getattr(args, "quant_noise_scalar", 0)494    # R4F config495    args.spectral_norm_classification_head = safe_getattr(496        args, "spectral_norm_classification_head", False497    )498@register_model_architecture("roberta", "roberta_prenorm")499def roberta_prenorm_architecture(args):500    args.layernorm_embedding = safe_getattr(args, "layernorm_embedding", False)501    args.encoder_normalize_before = safe_getattr(args, "encoder_normalize_before", True)502    base_architecture(args)503@register_model_architecture("roberta", "roberta_base")504def roberta_base_architecture(args):505    base_architecture(args)506@register_model_architecture("roberta", "roberta_large")507def roberta_large_architecture(args):508    args.encoder_layers = safe_getattr(args, "encoder_layers", 24)509    args.encoder_embed_dim = safe_getattr(args, "encoder_embed_dim", 1024)510    args.encoder_ffn_embed_dim = safe_getattr(args, "encoder_ffn_embed_dim", 4096)511    args.encoder_attention_heads = safe_getattr(args, "encoder_attention_heads", 16)512    base_architecture(args)513@register_model_architecture("roberta", "xlm")514def xlm_architecture(args):515    args.encoder_layers = safe_getattr(args, "encoder_layers", 16)516    args.encoder_embed_dim = safe_getattr(args, "encoder_embed_dim", 1280)517    args.encoder_ffn_embed_dim = safe_getattr(args, "encoder_ffn_embed_dim", 1280 * 4)518    args.encoder_attention_heads = safe_getattr(args, "encoder_attention_heads", 16)...

__init__.py

Source:__init__.py

...3import raven4from raven_aiohttp import AioHttpTransport5from sanic.handlers import ErrorHandler6from sanic import exceptions as sanic_exceptions7def safe_getattr(request, attr_name, default=None):8    # pylint:disable=bare-except9    try:10        return getattr(request, attr_name, default)11    except:12        return default13class SanicSentryErrorHandler(ErrorHandler):14    DEFAULT_EXCEPTIONS_TO_IGNORE = (sanic_exceptions.NotFound,)15    def __init__(self, dsn, exceptions_to_ignore=None, **sentry_kwargs):16        super(SanicSentryErrorHandler, self).__init__()17        self.exceptions_to_ignore = tuple(exceptions_to_ignore) if exceptions_to_ignore is not None else self.DEFAULT_EXCEPTIONS_TO_IGNORE18        # For sentry_kwargs see19        # https://docs.sentry.io/clients/python/advanced/#client-arguments20        self.sentry_client = raven.Client(dsn, transport=AioHttpTransport, **sentry_kwargs)21    def default(self, request, exception):22        if not isinstance(exception, self.exceptions_to_ignore):23            exc_info = (type(exception), exception, exception.__traceback__)24            extra = self._request_debug_info(request) if request else dict()25            self.sentry_client.captureException(exc_info, extra=extra)26        return super(SanicSentryErrorHandler, self).default(request, exception)27    def _request_debug_info(self, request):28        # pylint:disable=no-self-use29        return dict(30            url=safe_getattr(request, "url"),31            method=safe_getattr(request, "method"),32            headers=safe_getattr(request, "headers"),33            body=safe_getattr(request, "body"),34            query_string=safe_getattr(request, "query_string"),35        )36    def intercept_exception(self, function):37        """38        Decorator for Sanic exception views.39        You should use this decorator only if your exception handler returns its own response.40        If you're handler returns `None` the default exception handler will be called which41        means Sentry will be called twice.42        Example:43            >> @app.exception([Exception, ])44            >> @sentry_client.intercept_exception45            >> def handle_exception(request, exception):46            >>     pass47        """48        @wraps(function)...

inspect.py

Source:inspect.py

...8"""9def isdescriptor(x):10    """Check if the object is some kind of descriptor."""11    for item in '__get__', '__set__', '__delete__':12        if hasattr(safe_getattr(x, item, None), '__call__'):13            return True14    return False15def safe_getattr(obj, name, *defargs):16    """A getattr() that turns all exceptions into AttributeErrors."""17    try:18        return getattr(obj, name, *defargs)19    except Exception:20        # this is a catch-all for all the weird things that some modules do21        # with attribute access22        if defargs:23            return defargs[0]24        raise AttributeError(name)25def safe_getmembers(object, predicate=None):26    """A version of inspect.getmembers() that uses safe_getattr()."""27    results = []28    for key in dir(object):29        try:30            value = safe_getattr(object, key, None)31        except AttributeError:32            continue33        if not predicate or predicate(value):34            results.append((key, value))35    results.sort()...

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