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How to use to_impl method in Playwright Python

Best Python code snippet using playwright-python

_browser_context.py

Source:_browser_context.py

...355    @property356    def service_workers(self) -> List[Worker]:357        return list(self._service_workers)358    async def new_cdp_session(self, page: Union[Page, Frame]) -> CDPSession:359        page = to_impl(page)360        params = {}361        if isinstance(page, Page):362            params["page"] = page._channel363        elif isinstance(page, Frame):364            params["frame"] = page._channel365        else:366            raise Error("page: expected Page or Frame")367        return from_channel(await self._channel.send("newCDPSession", params))368    @property369    def tracing(self) -> Tracing:370        return self._tracing371    @property372    def request(self) -> "APIRequestContext":373        return self._request

generate_api.py

Source:generate_api.py

...119            "typing.Any" in value_str120            or "typing.Dict" in value_str121            or "Handle" in value_str122        ):123            tokens.append(f"{name}=mapping.to_impl({to_snake_case(name)})")124        elif (125            re.match(r"<class 'playwright\._impl\.[\w]+\.[\w]+", value_str)126            and "_api_structures" not in value_str127        ):128            tokens.append(f"{name}={to_snake_case(name)}._impl_obj")129        else:130            tokens.append(f"{name}={to_snake_case(name)}")131    return split.join(tokens)132def return_type(func: FunctionType) -> str:133    value = get_type_hints(func, globals())["return"]134    return process_type(value)135def short_name(t: Any) -> str:136    match = cast(137        Match[str], re.compile(r"playwright\._impl\.[^.]+\.([^']+)").search(str(t))...

op_lib.py

Source:op_lib.py

1# ------------------------------------------------------------2# Copyright (c) 2017-present, SeetaTech, Co.,Ltd.3#4# Licensed under the BSD 2-Clause License.5# You should have received a copy of the BSD 2-Clause License6# along with the software. If not, See,7#8#     <https://opensource.org/licenses/BSD-2-Clause>9#10# ------------------------------------------------------------11"""Operator library."""12from __future__ import absolute_import13from __future__ import division14from __future__ import print_function15import numpy16from dragon.core.autograph.op_schema import OpSchema17from dragon.core.framework import context18from dragon.core.framework import proto_util19from dragon.core.framework import tapes20from dragon.core.framework import workspace21from dragon.core.framework.tensor import Tensor22from dragon.core.util import nest23class OpExec(object):24    """The executable operator."""25    _created_instances = {}26    def __init__(self, op_type):27        self._op_type = op_type28        self._ignore_keys = {'outputs'}29        def_args = {}30        def_args_getter = OpSchema.get_args(op_type)31        if def_args_getter is not None:32            def_args = def_args_getter()33        for k, v in def_args.items():34            if k.endswith('_desc'):35                self._ignore_keys.add(k.split('_desc')[0])36        self._config_cache = {}37    @classmethod38    def get_instance(cls, op_type):39        """Return the executable operator."""40        try:41            instance = cls._created_instances[op_type]42        except KeyError:43            instance = OpExec(op_type)44            cls._created_instances[op_type] = instance45        return instance46    def get_config(self, **kwargs):47        """Return the execution config."""48        device = context.get_device()49        cache_key = self._op_type + '/' + str(device)50        for k, v in kwargs.items():51            if k not in self._ignore_keys:52                cache_key += '/' + str(v)53        try:54            return self._config_cache[cache_key]55        except KeyError:56            def_args, feed_dict = {}, {}57            def_args_getter = OpSchema.get_args(self._op_type)58            if def_args_getter is not None:59                def_args = def_args_getter(**kwargs)60            device = def_args.pop('device', device)61            no_grad = def_args.pop('no_grad', False)62            for k, v in def_args.items():63                if k.endswith('_desc') and v:64                    name = k.split('_desc')[0]65                    feed_dict[name] = v66                    def_args[k] = '$NAME/' + name67            op_def = proto_util.make_operator_def(68                op_type=self._op_type,69                name=kwargs.get('name', ''),70                device_option=device.to_proto(False),71                cache_key=cache_key,72                to_impl=True, **def_args)73            config = {'def': op_def,74                      'device': device,75                      'no_grad': no_grad,76                      'feed_dict': feed_dict}77            self._config_cache[cache_key] = config78            return config79class OpLib(object):80    """Library to apply the registered operators."""81    @staticmethod82    def add(op_type, inputs, **kwargs):83        """Add operator to output symbols."""84        op_tape = tapes.OrderedTape()85        graph_tape = tapes.get_tape()86        execute_ws = workspace.get_workspace()87        # Add inputs.88        enable_grad = False89        inputs = nest.flatten(inputs)90        for input in inputs:91            op_tape.add_source(input)92            if graph_tape and (input.requires_grad or93                               graph_tape.is_target(id(input))):94                enable_grad = True95        # Add extra inputs.96        for input in nest.flatten(kwargs.pop('extra_inputs', [])):97            op_tape.add_source(input)98            op_tape.add_target(input.id)99        # Add outputs.100        name = kwargs.pop('name', None)101        num_outputs = kwargs.pop('num_outputs', 1)102        outputs = []103        for i in range(num_outputs):104            outputs.append(Tensor(105                impl=execute_ws.create_tensor(scope='Tensor'),106                name=name if name else op_type + ':%d' % i,107                symbolic=True))108        # Create def.109        op_def = proto_util.make_operator_def(110            op_type=op_type,111            inputs=[input.id for input in inputs],112            outputs=[output.id for output in outputs],113            device_option=proto_util.get_default_device_option(),114            name=execute_ws.create_handle('Op'), **kwargs)115        # Record def.116        op_tape.add_element(op_def)117        graph_tape.add_element(op_def) if enable_grad else None118        # Set tape for outputs.119        for output in outputs:120            output._tape = op_tape121            output._requires_grad = enable_grad122        # Add spec to outputs.123        add_output_spec = OpSchema.get_spec(op_type)124        if add_output_spec is None:125            add_output_spec = OpSchema.get_spec('Unchanged')126        outputs = add_output_spec(kwargs, inputs, outputs)127        # Return single or repeated outputs.128        return outputs[0] if num_outputs == 1 else outputs129    @staticmethod130    def execute(op_type, inputs, **kwargs):131        """Execute an operator."""132        op_exec = OpExec.get_instance(op_type)133        run_config = op_exec.get_config(**kwargs)134        return OpLib.run(inputs, run_config, **kwargs)135    @staticmethod136    def run(inputs, run_config, **kwargs):137        """Run operator once."""138        graph_tape = tapes.get_tape()139        execute_ws = workspace.get_workspace()140        # Add inputs.141        input_names = []142        enable_grad = False143        for input in inputs:144            input_names.append(input.id)145            if graph_tape and (input.requires_grad or146                               graph_tape.is_target(id(input))):147                enable_grad = True148        # Unify grad modes.149        no_grad = run_config['no_grad']150        enable_grad = enable_grad and not no_grad151        if hasattr(graph_tape, '_exporting'):152            # Ensure the intermediates saved for the exporting graph.153            no_grad, enable_grad = False, True154        # Add outputs.155        outputs, output_names = [], []156        output_specs = list(kwargs.get('outputs', [None]))157        for i, spec in enumerate(output_specs):158            if spec is None:159                outputs.append(Tensor(160                    device=run_config['device'].copy(),161                    impl=execute_ws.create_tensor(162                        scope=context.get_variable_scope(enable_grad)),163                    deleter=execute_ws._handle_pool))164                output_names.append(outputs[i].id)165            else:166                assert isinstance(spec, Tensor)167                outputs.append(spec)168                output_names.append(spec.id)169                if enable_grad and output_names[-1] not in input_names:170                    raise RuntimeError('Output that requires gradient is not in inputs.')171        # Specialize def for given inputs and outputs.172        op_name = ''  # Optional operator name.173        op_def = run_config['def'].DeriveTo(input_names, output_names)174        # Record def if grad is enabled.175        if len(inputs) > 0 and not no_grad:176            if enable_grad:177                op_name = execute_ws.create_handle(op_def.type)178                op_def.name = op_name179                graph_tape.add_element(op_def)180                graph_tape.add_handle(op_name)181                for input in inputs:182                    graph_tape.add_source(input)183                for output in outputs:184                    output._requires_grad = True185            else:186                for output in outputs:187                    output._requires_grad = False188        # Ensure the named operator for the tracing graph.189        if hasattr(graph_tape, '_tracing') and not op_name:190            op_def.name = op_name = execute_ws.create_handle(op_def.type)191            graph_tape.add_handle(op_name)192        # Emit to dispatch this execution.193        for feed_key, value_type in run_config['feed_dict'].items():194            dest = execute_ws.create_tensor(op_name + '/' + feed_key)195            dest.FromNumpy(numpy.array(kwargs[feed_key], value_type), True)196        execute_ws.run_operator(op_def)197        # Return single or repeated outputs....

checkpoint.py

Source:checkpoint.py

1# ------------------------------------------------------------2# Copyright (c) 2017-present, SeetaTech, Co.,Ltd.3#4# Licensed under the BSD 2-Clause License.5# You should have received a copy of the BSD 2-Clause License6# along with the software. If not, See,7#8#     <https://opensource.org/licenses/BSD-2-Clause>9#10# ------------------------------------------------------------11"""Checkpoint utilities."""12from __future__ import absolute_import13from __future__ import division14from __future__ import print_function15from dragon.core.framework import context16from dragon.core.framework import proto_util17from dragon.core.framework import tapes18from dragon.core.framework import workspace19from dragon.core.util import decorator20from dragon.core.util import nest21from dragon.vm.torch.core.autograd import grad_mode22from dragon.vm.torch.core.tensor import Tensor23from dragon.vm.torch.core.nn.modules.container import Sequential24class CheckpointFunction(object):25    """Checkpointing function."""26    @staticmethod27    def apply(function, *args, **kwargs):28        """Apply function and create a checkpoint."""29        kwargs.pop('preserve_rng_state', True)30        variable_scope = kwargs.pop('variable_scope', 'Buffer')31        original_variable_scope = context.get_variable_scope(True)32        if kwargs:33            raise ValueError('Unexpected keyword arguments: ' +34                             ','.join(arg for arg in kwargs))35        # Run function.36        graph_tape = tapes.Tape()37        graph_tape._tracing = True  # Enable tracing.38        graph_tape._checkpointing = True  # Enable checkpointing.39        graph_tape._original_variable_scope = original_variable_scope40        with grad_mode.no_grad(), graph_tape:41            with context.variable_scope(variable_scope):42                outputs = function(*args)43        # Collect involving tensors.44        tensor_inputs, tensor_outputs = [], []45        for arg in args:46            if isinstance(arg, Tensor):47                tensor_inputs.append(arg)48        for arg in nest.flatten(outputs):49            if isinstance(arg, Tensor):50                tensor_outputs.append(arg)51        # Fill tape with function context.52        op_tape = tapes.OrderedTape()53        op_handle = workspace.get_workspace().create_handle('Checkpoint')54        op_tape.add_element(proto_util.make_operator_def(55            op_type='Checkpoint',56            name=op_handle,57            inputs=[input.id for input in tensor_inputs],58            outputs=[output.id for output in tensor_outputs],59            defs=[v.SerializeAs() for v in graph_tape.get_elements()],60            buffer_scope=variable_scope,61            to_impl=True))62        op_tape.add_handle(op_handle)63        op_tape.merge_handles(graph_tape.get_handles())64        # Save input tensors for backward.65        for input in tensor_inputs + graph_tape.get_sources():66            op_tape.add_source(input)67        # Save tape for backward.68        for output in tensor_outputs:69            output._tape = op_tape70            output._requires_grad = True71        return outputs72def checkpoint(function, *args, **kwargs):73    """Apply function and create a checkpoint.74    Parameters75    ----------76    function : callable77        The function to apply.78    Returns79    -------80    Any81        The function outputs.82    """83    if not grad_mode.is_grad_enabled():84        return function(*args, **kwargs)85    return CheckpointFunction.apply(function, *args, **kwargs)86def checkpoint_sequential(functions, input, segments=1, **kwargs):87    """Apply functions and create segmental checkpoints.88    Parameters89    ----------90    functions : Union[torch.nn.Sequential, Sequence[callable]]91        The functions to apply sequentially.92    input : dragon.vm.torch.Tensor93        The input tensor.94    segments : Union[int, Sequence[int]], optional95        The number or size of chunked checkpoints.96    Returns97    -------98    Any99        The function outputs.100    """101    def run_function(start, end, functions):102        def forward(input):103            for j in range(start, end):104                input = functions[j](input)105            with no_checkpoint():106                input = functions[end](input)107            return input108        return forward109    preserve_rng_state = kwargs.pop('preserve_rng_state', True)110    variable_scope = kwargs.pop('variable_scope', 'Buffer')111    if kwargs:112        raise ValueError('Unexpected keyword arguments: ' +113                         ','.join(arg for arg in kwargs))114    if isinstance(functions, Sequential):115        functions = list(functions.children())116    start, end = 0, len(functions) - 1117    if not grad_mode.is_grad_enabled():118        return run_function(start, end, functions)(input)119    if nest.is_sequence(segments):120        size_segments = segments121        if sum(size_segments) != len(functions):122            raise ValueError('Failed to chunk {} functions into {} segments.'123                             .format(len(functions), segments))124    else:125        size = (len(functions) + segments - 1) // segments126        last_size = len(functions) - size * (segments - 1)127        if last_size <= 0:128            raise ValueError('Failed to chunk {} functions into {} segments.'129                             .format(len(functions), segments))130        size_segments = [size] * (segments - 1) + [last_size]131    for size in size_segments:132        end = start + size - 1133        input = checkpoint(134            run_function(start, end, functions), input,135            preserve_rng_state=preserve_rng_state,136            variable_scope=variable_scope)137        start = end + 1138    return input139class no_checkpoint(decorator._DecoratorContextManager):140    """Context-manager to disable checkpointing."""141    def __init__(self):142        """Create a ``no_checkpoint`` context manager."""143        self._checkpointing = False144    def __enter__(self):145        graph_tape = tapes.get_tape()146        if hasattr(graph_tape, '_checkpointing'):147            self._checkpointing = True148            context._GLOBAL_VARIABLE_SCOPE_STACK.push(149                graph_tape._original_variable_scope)150    def __exit__(self, *args):151        if self._checkpointing:152            self._checkpointing = False153            context._GLOBAL_VARIABLE_SCOPE_STACK.pop()...

Playwright tutorial

LambdaTest’s Playwright tutorial will give you a broader idea about the Playwright automation framework, its unique features, and use cases with examples to exceed your understanding of Playwright testing. This tutorial will give A to Z guidance, from installing the Playwright framework to some best practices and advanced concepts.

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What is Component Testing: Component testing in Playwright is a unique feature that allows a tester to test a single component of a web application without integrating them with other elements. Learn how to perform Component testing on the Playwright automation framework.
Inputs And Buttons In Playwright: Every website has Input boxes and buttons; learn about testing inputs and buttons with different scenarios and examples.
Functions and Selectors in Playwright: Learn how to launch the Chromium browser with Playwright. Also, gain a better understanding of some important functions like “BrowserContext,” which allows you to run multiple browser sessions, and “newPage” which interacts with a page.
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Playwright Python Tutorial: Playwright automation framework support all major languages such as Python, JavaScript, TypeScript, .NET and etc. However, there are various advantages to Python end-to-end testing with Playwright because of its versatile utility. Get the hang of Playwright python testing with this chapter.
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