





import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
      import tensorflow as tf

      tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
      import torch

if is_datasets_available():
      from datasets import load_dataset


__lowercase				    =      5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Namespace):



  '''simple docstring'''






  return PTtoTFCommand(
      args.model_name  ,				args.local_dir  ,				args.max_error  ,				args.new_weights  ,				args.no_pr  ,				args.push  ,				args.extra_commit_description  ,				args.override_model_class  ,				)




class 	__snake_case					(      a_ ):






    '''simple docstring'''





    @staticmethod
    def 						A		( snake_case    :		ArgumentParser				):

      """simple docstring"""





      __lowerCAmelCase								=					parser.add_parser(
          '''pt-to-tf'''      ,    help=(
              '''CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint.'''
              ''' Can also be used to validate existing weights without opening PRs, with --no-pr.'''
          )      ,    )
      train_parser.add_argument(
          '''--model-name'''      ,    type=UpperCAmelCase_      ,    required=UpperCAmelCase_      ,    help='''The model name, including owner/organization, as seen on the hub.'''      ,    )
      train_parser.add_argument(
          '''--local-dir'''      ,    type=UpperCAmelCase_      ,    default=''''''      ,    help='''Optional local directory of the model repository. Defaults to /tmp/{model_name}'''      ,    )
      train_parser.add_argument(
          '''--max-error'''      ,    type=UpperCAmelCase_      ,    default=UpperCAmelCase_      ,    help=(
              f'''Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk.'''
          )      ,    )
      train_parser.add_argument(
          '''--new-weights'''      ,    action='''store_true'''      ,    help='''Optional flag to create new TensorFlow weights, even if they already exist.'''      ,    )
      train_parser.add_argument(
          '''--no-pr'''      ,    action='''store_true'''      ,    help='''Optional flag to NOT open a PR with converted weights.'''				)
      train_parser.add_argument(
          '''--push'''      ,    action='''store_true'''      ,    help='''Optional flag to push the weights directly to `main` (requires permissions)'''      ,    )
      train_parser.add_argument(
          '''--extra-commit-description'''      ,    type=UpperCAmelCase_      ,    default=''''''      ,    help='''Optional additional commit description to use when opening a PR (e.g. to tag the owner).'''      ,    )
      train_parser.add_argument(
          '''--override-model-class'''      ,    type=UpperCAmelCase_      ,    default=UpperCAmelCase_      ,    help='''If you think you know better than the auto-detector, you can specify the model class here. '''
          '''Can be either an AutoModel class or a specific model class like BertForSequenceClassification.'''      ,    )
      train_parser.set_defaults(func=UpperCAmelCase_				)





    @staticmethod
    def 						A		( snake_case    :		int      ,    snake_case    :		Dict				):

      """simple docstring"""





      __lowerCAmelCase								=					set(pt_outputs.keys()				)
      __lowerCAmelCase								=					set(tf_outputs.keys()				)
      if pt_out_attrs != tf_out_attrs:
        raise ValueError(
            f'''The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:'''
            f''' {tf_out_attrs})'''				)

      # 2. For each output attribute, computes the difference
      def _find_pt_tf_differences(snake_case    :		List[str]      ,    snake_case    :		List[Any]      ,    snake_case    :		Dict      ,    snake_case    :		Any=""				):
        # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
        # recursivelly, keeping the name of the attribute.
        if isinstance(UpperCAmelCase_      ,    torch.Tensor				):
          __lowerCAmelCase								=					np.max(np.abs(pt_out.numpy() - tf_out.numpy()				)				)
          __lowerCAmelCase								=					tensor_difference
        else:
          __lowerCAmelCase								=					attr_name
          for i, pt_item in enumerate(UpperCAmelCase_				):
            # If it is a named attribute, we keep the name. Otherwise, just its index.
            if isinstance(UpperCAmelCase_      ,    UpperCAmelCase_				):
              __lowerCAmelCase								=					root_name + pt_item
              __lowerCAmelCase								=					tf_out[pt_item]
              __lowerCAmelCase								=					pt_out[pt_item]
            else:
              __lowerCAmelCase								=					root_name + f'''[{i}]'''
              __lowerCAmelCase								=					tf_out[i]
            __lowerCAmelCase								=					_find_pt_tf_differences(UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_				)

        return differences

      return _find_pt_tf_differences(UpperCAmelCase_      ,    UpperCAmelCase_      ,    {}				)





    def __init__( self    :		Optional[Any]      ,    snake_case    :		str      ,    snake_case    :		str      ,    snake_case    :		float      ,    snake_case    :		bool      ,    snake_case    :		bool      ,    snake_case    :		bool      ,    snake_case    :		str      ,    snake_case    :		str      ,    *snake_case    :		List[Any]      ,    ):

      """simple docstring"""





      __lowerCAmelCase								=					logging.get_logger('''transformers-cli/pt_to_tf'''				)
      __lowerCAmelCase								=					model_name
      __lowerCAmelCase								=					local_dir if local_dir else os.path.join('''/tmp'''      ,    UpperCAmelCase_				)
      __lowerCAmelCase								=					max_error
      __lowerCAmelCase								=					new_weights
      __lowerCAmelCase								=					no_pr
      __lowerCAmelCase								=					push
      __lowerCAmelCase								=					extra_commit_description
      __lowerCAmelCase								=					override_model_class





    def 						A		( self    :		Union[str, Any]      ,    snake_case    :		Optional[Any]      ,    snake_case    :		Optional[Any]      ,    snake_case    :		int				):

      """simple docstring"""





      def _get_audio_input():
        __lowerCAmelCase								=					load_dataset('''hf-internal-testing/librispeech_asr_dummy'''      ,    '''clean'''      ,    split='''validation'''				)
        __lowerCAmelCase								=					ds.sort('''id'''				).select(range(2				)				)[:2]['audio']
        __lowerCAmelCase								=					[x['array'] for x in speech_samples]
        return raw_samples

      __lowerCAmelCase								=					type(pt_model.config				)
      if model_config_class in PROCESSOR_MAPPING:
        __lowerCAmelCase								=					AutoProcessor.from_pretrained(self._local_dir				)
        if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
          __lowerCAmelCase								=					processor.tokenizer.eos_token
      elif model_config_class in IMAGE_PROCESSOR_MAPPING:
        __lowerCAmelCase								=					AutoImageProcessor.from_pretrained(self._local_dir				)
      elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
        __lowerCAmelCase								=					AutoFeatureExtractor.from_pretrained(self._local_dir				)
      elif model_config_class in TOKENIZER_MAPPING:
        __lowerCAmelCase								=					AutoTokenizer.from_pretrained(self._local_dir				)
        if processor.pad_token is None:
          __lowerCAmelCase								=					processor.eos_token
      else:
        raise ValueError(f'''Unknown data processing type (model config type: {model_config_class})'''				)

      __lowerCAmelCase								=					set(inspect.signature(pt_model.forward				).parameters.keys()				)
      __lowerCAmelCase								=					{}
      if "input_ids" in model_forward_signature:
        processor_inputs.update(
            {
                '''text''': ['''Hi there!''', '''I am a batch with more than one row and different input lengths.'''],
                '''padding''': True,
                '''truncation''': True,
            }				)
      if "pixel_values" in model_forward_signature:
        __lowerCAmelCase								=					load_dataset('''cifar10'''      ,    '''plain_text'''      ,    split='''test'''				)[:2]['img']
        processor_inputs.update({'''images''': sample_images}				)
      if "input_features" in model_forward_signature:
        __lowerCAmelCase								=					inspect.signature(processor.feature_extractor				).parameters
        # Pad to the largest input length by default but take feature extractor default
        # padding value if it exists e.g. "max_length" and is not False or None
        if "padding" in feature_extractor_signature:
          __lowerCAmelCase								=					feature_extractor_signature['padding'].default
          if default_strategy is not False and default_strategy is not None:
            __lowerCAmelCase								=					default_strategy
          else:
            __lowerCAmelCase								=					True
        else:
          __lowerCAmelCase								=					True
        processor_inputs.update({'''audio''': _get_audio_input(), '''padding''': padding_strategy}				)
      if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
        processor_inputs.update({'''audio''': _get_audio_input(), '''padding''': True}				)
      __lowerCAmelCase								=					processor(**UpperCAmelCase_      ,    return_tensors='''pt'''				)
      __lowerCAmelCase								=					processor(**UpperCAmelCase_      ,    return_tensors='''tf'''				)

      # Extra input requirements, in addition to the input modality
      if (
          config.is_encoder_decoder
          or (hasattr(UpperCAmelCase_      ,    '''encoder'''				) and hasattr(UpperCAmelCase_      ,    '''decoder'''				))
          or "decoder_input_ids" in tf_dummy_inputs
      ):
        __lowerCAmelCase								=					np.asarray([[1], [1]]      ,    dtype=UpperCAmelCase_				) * (pt_model.config.decoder_start_token_id or 0)
        pt_input.update({'''decoder_input_ids''': torch.tensor(UpperCAmelCase_				)}				)
        tf_input.update({'''decoder_input_ids''': tf.convert_to_tensor(UpperCAmelCase_				)}				)

      return pt_input, tf_input





    def 						A		( self    :		int				):

      """simple docstring"""





      if version.parse(huggingface_hub.__version__				) < version.parse('''0.9.0'''				):
        raise ImportError(
            '''The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub'''
            ''' installation.'''				)
      else:
        from huggingface_hub import Repository, create_commit
        from huggingface_hub._commit_api import CommitOperationAdd

      # Fetch remote data
      __lowerCAmelCase								=					Repository(local_dir=self._local_dir      ,    clone_from=self._model_name				)

      # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
      __lowerCAmelCase								=					AutoConfig.from_pretrained(self._local_dir				)
      __lowerCAmelCase								=					config.architectures
      if self._override_model_class is not None:
        if self._override_model_class.startswith('''TF'''				):
          __lowerCAmelCase								=					[self._override_model_class[2:]]
        else:
          __lowerCAmelCase								=					[self._override_model_class]
        try:
          __lowerCAmelCase								=					getattr(import_module('''transformers'''				)      ,    architectures[0]				)
        except AttributeError:
          raise ValueError(f'''Model class {self._override_model_class} not found in transformers.'''				)
        try:
          __lowerCAmelCase								=					getattr(import_module('''transformers'''				)      ,    '''TF''' + architectures[0]				)
        except AttributeError:
          raise ValueError(f'''TF model class TF{self._override_model_class} not found in transformers.'''				)
      elif architectures is None:  # No architecture defined -- use auto classes
        __lowerCAmelCase								=					getattr(import_module('''transformers'''				)      ,    '''AutoModel'''				)
        __lowerCAmelCase								=					getattr(import_module('''transformers'''				)      ,    '''TFAutoModel'''				)
        self._logger.warning('''No detected architecture, using AutoModel/TFAutoModel'''				)
      else:  # Architecture defined -- use it
        if len(UpperCAmelCase_				) > 1:
          raise ValueError(f'''More than one architecture was found, aborting. (architectures = {architectures})'''				)
        self._logger.warning(f'''Detected architecture: {architectures[0]}'''				)
        __lowerCAmelCase								=					getattr(import_module('''transformers'''				)      ,    architectures[0]				)
        try:
          __lowerCAmelCase								=					getattr(import_module('''transformers'''				)      ,    '''TF''' + architectures[0]				)
        except AttributeError:
          raise AttributeError(f'''The TensorFlow equivalent of {architectures[0]} doesn\'t exist in transformers.'''				)

        # Check the TF dummy inputs to see what keys we need in the forward pass
      __lowerCAmelCase								=					tf_class.from_config(UpperCAmelCase_				)
      __lowerCAmelCase								=					tf_from_pt_model.dummy_inputs

      del tf_from_pt_model  # Try to keep only one model in memory at a time

      # Load the model and get some basic inputs
      __lowerCAmelCase								=					pt_class.from_pretrained(self._local_dir				)
      pt_model.eval()

      __lowerCAmelCase								=					self.get_inputs(UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_				)

      with torch.no_grad():
        __lowerCAmelCase								=					pt_model(**UpperCAmelCase_      ,    output_hidden_states=UpperCAmelCase_				)
      del pt_model  # will no longer be used, and may have a large memory footprint

      __lowerCAmelCase								=					tf_class.from_pretrained(self._local_dir      ,    from_pt=UpperCAmelCase_				)
      __lowerCAmelCase								=					tf_from_pt_model(**UpperCAmelCase_      ,    output_hidden_states=UpperCAmelCase_      ,    training=UpperCAmelCase_				)

      # Confirms that cross loading PT weights into TF worked.
      __lowerCAmelCase								=					self.find_pt_tf_differences(UpperCAmelCase_      ,    UpperCAmelCase_				)
      __lowerCAmelCase								=					{k: v for k, v in crossload_differences.items() if 'hidden' not in k}
      __lowerCAmelCase								=					{k: v for k, v in crossload_differences.items() if 'hidden' in k}
      if len(UpperCAmelCase_				) == 0 and architectures is not None:
        raise ValueError(
            f'''Something went wrong -- the config file has architectures ({architectures}), but no model head'''
            ''' output was found. All outputs start with \'hidden\''''				)
      __lowerCAmelCase								=					max(output_differences.values()				) if output_differences else 0.0
      __lowerCAmelCase								=					max(hidden_differences.values()				)
      if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
        raise ValueError(
            '''The cross-loaded TensorFlow model has different outputs, something went wrong!\n'''
            + f'''\nList of maximum output differences above the threshold ({self._max_error}):\n'''
            + '''\n'''.join([f'''{k}: {v:.3e}''' for k, v in output_differences.items() if v > self._max_error]				)
            + f'''\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n'''
            + '''\n'''.join([f'''{k}: {v:.3e}''' for k, v in hidden_differences.items() if v > self._max_error]				)				)

      # Save the weights in a TF format (if needed) and confirms that the results are still good
      __lowerCAmelCase								=					os.path.join(self._local_dir      ,    UpperCAmelCase_				)
      __lowerCAmelCase								=					os.path.join(self._local_dir      ,    UpperCAmelCase_				)
      if (not os.path.exists(UpperCAmelCase_				) and not os.path.exists(UpperCAmelCase_				)) or self._new_weights:
        tf_from_pt_model.save_pretrained(self._local_dir				)
      del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

      __lowerCAmelCase								=					tf_class.from_pretrained(self._local_dir				)
      __lowerCAmelCase								=					tf_model(**UpperCAmelCase_      ,    output_hidden_states=UpperCAmelCase_				)

      __lowerCAmelCase								=					self.find_pt_tf_differences(UpperCAmelCase_      ,    UpperCAmelCase_				)
      __lowerCAmelCase								=					{k: v for k, v in conversion_differences.items() if 'hidden' not in k}
      __lowerCAmelCase								=					{k: v for k, v in conversion_differences.items() if 'hidden' in k}
      if len(UpperCAmelCase_				) == 0 and architectures is not None:
        raise ValueError(
            f'''Something went wrong -- the config file has architectures ({architectures}), but no model head'''
            ''' output was found. All outputs start with \'hidden\''''				)
      __lowerCAmelCase								=					max(output_differences.values()				) if output_differences else 0.0
      __lowerCAmelCase								=					max(hidden_differences.values()				)
      if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
        raise ValueError(
            '''The converted TensorFlow model has different outputs, something went wrong!\n'''
            + f'''\nList of maximum output differences above the threshold ({self._max_error}):\n'''
            + '''\n'''.join([f'''{k}: {v:.3e}''' for k, v in output_differences.items() if v > self._max_error]				)
            + f'''\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n'''
            + '''\n'''.join([f'''{k}: {v:.3e}''' for k, v in hidden_differences.items() if v > self._max_error]				)				)

      __lowerCAmelCase								=					'Update TF weights' if self._new_weights else 'Add TF weights'
      if self._push:
        repo.git_add(auto_lfs_track=UpperCAmelCase_				)
        repo.git_commit(UpperCAmelCase_				)
        repo.git_push(blocking=UpperCAmelCase_				)  # this prints a progress bar with the upload
        self._logger.warning(f'''TF weights pushed into {self._model_name}'''				)
      elif not self._no_pr:
        self._logger.warning('''Uploading the weights into a new PR...'''				)
        __lowerCAmelCase								=					(
            'Model converted by the [`transformers`\' `pt_to_tf`'
            ' CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). '
            'All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n'
            f'''Maximum crossload output difference={max_crossload_output_diff:.3e}; '''
            f'''Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n'''
            f'''Maximum conversion output difference={max_conversion_output_diff:.3e}; '''
            f'''Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n'''
        )
        if self._max_error > MAX_ERROR:
          commit_descrition += (
              f'''\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!'''
          )
        if self._extra_commit_description:
          commit_descrition += "\n\n" + self._extra_commit_description

        # sharded model -> adds all related files (index and .h5 shards)
        if os.path.exists(UpperCAmelCase_				):
          __lowerCAmelCase								=					[
              CommitOperationAdd(path_in_repo=UpperCAmelCase_      ,    path_or_fileobj=UpperCAmelCase_				)
          ]
          for shard_path in tf.io.gfile.glob(self._local_dir + '''/tf_model-*.h5'''				):
            operations += [
                CommitOperationAdd(path_in_repo=os.path.basename(UpperCAmelCase_				)      ,    path_or_fileobj=UpperCAmelCase_				)
            ]
        else:
          __lowerCAmelCase								=					[CommitOperationAdd(path_in_repo=UpperCAmelCase_      ,    path_or_fileobj=UpperCAmelCase_				)]

        __lowerCAmelCase								=					create_commit(
            repo_id=self._model_name      ,    operations=UpperCAmelCase_      ,    commit_message=UpperCAmelCase_      ,    commit_description=UpperCAmelCase_      ,    repo_type='''model'''      ,    create_pr=UpperCAmelCase_      ,    ).pr_url
        self._logger.warning(f'''PR open in {hub_pr_url}'''				)

'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase       :     Tuple			=    {
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase       :     int			=    ["""MarkupLMTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase       :     Any			=    [
          """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MarkupLMForQuestionAnswering""",
          """MarkupLMForSequenceClassification""",
          """MarkupLMForTokenClassification""",
          """MarkupLMModel""",
          """MarkupLMPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
      from .feature_extraction_markuplm import MarkupLMFeatureExtractor
      from .processing_markuplm import MarkupLMProcessor
      from .tokenization_markuplm import MarkupLMTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_markuplm_fast import MarkupLMTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_markuplm import (
                MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                MarkupLMForQuestionAnswering,
                MarkupLMForSequenceClassification,
                MarkupLMForTokenClassification,
                MarkupLMModel,
                MarkupLMPreTrainedModel,
            )


else:
      import sys

      UpperCamelCase       :     Dict			=    _LazyModule(__name__, globals()["""__file__"""], _import_structure)

