

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


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



def   UpperCamelCase(   lowercase_				)      ->							str:





  '''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 					__lowerCamelCase							(      __snake_case      ):







      @staticmethod
      def       lowerCAmelCase_       (	lowerCamelCase  )		->			List[Any]:
        snake_case_					= 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=lowerCamelCase				,    required=lowerCamelCase				,    help="""The model name, including owner/organization, as seen on the hub."""				,    )
        train_parser.add_argument(
            """--local-dir"""				,    type=lowerCamelCase				,    default=""""""				,    help="""Optional local directory of the model repository. Defaults to /tmp/{model_name}"""				,    )
        train_parser.add_argument(
            """--max-error"""				,    type=lowerCamelCase				,    default=lowerCamelCase				,    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=lowerCamelCase				,    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=lowerCamelCase				,    default=lowerCamelCase				,    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=lowerCamelCase  )







      @staticmethod
      def       lowerCAmelCase_       (	lowerCamelCase				,    lowerCamelCase  )		->			Dict:
        snake_case_					= set(pt_outputs.keys()  )
        snake_case_					= 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(lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase=""  ):
          # 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(lowerCamelCase				,    torch.Tensor  ):
            snake_case_					= np.max(np.abs(pt_out.numpy() - tf_out.numpy()  )  )
            snake_case_					= tensor_difference
          else:
            snake_case_					= attr_name
            for i, pt_item in enumerate(lowerCamelCase  ):
              # If it is a named attribute, we keep the name. Otherwise, just its index.
              if isinstance(lowerCamelCase				,    lowerCamelCase  ):
                snake_case_					= root_name + pt_item
                snake_case_					= tf_out[pt_item]
                snake_case_					= pt_out[pt_item]
              else:
                snake_case_					= root_name + f'''[{i}]'''
                snake_case_					= tf_out[i]
              snake_case_					= _find_pt_tf_differences(lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )

          return differences

        return _find_pt_tf_differences(lowerCamelCase				,    lowerCamelCase				,    {}  )







      def __init__(	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    *lowerCamelCase				,    )		->			List[Any]:
        snake_case_					= logging.get_logger("""transformers-cli/pt_to_tf"""  )
        snake_case_					= model_name
        snake_case_					= local_dir if local_dir else os.path.join("""/tmp"""				,    lowerCamelCase  )
        snake_case_					= max_error
        snake_case_					= new_weights
        snake_case_					= no_pr
        snake_case_					= push
        snake_case_					= extra_commit_description
        snake_case_					= override_model_class







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			Any:

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

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

        snake_case_					= set(inspect.signature(pt_model.forward  ).parameters.keys()  )
        snake_case_					= {}
        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:
          snake_case_					= load_dataset("""cifar10"""				,    """plain_text"""				,    split="""test"""  )[:2]["""img"""]
          processor_inputs.update({"""images""": sample_images}  )
        if "input_features" in model_forward_signature:
          snake_case_					= 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:
            snake_case_					= feature_extractor_signature["""padding"""].default
            if default_strategy is not False and default_strategy is not None:
              snake_case_					= default_strategy
            else:
              snake_case_					= True
          else:
            snake_case_					= 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}  )
        snake_case_					= processor(**lowerCamelCase				,    return_tensors="""pt"""  )
        snake_case_					= processor(**lowerCamelCase				,    return_tensors="""tf"""  )

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

        return pt_input, tf_input







      def       lowerCAmelCase_       (	self  )		->			Optional[Any]:
        # hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens.
        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
        snake_case_					= 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
        snake_case_					= AutoConfig.from_pretrained(self._local_dir  )
        snake_case_					= config.architectures
        if self._override_model_class is not None:
          if self._override_model_class.startswith("""TF"""  ):
            snake_case_					= [self._override_model_class[2:]]
          else:
            snake_case_					= [self._override_model_class]
          try:
            snake_case_					= getattr(import_module("""transformers"""  )				,    architectures[0]  )
          except AttributeError:
            raise ValueError(f'''Model class {self._override_model_class} not found in transformers.'''  )
          try:
            snake_case_					= 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
          snake_case_					= getattr(import_module("""transformers"""  )				,    """AutoModel"""  )
          snake_case_					= getattr(import_module("""transformers"""  )				,    """TFAutoModel"""  )
          self._logger.warning("""No detected architecture, using AutoModel/TFAutoModel"""  )
        else:  # Architecture defined -- use it
          if len(lowerCamelCase  ) > 1:
            raise ValueError(f'''More than one architecture was found, aborting. (architectures = {architectures})'''  )
          self._logger.warning(f'''Detected architecture: {architectures[0]}'''  )
          snake_case_					= getattr(import_module("""transformers"""  )				,    architectures[0]  )
          try:
            snake_case_					= 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
        snake_case_					= tf_class.from_config(lowerCamelCase  )
        snake_case_					= 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
        snake_case_					= pt_class.from_pretrained(self._local_dir  )
        pt_model.eval()

        snake_case_		,							snake_case_					= self.get_inputs(lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )

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

        snake_case_					= tf_class.from_pretrained(self._local_dir				,    from_pt=lowerCamelCase  )
        snake_case_					= tf_from_pt_model(**lowerCamelCase				,    output_hidden_states=lowerCamelCase				,    training=lowerCamelCase  )

        # Confirms that cross loading PT weights into TF worked.
        snake_case_					= self.find_pt_tf_differences(lowerCamelCase				,    lowerCamelCase  )
        snake_case_					= {k: v for k, v in crossload_differences.items() if """hidden""" not in k}
        snake_case_					= {k: v for k, v in crossload_differences.items() if """hidden""" in k}
        if len(lowerCamelCase  ) == 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'"""  )
        snake_case_					= max(output_differences.values()  ) if output_differences else 0.0
        snake_case_					= 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
        snake_case_					= os.path.join(self._local_dir				,    lowerCamelCase  )
        snake_case_					= os.path.join(self._local_dir				,    lowerCamelCase  )
        if (not os.path.exists(lowerCamelCase  ) and not os.path.exists(lowerCamelCase  )) 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

        snake_case_					= tf_class.from_pretrained(self._local_dir  )
        snake_case_					= tf_model(**lowerCamelCase				,    output_hidden_states=lowerCamelCase  )

        snake_case_					= self.find_pt_tf_differences(lowerCamelCase				,    lowerCamelCase  )
        snake_case_					= {k: v for k, v in conversion_differences.items() if """hidden""" not in k}
        snake_case_					= {k: v for k, v in conversion_differences.items() if """hidden""" in k}
        if len(lowerCamelCase  ) == 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'"""  )
        snake_case_					= max(output_differences.values()  ) if output_differences else 0.0
        snake_case_					= 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]  )  )

        snake_case_					= """Update TF weights""" if self._new_weights else """Add TF weights"""
        if self._push:
          repo.git_add(auto_lfs_track=lowerCamelCase  )
          repo.git_commit(lowerCamelCase  )
          repo.git_push(blocking=lowerCamelCase  )  # 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..."""  )
          snake_case_					= (
              """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(lowerCamelCase  ):
            snake_case_					= [
                CommitOperationAdd(path_in_repo=lowerCamelCase				,    path_or_fileobj=lowerCamelCase  )
            ]
            for shard_path in tf.io.gfile.glob(self._local_dir + """/tf_model-*.h5"""  ):
              operations += [
                  CommitOperationAdd(path_in_repo=os.path.basename(lowerCamelCase  )				,    path_or_fileobj=lowerCamelCase  )
              ]
          else:
            snake_case_					= [CommitOperationAdd(path_in_repo=lowerCamelCase				,    path_or_fileobj=lowerCamelCase  )]

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


def   UpperCamelCase(   lowercase_ = 100     ,   lowercase_ = 100				)      ->							int:





  '''simple docstring'''





  return max(
      sum(int(lowercase_				) for x in str(base**power				)				)
      for base in range(lowercase_				)
      for power in range(lowercase_				)				)


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()