

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_							)	->				List[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   )						->       Optional[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   )						->       str:
        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							,  )						->       int:
        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   )						->       int:

        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   )						->       int:
        # 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_							)	->				str:





  '''simple docstring'''





  if len(lowercase_							) == 0 or not all(len(lowercase_							) == 2 for pair in coordinates							):
    raise ValueError("""The program cannot work out a fitting polynomial."""							)

  if len({tuple(lowercase_							) for pair in coordinates}							) != len(lowercase_							):
    raise ValueError("""The program cannot work out a fitting polynomial."""							)

  snake_case_       						=      {x for x, _ in coordinates}
  if len(lowercase_							) == 1:
    return f'''x={coordinates[0][0]}'''

  if len(lowercase_							) != len(lowercase_							):
    raise ValueError("""The program cannot work out a fitting polynomial."""							)

  snake_case_       						=      len(lowercase_							)

  snake_case_       						=      0
  snake_case_       						=      []
  # put the x and x to the power values in a matrix
  while count_of_line < x:
    snake_case_       						=      0
    snake_case_       						=      coordinates[count_of_line][0]
    snake_case_       						=      []
    while count_in_line < x:
      count_line.append(a ** (x - (count_in_line + 1))							)
      count_in_line += 1
    matrix.append(lowercase_							)
    count_of_line += 1

  snake_case_       						=      0
  # put the y values into a vector
  snake_case_       						=      []
  while count_of_line < x:
    vector.append(coordinates[count_of_line][1]							)
    count_of_line += 1

  snake_case_       						=      0

  while count < x:
    snake_case_       						=      0
    while zahlen < x:
      if count == zahlen:
        zahlen += 1
      if zahlen == x:
        break
      snake_case_       						=      matrix[zahlen][count] / matrix[count][count]
      for counting_columns, item in enumerate(matrix[count]							):
        # manipulating all the values in the matrix
        matrix[zahlen][counting_columns] -= item * bruch
      # manipulating the values in the vector
      vector[zahlen] -= vector[count] * bruch
      zahlen += 1
    count += 1

  snake_case_       						=      0
  # make solutions
  snake_case_       						=      []
  while count < x:
    solution.append(str(vector[count] / matrix[count][count]							)							)
    count += 1

  snake_case_       						=      0
  snake_case_       						=      """f(x)="""

  while count < x:
    snake_case_       						=      solution[count].split("""E"""							)
    if len(lowercase_							) > 1:
      snake_case_       						=      f'''{remove_e[0]}*10^{remove_e[1]}'''
    solved += f'''x^{x - (count + 1)}*{solution[count]}'''
    if count + 1 != x:
      solved += "+"
    count += 1

  return solved


if __name__ == "__main__":
    print(points_to_polynomial([]))
    print(points_to_polynomial([[]]))
    print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
    print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
    print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
    print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
    print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
    print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
    print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
    print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))