





"""simple docstring"""





from .stack import Stack




def 		lowerCAmelCase_	(  lowercase_   :							str		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Stack[str]												=       Stack()
 __SCREAMING_SNAKE_CASE :			List[Any]												=       {'''(''': ''')''', '''[''': ''']''', '''{''': '''}'''}
 for bracket in parentheses:
  if bracket in bracket_pairs:
   stack.push(lowercase_		)
  elif bracket in (")", "]", "}"):
   if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
    return False
 return stack.is_empty()


if __name__ == "__main__":
    from doctest import testmod

    testmod()

    _lowerCamelCase       =	['''((()))''', '''((())''', '''(()))''']
    print('''Balanced parentheses demonstration:\n''')
    for example in examples:
        _lowerCamelCase       =	'''''' if balanced_parentheses(example) else '''not '''
        print(f'{example} is {not_str}balanced')




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


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


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						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     (			lowerCAmelCase__				):

  @staticmethod
  def   lowerCAmelCase__   (    _A):
      SCREAMING_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=_A  ,						required=_A  ,						help='The model name, including owner/organization, as seen on the hub.'  ,						)
      train_parser.add_argument(
          '--local-dir'  ,						type=_A  ,						default=''  ,						help='Optional local directory of the model repository. Defaults to /tmp/{model_name}'  ,						)
      train_parser.add_argument(
          '--max-error'  ,						type=_A  ,						default=_A  ,						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=_A  ,						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=_A  ,						default=_A  ,						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=_A)

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

          return differences

      return _find_pt_tf_differences(_A  ,						_A  ,						{})

  def __init__(    self  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A  ,						*_A  ,						):
      SCREAMING_SNAKE_CASE_													=  logging.get_logger('transformers-cli/pt_to_tf')
      SCREAMING_SNAKE_CASE_													=  model_name
      SCREAMING_SNAKE_CASE_													=  local_dir if local_dir else os.path.join('/tmp'  ,						_A)
      SCREAMING_SNAKE_CASE_													=  max_error
      SCREAMING_SNAKE_CASE_													=  new_weights
      SCREAMING_SNAKE_CASE_													=  no_pr
      SCREAMING_SNAKE_CASE_													=  push
      SCREAMING_SNAKE_CASE_													=  extra_commit_description
      SCREAMING_SNAKE_CASE_													=  override_model_class

  def   lowerCAmelCase__   (    self  ,						_A  ,						_A  ,						_A):

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

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

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

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

      return pt_input, tf_input


  def   lowerCAmelCase__   (    self):
      # 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
      SCREAMING_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
      SCREAMING_SNAKE_CASE_													=  AutoConfig.from_pretrained(self._local_dir)
      SCREAMING_SNAKE_CASE_													=  config.architectures
      if self._override_model_class is not None:
          if self._override_model_class.startswith('TF'):
              SCREAMING_SNAKE_CASE_													=  [self._override_model_class[2:]]
          else:
              SCREAMING_SNAKE_CASE_													=  [self._override_model_class]
          try:
              SCREAMING_SNAKE_CASE_													=  getattr(import_module('transformers')  ,						architectures[0])
          except AttributeError:
              raise ValueError(f"""Model class {self._override_model_class} not found in transformers.""")
          try:
              SCREAMING_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
          SCREAMING_SNAKE_CASE_													=  getattr(import_module('transformers')  ,						'AutoModel')
          SCREAMING_SNAKE_CASE_													=  getattr(import_module('transformers')  ,						'TFAutoModel')
          self._logger.warning('No detected architecture, using AutoModel/TFAutoModel')
      else:  # Architecture defined -- use it
          if len(_A) > 1:
              raise ValueError(f"""More than one architecture was found, aborting. (architectures = {architectures})""")
          self._logger.warning(f"""Detected architecture: {architectures[0]}""")
          SCREAMING_SNAKE_CASE_													=  getattr(import_module('transformers')  ,						architectures[0])
          try:
              SCREAMING_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
      SCREAMING_SNAKE_CASE_													=  tf_class.from_config(_A)
      SCREAMING_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
      SCREAMING_SNAKE_CASE_													=  pt_class.from_pretrained(self._local_dir)
      pt_model.eval()

      SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  self.get_inputs(_A  ,						_A  ,						_A)

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

      SCREAMING_SNAKE_CASE_													=  tf_class.from_pretrained(self._local_dir  ,						from_pt=_A)
      SCREAMING_SNAKE_CASE_													=  tf_from_pt_model(**_A  ,						output_hidden_states=_A  ,						training=_A)

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

      SCREAMING_SNAKE_CASE_													=  tf_class.from_pretrained(self._local_dir)
      SCREAMING_SNAKE_CASE_													=  tf_model(**_A  ,						output_hidden_states=_A)

      SCREAMING_SNAKE_CASE_													=  self.find_pt_tf_differences(_A  ,						_A)
      SCREAMING_SNAKE_CASE_													=  {k: v for k, v in conversion_differences.items() if 'hidden' not in k}
      SCREAMING_SNAKE_CASE_													=  {k: v for k, v in conversion_differences.items() if 'hidden' in k}
      if len(_A) == 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\'')
      SCREAMING_SNAKE_CASE_													=  max(output_differences.values()) if output_differences else 0.0
      SCREAMING_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]))

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

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


