

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


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



def    snake_case_(_UpperCamelCase  )							->			Any:



  """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 						lowercase_					(						__lowercase  ):



      @staticmethod
      def 		UpperCamelCase_	(    A__   :       ArgumentParser	)					->  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=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 		UpperCamelCase_	(    A__   :       List[str]    ,      A__   :       Dict	)					->  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(A__   :       Dict    ,      A__   :       Union[str, Any]    ,      A__   :       List[Any]    ,      A__   :       List[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(A__    ,      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(A__	):
              # If it is a named attribute, we keep the name. Otherwise, just its index.
              if isinstance(A__    ,      A__	):
                _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(A__    ,      A__    ,      A__    ,      A__	)

          return differences

        return _find_pt_tf_differences(A__    ,      A__    ,      {}	)



      def __init__(    self   :       Dict    ,      A__   :       str    ,      A__   :       str    ,      A__   :       float    ,      A__   :       bool    ,      A__   :       bool    ,      A__   :       bool    ,      A__   :       str    ,      A__   :       str    ,      *A__   :       Dict    ,      )					->  Optional[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'''    ,      A__	)
        _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 		UpperCamelCase_	(    self   :       List[str]    ,      A__   :       Dict    ,      A__   :       Dict    ,      A__   :       Union[str, Any]	)					->  Union[str, 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(**A__    ,      return_tensors='''pt'''	)
        _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
        ):
          _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 		UpperCamelCase_	(    self   :       Optional[Any]	)					->  Optional[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(A__	) > 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(A__	)
        _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(A__    ,      A__    ,      A__	)

        with torch.no_grad():
          _snake_case		=							pt_model(**A__    ,      output_hidden_states=A__	)
        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=A__	)
        _snake_case		=							tf_from_pt_model(**A__    ,      output_hidden_states=A__    ,      training=A__	)

        # Confirms that cross loading PT weights into TF worked.
        _snake_case		=							self.find_pt_tf_differences(A__    ,      A__	)
        _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(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\''''	)
        _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    ,      A__	)
        _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

        _snake_case		=							tf_class.from_pretrained(self._local_dir	)
        _snake_case		=							tf_model(**A__    ,      output_hidden_states=A__	)

        _snake_case		=							self.find_pt_tf_differences(A__    ,      A__	)
        _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(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\''''	)
        _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=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...'''	)
          _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__	):
            _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:
            _snake_case		=							[CommitOperationAdd(path_in_repo=A__    ,      path_or_fileobj=A__	)]

          _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}"""	)




import random
import sys

from . import cryptomath_module as cryptomath

__A    	=			(
    r''' !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`'''
    r'''abcdefghijklmnopqrstuvwxyz{|}~'''
)



def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )							->			None:



  """simple docstring"""






  if mode == "encrypt":
    if key_a == 1:
      sys.exit(
          '''The affine cipher becomes weak when key '''
          '''A is set to 1. Choose different key'''  )
    if key_b == 0:
      sys.exit(
          '''The affine cipher becomes weak when key '''
          '''B is set to 0. Choose different key'''  )
  if key_a < 0 or key_b < 0 or key_b > len(_UpperCamelCase  ) - 1:
    sys.exit(
        '''Key A must be greater than 0 and key B must '''
        F"""be between 0 and {len(_UpperCamelCase  ) - 1}."""  )
  if cryptomath.gcd(_UpperCamelCase				, len(_UpperCamelCase  )  ) != 1:
    sys.exit(
        F"""Key A {key_a} and the symbol set size {len(_UpperCamelCase  )} """
        '''are not relatively prime. Choose a different key.'''  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			str:



  """simple docstring"""






  _snake_case,  _snake_case		=							divmod(_UpperCamelCase				, len(_UpperCamelCase  )  )
  check_keys(_UpperCamelCase				, _UpperCamelCase				, '''encrypt'''  )
  _snake_case		=							''''''
  for symbol in message:
    if symbol in SYMBOLS:
      _snake_case		=							SYMBOLS.find(_UpperCamelCase  )
      cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(_UpperCamelCase  )]
    else:
      cipher_text += symbol
  return cipher_text



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			str:



  """simple docstring"""






  _snake_case,  _snake_case		=							divmod(_UpperCamelCase				, len(_UpperCamelCase  )  )
  check_keys(_UpperCamelCase				, _UpperCamelCase				, '''decrypt'''  )
  _snake_case		=							''''''
  _snake_case		=							cryptomath.find_mod_inverse(_UpperCamelCase				, len(_UpperCamelCase  )  )
  for symbol in message:
    if symbol in SYMBOLS:
      _snake_case		=							SYMBOLS.find(_UpperCamelCase  )
      plain_text += SYMBOLS[
          (sym_index - key_b) * mod_inverse_of_key_a % len(_UpperCamelCase  )
      ]
    else:
      plain_text += symbol
  return plain_text



def    snake_case_()							->			int:



  """simple docstring"""






  while True:
    _snake_case		=							random.randint(2				, len(_UpperCamelCase  )  )
    _snake_case		=							random.randint(2				, len(_UpperCamelCase  )  )
    if cryptomath.gcd(_UpperCamelCase				, len(_UpperCamelCase  )  ) == 1 and key_b % len(_UpperCamelCase  ) != 0:
      return key_b * len(_UpperCamelCase  ) + key_b



def    snake_case_()							->			None:



  """simple docstring"""






  _snake_case		=							input('''Enter message: '''  ).strip()
  _snake_case		=							int(input('''Enter key [2000 - 9000]: '''  ).strip()  )
  _snake_case		=							input('''Encrypt/Decrypt [E/D]: '''  ).strip().lower()

  if mode.startswith('''e'''  ):
    _snake_case		=							'''encrypt'''
    _snake_case		=							encrypt_message(_UpperCamelCase				, _UpperCamelCase  )
  elif mode.startswith('''d'''  ):
    _snake_case		=							'''decrypt'''
    _snake_case		=							decrypt_message(_UpperCamelCase				, _UpperCamelCase  )
  print(F"""\n{mode.title()}ed text: \n{translated}"""  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 # main()

