code: 
  
  
  '''simple docstring'''
  
  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							:     Optional[int]								=			5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors
  
  def _SCREAMING_SNAKE_CASE   (A ) -> 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		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        @staticmethod
        def    UpperCamelCase__	(UpperCamelCase					:					ArgumentParser				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				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    UpperCamelCase__	(UpperCamelCase					:					str     ,		UpperCamelCase					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				set(pt_outputs.keys()				)
               lowercase__       =				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(UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					Any     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					Optional[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				):
                             lowercase__       =				np.max(np.abs(pt_out.numpy() - tf_out.numpy()				)				)
                             lowercase__       =				tensor_difference
                      else:
                             lowercase__       =				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				):
                                           lowercase__       =				root_name + pt_item
                                           lowercase__       =				tf_out[pt_item]
                                           lowercase__       =				pt_out[pt_item]
                                    else:
                                           lowercase__       =				root_name + f"[{i}]"
                                           lowercase__       =				tf_out[i]
                                    lowercase__       =				_find_pt_tf_differences(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				)
  
                      return differences
  
               return _find_pt_tf_differences(UpperCamelCase     ,		UpperCamelCase     ,		{}				)
  
  
  
        def __init__(self					:					Optional[Any]     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					float     ,		UpperCamelCase					:					bool     ,		UpperCamelCase					:					bool     ,		UpperCamelCase					:					bool     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					str     ,		*UpperCamelCase					:					Tuple     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				logging.get_logger('''transformers-cli/pt_to_tf'''				)
               lowercase__       =				model_name
               lowercase__       =				local_dir if local_dir else os.path.join('''/tmp'''     ,		UpperCamelCase				)
               lowercase__       =				max_error
               lowercase__       =				new_weights
               lowercase__       =				no_pr
               lowercase__       =				push
               lowercase__       =				extra_commit_description
               lowercase__       =				override_model_class
  
  
  
        def    UpperCamelCase__	(self					:					List[str]     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               def _get_audio_input():
                      lowercase__       =				load_dataset('''hf-internal-testing/librispeech_asr_dummy'''     ,		'''clean'''     ,		split='''validation'''				)
                      lowercase__       =				ds.sort('''id'''				).select(range(2				)				)[:2]['''audio''']
                      lowercase__       =				[x['''array'''] for x in speech_samples]
                      return raw_samples
  
               lowercase__       =				type(pt_model.config				)
               if model_config_class in PROCESSOR_MAPPING:
                      lowercase__       =				AutoProcessor.from_pretrained(self._local_dir				)
                      if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
                             lowercase__       =				processor.tokenizer.eos_token
               elif model_config_class in IMAGE_PROCESSOR_MAPPING:
                      lowercase__       =				AutoImageProcessor.from_pretrained(self._local_dir				)
               elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
                      lowercase__       =				AutoFeatureExtractor.from_pretrained(self._local_dir				)
               elif model_config_class in TOKENIZER_MAPPING:
                      lowercase__       =				AutoTokenizer.from_pretrained(self._local_dir				)
                      if processor.pad_token is None:
                             lowercase__       =				processor.eos_token
               else:
                      raise ValueError(f"Unknown data processing type (model config type: {model_config_class})"				)
  
               lowercase__       =				set(inspect.signature(pt_model.forward				).parameters.keys()				)
               lowercase__       =				{}
               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:
                      lowercase__       =				load_dataset('''cifar10'''     ,		'''plain_text'''     ,		split='''test'''				)[:2]['''img''']
                      processor_inputs.update({'''images''': sample_images}				)
               if "input_features" in model_forward_signature:
                      lowercase__       =				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:
                             lowercase__       =				feature_extractor_signature['''padding'''].default
                             if default_strategy is not False and default_strategy is not None:
                                    lowercase__       =				default_strategy
                             else:
                                    lowercase__       =				True
                      else:
                             lowercase__       =				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}				)
               lowercase__       =				processor(**UpperCamelCase     ,		return_tensors='''pt'''				)
               lowercase__       =				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
               ):
                      lowercase__       =				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    UpperCamelCase__	(self					:					str				):
  
               '''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
               lowercase__       =				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
               lowercase__       =				AutoConfig.from_pretrained(self._local_dir				)
               lowercase__       =				config.architectures
               if self._override_model_class is not None:
                      if self._override_model_class.startswith('''TF'''				):
                             lowercase__       =				[self._override_model_class[2:]]
                      else:
                             lowercase__       =				[self._override_model_class]
                      try:
                             lowercase__       =				getattr(import_module('''transformers'''				)     ,		architectures[0]				)
                      except AttributeError:
                             raise ValueError(f"Model class {self._override_model_class} not found in transformers."				)
                      try:
                             lowercase__       =				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
                      lowercase__       =				getattr(import_module('''transformers'''				)     ,		'''AutoModel'''				)
                      lowercase__       =				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]}"				)
                      lowercase__       =				getattr(import_module('''transformers'''				)     ,		architectures[0]				)
                      try:
                             lowercase__       =				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
               lowercase__       =				tf_class.from_config(UpperCamelCase				)
               lowercase__       =				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
               lowercase__       =				pt_class.from_pretrained(self._local_dir				)
               pt_model.eval()
  
               lowercase__    ,lowercase__       =				self.get_inputs(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				)
  
               with torch.no_grad():
                      lowercase__       =				pt_model(**UpperCamelCase     ,		output_hidden_states=UpperCamelCase				)
               del pt_model  # will no longer be used, and may have a large memory footprint
  
               lowercase__       =				tf_class.from_pretrained(self._local_dir     ,		from_pt=UpperCamelCase				)
               lowercase__       =				tf_from_pt_model(**UpperCamelCase     ,		output_hidden_states=UpperCamelCase     ,		training=UpperCamelCase				)
  
               # Confirms that cross loading PT weights into TF worked.
               lowercase__       =				self.find_pt_tf_differences(UpperCamelCase     ,		UpperCamelCase				)
               lowercase__       =				{k: v for k, v in crossload_differences.items() if '''hidden''' not in k}
               lowercase__       =				{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\''''				)
               lowercase__       =				max(output_differences.values()				) if output_differences else 0.0
               lowercase__       =				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
               lowercase__       =				os.path.join(self._local_dir     ,		UpperCamelCase				)
               lowercase__       =				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
  
               lowercase__       =				tf_class.from_pretrained(self._local_dir				)
               lowercase__       =				tf_model(**UpperCamelCase     ,		output_hidden_states=UpperCamelCase				)
  
               lowercase__       =				self.find_pt_tf_differences(UpperCamelCase     ,		UpperCamelCase				)
               lowercase__       =				{k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
               lowercase__       =				{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\''''				)
               lowercase__       =				max(output_differences.values()				) if output_differences else 0.0
               lowercase__       =				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]				)				)
  
               lowercase__       =				'''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...'''				)
                      lowercase__       =				(
                          '''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				):
                             lowercase__       =				[
                                 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:
                             lowercase__       =				[CommitOperationAdd(path_in_repo=UpperCamelCase     ,		path_or_fileobj=UpperCamelCase				)]
  
                      lowercase__       =				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}"				)
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  import json
  import os
  from functools import lru_cache
  from typing import TYPE_CHECKING, List, Optional, Tuple, Union
  
  import numpy as np
  import regex as re
  
  from ...utils import is_tf_available, is_torch_available, logging
  
  
  if TYPE_CHECKING:
   if is_torch_available():
    import torch
   if is_tf_available():
    import tensorflow as tf
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  
  
  lowerCamelCase							:     Union[str, Any]								=			logging.get_logger(__name__)
  
  lowerCamelCase							:     Tuple								=			{
      'vocab_file': 'vocab.json',
      'merges_file': 'merges.txt',
  }
  
  lowerCamelCase							:     Tuple								=			{
      'vocab_file': {
          'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json',
      },
      'merges_file': {
          'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt',
      },
  }
  
  lowerCamelCase							:     Optional[int]								=			{
      'Salesforce/codegen-350M-mono': 2_048,
  }
  
  @lru_cache()
  def _SCREAMING_SNAKE_CASE   () -> Optional[int]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				(
             list(range(ord('''!''' )						,				ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' )						,				ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' )						,				ord('''ÿ''' ) + 1 ) )
         )
         lowercase__       =				bs[:]
         lowercase__       =				0
         for b in range(2**8 ):
                if b not in bs:
                       bs.append(A )
                       cs.append(2**8 + n )
                       n += 1
         lowercase__       =				[chr(A ) for n in cs]
         return dict(zip(A						,				A ) )
  
  def _SCREAMING_SNAKE_CASE   (A ) -> Tuple:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				set()
         lowercase__       =				word[0]
         for char in word[1:]:
                pairs.add((prev_char, char) )
                lowercase__       =				char
         return pairs
  
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  int						      =							VOCAB_FILES_NAMES
        lowerCAmelCase__      :  Union[str, Any]						      =							PRETRAINED_VOCAB_FILES_MAP
        lowerCAmelCase__      :  Optional[int]						      =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
        lowerCAmelCase__      :  Tuple						      =							["""input_ids""", """attention_mask"""]
  
  
  
        def __init__(self					:					List[Any]     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					Optional[int]="replace"     ,		UpperCamelCase					:					Optional[Any]="<|endoftext|>"     ,		UpperCamelCase					:					str="<|endoftext|>"     ,		UpperCamelCase					:					Optional[int]="<|endoftext|>"     ,		UpperCamelCase					:					Optional[int]=None     ,		UpperCamelCase					:					str=False     ,		UpperCamelCase					:					Any=False     ,		**UpperCamelCase					:					Dict     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				AddedToken(UpperCamelCase     ,		lstrip=UpperCamelCase     ,		rstrip=UpperCamelCase				) if isinstance(UpperCamelCase     ,		UpperCamelCase				) else bos_token
               lowercase__       =				AddedToken(UpperCamelCase     ,		lstrip=UpperCamelCase     ,		rstrip=UpperCamelCase				) if isinstance(UpperCamelCase     ,		UpperCamelCase				) else eos_token
               lowercase__       =				AddedToken(UpperCamelCase     ,		lstrip=UpperCamelCase     ,		rstrip=UpperCamelCase				) if isinstance(UpperCamelCase     ,		UpperCamelCase				) else unk_token
               lowercase__       =				AddedToken(UpperCamelCase     ,		lstrip=UpperCamelCase     ,		rstrip=UpperCamelCase				) if isinstance(UpperCamelCase     ,		UpperCamelCase				) else pad_token
               super().__init__(
                   errors=UpperCamelCase     ,		unk_token=UpperCamelCase     ,		bos_token=UpperCamelCase     ,		eos_token=UpperCamelCase     ,		pad_token=UpperCamelCase     ,		add_prefix_space=UpperCamelCase     ,		add_bos_token=UpperCamelCase     ,		**UpperCamelCase     ,		)
               lowercase__       =				add_bos_token
  
               with open(UpperCamelCase     ,		encoding='''utf-8'''				) as vocab_handle:
                      lowercase__       =				json.load(UpperCamelCase				)
               lowercase__       =				{v: k for k, v in self.encoder.items()}
               lowercase__       =				errors  # how to handle errors in decoding
               lowercase__       =				bytes_to_unicode()
               lowercase__       =				{v: k for k, v in self.byte_encoder.items()}
               with open(UpperCamelCase     ,		encoding='''utf-8'''				) as merges_handle:
                      lowercase__       =				merges_handle.read().split('''\n'''				)[1:-1]
               lowercase__       =				[tuple(merge.split()				) for merge in bpe_merges]
               lowercase__       =				dict(zip(UpperCamelCase     ,		range(len(UpperCamelCase				)				)				)				)
               lowercase__       =				{}
               lowercase__       =				add_prefix_space
  
               # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
               lowercase__       =				re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''				)
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Any				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return len(self.encoder				)
  
  
  
        def    UpperCamelCase__	(self					:					Optional[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return dict(self.encoder     ,		**self.added_tokens_encoder				)
  
  
  
        def    UpperCamelCase__	(self					:					Union[str, Any]     ,		UpperCamelCase					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if token in self.cache:
                      return self.cache[token]
               lowercase__       =				tuple(UpperCamelCase				)
               lowercase__       =				get_pairs(UpperCamelCase				)
  
               if not pairs:
                      return token
  
               while True:
                      lowercase__       =				min(UpperCamelCase     ,		key=lambda UpperCamelCase				: self.bpe_ranks.get(UpperCamelCase     ,		float('''inf'''				)				)				)
                      if bigram not in self.bpe_ranks:
                             break
                      lowercase__    ,lowercase__       =				bigram
                      lowercase__       =				[]
                      lowercase__       =				0
                      while i < len(UpperCamelCase				):
                             try:
                                    lowercase__       =				word.index(UpperCamelCase     ,		UpperCamelCase				)
                             except ValueError:
                                    new_word.extend(word[i:]				)
                                    break
                             else:
                                    new_word.extend(word[i:j]				)
                                    lowercase__       =				j
  
                             if word[i] == first and i < len(UpperCamelCase				) - 1 and word[i + 1] == second:
                                    new_word.append(first + second				)
                                    i += 2
                             else:
                                    new_word.append(word[i]				)
                                    i += 1
                      lowercase__       =				tuple(UpperCamelCase				)
                      lowercase__       =				new_word
                      if len(UpperCamelCase				) == 1:
                             break
                      else:
                             lowercase__       =				get_pairs(UpperCamelCase				)
               lowercase__       =				''' '''.join(UpperCamelCase				)
               lowercase__       =				word
               return word
  
  
  
        def    UpperCamelCase__	(self					:					Any     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					str=None				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if self.add_bos_token:
                      lowercase__       =				[self.bos_token_id]
               else:
                      lowercase__       =				[]
  
               lowercase__       =				bos_token_ids + token_ids_a
  
               if token_ids_a is None:
                      return output
  
               return output + bos_token_ids + token_ids_a
  
  
  
        def    UpperCamelCase__	(self					:					Optional[Any]     ,		UpperCamelCase					:					str				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				[]
               for token in re.findall(self.pat     ,		UpperCamelCase				):
                      lowercase__       =				''''''.join(
                          self.byte_encoder[b] for b in token.encode('''utf-8'''				)				)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                      bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase				).split(''' '''				)				)
               return bpe_tokens
  
  
  
        def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					str				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return self.encoder.get(UpperCamelCase     ,		self.encoder.get(self.unk_token				)				)
  
  
  
        def    UpperCamelCase__	(self					:					Any     ,		UpperCamelCase					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return self.decoder.get(UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					List[Any]     ,		UpperCamelCase					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				''''''.join(UpperCamelCase				)
               lowercase__       =				bytearray([self.byte_decoder[c] for c in text]				).decode('''utf-8'''     ,		errors=self.errors				)
               return text
  
  
  
        def    UpperCamelCase__	(self					:					int     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					Optional[str] = None				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if not os.path.isdir(UpperCamelCase				):
                      logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
                      return
               lowercase__       =				os.path.join(
                   UpperCamelCase     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
               lowercase__       =				os.path.join(
                   UpperCamelCase     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']				)
  
               with open(UpperCamelCase     ,		'''w'''     ,		encoding='''utf-8'''				) as f:
                      f.write(json.dumps(self.encoder     ,		indent=2     ,		sort_keys=UpperCamelCase     ,		ensure_ascii=UpperCamelCase				) + '''\n'''				)
  
               lowercase__       =				0
               with open(UpperCamelCase     ,		'''w'''     ,		encoding='''utf-8'''				) as writer:
                      writer.write('''#version: 0.2\n'''				)
                      for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,		key=lambda UpperCamelCase				: kv[1]				):
                             if index != token_index:
                                    logger.warning(
                                        f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                                        ''' Please check that the tokenizer is not corrupted!'''				)
                                    lowercase__       =				token_index
                             writer.write(''' '''.join(UpperCamelCase				) + '''\n'''				)
                             index += 1
  
               return vocab_file, merge_file
  
  
  
        def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					int=False     ,		**UpperCamelCase					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				kwargs.pop('''add_prefix_space'''     ,		self.add_prefix_space				)
               if is_split_into_words or add_prefix_space:
                      lowercase__       =				''' ''' + text
               return (text, kwargs)
  
  
  
        def    UpperCamelCase__	(self					:					Tuple     ,		UpperCamelCase					:					Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]     ,		UpperCamelCase					:					bool = False     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Optional[List[str]] = None     ,		**UpperCamelCase					:					Optional[Any]     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				super()._decode(
                   token_ids=UpperCamelCase     ,		skip_special_tokens=UpperCamelCase     ,		clean_up_tokenization_spaces=UpperCamelCase     ,		**UpperCamelCase     ,		)
  
               if truncate_before_pattern is not None and len(UpperCamelCase				) > 0:
                      lowercase__       =				self.truncate(UpperCamelCase     ,		UpperCamelCase				)
  
               return decoded_text
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               def find_re(UpperCamelCase					:					Dict     ,		UpperCamelCase					:					Dict     ,		UpperCamelCase					:					Optional[Any]				):
                      lowercase__       =				pattern.search(UpperCamelCase     ,		UpperCamelCase				)
                      return m.start() if m else -1
  
               lowercase__       =				[re.compile(UpperCamelCase     ,		re.MULTILINE				) for pattern in truncate_before_pattern]
  
               lowercase__       =				list(re.finditer('''^print'''     ,		UpperCamelCase     ,		re.MULTILINE				)				)
  
               if len(UpperCamelCase				) > 1:
                      lowercase__       =				completion[: prints[1].start()]
  
               lowercase__       =				list(re.finditer('''^def'''     ,		UpperCamelCase     ,		re.MULTILINE				)				)
  
               if len(UpperCamelCase				) > 1:
                      lowercase__       =				completion[: defs[1].start()]
  
               lowercase__       =				0
  
               lowercase__       =				[
                   pos for pos in [find_re(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				) for terminal in terminals] if pos != -1
               ]
  
               if len(UpperCamelCase				) > 0:
                      return completion[: min(UpperCamelCase				)]
               else:
                      return completion
  
  
  
  
  