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



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Namespace      )				->   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 __snake_case							(		__snake_case						):




   @staticmethod
   def       __a		(       _lowercase     :							Tuple ):



     """simple docstring"""






     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=__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       __a		(       _lowercase     :							Any			,      _lowercase     :							Dict ):



     """simple docstring"""






     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(_lowercase     :							str			,      _lowercase     :							Dict			,      _lowercase     :							Any			,      _lowercase     :							str="" ):
       # 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 ):
         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(__UpperCamelCase ):
           # If it is a named attribute, we keep the name. Otherwise, just its index.
           if isinstance(__UpperCamelCase			,      __UpperCamelCase ):
             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(__UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase )

       return differences

     return _find_pt_tf_differences(__UpperCamelCase			,      __UpperCamelCase			,      {} )




   def __init__(       self     :							Tuple			,      _lowercase     :							Tuple			,      _lowercase     :							Any			,      _lowercase     :							Optional[Any]			,      _lowercase     :							Tuple			,      _lowercase     :							str			,      _lowercase     :							Tuple			,      _lowercase     :							Optional[int]			,      _lowercase     :							Dict			,      *_lowercase     :							int			,      ):



     """simple docstring"""






     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"""			,      __UpperCamelCase )
     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       __a		(       self     :							Tuple			,      _lowercase     :							List[Any]			,      _lowercase     :							int			,      _lowercase     :							int ):



     """simple docstring"""






     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(**__UpperCamelCase			,      return_tensors="""pt""" )
     SCREAMING_SNAKE_CASE__     				= 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
     ):
       SCREAMING_SNAKE_CASE__     				= 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       __a		(       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
     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(__UpperCamelCase ) > 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(__UpperCamelCase )
     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(__UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase )

     with torch.no_grad():
       SCREAMING_SNAKE_CASE__     				= pt_model(**__UpperCamelCase			,      output_hidden_states=__UpperCamelCase )
     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=__UpperCamelCase )
     SCREAMING_SNAKE_CASE__     				= tf_from_pt_model(**__UpperCamelCase			,      output_hidden_states=__UpperCamelCase			,      training=__UpperCamelCase )

     # Confirms that cross loading PT weights into TF worked.
     SCREAMING_SNAKE_CASE__     				= self.find_pt_tf_differences(__UpperCamelCase			,      __UpperCamelCase )
     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(__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'""" )
     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			,      __UpperCamelCase )
     SCREAMING_SNAKE_CASE__     				= 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

     SCREAMING_SNAKE_CASE__     				= tf_class.from_pretrained(self._local_dir )
     SCREAMING_SNAKE_CASE__     				= tf_model(**__UpperCamelCase			,      output_hidden_states=__UpperCamelCase )

     SCREAMING_SNAKE_CASE__     				= self.find_pt_tf_differences(__UpperCamelCase			,      __UpperCamelCase )
     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(__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'""" )
     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=__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...""" )
       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(__UpperCamelCase ):
         SCREAMING_SNAKE_CASE__     				= [
             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:
         SCREAMING_SNAKE_CASE__     				= [CommitOperationAdd(path_in_repo=__UpperCamelCase			,      path_or_fileobj=__UpperCamelCase )]

       SCREAMING_SNAKE_CASE__     				= 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}""" )


import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__magic_name__  :  List[Any]     			=   logging.get_logger(__name__)

__magic_name__  :  Any     			=   {
    'src_vocab_file': 'vocab-src.json',
    'tgt_vocab_file': 'vocab-tgt.json',
    'merges_file': 'merges.txt',
}

__magic_name__  :  Dict     			=   {
    'src_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json'
    },
    'tgt_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json'
    },
    'merges_file': {'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt'},
}

__magic_name__  :  Any     			=   {'stas/tiny-wmt19-en-de': 1_0_2_4}
__magic_name__  :  int     			=   {
    'stas/tiny-wmt19-en-de': {
        'langs': ['en', 'de'],
        'model_max_length': 1_0_2_4,
        'special_tokens_map_file': None,
        'full_tokenizer_file': None,
    }
}





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			Dict:
    A_								=					set()
    A_								=					word[0]
    for char in word[1:]:
        pairs.add((prev_char, char) )
        A_								=					char
    return pairs





def        lowerCAmelCase       (				snake_case__   :      Optional[int] )->			List[Any]:
    A_								=					text.replace("，"				,					"," )
    A_								=					re.sub(r"。\s*"				,					". "				,					snake_case__ )
    A_								=					text.replace("、"				,					"," )
    A_								=					text.replace("”"				,					"\"" )
    A_								=					text.replace("“"				,					"\"" )
    A_								=					text.replace("∶"				,					":" )
    A_								=					text.replace("："				,					":" )
    A_								=					text.replace("？"				,					"?" )
    A_								=					text.replace("《"				,					"\"" )
    A_								=					text.replace("》"				,					"\"" )
    A_								=					text.replace("）"				,					")" )
    A_								=					text.replace("！"				,					"!" )
    A_								=					text.replace("（"				,					"(" )
    A_								=					text.replace("；"				,					";" )
    A_								=					text.replace("１"				,					"1" )
    A_								=					text.replace("」"				,					"\"" )
    A_								=					text.replace("「"				,					"\"" )
    A_								=					text.replace("０"				,					"0" )
    A_								=					text.replace("３"				,					"3" )
    A_								=					text.replace("２"				,					"2" )
    A_								=					text.replace("５"				,					"5" )
    A_								=					text.replace("６"				,					"6" )
    A_								=					text.replace("９"				,					"9" )
    A_								=					text.replace("７"				,					"7" )
    A_								=					text.replace("８"				,					"8" )
    A_								=					text.replace("４"				,					"4" )
    A_								=					re.sub(r"．\s*"				,					". "				,					snake_case__ )
    A_								=					text.replace("～"				,					"~" )
    A_								=					text.replace("’"				,					"'" )
    A_								=					text.replace("…"				,					"..." )
    A_								=					text.replace("━"				,					"-" )
    A_								=					text.replace("〈"				,					"<" )
    A_								=					text.replace("〉"				,					">" )
    A_								=					text.replace("【"				,					"[" )
    A_								=					text.replace("】"				,					"]" )
    A_								=					text.replace("％"				,					"%" )
    return text





def        lowerCAmelCase       (				snake_case__   :      List[Any] )->			List[Any]:
    A_								=					[]
    for char in text:
        A_								=					unicodedata.category(snake_case__ )
        if cat.startswith("C" ):
            continue
        output.append(snake_case__ )
    return "".join(snake_case__ )



class 							lowerCamelCase       (		__snake_case					):



  """simple docstring"""





  lowerCAmelCase_								=		VOCAB_FILES_NAMES
  lowerCAmelCase_								=		PRETRAINED_VOCAB_FILES_MAP
  lowerCAmelCase_								=		PRETRAINED_INIT_CONFIGURATION
  lowerCAmelCase_								=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCAmelCase_								=		["""input_ids""", """attention_mask"""]

  def __init__(					self						,    __UpperCamelCase=None						,    __UpperCamelCase=None						,    __UpperCamelCase=None						,    __UpperCamelCase=None						,    __UpperCamelCase=False						,    __UpperCamelCase="<unk>"						,    __UpperCamelCase="<s>"						,    __UpperCamelCase="</s>"						,    __UpperCamelCase="<pad>"						,    **__UpperCamelCase						,    ):
      super().__init__(
          langs=__UpperCamelCase						,    src_vocab_file=__UpperCamelCase						,    tgt_vocab_file=__UpperCamelCase						,    merges_file=__UpperCamelCase						,    do_lower_case=__UpperCamelCase						,    unk_token=__UpperCamelCase						,    bos_token=__UpperCamelCase						,    sep_token=__UpperCamelCase						,    pad_token=__UpperCamelCase						,    **__UpperCamelCase						,    )

      try:
          import sacremoses
      except ImportError:
          raise ImportError(
              "You need to install sacremoses to use XLMTokenizer. "
              "See https://pypi.org/project/sacremoses/ for installation."					)

      A_								=					sacremoses

      A_								=					src_vocab_file
      A_								=					tgt_vocab_file
      A_								=					merges_file
      A_								=					do_lower_case

      # cache of sm.MosesPunctNormalizer instance
      A_								=					{}
      # cache of sm.MosesTokenizer instance
      A_								=					{}
      A_								=					{}

      if langs and len(__UpperCamelCase					) == 2:
          A_	,	A_								=					langs
      else:
          raise ValueError(
              f'arg `langs` needs to be a list of 2 langs, e.g. [\'en\', \'ru\'], but got {langs}. '
              "Usually that means that tokenizer can't find a mapping for the given model path "
              "in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer."					)

      with open(__UpperCamelCase						,    encoding="utf-8"					) as src_vocab_handle:
          A_								=					json.load(__UpperCamelCase					)
      with open(__UpperCamelCase						,    encoding="utf-8"					) as tgt_vocab_handle:
          A_								=					json.load(__UpperCamelCase					)
          A_								=					{v: k for k, v in tgt_vocab.items()}
      with open(__UpperCamelCase						,    encoding="utf-8"					) as merges_handle:
          A_								=					merges_handle.read().split("\n"					)[:-1]
      A_								=					[tuple(merge.split()[:2]					) for merge in merges]
      A_								=					dict(zip(__UpperCamelCase						,    range(len(__UpperCamelCase					)					)					)					)
      A_								=					{}

  def 					lowercase_						(					self					):
      return self.get_src_vocab()

  @property
  def 					lowercase_						(					self					):
      return self.src_vocab_size

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      if lang not in self.cache_moses_punct_normalizer:
          A_								=					self.sm.MosesPunctNormalizer(lang=__UpperCamelCase					)
          A_								=					punct_normalizer
      return self.cache_moses_punct_normalizer[lang].normalize(__UpperCamelCase					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      if lang not in self.cache_moses_tokenizer:
          A_								=					self.sm.MosesTokenizer(lang=__UpperCamelCase					)
          A_								=					moses_tokenizer
      return self.cache_moses_tokenizer[lang].tokenize(
          __UpperCamelCase						,    aggressive_dash_splits=__UpperCamelCase						,    return_str=__UpperCamelCase						,    escape=__UpperCamelCase					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      if lang not in self.cache_moses_detokenizer:
          A_								=					self.sm.MosesDetokenizer(lang=__UpperCamelCase					)
          A_								=					moses_detokenizer
      return self.cache_moses_detokenizer[lang].detokenize(__UpperCamelCase					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					replace_unicode_punct(__UpperCamelCase					)
      A_								=					self.moses_punct_norm(__UpperCamelCase						,    __UpperCamelCase					)
      A_								=					remove_non_printing_char(__UpperCamelCase					)
      return text

  @property
  def 					lowercase_						(					self					):
      return len(self.encoder					)

  @property
  def 					lowercase_						(					self					):
      return len(self.decoder					)

  def 					lowercase_						(					self					):
      return dict(self.encoder						,    **self.added_tokens_encoder					)

  def 					lowercase_						(					self					):
      return dict(self.decoder						,    **self.added_tokens_decoder					)

  def 					lowercase_						(					self						,    __UpperCamelCase					):
      A_								=					tuple(token[:-1]					) + (token[-1] + "</w>",)
      if token in self.cache:
          return self.cache[token]
      A_								=					get_pairs(__UpperCamelCase					)

      if not pairs:
          return token + "</w>"

      while True:
          A_								=					min(__UpperCamelCase						,    key=lambda __UpperCamelCase					: self.bpe_ranks.get(__UpperCamelCase						,    float("inf"					)					)					)
          if bigram not in self.bpe_ranks:
              break
          A_	,	A_								=					bigram
          A_								=					[]
          A_								=					0
          while i < len(__UpperCamelCase					):
              try:
                  A_								=					word.index(__UpperCamelCase						,    __UpperCamelCase					)
              except ValueError:
                  new_word.extend(word[i:]					)
                  break
              else:
                  new_word.extend(word[i:j]					)
                  A_								=					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
          A_								=					tuple(__UpperCamelCase					)
          A_								=					new_word
          if len(__UpperCamelCase					) == 1:
              break
          else:
              A_								=					get_pairs(__UpperCamelCase					)
      A_								=					" ".join(__UpperCamelCase					)
      if word == "\n  </w>":
          A_								=					"\n</w>"
      A_								=					word
      return word

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase="en"						,    __UpperCamelCase=False					):
      A_								=					self.src_lang

      if self.do_lower_case:
          A_								=					text.lower()

      if bypass_tokenizer:
          A_								=					text.split()
      else:
          A_								=					self.moses_pipeline(__UpperCamelCase						,    lang=__UpperCamelCase					)
          A_								=					self.moses_tokenize(__UpperCamelCase						,    lang=__UpperCamelCase					)

      A_								=					[]
      for token in text:
          if token:
              split_tokens.extend(list(self.bpe(__UpperCamelCase					).split(" "					)					)					)

      return split_tokens

  def 					lowercase_						(					self						,    __UpperCamelCase					):
      return self.encoder.get(__UpperCamelCase						,    self.encoder.get(self.unk_token					)					)

  def 					lowercase_						(					self						,    __UpperCamelCase					):
      return self.decoder.get(__UpperCamelCase						,    self.unk_token					)

  def 					lowercase_						(					self						,    __UpperCamelCase					):
      A_								=					[t.replace(" "						,    ""					).replace("</w>"						,    " "					) for t in tokens]
      A_								=					"".join(__UpperCamelCase					).split()
      # detokenize
      A_								=					self.moses_detokenize(__UpperCamelCase						,    self.tgt_lang					)
      return text

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase = None					):
      A_								=					[self.sep_token_id]

      # no bos used in fairseq
      if token_ids_a is None:
          return token_ids_a + sep
      return token_ids_a + sep + token_ids_a + sep

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase = None						,    __UpperCamelCase = False					):

      if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=__UpperCamelCase						,    token_ids_a=__UpperCamelCase						,    already_has_special_tokens=__UpperCamelCase					)
      # no bos used in fairseq
      if token_ids_a is not None:
          return ([0] * len(__UpperCamelCase					)) + [1] + ([0] * len(__UpperCamelCase					)) + [1]
      return ([0] * len(__UpperCamelCase					)) + [1]

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase = None					):
      A_								=					[self.sep_token_id]

      # no bos used in fairseq
      if token_ids_a is None:
          return len(token_ids_a + sep					) * [0]
      return len(token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase = None					):
      if not os.path.isdir(__UpperCamelCase					):
          logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
          return

      A_								=					os.path.join(
          __UpperCamelCase						,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["src_vocab_file"]					)
      A_								=					os.path.join(
          __UpperCamelCase						,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["tgt_vocab_file"]					)
      A_								=					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"					)

      with open(__UpperCamelCase						,    "w"						,    encoding="utf-8"					) as f:
          A_								=					{v: k for k, v in self.decoder.items()}
          f.write(json.dumps(__UpperCamelCase						,    indent=2						,    sort_keys=__UpperCamelCase						,    ensure_ascii=__UpperCamelCase					) + "\n"					)

      A_								=					0
      with open(__UpperCamelCase						,    "w"						,    encoding="utf-8"					) as writer:
          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 {merges_file}: BPE merge indices are not consecutive.'
                      " Please check that the tokenizer is not corrupted!"					)
                  A_								=					token_index
              writer.write(" ".join(__UpperCamelCase					) + "\n"					)
              index += 1

      return src_vocab_file, tgt_vocab_file, merges_file

  def __getstate__(					self					):
      A_								=					self.__dict__.copy()
      A_								=					None
      return state

  def __setstate__(					self						,    __UpperCamelCase					):
      A_								=					d

      try:
          import sacremoses
      except ImportError:
          raise ImportError(
              "You need to install sacremoses to use XLMTokenizer. "
              "See https://pypi.org/project/sacremoses/ for installation."					)

      A_								=					sacremoses






