


import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


__UpperCAmelCase : List[str] 			=				logging.get_logger(__name__)


__UpperCAmelCase : Optional[int] 			=				{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase : Union[str, Any] 			=				{
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

__UpperCAmelCase : List[Any] 			=				{
    "allenai/led-base-16384": 16384,
}



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Optional[int]							=					VOCAB_FILES_NAMES
       __UpperCamelCase      :     str							=					PRETRAINED_VOCAB_FILES_MAP
       __UpperCamelCase      :     Optional[Any]							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCamelCase      :     Optional[int]							=					LEDTokenizer
       __UpperCamelCase      :     Optional[Any]							=					["input_ids", "attention_mask"]






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE="replace" ,					__SCREAMING_SNAKE_CASE="<s>" ,					__SCREAMING_SNAKE_CASE="</s>" ,					__SCREAMING_SNAKE_CASE="</s>" ,					__SCREAMING_SNAKE_CASE="<s>" ,					__SCREAMING_SNAKE_CASE="<unk>" ,					__SCREAMING_SNAKE_CASE="<pad>" ,					__SCREAMING_SNAKE_CASE="<mask>" ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE=True ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              super().__init__(
                  __SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					tokenizer_file=__SCREAMING_SNAKE_CASE ,					errors=__SCREAMING_SNAKE_CASE ,					bos_token=__SCREAMING_SNAKE_CASE ,					eos_token=__SCREAMING_SNAKE_CASE ,					sep_token=__SCREAMING_SNAKE_CASE ,					cls_token=__SCREAMING_SNAKE_CASE ,					unk_token=__SCREAMING_SNAKE_CASE ,					pad_token=__SCREAMING_SNAKE_CASE ,					mask_token=__SCREAMING_SNAKE_CASE ,					add_prefix_space=__SCREAMING_SNAKE_CASE ,					trim_offsets=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE ,					)

              UpperCamelCase    :	List[Any]   	=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
              if pre_tok_state.get('''add_prefix_space''' ,					__SCREAMING_SNAKE_CASE						) != add_prefix_space:
                     UpperCamelCase    :	Union[str, Any]   	=		getattr(__SCREAMING_SNAKE_CASE ,					pre_tok_state.pop('''type'''						)						)
                     UpperCamelCase    :	Dict   	=		add_prefix_space
                     UpperCamelCase    :	Dict   	=		pre_tok_class(**__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	List[str]   	=		add_prefix_space

              # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
              UpperCamelCase    :	Union[str, Any]   	=		'''post_processor'''
              UpperCamelCase    :	int   	=		getattr(self.backend_tokenizer ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
              if tokenizer_component_instance:
                     UpperCamelCase    :	Any   	=		json.loads(tokenizer_component_instance.__getstate__()						)

                     # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                     if "sep" in state:
                            UpperCamelCase    :	int   	=		tuple(state['''sep''']						)
                     if "cls" in state:
                            UpperCamelCase    :	int   	=		tuple(state['''cls''']						)

                     UpperCamelCase    :	Union[str, Any]   	=		False

                     if state.get('''add_prefix_space''' ,					__SCREAMING_SNAKE_CASE						) != add_prefix_space:
                            UpperCamelCase    :	List[Any]   	=		add_prefix_space
                            UpperCamelCase    :	List[Any]   	=		True

                     if state.get('''trim_offsets''' ,					__SCREAMING_SNAKE_CASE						) != trim_offsets:
                            UpperCamelCase    :	Dict   	=		trim_offsets
                            UpperCamelCase    :	Any   	=		True

                     if changes_to_apply:
                            UpperCamelCase    :	Optional[Any]   	=		getattr(__SCREAMING_SNAKE_CASE ,					state.pop('''type'''						)						)
                            UpperCamelCase    :	Any   	=		component_class(**__SCREAMING_SNAKE_CASE						)
                            setattr(self.backend_tokenizer ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)






       @property
       # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
       def  _lowercase   (     self						):




              """simple docstring"""




              if self._mask_token is None:
                     if self.verbose:
                            logger.error('''Using mask_token, but it is not set yet.'''						)
                     return None
              return str(self._mask_token						)






       @mask_token.setter
       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		AddedToken(__SCREAMING_SNAKE_CASE ,					lstrip=__SCREAMING_SNAKE_CASE ,					rstrip=__SCREAMING_SNAKE_CASE						) if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						) else value
              UpperCamelCase    :	Dict   	=		value






       def  _lowercase   (     self ,					*__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		kwargs.get('''is_split_into_words''' ,					__SCREAMING_SNAKE_CASE						)

              if is_split_into_words and not self.add_prefix_space:
                     raise ValueError(
                         f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                         '''to use it with pretokenized inputs.'''						)

              return super()._batch_encode_plus(*__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					*__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		kwargs.get('''is_split_into_words''' ,					__SCREAMING_SNAKE_CASE						)

              if is_split_into_words and not self.add_prefix_space:
                     raise ValueError(
                         f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                         '''to use it with pretokenized inputs.'''						)

              return super()._encode_plus(*__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		self._tokenizer.model.save(__SCREAMING_SNAKE_CASE ,					name=__SCREAMING_SNAKE_CASE						)
              return tuple(__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=None						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		[self.bos_token_id] + token_ids_a + [self.eos_token_id]
              if token_ids_a is None:
                     return output

              return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		[self.sep_token_id]
              UpperCamelCase    :	List[Any]   	=		[self.cls_token_id]

              if token_ids_a is None:
                     return len(cls + token_ids_a + sep						) * [0]
              return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = PaddingStrategy.DO_NOT_PAD ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					):




              """simple docstring"""




              UpperCamelCase    :	str   	=		super()._pad(
                  encoded_inputs=__SCREAMING_SNAKE_CASE ,					max_length=__SCREAMING_SNAKE_CASE ,					padding_strategy=__SCREAMING_SNAKE_CASE ,					pad_to_multiple_of=__SCREAMING_SNAKE_CASE ,					return_attention_mask=__SCREAMING_SNAKE_CASE ,					)

              # Load from model defaults
              if return_attention_mask is None:
                     UpperCamelCase    :	Any   	=		'''attention_mask''' in self.model_input_names

              if return_attention_mask and "global_attention_mask" in encoded_inputs:
                     UpperCamelCase    :	int   	=		encoded_inputs[self.model_input_names[0]]
                     # `global_attention_mask` need to have the same length as other (sequential) inputs.
                     UpperCamelCase    :	Dict   	=		len(encoded_inputs['''global_attention_mask''']						) != len(__SCREAMING_SNAKE_CASE						)

                     if needs_to_be_padded:
                            UpperCamelCase    :	Dict   	=		len(__SCREAMING_SNAKE_CASE						) - len(encoded_inputs['''global_attention_mask''']						)

                            if self.padding_side == "right":
                                   # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                                   UpperCamelCase    :	str   	=		(
                                       encoded_inputs['''global_attention_mask'''] + [-1] * difference
                                   )
                            elif self.padding_side == "left":
                                   UpperCamelCase    :	str   	=		[-1] * difference + encoded_inputs[
                                       '''global_attention_mask'''
                                   ]
                            else:
                                   raise ValueError('''Invalid padding strategy:''' + str(self.padding_side						)						)

              return encoded_inputs





import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
  from cookiecutter.main import cookiecutter

  __UpperCAmelCase : List[Any] 			=				True
except ImportError:
  __UpperCAmelCase : List[str] 			=				False

__UpperCAmelCase : Any 			=				logging.get_logger(__name__)  # pylint: disable=invalid-name


def      a	(							SCREAMING_SNAKE_CASE_      :		Namespace       ):






       """simple docstring"""





       return AddNewModelCommand(args.testing							,			args.testing_file							,			path=args.path       )



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''






       @staticmethod
       def  _lowercase   (     __SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		parser.add_parser('''add-new-model'''						)
              add_new_model_parser.add_argument('''--testing''' ,					action='''store_true''' ,					help='''If in testing mode.'''						)
              add_new_model_parser.add_argument('''--testing_file''' ,					type=__SCREAMING_SNAKE_CASE ,					help='''Configuration file on which to run.'''						)
              add_new_model_parser.add_argument(
                  '''--path''' ,					type=__SCREAMING_SNAKE_CASE ,					help='''Path to cookiecutter. Should only be used for testing purposes.'''						)
              add_new_model_parser.set_defaults(func=__SCREAMING_SNAKE_CASE						)






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=None ,					*__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		testing
              UpperCamelCase    :	Any   	=		testing_file
              UpperCamelCase    :	Dict   	=		path






       def  _lowercase   (     self						):




              """simple docstring"""




              warnings.warn(
                  '''The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '''
                  '''It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '''
                  '''checks, you should use `transformers-cli add-new-model-like` instead.'''						)
              if not _has_cookiecutter:
                     raise ImportError(
                         '''Model creation dependencies are required to use the `add_new_model` command. Install them by running '''
                         '''the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n'''						)
              # Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
              UpperCamelCase    :	List[str]   	=		[directory for directory in os.listdir() if '''cookiecutter-template-''' == directory[:22]]
              if len(__SCREAMING_SNAKE_CASE						) > 0:
                     raise ValueError(
                         '''Several directories starting with `cookiecutter-template-` in current working directory. '''
                         '''Please clean your directory by removing all folders starting with `cookiecutter-template-` or '''
                         '''change your working directory.'''						)

              UpperCamelCase    :	Dict   	=		(
                  Path(__SCREAMING_SNAKE_CASE						).parent.parent.parent.parent if self._path is None else Path(self._path						).parent.parent
              )
              UpperCamelCase    :	List[Any]   	=		path_to_transformer_root / '''templates''' / '''adding_a_new_model'''

              # Execute cookiecutter
              if not self._testing:
                     cookiecutter(str(__SCREAMING_SNAKE_CASE						)						)
              else:
                     with open(self._testing_file ,					'''r'''						) as configuration_file:
                            UpperCamelCase    :	Tuple   	=		json.load(__SCREAMING_SNAKE_CASE						)

                     cookiecutter(
                         str(path_to_cookiecutter if self._path is None else self._path						) ,					no_input=__SCREAMING_SNAKE_CASE ,					extra_context=__SCREAMING_SNAKE_CASE ,					)

              UpperCamelCase    :	Dict   	=		[directory for directory in os.listdir() if '''cookiecutter-template-''' in directory[:22]][0]

              # Retrieve configuration
              with open(directory + '''/configuration.json''' ,					'''r'''						) as configuration_file:
                     UpperCamelCase    :	Tuple   	=		json.load(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Dict   	=		configuration['''lowercase_modelname''']
              UpperCamelCase    :	int   	=		configuration['''generate_tensorflow_pytorch_and_flax''']
              os.remove(f"""{directory}/configuration.json"""						)

              UpperCamelCase    :	str   	=		'''PyTorch''' in generate_tensorflow_pytorch_and_flax
              UpperCamelCase    :	Any   	=		'''TensorFlow''' in generate_tensorflow_pytorch_and_flax
              UpperCamelCase    :	Union[str, Any]   	=		'''Flax''' in generate_tensorflow_pytorch_and_flax

              UpperCamelCase    :	Optional[Any]   	=		f"""{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"""
              os.makedirs(__SCREAMING_SNAKE_CASE ,					exist_ok=__SCREAMING_SNAKE_CASE						)
              os.makedirs(f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}""" ,					exist_ok=__SCREAMING_SNAKE_CASE						)

              # Tests require submodules as they have parent imports
              with open(f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py""" ,					'''w'''						):
                     pass

              shutil.move(
                  f"""{directory}/__init__.py""" ,					f"""{model_dir}/__init__.py""" ,					)
              shutil.move(
                  f"""{directory}/configuration_{lowercase_model_name}.py""" ,					f"""{model_dir}/configuration_{lowercase_model_name}.py""" ,					)

              def remove_copy_lines(__SCREAMING_SNAKE_CASE						):
                     with open(__SCREAMING_SNAKE_CASE ,					'''r'''						) as f:
                            UpperCamelCase    :	Any   	=		f.readlines()
                     with open(__SCREAMING_SNAKE_CASE ,					'''w'''						) as f:
                            for line in lines:
                                   if "# Copied from transformers." not in line:
                                          f.write(__SCREAMING_SNAKE_CASE						)

              if output_pytorch:
                     if not self._testing:
                            remove_copy_lines(f"""{directory}/modeling_{lowercase_model_name}.py"""						)

                     shutil.move(
                         f"""{directory}/modeling_{lowercase_model_name}.py""" ,					f"""{model_dir}/modeling_{lowercase_model_name}.py""" ,					)

                     shutil.move(
                         f"""{directory}/test_modeling_{lowercase_model_name}.py""" ,					f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py""" ,					)
              else:
                     os.remove(f"""{directory}/modeling_{lowercase_model_name}.py"""						)
                     os.remove(f"""{directory}/test_modeling_{lowercase_model_name}.py"""						)

              if output_tensorflow:
                     if not self._testing:
                            remove_copy_lines(f"""{directory}/modeling_tf_{lowercase_model_name}.py"""						)

                     shutil.move(
                         f"""{directory}/modeling_tf_{lowercase_model_name}.py""" ,					f"""{model_dir}/modeling_tf_{lowercase_model_name}.py""" ,					)

                     shutil.move(
                         f"""{directory}/test_modeling_tf_{lowercase_model_name}.py""" ,					f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py""" ,					)
              else:
                     os.remove(f"""{directory}/modeling_tf_{lowercase_model_name}.py"""						)
                     os.remove(f"""{directory}/test_modeling_tf_{lowercase_model_name}.py"""						)

              if output_flax:
                     if not self._testing:
                            remove_copy_lines(f"""{directory}/modeling_flax_{lowercase_model_name}.py"""						)

                     shutil.move(
                         f"""{directory}/modeling_flax_{lowercase_model_name}.py""" ,					f"""{model_dir}/modeling_flax_{lowercase_model_name}.py""" ,					)

                     shutil.move(
                         f"""{directory}/test_modeling_flax_{lowercase_model_name}.py""" ,					f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py""" ,					)
              else:
                     os.remove(f"""{directory}/modeling_flax_{lowercase_model_name}.py"""						)
                     os.remove(f"""{directory}/test_modeling_flax_{lowercase_model_name}.py"""						)

              shutil.move(
                  f"""{directory}/{lowercase_model_name}.md""" ,					f"""{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md""" ,					)

              shutil.move(
                  f"""{directory}/tokenization_{lowercase_model_name}.py""" ,					f"""{model_dir}/tokenization_{lowercase_model_name}.py""" ,					)

              shutil.move(
                  f"""{directory}/tokenization_fast_{lowercase_model_name}.py""" ,					f"""{model_dir}/tokenization_{lowercase_model_name}_fast.py""" ,					)

              from os import fdopen, remove
              from shutil import copymode, move
              from tempfile import mkstemp

              def replace(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                     # Create temp file
                     UpperCamelCase	, UpperCamelCase    :	Optional[Any]   	=		mkstemp()
                     UpperCamelCase    :	Tuple   	=		False
                     with fdopen(__SCREAMING_SNAKE_CASE ,					'''w'''						) as new_file:
                            with open(__SCREAMING_SNAKE_CASE						) as old_file:
                                   for line in old_file:
                                          new_file.write(__SCREAMING_SNAKE_CASE						)
                                          if line_to_copy_below in line:
                                                 UpperCamelCase    :	Optional[int]   	=		True
                                                 for line_to_copy in lines_to_copy:
                                                        new_file.write(__SCREAMING_SNAKE_CASE						)

                     if not line_found:
                            raise ValueError(f"""Line {line_to_copy_below} was not found in file."""						)

                     # Copy the file permissions from the old file to the new file
                     copymode(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
                     # Remove original file
                     remove(__SCREAMING_SNAKE_CASE						)
                     # Move new file
                     move(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

              def skip_units(__SCREAMING_SNAKE_CASE						):
                     return (
                         ("generating PyTorch" in line and not output_pytorch)
                         or ("generating TensorFlow" in line and not output_tensorflow)
                         or ("generating Flax" in line and not output_flax)
                     )

              def replace_in_files(__SCREAMING_SNAKE_CASE						):
                     with open(__SCREAMING_SNAKE_CASE						) as datafile:
                            UpperCamelCase    :	int   	=		[]
                            UpperCamelCase    :	Dict   	=		False
                            UpperCamelCase    :	List[Any]   	=		False
                            for line in datafile:
                                   if "# To replace in: " in line and "##" not in line:
                                          UpperCamelCase    :	Dict   	=		line.split('''"'''						)[1]
                                          UpperCamelCase    :	int   	=		skip_units(__SCREAMING_SNAKE_CASE						)
                                   elif "# Below: " in line and "##" not in line:
                                          UpperCamelCase    :	Dict   	=		line.split('''"'''						)[1]
                                          UpperCamelCase    :	List[str]   	=		skip_units(__SCREAMING_SNAKE_CASE						)
                                   elif "# End." in line and "##" not in line:
                                          if not skip_file and not skip_snippet:
                                                 replace(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

                                          UpperCamelCase    :	Dict   	=		[]
                                   elif "# Replace with" in line and "##" not in line:
                                          UpperCamelCase    :	Tuple   	=		[]
                                   elif "##" not in line:
                                          lines_to_copy.append(__SCREAMING_SNAKE_CASE						)

                     remove(__SCREAMING_SNAKE_CASE						)

              replace_in_files(f"""{directory}/to_replace_{lowercase_model_name}.py"""						)
              os.rmdir(__SCREAMING_SNAKE_CASE						)

