



import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
UpperCAmelCase__     = "src/transformers"
UpperCAmelCase__     = "docs/source/en/tasks"





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     Union[str, Any]					)       ->				int:







			'''simple docstring'''
			with open(_UpperCAmelCase							,     'r'							,     encoding='utf-8'							,     newline='\n'					) as f:
						_UpperCAmelCase	 =     f.readlines()
			# Find the start prompt.
			_UpperCAmelCase	 =     0
			while not lines[start_index].startswith(_UpperCAmelCase					):
						start_index += 1
			start_index += 1

			_UpperCAmelCase	 =     start_index
			while not lines[end_index].startswith(_UpperCAmelCase					):
						end_index += 1
			end_index -= 1

			while len(lines[start_index]					) <= 1:
						start_index += 1
			while len(lines[end_index]					) <= 1:
						end_index -= 1
			end_index += 1
			return "".join(lines[start_index:end_index]					), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase__     = direct_transformers_import(TRANSFORMERS_PATH)

UpperCAmelCase__     = {
    "asr.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    "audio_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    "language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    "image_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    "masked_language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    "multiple_choice.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    "object_detection.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    "question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    "semantic_segmentation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    "sequence_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    "summarization.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "token_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    "translation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "video_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    "document_question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    "monocular_depth_estimation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
UpperCAmelCase__     = {
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}





def 							A			(  _UpperCAmelCase       :     str					)       ->				List[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     TASK_GUIDE_TO_MODELS[task_guide]
			_UpperCAmelCase	 =     SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_UpperCAmelCase							,     set()					)
			_UpperCAmelCase	 =     {
			    code: name
			    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
			    if (code in model_maping_names or code in special_model_types)
			}
			return ", ".join([F"[{name}](../model_doc/{code})" for code, name in model_names.items()]					) + "\n"





def 							A			(  _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     List[str]=False					)       ->				Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase	 =     _find_text_in_file(
			    filename=os.path.join(_UpperCAmelCase							,     _UpperCAmelCase					)							,     start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'							,     end_prompt='<!--End of the generated tip-->'							,     )

			_UpperCAmelCase	 =     get_model_list_for_task(_UpperCAmelCase					)

			if current_list != new_list:
						if overwrite:
									with open(os.path.join(_UpperCAmelCase							,     _UpperCAmelCase					)							,     'w'							,     encoding='utf-8'							,     newline='\n'					) as f:
												f.writelines(lines[:start_index] + [new_list] + lines[end_index:]					)
						else:
									raise ValueError(
									    F"The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"
									    ' to fix this.'					)


if __name__ == "__main__":
							UpperCAmelCase__     = argparse.ArgumentParser()
							parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
							UpperCAmelCase__     = parser.parse_args()

							for task_guide in TASK_GUIDE_TO_MODELS.keys():
														check_model_list_for_task(task_guide, args.fix_and_overwrite)





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_xlnet import XLNetTokenizer
else:
							UpperCAmelCase__     = None


UpperCAmelCase__     = logging.get_logger(__name__)

UpperCAmelCase__     = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__     = {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

UpperCAmelCase__     = {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

UpperCAmelCase__     = "▁"

# Segments (not really needed)
UpperCAmelCase__     = 0
UpperCAmelCase__     = 1
UpperCAmelCase__     = 2
UpperCAmelCase__     = 3
UpperCAmelCase__     = 4







class     __lowerCAmelCase					(    A		):
			UpperCamelCase           =  VOCAB_FILES_NAMES
			UpperCamelCase           =  PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase           =  '''left'''
			UpperCamelCase           =  XLNetTokenizer



			def __init__(		self     :						Any				,	A     :						Union[str, Any]=None				,	A     :						str=None				,	A     :						Tuple=False				,	A     :						Tuple=True				,	A     :						Any=False				,	A     :						List[str]="<s>"				,	A     :						List[str]="</s>"				,	A     :						Optional[int]="<unk>"				,	A     :						Tuple="<sep>"				,	A     :						str="<pad>"				,	A     :						Dict="<cls>"				,	A     :						Dict="<mask>"				,	A     :						Optional[Any]=["<eop>", "<eod>"]				,	**A     :						Optional[Any]				,	)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     AddedToken(A				,	lstrip=A				,	rstrip=A) if isinstance(A				,	A) else mask_token

						super().__init__(
						    vocab_file=A				,	tokenizer_file=A				,	do_lower_case=A				,	remove_space=A				,	keep_accents=A				,	bos_token=A				,	eos_token=A				,	unk_token=A				,	sep_token=A				,	pad_token=A				,	cls_token=A				,	mask_token=A				,	additional_special_tokens=A				,	**A				,	)

						_UpperCAmelCase	 =     3
						_UpperCAmelCase	 =     do_lower_case
						_UpperCAmelCase	 =     remove_space
						_UpperCAmelCase	 =     keep_accents
						_UpperCAmelCase	 =     vocab_file
						_UpperCAmelCase	 =     False if not self.vocab_file else True



			def 					_lowerCamelCase (		self     :						Tuple				,	A     :						List[int]				,	A     :						Optional[List[int]] = None)     ->					List[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     [self.sep_token_id]
						_UpperCAmelCase	 =     [self.cls_token_id]
						if token_ids_a is None:
									return token_ids_a + sep + cls
						return token_ids_a + sep + token_ids_a + sep + cls



			def 					_lowerCamelCase (		self     :						Tuple				,	A     :						List[int]				,	A     :						Optional[List[int]] = None)     ->					List[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     [self.sep_token_id]
						_UpperCAmelCase	 =     [2]

						if token_ids_a is None:
									return len(token_ids_a + sep) * [0] + cls_segment_id
						return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id



			def 					_lowerCamelCase (		self     :						List[str]				,	A     :						str				,	A     :						Optional[str] = None)     ->					Tuple[str]:



						"""simple docstring"""






						if not self.can_save_slow_tokenizer:
									raise ValueError(
									    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
									    'tokenizer.')

						if not os.path.isdir(A):
									logger.error(F"Vocabulary path ({save_directory}) should be a directory")
									return
						_UpperCAmelCase	 =     os.path.join(
						    A				,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

						if os.path.abspath(self.vocab_file) != os.path.abspath(A):
									copyfile(self.vocab_file				,	A)

						return (out_vocab_file,)
