






"""simple docstring"""
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
_lowerCAmelCase         =							"src/transformers"
_lowerCAmelCase         =							"docs/source/en/tasks"






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	with open(snake_case__   ,   """r"""   ,   encoding="""utf-8"""   ,   newline="""\n"""					) as f:
		A_		:						Optional[int]	    =   f.readlines()
	# Find the start prompt.
	A_		:						Any	    =   0
	while not lines[start_index].startswith(snake_case__					):
		start_index += 1
	start_index += 1

	A_		:						List[Any]	    =   start_index
	while not lines[end_index].startswith(snake_case__					):
		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.
_lowerCAmelCase         =							direct_transformers_import(TRANSFORMERS_PATH)

_lowerCAmelCase         =							{
    "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`).
_lowerCAmelCase         =							{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						int	    =   TASK_GUIDE_TO_MODELS[task_guide]
	A_		:						Any	    =   SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(snake_case__   ,   set()					)
	A_		:						Any	    =   {
	    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 			__UpperCamelCase	(		snake_case__   ,   snake_case__=False					):
	A_		:						List[Any]	    =   _find_text_in_file(
	    filename=os.path.join(snake_case__   ,   snake_case__					)   ,   start_prompt="""<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"""   ,   end_prompt="""<!--End of the generated tip-->"""   ,   )

	A_		:						int	    =   get_model_list_for_task(snake_case__					)

	if current_list != new_list:
		if overwrite:
			with open(os.path.join(snake_case__   ,   snake_case__					)   ,   """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__":
						_lowerCAmelCase         =							argparse.ArgumentParser()
						parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
						_lowerCAmelCase         =							parser.parse_args()

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









"""simple docstring"""
import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				Dict    =				FunnelTokenizer
	_A       :				Union[str, Any]    =				FunnelTokenizerFast
	_A       :				List[str]    =				True
	_A       :				List[Any]    =				True





	def    lowerCamelCase(self							):
		super().setUp()

		A_		:						Tuple	    =   [
		    """<unk>""",
		    """<cls>""",
		    """<sep>""",
		    """want""",
		    """##want""",
		    """##ed""",
		    """wa""",
		    """un""",
		    """runn""",
		    """##ing""",
		    """,""",
		    """low""",
		    """lowest""",
		]
		A_		:						str	    =   os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""vocab_file"""]							)
		with open(self.vocab_file     ,     """w"""     ,     encoding="""utf-8"""							) as vocab_writer:
			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]							)							)





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		return FunnelTokenizer.from_pretrained(self.tmpdirname     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		return FunnelTokenizerFast.from_pretrained(self.tmpdirname     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						Union[str, Any]	    =   """UNwant\u00E9d,running"""
		A_		:						int	    =   """unwanted, running"""
		return input_text, output_text





	def    lowerCamelCase(self							):
		A_		:						Optional[Any]	    =   self.tokenizer_class(self.vocab_file							)

		A_		:						Any	    =   tokenizer.tokenize("""UNwant\u00E9d,running"""							)
		self.assertListEqual(lowerCAmelCase_     ,     ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]							)
		self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_							)     ,     [7, 4, 5, 10, 8, 9]							)





	def    lowerCamelCase(self							):
		A_		:						List[Any]	    =   self.get_tokenizers(do_lower_case=lowerCAmelCase_							)
		for tokenizer in tokenizers:
			A_		:						Any	    =   tokenizer("""UNwant\u00E9d,running"""							)
			A_		:						Dict	    =   len(inputs["""input_ids"""]							) - 1
			self.assertListEqual(inputs["""token_type_ids"""]     ,     [2] + [0] * sentence_len							)

			A_		:						Tuple	    =   tokenizer("""UNwant\u00E9d,running"""     ,     """UNwant\u00E9d,running"""							)
			self.assertListEqual(inputs["""token_type_ids"""]     ,     [2] + [0] * sentence_len + [1] * sentence_len							)

