
import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__a         =   get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
class 					lowercase__(							UpperCAmelCase					,		unittest.TestCase				):



	"""simple docstring"""




	a     :Optional[int]       	=      XLMProphetNetTokenizer
	a     :Any       	=      False
	a     :Optional[int]       	=      True
	def 		_lowercase    (			self    :     Optional[int]			)			->	List[str]:
					super().setUp()

					# We have a SentencePiece fixture for testing
					lowercase_	    =   XLMProphetNetTokenizer(SCREAMING_SNAKE_CASE_			,				keep_accents=SCREAMING_SNAKE_CASE_			)
					tokenizer.save_pretrained(self.tmpdirname			)
	def 		_lowercase    (			self    :     Any			)			->	Optional[Any]:
					lowercase_	    =   '''[PAD]'''
					lowercase_	    =   0

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE_			)			,				SCREAMING_SNAKE_CASE_			)
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE_			)			,				SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Optional[Any]			)			->	int:
					lowercase_	    =   list(self.get_tokenizer().get_vocab().keys()			)

					self.assertEqual(vocab_keys[0]			,				'''[PAD]'''			)
					self.assertEqual(vocab_keys[1]			,				'''[CLS]'''			)
					self.assertEqual(vocab_keys[-1]			,				'''j'''			)
					self.assertEqual(len(SCREAMING_SNAKE_CASE_			)			,				1_0_1_2			)
	def 		_lowercase    (			self    :     int			)			->	int:
					self.assertEqual(self.get_tokenizer().vocab_size			,				1_0_1_2			)
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	Union[str, Any]:
					lowercase_	    =   XLMProphetNetTokenizer(SCREAMING_SNAKE_CASE_			,				keep_accents=SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   tokenizer.tokenize('''This is a test'''			)
					self.assertListEqual(SCREAMING_SNAKE_CASE_			,				['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']			)

					self.assertListEqual(
					    tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_			)			,				[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]			,				)

					lowercase_	    =   tokenizer.tokenize('''I was born in 92000, and this is falsé.'''			)
					self.assertListEqual(
					    SCREAMING_SNAKE_CASE_			,				[
					        SPIECE_UNDERLINE + '''I''',
					        SPIECE_UNDERLINE + '''was''',
					        SPIECE_UNDERLINE + '''b''',
					        '''or''',
					        '''n''',
					        SPIECE_UNDERLINE + '''in''',
					        SPIECE_UNDERLINE + '''''',
					        '''9''',
					        '''2''',
					        '''0''',
					        '''0''',
					        '''0''',
					        ''',''',
					        SPIECE_UNDERLINE + '''and''',
					        SPIECE_UNDERLINE + '''this''',
					        SPIECE_UNDERLINE + '''is''',
					        SPIECE_UNDERLINE + '''f''',
					        '''al''',
					        '''s''',
					        '''é''',
					        '''.''',
					    ]			,				)
					lowercase_	    =   tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_			)
					self.assertListEqual(
					    SCREAMING_SNAKE_CASE_			,				[
					        value + tokenizer.fairseq_offset
					        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, -9, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, -9, 4]
					    ]			,				)

					lowercase_	    =   tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_			)
					self.assertListEqual(
					    SCREAMING_SNAKE_CASE_			,				[
					        SPIECE_UNDERLINE + '''I''',
					        SPIECE_UNDERLINE + '''was''',
					        SPIECE_UNDERLINE + '''b''',
					        '''or''',
					        '''n''',
					        SPIECE_UNDERLINE + '''in''',
					        SPIECE_UNDERLINE + '''''',
					        '''[UNK]''',
					        '''2''',
					        '''0''',
					        '''0''',
					        '''0''',
					        ''',''',
					        SPIECE_UNDERLINE + '''and''',
					        SPIECE_UNDERLINE + '''this''',
					        SPIECE_UNDERLINE + '''is''',
					        SPIECE_UNDERLINE + '''f''',
					        '''al''',
					        '''s''',
					        '''[UNK]''',
					        '''.''',
					    ]			,				)
	@cached_property
	def 		_lowercase    (			self    :     Tuple			)			->	List[Any]:
					return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''			)
	@slow
	def 		_lowercase    (			self    :     Optional[int]			)			->	Union[str, Any]:
					lowercase_	    =   '''Hello World!'''
					lowercase_	    =   [3_5_3_8_9, 6_6_7_2, 4_9, 2]
					self.assertListEqual(SCREAMING_SNAKE_CASE_			,				self.big_tokenizer.encode(SCREAMING_SNAKE_CASE_			)			)




	@slow
	def 		_lowercase    (			self    :     Any			)			->	List[Any]:
					# fmt: off
					lowercase_	    =   {'''input_ids''': [[1_1_0_7_3, 8_2_7_8_3, 1_8, 2_6, 8_2_7_8_3, 5_4_9, 5_1_5_4_0, 2_4_8, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 2_1_5_1_8_6, 1_3_2_5, 1_4_7, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 5_6_3_7_0, 5_3, 1_2_2_0_2_0, 2_0, 1_6_4_7_7, 2_7, 8_7_3_5_5, 4_5_4_8, 2_0, 4_7_2_8, 7_8_3_9_2, 1_7, 1_5_9_9_6_9, 1_8, 2_6, 2_4_4_9_1, 6_2_9, 1_5, 5_3_8, 2_2_7_0_4, 5_4_3_9, 1_5, 2_7_8_8, 2_4_4_9_1, 9_8_8_5, 1_5, 4_3_5_3_4, 6_0_5, 1_5, 8_1_4, 1_8_4_0_3, 3_3_2_0_0, 2_9, 1_5, 4_3_5_3_4, 2_4_4_5_8, 1_2_4_1_0, 1_1_1, 2_4_9_6_6, 8_3_6_6_9, 9_6_3_7, 1_4_4_0_6_8, 2_6, 8_5_0, 2_2_3_4_6, 2_7, 1_4_7, 2_4_9_6_6, 8_3_6_6_9, 8_3_4_9_0, 2_6, 3_9_1_1_3, 7_3_5, 2_7, 6_8_9, 6_5_6, 2_8_0_0, 1_3_3_9, 4_6_0_0, 5_3, 1_2_2_0_2_0, 1_1_5_7_8_5, 3_4, 8_1_6, 1_3_3_9, 4_6_8_8_7, 1_8, 1_4_7, 5_3_9_0_5, 1_9_5_1, 4_2_2_3_8, 4_1_1_7_0, 1_7_7_3_2, 8_3_4, 4_3_6, 1_5, 2_7_5_2_3, 9_8_7_3_3, 2_1_7, 1_4_7, 5_5_4_2, 4_9_8_1, 9_3_0, 1_7_3_4_7, 1_6, 2], [2_0_0_9_1, 6_2_9, 9_4, 8_2_7_8_6, 5_8, 4_9_0, 2_0, 1_5_2_8, 8_4, 5_3_9_0_5, 3_4_4, 8_0_5_9_2, 1_1_0_1_2_8, 1_8_8_2_2, 5_2_6_7, 1_3_0_6, 6_2, 1_5_2_5_3_7, 3_0_8, 7_9_9_7, 4_0_1, 1_2_4_4_2_7, 5_4_9, 3_5_4_4_2, 2_2_5, 1_0_9, 1_5_0_5_5, 2_5_7_4_8, 1_4_7, 7_1_1_9, 4_3_7_1_2, 3_4, 7_6_7, 1_3_5_3_6_6, 1_8, 1_6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_9_2, 6_3_7_8_4, 1_1_9_4_6_6, 1_7, 1_4_7_8_0_8, 8_8_2_1_4, 1_8, 6_5_6, 8_1, 3_2, 3_2_9_6, 1_0_2_8_0, 1_6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
					# fmt: on

					self.tokenizer_integration_test_util(
					    expected_encoding=SCREAMING_SNAKE_CASE_			,				model_name='''microsoft/xprophetnet-large-wiki100-cased'''			,				revision='''1acad1643ddd54a44df6a1b797ada8373685d90e'''			,				)






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
__a         =   'src/transformers'
__a         =   'docs/source/en/tasks'




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Union[str, Any]		,  snake_case__:   Any   ):


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

				lowercase_	    =   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.
__a         =   direct_transformers_import(TRANSFORMERS_PATH)

__a         =   {
    '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`).
__a         =   {
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}




def 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				lowercase_	    =   TASK_GUIDE_TO_MODELS[task_guide]
				lowercase_	    =   SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(snake_case__		,  set()   )
				lowercase_	    =   {
				    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		(				snake_case__:   List[Any]		,  snake_case__:   Optional[int]=False   ):


				'''simple docstring'''
				lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_	    =   _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-->'''		,  )

				lowercase_	    =   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__":
						__a         =   argparse.ArgumentParser()
						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
						__a         =   parser.parse_args()

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




