'''simple docstring'''


import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase	:       Union[str, Any]						       =	get_tests_dir("fixtures/test_sentencepiece.model")

if is_sentencepiece_available():
					import sentencepiece as sp


__lowerCAmelCase	:       str						       =	5
__lowerCAmelCase	:       Union[str, Any]						       =	10






@require_sentencepiece
@require_tokenizers
class 						A   (   UpperCAmelCase       ,	unittest.TestCase   ):
							a_        =      SpeechaTextTokenizer
							a_        =      False
							a_        =      True





							def       snake_case__						(      self				:							Tuple	)							->	List[Any]:
								super().setUp()

								__UpperCAmelCase							=			sp.SentencePieceProcessor()
								spm_model.Load(__a	)
								__UpperCAmelCase							=			['''<s>''', '''<pad>''', '''</s>''', '''<unk>''']

								vocab += [spm_model.IdToPiece(id_	) for id_ in range(len(__a	)	)]
								__UpperCAmelCase							=			dict(zip(__a     ,		range(len(__a	)	)	)	)

								__UpperCAmelCase							=			Path(self.tmpdirname	)
								save_json(__a     ,		save_dir / VOCAB_FILES_NAMES['''vocab_file''']	)
								if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
									copyfile(__a     ,		save_dir / VOCAB_FILES_NAMES['''spm_file''']	)

								__UpperCAmelCase							=			SpeechaTextTokenizer.from_pretrained(self.tmpdirname	)
								tokenizer.save_pretrained(self.tmpdirname	)





							def       snake_case__						(      self				:							List[str]	)							->	Optional[int]:
								__UpperCAmelCase							=			'''<pad>'''
								__UpperCAmelCase							=			1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(__a	)     ,		__a	)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(__a	)     ,		__a	)





							def       snake_case__						(      self				:							List[Any]	)							->	Dict:
								__UpperCAmelCase							=			list(self.get_tokenizer().get_vocab().keys()	)

								self.assertEqual(vocab_keys[0]     ,		'''<s>'''	)
								self.assertEqual(vocab_keys[1]     ,		'''<pad>'''	)
								self.assertEqual(vocab_keys[-1]     ,		'''j'''	)
								self.assertEqual(len(__a	)     ,		1_0_0_1	)





							def       snake_case__						(      self				:							List[Any]	)							->	List[str]:
								self.assertEqual(self.get_tokenizer().vocab_size     ,		1_0_0_1	)





							def       snake_case__						(      self				:							Tuple	)							->	Optional[Any]:
								__UpperCAmelCase							=			SpeechaTextTokenizer.from_pretrained(self.tmpdirname	)

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

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(__a	)     ,		[2_8_9, 5_0, 1_4, 1_7_4, 3_8_6]     ,		)

								__UpperCAmelCase							=			tokenizer.tokenize('''I was born in 92000, and this is falsé.'''	)
								self.assertListEqual(
								    __a     ,		[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''', '''.''']     ,		)
								__UpperCAmelCase							=			tokenizer.convert_tokens_to_ids(__a	)
								self.assertListEqual(__a     ,		[1_2, 2_5, 8_8, 5_9, 2_8, 2_3, 1_1, 4, 6_0_6, 3_5_1, 3_5_1, 3_5_1, 7, 1_6, 7_0, 5_0, 7_6, 8_4, 1_0, 4, 8]	)

								__UpperCAmelCase							=			tokenizer.convert_ids_to_tokens(__a	)
								self.assertListEqual(
								    __a     ,		[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>''', '''.''']     ,		)






							@slow
							def       snake_case__						(      self				:							Tuple	)							->	Optional[int]:
								# fmt: off
								__UpperCAmelCase							=			{'''input_ids''': [[3_7_9_1, 7_9_7, 3_1, 1_1, 6_4, 7_9_7, 3_1, 2_4_2_9, 4_3_3, 1_2, 1_1_7_6, 1_2, 2_0, 7_8_6, 9_1_5, 1_4_2, 2_4_1_3, 2_4_0, 3_7, 3_2_3_8, 7_9_7, 3_1, 1_1, 3_5, 9_3, 9_1_5, 1_4_2, 2_4_1_3, 2_4_0, 3_7, 5_5_4_0, 5_6_7, 1_2_7_6, 9_3, 3_7, 6_1_0, 4_0, 6_2, 4_5_5, 6_5_7, 1_0_4_2, 1_2_3, 7_8_0, 1_7_7, 3_7, 3_0_9, 2_4_1, 1_2_9_8, 5_1_4, 2_0, 2_9_2, 2_7_3_7, 1_1_4, 2_4_6_9, 2_4_1, 8_5, 6_4, 3_0_2, 5_4_8, 5_2_8, 4_2_3, 4, 5_0_9, 4_0_6, 4_2_3, 3_7, 6_0_1, 4, 7_7_7, 3_0_2, 5_4_8, 5_2_8, 4_2_3, 2_8_4, 4, 3_3_8_8, 5_1_1, 4_5_9, 4, 3_5_5_5, 4_0, 3_2_1, 3_0_2, 7_0_5, 4, 3_3_8_8, 5_1_1, 5_8_3, 3_2_6, 5, 5, 5, 6_2, 3_3_1_0, 5_6_0, 1_7_7, 2_6_8_0, 2_1_7, 1_5_0_8, 3_2, 3_1, 8_5_3, 4_1_8, 6_4, 5_8_3, 5_1_1, 1_6_0_5, 6_2, 3_5, 9_3, 5_6_0, 1_7_7, 2_6_8_0, 2_1_7, 1_5_0_8, 1_5_2_1, 6_4, 5_8_3, 5_1_1, 5_1_9, 6_2, 2_0, 1_5_1_5, 7_6_4, 2_0, 1_4_9, 2_6_1, 5_6_2_5, 7_9_7_2, 2_0, 5_5_4_0, 5_6_7, 1_2_7_6, 9_3, 3_9_2_5, 1_6_7_5, 1_1, 1_5, 8_0_2, 7_9_7_2, 5_7_6, 2_1_7, 1_5_0_8, 1_1, 3_5, 9_3, 1_2_5_3, 2_4_4_1, 1_5, 2_8_9, 6_5_2, 3_1, 4_1_6, 3_2_1, 3_8_4_2, 1_1_5, 4_0, 9_1_1, 8, 4_7_6, 6_1_9, 4, 3_8_0, 1_4_2, 4_2_3, 3_3_5, 2_4_0, 3_5, 9_3, 2_6_4, 8, 1_1, 3_3_5, 5_6_9, 4_2_0, 1_6_3, 5, 2], [2_6_0, 5_4_8, 5_2_8, 4_2_3, 2_0, 4_5_1, 2_0, 2_6_8_1, 1_1_5_3, 3_4_3_4, 2_0, 5_5_4_0, 3_7, 5_6_7, 1_2_6, 1_2_5_3, 2_4_4_1, 3_3_7_6, 4_4_9, 2_1_0, 4_3_1, 1_5_6_3, 1_7_7, 7_6_7, 5_5_4_0, 1_1, 1_2_0_3, 4_7_2, 1_1, 2_9_5_3, 6_8_5, 2_8_5, 3_6_4, 7_0_6, 1_1_5_3, 2_0, 6_7_9_9, 2_0, 2_8_6_9, 2_0, 4_4_6_4, 1_2_6, 4_0, 2_4_2_9, 2_0, 1_0_4_0, 8_6_6, 2_6_6_4, 4_1_8, 2_0, 3_1_8, 2_0, 1_7_2_6, 1_8_6, 2_0, 2_6_5, 5_2_2, 3_5, 9_3, 2_1_9_1, 4_6_3_4, 2_0, 1_0_4_0, 1_2, 6_7_9_9, 1_5, 2_2_8, 2_3_5_6, 1_4_2, 3_1, 1_1, 5, 2, 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], [2_5_7_5, 2_6_6_6, 6_8_4, 1_5_8_2, 1_1_7_6, 1_2, 6_2_7, 1_4_9, 6_1_9, 2_0, 4_9_0_2, 5_6_3, 1_1, 2_0, 1_4_9, 2_6_1, 3_4_2_0, 2_3_5_6, 1_7_4, 1_4_2, 4_7_1_4, 1_3_1, 5, 2, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''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, 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, 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, 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, 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=__a     ,		model_name='''facebook/s2t-small-mustc-en-de-st'''     ,		revision='''a14f04cf0776c02f62a8cb800cf7909e15ea23ad'''     ,		)






@require_sentencepiece
class 						A   (   unittest.TestCase   ):
							a_        =      '''valhalla/s2t_mustc_multilinguial_medium'''

							a_        =      '''C\'est trop cool'''
							a_        =      '''Esto es genial'''





							@classmethod
							def       snake_case__						(      cls				:							Any	)							->	List[Any]:
								__UpperCAmelCase							=			SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name	)
								return cls





							def       snake_case__						(      self				:							List[Any]	)							->	List[str]:
								self.assertEqual(self.tokenizer.lang_code_to_id['''pt''']     ,		4	)
								self.assertEqual(self.tokenizer.lang_code_to_id['''ru''']     ,		6	)
								self.assertEqual(self.tokenizer.lang_code_to_id['''it''']     ,		9	)
								self.assertEqual(self.tokenizer.lang_code_to_id['''de''']     ,		1_1	)





							def       snake_case__						(      self				:							Tuple	)							->	Tuple:
								self.assertEqual(self.tokenizer.vocab_size     ,		1_0_0_0_0	)





							def       snake_case__						(      self				:							int	)							->	Dict:
								self.assertIn(__a     ,		self.tokenizer.all_special_ids	)
								__UpperCAmelCase							=			[ES_CODE, 4, 1_6_0_1, 4_7, 7_6_4_7, 2]
								__UpperCAmelCase							=			self.tokenizer.decode(__a     ,		skip_special_tokens=__a	)
								__UpperCAmelCase							=			self.tokenizer.decode(generated_ids[1:]     ,		skip_special_tokens=__a	)
								self.assertEqual(__a     ,		__a	)
								self.assertNotIn(self.tokenizer.eos_token     ,		__a	)





							def       snake_case__						(      self				:							Optional[Any]	)							->	Optional[int]:
								__UpperCAmelCase							=			'''fr'''
								__UpperCAmelCase							=			self.tokenizer(self.french_text	).input_ids
								self.assertEqual(encoded[0]     ,		__a	)
								self.assertEqual(encoded[-1]     ,		self.tokenizer.eos_token_id	)






							def       snake_case__						(      self				:							Any	)							->	Tuple:
								__UpperCAmelCase							=			'''fr'''
								self.assertListEqual(self.tokenizer.prefix_tokens     ,		[FR_CODE]	)

								__UpperCAmelCase							=			'''es'''
								self.assertListEqual(self.tokenizer.prefix_tokens     ,		[ES_CODE]	)

'''simple docstring'''


import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

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/update_metadata.py
__lowerCAmelCase	:       List[Any]						       =	"src/transformers"


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


# Regexes that match TF/Flax/PT model names.
__lowerCAmelCase	:       int						       =	re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
__lowerCAmelCase	:       List[Any]						       =	re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__lowerCAmelCase	:       List[str]						       =	re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__lowerCAmelCase	:       Optional[int]						       =	[
    ("pretraining", "MODEL_FOR_PRETRAINING_MAPPING_NAMES", "AutoModelForPreTraining"),
    ("feature-extraction", "MODEL_MAPPING_NAMES", "AutoModel"),
    ("audio-classification", "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES", "AutoModelForAudioClassification"),
    ("text-generation", "MODEL_FOR_CAUSAL_LM_MAPPING_NAMES", "AutoModelForCausalLM"),
    ("automatic-speech-recognition", "MODEL_FOR_CTC_MAPPING_NAMES", "AutoModelForCTC"),
    ("image-classification", "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForImageClassification"),
    ("image-segmentation", "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES", "AutoModelForImageSegmentation"),
    ("fill-mask", "MODEL_FOR_MASKED_LM_MAPPING_NAMES", "AutoModelForMaskedLM"),
    ("object-detection", "MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES", "AutoModelForObjectDetection"),
    (
        "zero-shot-object-detection",
        "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES",
        "AutoModelForZeroShotObjectDetection",
    ),
    ("question-answering", "MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES", "AutoModelForQuestionAnswering"),
    ("text2text-generation", "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES", "AutoModelForSeq2SeqLM"),
    ("text-classification", "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForSequenceClassification"),
    ("automatic-speech-recognition", "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES", "AutoModelForSpeechSeq2Seq"),
    (
        "table-question-answering",
        "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForTableQuestionAnswering",
    ),
    ("token-classification", "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES", "AutoModelForTokenClassification"),
    ("multiple-choice", "MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES", "AutoModelForMultipleChoice"),
    (
        "next-sentence-prediction",
        "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES",
        "AutoModelForNextSentencePrediction",
    ),
    (
        "audio-frame-classification",
        "MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES",
        "AutoModelForAudioFrameClassification",
    ),
    ("audio-xvector", "MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES", "AutoModelForAudioXVector"),
    (
        "document-question-answering",
        "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForDocumentQuestionAnswering",
    ),
    (
        "visual-question-answering",
        "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForVisualQuestionAnswering",
    ),
    ("image-to-text", "MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES", "AutoModelForVision2Seq"),
    (
        "zero-shot-image-classification",
        "MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES",
        "AutoModelForZeroShotImageClassification",
    ),
    ("depth-estimation", "MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES", "AutoModelForDepthEstimation"),
    ("video-classification", "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES", "AutoModelForVideoClassification"),
    ("mask-generation", "MODEL_FOR_MASK_GENERATION_MAPPING_NAMES", "AutoModelForMaskGeneration"),
]





def       lowerCAmelCase					(		UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''    ,					UpperCamelCase__       )
	return [m.group(0       ) for m in matches]





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	__UpperCAmelCase							=			{
	    config.replace('''Config'''    ,					''''''       ): model_type for model_type, config in config_maping_names.items()
	}

	# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
	__UpperCAmelCase							=			collections.defaultdict(UpperCamelCase__       )
	__UpperCAmelCase							=			collections.defaultdict(UpperCamelCase__       )
	__UpperCAmelCase							=			collections.defaultdict(UpperCamelCase__       )

	# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
	for attr_name in dir(UpperCamelCase__       ):
		__UpperCAmelCase							=			None
		if _re_tf_models.match(UpperCamelCase__       ) is not None:
			__UpperCAmelCase							=			tf_models
			__UpperCAmelCase							=			_re_tf_models.match(UpperCamelCase__       ).groups()[0]
		elif _re_flax_models.match(UpperCamelCase__       ) is not None:
			__UpperCAmelCase							=			flax_models
			__UpperCAmelCase							=			_re_flax_models.match(UpperCamelCase__       ).groups()[0]
		elif _re_pt_models.match(UpperCamelCase__       ) is not None:
			__UpperCAmelCase							=			pt_models
			__UpperCAmelCase							=			_re_pt_models.match(UpperCamelCase__       ).groups()[0]

		if lookup_dict is not None:
			while len(UpperCamelCase__       ) > 0:
				if attr_name in model_prefix_to_model_type:
					__UpperCAmelCase							=			True
					break
				# Try again after removing the last word in the name
				__UpperCAmelCase							=			''''''.join(camel_case_split(UpperCamelCase__       )[:-1]       )

	__UpperCAmelCase							=			set(list(pt_models.keys()       ) + list(tf_models.keys()       ) + list(flax_models.keys()       )       )
	__UpperCAmelCase							=			list(UpperCamelCase__       )
	all_models.sort()

	__UpperCAmelCase							=			{'''model_type''': all_models}
	__UpperCAmelCase							=			[pt_models[t] for t in all_models]
	__UpperCAmelCase							=			[tf_models[t] for t in all_models]
	__UpperCAmelCase							=			[flax_models[t] for t in all_models]

	# Now let's use the auto-mapping names to make sure
	__UpperCAmelCase							=			{}
	for t in all_models:
		if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
			__UpperCAmelCase							=			'''AutoProcessor'''
		elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
			__UpperCAmelCase							=			'''AutoTokenizer'''
		elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
			__UpperCAmelCase							=			'''AutoFeatureExtractor'''
		else:
			# Default to AutoTokenizer if a model has nothing, for backward compatibility.
			__UpperCAmelCase							=			'''AutoTokenizer'''

	__UpperCAmelCase							=			[processors[t] for t in all_models]

	return pd.DataFrame(UpperCamelCase__       )





def       lowerCAmelCase					(		UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[
	    transformers_module.models.auto.modeling_auto,
	    transformers_module.models.auto.modeling_tf_auto,
	    transformers_module.models.auto.modeling_flax_auto,
	]
	for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
		__UpperCAmelCase							=			[model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
		__UpperCAmelCase							=			[auto_class, f"""TF_{auto_class}""", f"""Flax_{auto_class}"""]
		# Loop through all three frameworks
		for module, cls, mapping in zip(UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__       ):
			# The type of pipeline may not exist in this framework
			if not hasattr(UpperCamelCase__    ,					UpperCamelCase__       ):
				continue
			# First extract all model_names
			__UpperCAmelCase							=			[]
			for name in getattr(UpperCamelCase__    ,					UpperCamelCase__       ).values():
				if isinstance(UpperCamelCase__    ,					UpperCamelCase__       ):
					model_names.append(UpperCamelCase__       )
				else:
					model_names.extend(list(UpperCamelCase__       )       )

            # Add pipeline tag and auto model class for those models
			table.update({model_name: (pipeline_tag, cls) for model_name in model_names}       )

	return table





def       lowerCAmelCase					(		UpperCamelCase__				:   List[str]    ,					UpperCamelCase__				:   Dict       ):


	"""simple docstring"""
	__UpperCAmelCase							=			get_frameworks_table()
	__UpperCAmelCase							=			Dataset.from_pandas(UpperCamelCase__       )

	__UpperCAmelCase							=			hf_hub_download(
	    '''huggingface/transformers-metadata'''    ,					'''pipeline_tags.json'''    ,					repo_type='''dataset'''    ,					token=UpperCamelCase__       )
	__UpperCAmelCase							=			Dataset.from_json(UpperCamelCase__       )
	__UpperCAmelCase							=			{
	    tags_dataset[i]['''model_class''']: (tags_dataset[i]['''pipeline_tag'''], tags_dataset[i]['''auto_class'''])
	    for i in range(len(UpperCamelCase__       )       )
	}
	__UpperCAmelCase							=			update_pipeline_and_auto_class_table(UpperCamelCase__       )

	# Sort the model classes to avoid some nondeterministic updates to create false update commits.
	__UpperCAmelCase							=			sorted(table.keys()       )
	__UpperCAmelCase							=			pd.DataFrame(
	    {
	        '''model_class''': model_classes,
	        '''pipeline_tag''': [table[m][0] for m in model_classes],
	        '''auto_class''': [table[m][1] for m in model_classes],
	    }       )
	__UpperCAmelCase							=			Dataset.from_pandas(UpperCamelCase__       )

	with tempfile.TemporaryDirectory() as tmp_dir:
		frameworks_dataset.to_json(os.path.join(UpperCamelCase__    ,					'''frameworks.json'''       )       )
		tags_dataset.to_json(os.path.join(UpperCamelCase__    ,					'''pipeline_tags.json'''       )       )

		if commit_sha is not None:
			__UpperCAmelCase							=			(
			    f"""Update with commit {commit_sha}\n\nSee: """
			    f"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
			)
		else:
			__UpperCAmelCase							=			'''Update'''

		upload_folder(
		    repo_id='''huggingface/transformers-metadata'''    ,					folder_path=UpperCamelCase__    ,					repo_type='''dataset'''    ,					token=UpperCamelCase__    ,					commit_message=UpperCamelCase__    ,					)





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
	__UpperCAmelCase							=			transformers_module.pipelines.SUPPORTED_TASKS
	__UpperCAmelCase							=			[]
	for key in pipeline_tasks:
		if key not in in_table:
			__UpperCAmelCase							=			pipeline_tasks[key]['''pt''']
			if isinstance(UpperCamelCase__    ,					(list, tuple)       ):
				__UpperCAmelCase							=			model[0]
			__UpperCAmelCase							=			model.__name__
			if model not in in_table.values():
				missing.append(UpperCamelCase__       )

	if len(UpperCamelCase__       ) > 0:
		__UpperCAmelCase							=			''', '''.join(UpperCamelCase__       )
		raise ValueError(
		    '''The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '''
		    f"""`utils/update_metadata.py`: {msg}. Please add them!"""       )


if __name__ == "__main__":
					__lowerCAmelCase	:       Union[str, Any]						       =	argparse.ArgumentParser()
					parser.add_argument("--token", type=str, help="The token to use to push to the transformers-metadata dataset.")
					parser.add_argument("--commit_sha", type=str, help="The sha of the commit going with this update.")
					parser.add_argument("--check-only", action="store_true", help="Activate to just check all pipelines are present.")
					__lowerCAmelCase	:       Tuple						       =	parser.parse_args()

					if args.check_only:
										check_pipeline_tags()
					else:
										update_metadata(args.token, args.commit_sha)
