


'''simple docstring'''






import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 a_					:    List[Any]          =    "platform"
 import jax
 import jax.numpy as jnp
 import numpy as np

 from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel




@require_flax
class  a     :
 _lowerCAmelCase 					=	PegasusConfig
 _lowerCAmelCase 					=	{}
 _lowerCAmelCase 					=	"""gelu"""
 def __init__( self      ,				__magic_name__      ,				__magic_name__=13      ,				__magic_name__=7      ,				__magic_name__=True      ,				__magic_name__=False      ,				__magic_name__=99      ,				__magic_name__=32      ,				__magic_name__=5      ,				__magic_name__=4      ,				__magic_name__=37      ,				__magic_name__=0.1      ,				__magic_name__=0.1      ,				__magic_name__=20      ,				__magic_name__=2      ,				__magic_name__=1      ,				__magic_name__=0      ,				)					->							Optional[int]:
      _a			      =							parent
      _a			      =							batch_size
      _a			      =							seq_length
      _a			      =							is_training
      _a			      =							use_labels
      _a			      =							vocab_size
      _a			      =							hidden_size
      _a			      =							num_hidden_layers
      _a			      =							num_attention_heads
      _a			      =							intermediate_size

      _a			      =							hidden_dropout_prob
      _a			      =							attention_probs_dropout_prob
      _a			      =							max_position_embeddings
      _a			      =							eos_token_id
      _a			      =							pad_token_id
      _a			      =							bos_token_id
 def 							__UpperCAmelCase		( self						)					->							int:
      _a			      =							ids_tensor([self.batch_size, self.seq_length - 1]      ,				self.vocab_size						).clip(3      ,				self.vocab_size						)
      _a			      =							np.expand_dims(np.array([self.eos_token_id] * self.batch_size						)      ,				1						)
      _a			      =							np.concatenate([input_ids, eos_tensor]      ,				axis=1						)

      _a			      =							ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size						)

      _a			      =							self.config_cls(
          vocab_size=self.vocab_size      ,				d_model=self.hidden_size      ,				encoder_layers=self.num_hidden_layers      ,				decoder_layers=self.num_hidden_layers      ,				encoder_attention_heads=self.num_attention_heads      ,				decoder_attention_heads=self.num_attention_heads      ,				encoder_ffn_dim=self.intermediate_size      ,				decoder_ffn_dim=self.intermediate_size      ,				dropout=self.hidden_dropout_prob      ,				attention_dropout=self.attention_probs_dropout_prob      ,				max_position_embeddings=self.max_position_embeddings      ,				eos_token_ids=[2]      ,				bos_token_id=self.bos_token_id      ,				pad_token_id=self.pad_token_id      ,				decoder_start_token_id=self.pad_token_id      ,				**self.config_updates      ,				)
      _a			      =							prepare_pegasus_inputs_dict(__magic_name__      ,				__magic_name__      ,				__magic_name__						)
      return config, inputs_dict
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Union[str, Any]:
      _a			      =							20
      _a			      =							model_class_name(__magic_name__						)

      _a			      =							model.encode(inputs_dict['input_ids']						)

      _a      ,   _a			      =							(
          inputs_dict['decoder_input_ids'],
          inputs_dict['decoder_attention_mask'],
      )

      _a			      =							model.init_cache(decoder_input_ids.shape[0]      ,				__magic_name__      ,				__magic_name__						)
      _a			      =							jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,				dtype='i4'						)

      _a			      =							jnp.broadcast_to(
          jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]      ,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,				)
      _a			      =							model.decode(
          decoder_input_ids[:, :-1]      ,				__magic_name__      ,				decoder_attention_mask=__magic_name__      ,				past_key_values=__magic_name__      ,				decoder_position_ids=__magic_name__      ,				)

      _a			      =							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,				dtype='i4'						)
      _a			      =							model.decode(
          decoder_input_ids[:, -1:]      ,				__magic_name__      ,				decoder_attention_mask=__magic_name__      ,				past_key_values=outputs_cache.past_key_values      ,				decoder_position_ids=__magic_name__      ,				)

      _a			      =							model.decode(__magic_name__      ,				__magic_name__						)

      _a			      =							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
      self.parent.assertTrue(diff < 1e-3      ,				msg=f'Max diff is {diff}'						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Union[str, Any]:
      _a			      =							20
      _a			      =							model_class_name(__magic_name__						)

      _a			      =							model.encode(inputs_dict['input_ids']						)

      _a      ,   _a			      =							(
          inputs_dict['decoder_input_ids'],
          inputs_dict['decoder_attention_mask'],
      )

      _a			      =							jnp.concatenate(
          [
              decoder_attention_mask,
              jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
          ]      ,				axis=-1      ,				)

      _a			      =							model.init_cache(decoder_input_ids.shape[0]      ,				__magic_name__      ,				__magic_name__						)
      _a			      =							jnp.broadcast_to(
          jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]      ,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,				)

      _a			      =							model.decode(
          decoder_input_ids[:, :-1]      ,				__magic_name__      ,				decoder_attention_mask=__magic_name__      ,				past_key_values=__magic_name__      ,				decoder_position_ids=__magic_name__      ,				)
      _a			      =							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,				dtype='i4'						)
      _a			      =							model.decode(
          decoder_input_ids[:, -1:]      ,				__magic_name__      ,				past_key_values=outputs_cache.past_key_values      ,				decoder_attention_mask=__magic_name__      ,				decoder_position_ids=__magic_name__      ,				)

      _a			      =							model.decode(__magic_name__      ,				__magic_name__      ,				decoder_attention_mask=__magic_name__						)

      _a			      =							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
      self.parent.assertTrue(diff < 1e-3      ,				msg=f'Max diff is {diff}'						)






def 	_A				(lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :List[str]=None				,						lowerCAmelCase__  :List[Any]=None				,						)   ->  str:




     '''simple docstring'''



     if attention_mask is None:
          _a			      =							np.not_equal(lowerCAmelCase__				,						config.pad_token_id		).astype(np.inta		)
     if decoder_attention_mask is None:
          _a			      =							np.concatenate(
              [
                  np.ones(decoder_input_ids[:, :1].shape				,						dtype=np.inta		),
                  np.not_equal(decoder_input_ids[:, 1:]				,						config.pad_token_id		).astype(np.inta		),
              ]				,						axis=-1				,						)
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": decoder_attention_mask,
     }






@require_flax
class  a     (     _SCREAMING_SNAKE_CASE  ,				unittest.TestCase       ):
 _lowerCAmelCase 					=	(
     (
         FlaxPegasusForConditionalGeneration,
         FlaxPegasusModel,
     )
     if is_flax_available()
     else ()
 )
 _lowerCAmelCase 					=	(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
 _lowerCAmelCase 					=	True
 _lowerCAmelCase 					=	False
 _lowerCAmelCase 					=	False
 _lowerCAmelCase 					=	False
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      _a			      =							FlaxPegasusModelTester(self						)
      _a			      =							ConfigTester(self      ,				config_class=__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      self.config_tester.run_common_tests()
 def 							__UpperCAmelCase		( self						)					->							Dict:
      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(__magic_name__      ,				__magic_name__      ,				__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							List[str]:
      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward_with_attn_mask(__magic_name__      ,				__magic_name__      ,				__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__						):
                _a			      =							self._prepare_for_class(__magic_name__      ,				__magic_name__						)
                _a			      =							model_class(__magic_name__						)

                @jax.jit
                def encode_jitted(__magic_name__      ,				__magic_name__=None      ,				**__magic_name__						):
                     return model.encode(input_ids=__magic_name__      ,				attention_mask=__magic_name__						)

                with self.subTest('JIT Enabled'						):
                     _a			      =							encode_jitted(**__magic_name__						).to_tuple()

                with self.subTest('JIT Disabled'						):
                     with jax.disable_jit():
                          _a			      =							encode_jitted(**__magic_name__						).to_tuple()

                self.assertEqual(len(__magic_name__						)      ,				len(__magic_name__						)						)
                for jitted_output, output in zip(__magic_name__      ,				__magic_name__						):
                     self.assertEqual(jitted_output.shape      ,				output.shape						)
 def 							__UpperCAmelCase		( self						)					->							List[Any]:
      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__						):
                _a			      =							model_class(__magic_name__						)
                _a			      =							model.encode(inputs_dict['input_ids']      ,				inputs_dict['attention_mask']						)

                _a			      =							{
                    'decoder_input_ids': inputs_dict['decoder_input_ids'],
                    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                    'encoder_outputs': encoder_outputs,
                }

                @jax.jit
                def decode_jitted(__magic_name__      ,				__magic_name__      ,				__magic_name__						):
                     return model.decode(
                         decoder_input_ids=__magic_name__      ,				decoder_attention_mask=__magic_name__      ,				encoder_outputs=__magic_name__      ,				)

                with self.subTest('JIT Enabled'						):
                     _a			      =							decode_jitted(**__magic_name__						).to_tuple()

                with self.subTest('JIT Disabled'						):
                     with jax.disable_jit():
                          _a			      =							decode_jitted(**__magic_name__						).to_tuple()

                self.assertEqual(len(__magic_name__						)      ,				len(__magic_name__						)						)
                for jitted_output, output in zip(__magic_name__      ,				__magic_name__						):
                     self.assertEqual(jitted_output.shape      ,				output.shape						)
 @slow
 def 							__UpperCAmelCase		( self						)					->							int:
      for model_class_name in self.all_model_classes:
           _a			      =							model_class_name.from_pretrained('google/pegasus-large'      ,				from_pt=__magic_name__						)
           _a			      =							np.ones((1, 1)						)
           _a			      =							model(__magic_name__						)
           self.assertIsNotNone(__magic_name__						)




 @slow
 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      _a			      =							FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum'						)
      _a			      =							PegasusTokenizer.from_pretrained('google/pegasus-xsum'						)

      _a			      =							[
          ' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
          ' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ',
      ]

      _a			      =							[
          'California\'s largest electricity provider has turned off power to hundreds of thousands of customers.',
          'Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.',
      ]

      _a			      =							tokenizer(__magic_name__      ,				return_tensors='np'      ,				truncation=__magic_name__      ,				max_length=5_12      ,				padding=__magic_name__						)
      _a			      =							model.generate(**__magic_name__      ,				num_beams=2						).sequences
      _a			      =							tokenizer.batch_decode(__magic_name__      ,				skip_special_tokens=__magic_name__						)
      assert tgt_text == decoded





'''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
a_					:    int          =    "src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
a_					:    Optional[int]          =    direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
a_					:    str          =    re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
a_					:    Union[str, 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.
a_					:    List[Any]          =    re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
a_					:    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 	_A				(lowerCAmelCase__  :List[Any]		)   ->  str:




     '''simple docstring'''



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






def 	_A				()   ->  Union[str, Any]:




     '''simple docstring'''



     _a			      =							transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     _a			      =							{
         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.
     _a			      =							collections.defaultdict(lowerCAmelCase__		)
     _a			      =							collections.defaultdict(lowerCAmelCase__		)
     _a			      =							collections.defaultdict(lowerCAmelCase__		)

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

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

     _a			      =							set(list(pt_models.keys()		) + list(tf_models.keys()		) + list(flax_models.keys()		)		)
     _a			      =							list(lowerCAmelCase__		)
     all_models.sort()

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

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

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

     return pd.DataFrame(lowerCAmelCase__		)






def 	_A				(lowerCAmelCase__  :Dict		)   ->  str:




     '''simple docstring'''



     _a			      =							[
         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:
          _a			      =							[model_mapping, f'TF_{model_mapping}', f'FLAX_{model_mapping}']
          _a			      =							[auto_class, f'TF_{auto_class}', f'Flax_{auto_class}']
          # Loop through all three frameworks
          for module, cls, mapping in zip(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		):
               # The type of pipeline may not exist in this framework
               if not hasattr(lowerCAmelCase__				,						lowerCAmelCase__		):
                    continue
               # First extract all model_names
               _a			      =							[]
               for name in getattr(lowerCAmelCase__				,						lowerCAmelCase__		).values():
                    if isinstance(lowerCAmelCase__				,						lowerCAmelCase__		):
                         model_names.append(lowerCAmelCase__		)
                    else:
                         model_names.extend(list(lowerCAmelCase__		)		)

            # 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 	_A				(lowerCAmelCase__  :Any				,						lowerCAmelCase__  :str		)   ->  Union[str, Any]:




     '''simple docstring'''



     _a			      =							get_frameworks_table()
     _a			      =							Dataset.from_pandas(lowerCAmelCase__		)

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

     # Sort the model classes to avoid some nondeterministic updates to create false update commits.
     _a			      =							sorted(table.keys()		)
     _a			      =							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],
         }		)
     _a			      =							Dataset.from_pandas(lowerCAmelCase__		)

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

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

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






def 	_A				()   ->  str:




     '''simple docstring'''



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

     if len(lowerCAmelCase__		) > 0:
          _a			      =							', '.join(lowerCAmelCase__		)
          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__":
 a_					:    Dict          =    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.")
 a_					:    Union[str, Any]          =    parser.parse_args()

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

