


'''simple docstring'''






import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
 from transformers.models.mobilevitva.modeling_mobilevitva import (
     MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
     make_divisible,
 )


if is_vision_available():
 from PIL import Image

 from transformers import MobileViTImageProcessor




class  a     (     _SCREAMING_SNAKE_CASE       ):
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      _a			      =							self.config_class(**self.inputs_dict						)
      self.parent.assertTrue(hasattr(__magic_name__      ,				'width_multiplier'						)						)





class  a     :
 def __init__( self      ,				__magic_name__      ,				__magic_name__=13      ,				__magic_name__=64      ,				__magic_name__=2      ,				__magic_name__=3      ,				__magic_name__="swish"      ,				__magic_name__=3      ,				__magic_name__=32      ,				__magic_name__=0.1      ,				__magic_name__=0.0_2      ,				__magic_name__=True      ,				__magic_name__=True      ,				__magic_name__=10      ,				__magic_name__=None      ,				__magic_name__=0.2_5      ,				__magic_name__=0.0      ,				__magic_name__=0.0      ,				)					->							str:
      _a			      =							parent
      _a			      =							batch_size
      _a			      =							image_size
      _a			      =							patch_size
      _a			      =							num_channels
      _a			      =							make_divisible(5_12 * width_multiplier      ,				divisor=8						)
      _a			      =							hidden_act
      _a			      =							conv_kernel_size
      _a			      =							output_stride
      _a			      =							classifier_dropout_prob
      _a			      =							use_labels
      _a			      =							is_training
      _a			      =							num_labels
      _a			      =							initializer_range
      _a			      =							scope
      _a			      =							width_multiplier
      _a			      =							ffn_dropout
      _a			      =							attn_dropout
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      _a			      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

      _a			      =							None
      _a			      =							None
      if self.use_labels:
           _a			      =							ids_tensor([self.batch_size]      ,				self.num_labels						)
           _a			      =							ids_tensor([self.batch_size, self.image_size, self.image_size]      ,				self.num_labels						)

      _a			      =							self.get_config()

      return config, pixel_values, labels, pixel_labels
 def 							__UpperCAmelCase		( self						)					->							str:
      return MobileViTVaConfig(
          image_size=self.image_size      ,				patch_size=self.patch_size      ,				num_channels=self.num_channels      ,				hidden_act=self.hidden_act      ,				conv_kernel_size=self.conv_kernel_size      ,				output_stride=self.output_stride      ,				classifier_dropout_prob=self.classifier_dropout_prob      ,				initializer_range=self.initializer_range      ,				width_multiplier=self.width_multiplier      ,				ffn_dropout=self.ffn_dropout_prob      ,				attn_dropout=self.attn_dropout_prob      ,				)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							int:
      _a			      =							MobileViTVaModel(config=__magic_name__						)
      model.to(__magic_name__						)
      model.eval()
      _a			      =							model(__magic_name__						)
      self.parent.assertEqual(
          result.last_hidden_state.shape      ,				(
              self.batch_size,
              self.last_hidden_size,
              self.image_size // self.output_stride,
              self.image_size // self.output_stride,
          )      ,				)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Optional[Any]:
      _a			      =							self.num_labels
      _a			      =							MobileViTVaForImageClassification(__magic_name__						)
      model.to(__magic_name__						)
      model.eval()
      _a			      =							model(__magic_name__      ,				labels=__magic_name__						)
      self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_labels)						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							str:
      _a			      =							self.num_labels
      _a			      =							MobileViTVaForSemanticSegmentation(__magic_name__						)
      model.to(__magic_name__						)
      model.eval()
      _a			      =							model(__magic_name__						)
      self.parent.assertEqual(
          result.logits.shape      ,				(
              self.batch_size,
              self.num_labels,
              self.image_size // self.output_stride,
              self.image_size // self.output_stride,
          )      ,				)
      _a			      =							model(__magic_name__      ,				labels=__magic_name__						)
      self.parent.assertEqual(
          result.logits.shape      ,				(
              self.batch_size,
              self.num_labels,
              self.image_size // self.output_stride,
              self.image_size // self.output_stride,
          )      ,				)




 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      _a			      =							self.prepare_config_and_inputs()
      _a      ,   _a      ,   _a      ,   _a			      =							config_and_inputs
      _a			      =							{'pixel_values': pixel_values}
      return config, inputs_dict





@require_torch
class  a     (     _SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				unittest.TestCase       ):
 _lowerCAmelCase 					=	(
     (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
     if is_torch_available()
     else ()
 )

 _lowerCAmelCase 					=	(
     {
         """feature-extraction""": MobileViTVaModel,
         """image-classification""": MobileViTVaForImageClassification,
         """image-segmentation""": MobileViTVaForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 _lowerCAmelCase 					=	False
 _lowerCAmelCase 					=	False
 _lowerCAmelCase 					=	False
 _lowerCAmelCase 					=	False
 def 							__UpperCAmelCase		( self						)					->							List[Any]:
      _a			      =							MobileViTVaModelTester(self						)
      _a			      =							MobileViTVaConfigTester(self      ,				config_class=__magic_name__      ,				has_text_modality=__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      self.config_tester.run_common_tests()
 @unittest.skip(reason='MobileViTV2 does not use inputs_embeds'						)
 def 							__UpperCAmelCase		( self						)					->							List[str]:
      pass
 @unittest.skip(reason='MobileViTV2 does not support input and output embeddings'						)
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      pass
 @unittest.skip(reason='MobileViTV2 does not output attentions'						)
 def 							__UpperCAmelCase		( self						)					->							str:
      pass
 @require_torch_multi_gpu
 @unittest.skip(reason='Got `CUDA error: misaligned address` for tests after this one being run.'						)
 def 							__UpperCAmelCase		( self						)					->							Any:
      pass
 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'						)
 def 							__UpperCAmelCase		( self						)					->							List[str]:
      pass
 def 							__UpperCAmelCase		( self						)					->							Any:
      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a			      =							model_class(__magic_name__						)
           _a			      =							inspect.signature(model.forward						)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           _a			      =							[*signature.parameters.keys()]

           _a			      =							['pixel_values']
           self.assertListEqual(arg_names[:1]      ,				__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							List[Any]:
      _a			      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							Dict:
      def check_hidden_states_output(__magic_name__      ,				__magic_name__      ,				__magic_name__						):
           _a			      =							model_class(__magic_name__						)
           model.to(__magic_name__						)
           model.eval()

           with torch.no_grad():
                _a			      =							model(**self._prepare_for_class(__magic_name__      ,				__magic_name__						)						)

           _a			      =							outputs.hidden_states

           _a			      =							5
           self.assertEqual(len(__magic_name__						)      ,				__magic_name__						)

           # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
           # with the width and height being successively divided by 2.
           _a			      =							2
           for i in range(len(__magic_name__						)						):
                self.assertListEqual(
                    list(hidden_states[i].shape[-2:]						)      ,				[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]      ,				)
                divisor *= 2

           self.assertEqual(self.model_tester.output_stride      ,				divisor // 2						)

      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a			      =							True
           check_hidden_states_output(__magic_name__      ,				__magic_name__      ,				__magic_name__						)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _a			      =							True

           check_hidden_states_output(__magic_name__      ,				__magic_name__      ,				__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      _a			      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							Dict:
      _a			      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*__magic_name__						)




 @slow
 def 							__UpperCAmelCase		( self						)					->							int:
      for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a			      =							MobileViTVaModel.from_pretrained(__magic_name__						)
           self.assertIsNotNone(__magic_name__						)






def 	_A				()   ->  Any:




     '''simple docstring'''



     _a			      =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
     return image






@require_torch
@require_vision
class  a     (     unittest.TestCase       ):
 @cached_property
 def 							__UpperCAmelCase		( self						)					->							List[str]:
      return (
          MobileViTImageProcessor.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'						)
          if is_vision_available()
          else None
      )
 @slow
 def 							__UpperCAmelCase		( self						)					->							List[str]:
      _a			      =							MobileViTVaForImageClassification.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'						).to(
          __magic_name__						)

      _a			      =							self.default_image_processor
      _a			      =							prepare_img()
      _a			      =							image_processor(images=__magic_name__      ,				return_tensors='pt'						).to(__magic_name__						)

      # forward pass
      with torch.no_grad():
           _a			      =							model(**__magic_name__						)

      # verify the logits
      _a			      =							torch.Size((1, 10_00)						)
      self.assertEqual(outputs.logits.shape      ,				__magic_name__						)

      _a			      =							torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]						).to(__magic_name__						)

      self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,				__magic_name__      ,				atol=1e-4						)						)
 @slow
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      _a			      =							MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'						)
      _a			      =							model.to(__magic_name__						)

      _a			      =							MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'						)

      _a			      =							prepare_img()
      _a			      =							image_processor(images=__magic_name__      ,				return_tensors='pt'						).to(__magic_name__						)

      # forward pass
      with torch.no_grad():
           _a			      =							model(**__magic_name__						)
      _a			      =							outputs.logits

      # verify the logits
      _a			      =							torch.Size((1, 21, 32, 32)						)
      self.assertEqual(logits.shape      ,				__magic_name__						)

      _a			      =							torch.tensor(
          [
              [[7.0_8_6_3, 7.1_5_2_5, 6.8_2_0_1], [6.6_9_3_1, 6.8_7_7_0, 6.8_9_3_3], [6.2_9_7_8, 7.0_3_6_6, 6.9_6_3_6]],
              [[-3.7_1_3_4, -3.6_7_1_2, -3.6_6_7_5], [-3.5_8_2_5, -3.3_5_4_9, -3.4_7_7_7], [-3.3_4_3_5, -3.3_9_7_9, -3.2_8_5_7]],
              [[-2.9_3_2_9, -2.8_0_0_3, -2.7_3_6_9], [-3.0_5_6_4, -2.4_7_8_0, -2.0_2_0_7], [-2.6_8_8_9, -1.9_2_9_8, -1.7_6_4_0]],
          ]      ,				device=__magic_name__      ,				)

      self.assertTrue(torch.allclose(logits[0, :3, :3, :3]      ,				__magic_name__      ,				atol=1e-4						)						)




 @slow
 def 							__UpperCAmelCase		( self						)					->							int:
      _a			      =							MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'						)
      _a			      =							model.to(__magic_name__						)

      _a			      =							MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'						)

      _a			      =							prepare_img()
      _a			      =							image_processor(images=__magic_name__      ,				return_tensors='pt'						).to(__magic_name__						)

      # forward pass
      with torch.no_grad():
           _a			      =							model(**__magic_name__						)

      _a			      =							outputs.logits.detach().cpu()

      _a			      =							image_processor.post_process_semantic_segmentation(outputs=__magic_name__      ,				target_sizes=[(50, 60)]						)
      _a			      =							torch.Size((50, 60)						)
      self.assertEqual(segmentation[0].shape      ,				__magic_name__						)

      _a			      =							image_processor.post_process_semantic_segmentation(outputs=__magic_name__						)
      _a			      =							torch.Size((32, 32)						)
      self.assertEqual(segmentation[0].shape      ,				__magic_name__						)





'''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)

