


'''simple docstring'''






import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
 from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor




class  a     :
 def __init__( self      ,				__magic_name__      ,				__magic_name__=13      ,				__magic_name__=32      ,				__magic_name__=2      ,				__magic_name__=3      ,				__magic_name__=16      ,				__magic_name__=[1, 2, 1]      ,				__magic_name__=[2, 2, 4]      ,				__magic_name__=2      ,				__magic_name__=2.0      ,				__magic_name__=True      ,				__magic_name__=0.0      ,				__magic_name__=0.0      ,				__magic_name__=0.1      ,				__magic_name__="gelu"      ,				__magic_name__=False      ,				__magic_name__=True      ,				__magic_name__=0.0_2      ,				__magic_name__=1e-5      ,				__magic_name__=True      ,				__magic_name__=None      ,				__magic_name__=True      ,				__magic_name__=10      ,				__magic_name__=8      ,				)					->							Optional[int]:
      _a			      =							parent
      _a			      =							batch_size
      _a			      =							image_size
      _a			      =							patch_size
      _a			      =							num_channels
      _a			      =							embed_dim
      _a			      =							depths
      _a			      =							num_heads
      _a			      =							window_size
      _a			      =							mlp_ratio
      _a			      =							qkv_bias
      _a			      =							hidden_dropout_prob
      _a			      =							attention_probs_dropout_prob
      _a			      =							drop_path_rate
      _a			      =							hidden_act
      _a			      =							use_absolute_embeddings
      _a			      =							patch_norm
      _a			      =							layer_norm_eps
      _a			      =							initializer_range
      _a			      =							is_training
      _a			      =							scope
      _a			      =							use_labels
      _a			      =							type_sequence_label_size
      _a			      =							encoder_stride
 def 							__UpperCAmelCase		( self						)					->							List[Any]:
      _a			      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

      _a			      =							self.get_config()

      return config, pixel_values, labels
 def 							__UpperCAmelCase		( self						)					->							int:
      return SwinvaConfig(
          image_size=self.image_size      ,				patch_size=self.patch_size      ,				num_channels=self.num_channels      ,				embed_dim=self.embed_dim      ,				depths=self.depths      ,				num_heads=self.num_heads      ,				window_size=self.window_size      ,				mlp_ratio=self.mlp_ratio      ,				qkv_bias=self.qkv_bias      ,				hidden_dropout_prob=self.hidden_dropout_prob      ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,				drop_path_rate=self.drop_path_rate      ,				hidden_act=self.hidden_act      ,				use_absolute_embeddings=self.use_absolute_embeddings      ,				path_norm=self.patch_norm      ,				layer_norm_eps=self.layer_norm_eps      ,				initializer_range=self.initializer_range      ,				encoder_stride=self.encoder_stride      ,				)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Union[str, Any]:
      _a			      =							SwinvaModel(config=__magic_name__						)
      model.to(__magic_name__						)
      model.eval()
      _a			      =							model(__magic_name__						)

      _a			      =							((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
      _a			      =							int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

      self.parent.assertEqual(result.last_hidden_state.shape      ,				(self.batch_size, expected_seq_len, expected_dim)						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							str:
      _a			      =							SwinvaForMaskedImageModeling(config=__magic_name__						)
      model.to(__magic_name__						)
      model.eval()
      _a			      =							model(__magic_name__						)
      self.parent.assertEqual(
          result.logits.shape      ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)						)

      # test greyscale images
      _a			      =							1
      _a			      =							SwinvaForMaskedImageModeling(__magic_name__						)
      model.to(__magic_name__						)
      model.eval()

      _a			      =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
      _a			      =							model(__magic_name__						)
      self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, 1, self.image_size, self.image_size)						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Optional[Any]:
      _a			      =							self.type_sequence_label_size
      _a			      =							SwinvaForImageClassification(__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.type_sequence_label_size)						)




 def 							__UpperCAmelCase		( self						)					->							List[str]:
      _a			      =							self.prepare_config_and_inputs()
      _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 					=	(
     (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
 )
 _lowerCAmelCase 					=	(
     {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
     if is_torch_available()
     else {}
 )

 _lowerCAmelCase 					=	False
 _lowerCAmelCase 					=	False
 _lowerCAmelCase 					=	False
 _lowerCAmelCase 					=	False
 def 							__UpperCAmelCase		( self						)					->							List[str]:
      _a			      =							SwinvaModelTester(self						)
      _a			      =							ConfigTester(self      ,				config_class=__magic_name__      ,				embed_dim=37						)
 def 							__UpperCAmelCase		( self						)					->							int:
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      _a			      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__magic_name__						)
 @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'						)
 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      pass
 @unittest.skip(reason='Swinv2 does not use inputs_embeds'						)
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      pass
 def 							__UpperCAmelCase		( self						)					->							str:
      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a			      =							model_class(__magic_name__						)
           self.assertIsInstance(model.get_input_embeddings()      ,				(nn.Module)						)
           _a			      =							model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(__magic_name__      ,				nn.Linear						)						)
 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						)					->							Union[str, Any]:
      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()
      _a			      =							True

      for model_class in self.all_model_classes:
           _a			      =							True
           _a			      =							False
           _a			      =							True
           _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.attentions
           _a			      =							len(self.model_tester.depths						)
           self.assertEqual(len(__magic_name__						)      ,				__magic_name__						)

           # check that output_attentions also work using config
           del inputs_dict["output_attentions"]
           _a			      =							True
           _a			      =							config.window_size**2
           _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.attentions
           self.assertEqual(len(__magic_name__						)      ,				__magic_name__						)

           self.assertListEqual(
               list(attentions[0].shape[-3:]						)      ,				[self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,				)
           _a			      =							len(__magic_name__						)

           # Check attention is always last and order is fine
           _a			      =							True
           _a			      =							True
           _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__						)						)

           if hasattr(self.model_tester      ,				'num_hidden_states_types'						):
                _a			      =							self.model_tester.num_hidden_states_types
           else:
                # also another +1 for reshaped_hidden_states
                _a			      =							2
           self.assertEqual(out_len + added_hidden_states      ,				len(__magic_name__						)						)

           _a			      =							outputs.attentions

           self.assertEqual(len(__magic_name__						)      ,				__magic_name__						)

           self.assertListEqual(
               list(self_attentions[0].shape[-3:]						)      ,				[self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,				)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							List[Any]:
      _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			      =							getattr(
          self.model_tester      ,				'expected_num_hidden_layers'      ,				len(self.model_tester.depths						) + 1						)
      self.assertEqual(len(__magic_name__						)      ,				__magic_name__						)

      # Swinv2 has a different seq_length
      _a			      =							(
          config.patch_size
          if isinstance(config.patch_size      ,				collections.abc.Iterable						)
          else (config.patch_size, config.patch_size)
      )

      _a			      =							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

      self.assertListEqual(
          list(hidden_states[0].shape[-2:]						)      ,				[num_patches, self.model_tester.embed_dim]      ,				)

      _a			      =							outputs.reshaped_hidden_states
      self.assertEqual(len(__magic_name__						)      ,				__magic_name__						)

      _a      ,   _a      ,   _a      ,   _a			      =							reshaped_hidden_states[0].shape
      _a			      =							(
          reshaped_hidden_states[0].view(__magic_name__      ,				__magic_name__      ,				height * width						).permute(0      ,				2      ,				1						)
      )
      self.assertListEqual(
          list(reshaped_hidden_states.shape[-2:]						)      ,				[num_patches, self.model_tester.embed_dim]      ,				)
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()

      _a			      =							(
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size      ,				collections.abc.Iterable						)
          else (self.model_tester.image_size, self.model_tester.image_size)
      )

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

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

           self.check_hidden_states_output(__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()
      _a			      =							3

      _a			      =							(
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size      ,				collections.abc.Iterable						)
          else (self.model_tester.image_size, self.model_tester.image_size)
      )
      _a			      =							(
          config.patch_size
          if isinstance(config.patch_size      ,				collections.abc.Iterable						)
          else (config.patch_size, config.patch_size)
      )

      _a			      =							image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
      _a			      =							image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

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

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _a			      =							True
           self.check_hidden_states_output(__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				(padded_height, padded_width)						)
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      _a			      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							str:
      _a			      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__magic_name__						)
 @slow
 def 							__UpperCAmelCase		( self						)					->							str:
      for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a			      =							SwinvaModel.from_pretrained(__magic_name__						)
           self.assertIsNotNone(__magic_name__						)




 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()

      _a			      =							_config_zero_init(__magic_name__						)
      for model_class in self.all_model_classes:
           _a			      =							model_class(config=__magic_name__						)
           for name, param in model.named_parameters():
                if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
                     self.assertIn(
                         ((param.data.mean() * 1e9).round() / 1e9).item()      ,				[0.0, 1.0]      ,				msg=f'Parameter {name} of model {model_class} seems not properly initialized'      ,				)





@require_vision
@require_torch
class  a     (     unittest.TestCase       ):
 @cached_property
 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      return (
          AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'						)
          if is_vision_available()
          else None
      )




 @slow
 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      _a			      =							SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'						).to(
          __magic_name__						)
      _a			      =							self.default_image_processor

      _a			      =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
      _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([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6]						).to(__magic_name__						)
      self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,				__magic_name__      ,				atol=1e-4						)						)





'''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)

