import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__a  :     Tuple       =  {"""LayoutLMv2Config""", """LayoutLMv3Config"""}




@is_pipeline_test
class    _UpperCamelCase      (						unittest.TestCase ):


			"""simple docstring"""






			__a  : Optional[Any]			 =						MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
			__a  : Dict			 =						TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

			if model_mapping is not None:
						__a  : Union[str, Any]			 =						{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
			if tf_model_mapping is not None:
						__a  : Dict			 =						{
						    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
						}





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					Optional[int]:

							'''simple docstring'''


							__lowercase 			=							ZeroShotClassificationPipeline(
							    model=lowerCAmelCase__      ,   tokenizer=lowerCAmelCase__      ,   candidate_labels=['''polics''', '''health''']						)
							return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					Optional[int]:

							'''simple docstring'''


							__lowercase 			=							classifier('''Who are you voting for in 2020?'''      ,   candidate_labels='''politics'''						)
							self.assertEqual(lowerCAmelCase__      ,   {'''sequence''': ANY(lowerCAmelCase__						), '''labels''': [ANY(lowerCAmelCase__						)], '''scores''': [ANY(lowerCAmelCase__						)]}						)

							# No kwarg
							__lowercase 			=							classifier('''Who are you voting for in 2020?'''      ,   ['''politics''']						)
							self.assertEqual(lowerCAmelCase__      ,   {'''sequence''': ANY(lowerCAmelCase__						), '''labels''': [ANY(lowerCAmelCase__						)], '''scores''': [ANY(lowerCAmelCase__						)]}						)

							__lowercase 			=							classifier('''Who are you voting for in 2020?'''      ,   candidate_labels=['''politics''']						)
							self.assertEqual(lowerCAmelCase__      ,   {'''sequence''': ANY(lowerCAmelCase__						), '''labels''': [ANY(lowerCAmelCase__						)], '''scores''': [ANY(lowerCAmelCase__						)]}						)

							__lowercase 			=							classifier('''Who are you voting for in 2020?'''      ,   candidate_labels='''politics, public health'''						)
							self.assertEqual(
							    lowerCAmelCase__      ,   {'''sequence''': ANY(lowerCAmelCase__						), '''labels''': [ANY(lowerCAmelCase__						), ANY(lowerCAmelCase__						)], '''scores''': [ANY(lowerCAmelCase__						), ANY(lowerCAmelCase__						)]}						)
							self.assertAlmostEqual(sum(nested_simplify(outputs['''scores''']						)						)      ,   1.0						)

							__lowercase 			=							classifier('''Who are you voting for in 2020?'''      ,   candidate_labels=['''politics''', '''public health''']						)
							self.assertEqual(
							    lowerCAmelCase__      ,   {'''sequence''': ANY(lowerCAmelCase__						), '''labels''': [ANY(lowerCAmelCase__						), ANY(lowerCAmelCase__						)], '''scores''': [ANY(lowerCAmelCase__						), ANY(lowerCAmelCase__						)]}						)
							self.assertAlmostEqual(sum(nested_simplify(outputs['''scores''']						)						)      ,   1.0						)

							__lowercase 			=							classifier(
							    '''Who are you voting for in 2020?'''      ,   candidate_labels='''politics'''      ,   hypothesis_template='''This text is about {}'''						)
							self.assertEqual(lowerCAmelCase__      ,   {'''sequence''': ANY(lowerCAmelCase__						), '''labels''': [ANY(lowerCAmelCase__						)], '''scores''': [ANY(lowerCAmelCase__						)]}						)

							# https://github.com/huggingface/transformers/issues/13846
							__lowercase 			=							classifier(['''I am happy''']      ,   ['''positive''', '''negative''']						)
							self.assertEqual(
							    lowerCAmelCase__      ,   [
							        {'''sequence''': ANY(lowerCAmelCase__						), '''labels''': [ANY(lowerCAmelCase__						), ANY(lowerCAmelCase__						)], '''scores''': [ANY(lowerCAmelCase__						), ANY(lowerCAmelCase__						)]}
							        for i in range(1						)
							    ]      ,   )
							__lowercase 			=							classifier(['''I am happy''', '''I am sad''']      ,   ['''positive''', '''negative''']						)
							self.assertEqual(
							    lowerCAmelCase__      ,   [
							        {'''sequence''': ANY(lowerCAmelCase__						), '''labels''': [ANY(lowerCAmelCase__						), ANY(lowerCAmelCase__						)], '''scores''': [ANY(lowerCAmelCase__						), ANY(lowerCAmelCase__						)]}
							        for i in range(2						)
							    ]      ,   )

							with self.assertRaises(lowerCAmelCase__						):
											classifier(''''''      ,   candidate_labels='''politics'''						)

							with self.assertRaises(lowerCAmelCase__						):
											classifier(lowerCAmelCase__      ,   candidate_labels='''politics'''						)

							with self.assertRaises(lowerCAmelCase__						):
											classifier('''Who are you voting for in 2020?'''      ,   candidate_labels=''''''						)

							with self.assertRaises(lowerCAmelCase__						):
											classifier('''Who are you voting for in 2020?'''      ,   candidate_labels=lowerCAmelCase__						)

							with self.assertRaises(lowerCAmelCase__						):
											classifier(
											    '''Who are you voting for in 2020?'''      ,   candidate_labels='''politics'''      ,   hypothesis_template='''Not formatting template'''      ,   )

							with self.assertRaises(lowerCAmelCase__						):
											classifier(
											    '''Who are you voting for in 2020?'''      ,   candidate_labels='''politics'''      ,   hypothesis_template=lowerCAmelCase__      ,   )

							self.run_entailment_id(lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__						)     ->					Optional[int]:

							'''simple docstring'''


							__lowercase 			=							zero_shot_classifier.model.config
							__lowercase 			=							config.labelaid
							__lowercase 			=							zero_shot_classifier.entailment_id

							__lowercase 			=							{"""LABEL_0""": 0, """LABEL_1""": 1, """LABEL_2""": 2}
							self.assertEqual(zero_shot_classifier.entailment_id      ,   -1						)

							__lowercase 			=							{"""entailment""": 0, """neutral""": 1, """contradiction""": 2}
							self.assertEqual(zero_shot_classifier.entailment_id      ,   0						)

							__lowercase 			=							{"""ENTAIL""": 0, """NON-ENTAIL""": 1}
							self.assertEqual(zero_shot_classifier.entailment_id      ,   0						)

							__lowercase 			=							{"""ENTAIL""": 2, """NEUTRAL""": 1, """CONTR""": 0}
							self.assertEqual(zero_shot_classifier.entailment_id      ,   2						)

							__lowercase 			=							original_labelaid
							self.assertEqual(lowerCAmelCase__      ,   zero_shot_classifier.entailment_id						)





			@require_torch
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							__lowercase 			=							pipeline(
							    '''zero-shot-classification'''      ,   model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''      ,   framework='''pt'''      ,   )
							# There was a regression in 4.10 for this
							# Adding a test so we don't make the mistake again.
							# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
							zero_shot_classifier(
							    '''Who are you voting for in 2020?''' * 1_00      ,   candidate_labels=['''politics''', '''public health''', '''science''']						)





			@require_torch
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							__lowercase 			=							pipeline(
							    '''zero-shot-classification'''      ,   model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''      ,   framework='''pt'''      ,   )
							__lowercase 			=							zero_shot_classifier(
							    '''Who are you voting for in 2020?'''      ,   candidate_labels=['''politics''', '''public health''', '''science''']						)

							self.assertEqual(
							    nested_simplify(lowerCAmelCase__						)      ,   {
							        '''sequence''': '''Who are you voting for in 2020?''',
							        '''labels''': ['''science''', '''public health''', '''politics'''],
							        '''scores''': [0.333, 0.333, 0.333],
							    }      ,   )





			@require_tf
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							__lowercase 			=							pipeline(
							    '''zero-shot-classification'''      ,   model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''      ,   framework='''tf'''      ,   )
							__lowercase 			=							zero_shot_classifier(
							    '''Who are you voting for in 2020?'''      ,   candidate_labels=['''politics''', '''public health''', '''science''']						)

							self.assertEqual(
							    nested_simplify(lowerCAmelCase__						)      ,   {
							        '''sequence''': '''Who are you voting for in 2020?''',
							        '''labels''': ['''science''', '''public health''', '''politics'''],
							        '''scores''': [0.333, 0.333, 0.333],
							    }      ,   )





			@slow
			@require_torch
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[int]:

							'''simple docstring'''


							__lowercase 			=							pipeline('''zero-shot-classification'''      ,   model='''roberta-large-mnli'''      ,   framework='''pt'''						)
							__lowercase 			=							zero_shot_classifier(
							    '''Who are you voting for in 2020?'''      ,   candidate_labels=['''politics''', '''public health''', '''science''']						)

							self.assertEqual(
							    nested_simplify(lowerCAmelCase__						)      ,   {
							        '''sequence''': '''Who are you voting for in 2020?''',
							        '''labels''': ['''politics''', '''public health''', '''science'''],
							        '''scores''': [0.976, 0.015, 0.009],
							    }      ,   )
							__lowercase 			=							zero_shot_classifier(
							    '''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
							    ''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
							    ''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
							    ''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
							    ''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
							    ''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
							    ''' English-to-German translation task, improving over the existing best results, including ensembles by'''
							    ''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
							    ''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
							    ''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
							    ''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
							    ''' large and limited training data.'''      ,   candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision''']      ,   multi_label=lowerCAmelCase__      ,   )
							self.assertEqual(
							    nested_simplify(lowerCAmelCase__						)      ,   {
							        '''sequence''': (
							            '''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
							            ''' networks in an encoder-decoder configuration. The best performing models also connect the'''
							            ''' encoder and decoder through an attention mechanism. We propose a new simple network'''
							            ''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
							            ''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
							            ''' superior in quality while being more parallelizable and requiring significantly less time to'''
							            ''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
							            ''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
							            ''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
							            ''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
							            ''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
							            ''' other tasks by applying it successfully to English constituency parsing both with large and'''
							            ''' limited training data.'''
							        ),
							        '''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
							        '''scores''': [0.817, 0.713, 0.018, 0.018],
							    }      ,   )





			@slow
			@require_tf
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


							__lowercase 			=							pipeline('''zero-shot-classification'''      ,   model='''roberta-large-mnli'''      ,   framework='''tf'''						)
							__lowercase 			=							zero_shot_classifier(
							    '''Who are you voting for in 2020?'''      ,   candidate_labels=['''politics''', '''public health''', '''science''']						)

							self.assertEqual(
							    nested_simplify(lowerCAmelCase__						)      ,   {
							        '''sequence''': '''Who are you voting for in 2020?''',
							        '''labels''': ['''politics''', '''public health''', '''science'''],
							        '''scores''': [0.976, 0.015, 0.009],
							    }      ,   )
							__lowercase 			=							zero_shot_classifier(
							    '''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
							    ''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
							    ''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
							    ''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
							    ''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
							    ''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
							    ''' English-to-German translation task, improving over the existing best results, including ensembles by'''
							    ''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
							    ''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
							    ''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
							    ''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
							    ''' large and limited training data.'''      ,   candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision''']      ,   multi_label=lowerCAmelCase__      ,   )
							self.assertEqual(
							    nested_simplify(lowerCAmelCase__						)      ,   {
							        '''sequence''': (
							            '''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
							            ''' networks in an encoder-decoder configuration. The best performing models also connect the'''
							            ''' encoder and decoder through an attention mechanism. We propose a new simple network'''
							            ''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
							            ''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
							            ''' superior in quality while being more parallelizable and requiring significantly less time to'''
							            ''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
							            ''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
							            ''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
							            ''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
							            ''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
							            ''' other tasks by applying it successfully to English constituency parsing both with large and'''
							            ''' limited training data.'''
							        ),
							        '''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
							        '''scores''': [0.817, 0.713, 0.018, 0.018],
							    }      ,   )

import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       if "cls_token" in name:
              lowercase							:     List[Any]       	=      name.replace("""cls_token"""					,	"""vit.embeddings.cls_token"""				)
       if "mask_token" in name:
              lowercase							:     Any       	=      name.replace("""mask_token"""					,	"""decoder.mask_token"""				)
       if "decoder_pos_embed" in name:
              lowercase							:     str       	=      name.replace("""decoder_pos_embed"""					,	"""decoder.decoder_pos_embed"""				)
       if "pos_embed" in name and "decoder" not in name:
              lowercase							:     List[str]       	=      name.replace("""pos_embed"""					,	"""vit.embeddings.position_embeddings"""				)
       if "patch_embed.proj" in name:
              lowercase							:     Tuple       	=      name.replace("""patch_embed.proj"""					,	"""vit.embeddings.patch_embeddings.projection"""				)
       if "patch_embed.norm" in name:
              lowercase							:     int       	=      name.replace("""patch_embed.norm"""					,	"""vit.embeddings.norm"""				)
       if "decoder_blocks" in name:
              lowercase							:     Tuple       	=      name.replace("""decoder_blocks"""					,	"""decoder.decoder_layers"""				)
       if "blocks" in name:
              lowercase							:     List[Any]       	=      name.replace("""blocks"""					,	"""vit.encoder.layer"""				)
       if "attn.proj" in name:
              lowercase							:     List[str]       	=      name.replace("""attn.proj"""					,	"""attention.output.dense"""				)
       if "attn" in name:
              lowercase							:     Union[str, Any]       	=      name.replace("""attn"""					,	"""attention.self"""				)
       if "norm1" in name:
              lowercase							:     Optional[Any]       	=      name.replace("""norm1"""					,	"""layernorm_before"""				)
       if "norm2" in name:
              lowercase							:     Union[str, Any]       	=      name.replace("""norm2"""					,	"""layernorm_after"""				)
       if "mlp.fc1" in name:
              lowercase							:     Dict       	=      name.replace("""mlp.fc1"""					,	"""intermediate.dense"""				)
       if "mlp.fc2" in name:
              lowercase							:     Dict       	=      name.replace("""mlp.fc2"""					,	"""output.dense"""				)
       if "decoder_embed" in name:
              lowercase							:     List[str]       	=      name.replace("""decoder_embed"""					,	"""decoder.decoder_embed"""				)
       if "decoder_norm" in name:
              lowercase							:     Dict       	=      name.replace("""decoder_norm"""					,	"""decoder.decoder_norm"""				)
       if "decoder_pred" in name:
              lowercase							:     List[str]       	=      name.replace("""decoder_pred"""					,	"""decoder.decoder_pred"""				)
       if "norm.weight" in name and "decoder" not in name:
              lowercase							:     Tuple       	=      name.replace("""norm.weight"""					,	"""vit.layernorm.weight"""				)
       if "norm.bias" in name and "decoder" not in name:
              lowercase							:     int       	=      name.replace("""norm.bias"""					,	"""vit.layernorm.bias"""				)

       return name







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       for key in orig_state_dict.copy().keys():
              lowercase							:     List[Any]       	=      orig_state_dict.pop(SCREAMING_SNAKE_CASE__				)

              if "qkv" in key:
                     lowercase							:     int       	=      key.split("""."""				)
                     lowercase							:     List[str]       	=      int(key_split[1]				)
                     if "decoder_blocks" in key:
                            lowercase							:     Tuple       	=      config.decoder_hidden_size
                            lowercase							:     int       	=      """decoder.decoder_layers."""
                            if "weight" in key:
                                   lowercase							:     List[Any]       	=      val[:dim, :]
                                   lowercase							:     Tuple       	=      val[dim : dim * 2, :]
                                   lowercase							:     List[Any]       	=      val[-dim:, :]
                            elif "bias" in key:
                                   lowercase							:     str       	=      val[:dim]
                                   lowercase							:     Dict       	=      val[dim : dim * 2]
                                   lowercase							:     Union[str, Any]       	=      val[-dim:]
                     else:
                            lowercase							:     Tuple       	=      config.hidden_size
                            lowercase							:     Union[str, Any]       	=      """vit.encoder.layer."""
                            if "weight" in key:
                                   lowercase							:     Tuple       	=      val[:dim, :]
                                   lowercase							:     List[str]       	=      val[dim : dim * 2, :]
                                   lowercase							:     Dict       	=      val[-dim:, :]
                            elif "bias" in key:
                                   lowercase							:     Any       	=      val[:dim]
                                   lowercase							:     str       	=      val[dim : dim * 2]
                                   lowercase							:     Union[str, Any]       	=      val[-dim:]

              else:
                     lowercase							:     Union[str, Any]       	=      val

       return orig_state_dict







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    int:
       lowercase							:     int       	=      ViTMAEConfig()
       if "large" in checkpoint_url:
              lowercase							:     Dict       	=      1_024
              lowercase							:     str       	=      4_096
              lowercase							:     Optional[Any]       	=      24
              lowercase							:     Optional[Any]       	=      16
       elif "huge" in checkpoint_url:
              lowercase							:     int       	=      14
              lowercase							:     List[Any]       	=      1_280
              lowercase							:     int       	=      5_120
              lowercase							:     List[Any]       	=      32
              lowercase							:     Any       	=      16

       lowercase							:     List[str]       	=      ViTMAEForPreTraining(SCREAMING_SNAKE_CASE__				)

       lowercase							:     Optional[Any]       	=      torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__					,	map_location="""cpu"""				)["""model"""]

       lowercase							:     Tuple       	=      ViTMAEImageProcessor(size=config.image_size				)

       lowercase							:     Optional[int]       	=      convert_state_dict(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       model.load_state_dict(SCREAMING_SNAKE_CASE__				)
       model.eval()

       lowercase							:     Union[str, Any]       	=      """https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

       lowercase							:     Union[str, Any]       	=      Image.open(requests.get(SCREAMING_SNAKE_CASE__					,	stream=SCREAMING_SNAKE_CASE__				).raw				)
       lowercase							:     Optional[Any]       	=      ViTMAEImageProcessor(size=config.image_size				)
       lowercase							:     List[Any]       	=      image_processor(images=SCREAMING_SNAKE_CASE__					,	return_tensors="""pt"""				)

       # forward pass
       torch.manual_seed(2				)
       lowercase							:     int       	=      model(**SCREAMING_SNAKE_CASE__				)
       lowercase							:     str       	=      outputs.logits

       if "large" in checkpoint_url:
              lowercase							:     List[Any]       	=      torch.tensor(
                  [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]				)
       elif "huge" in checkpoint_url:
              lowercase							:     Tuple       	=      torch.tensor(
                  [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]				)
       else:
              lowercase							:     List[str]       	=      torch.tensor(
                  [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]				)

       # verify logits
       assert torch.allclose(logits[0, :3, :3]					,	SCREAMING_SNAKE_CASE__					,	atol=1e-4				)

       print(f"Saving model to {pytorch_dump_folder_path}"				)
       model.save_pretrained(SCREAMING_SNAKE_CASE__				)

       print(f"Saving image processor to {pytorch_dump_folder_path}"				)
       image_processor.save_pretrained(SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
  lowercase  :					Union[str, Any]						    =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth""",
      type=str,
      help="""URL of the checkpoint you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  lowercase  :					List[Any]						    =							parser.parse_args()
  convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
