

'''simple docstring'''



import argparse
import json
from pathlib import Path

import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download

from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase					:			Tuple       =	logging.get_logger(__name__)







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = ASTConfig()

     if "10-10" in model_name:
          pass
     elif "speech-commands" in model_name:
          __SCREAMING_SNAKE_CASE      = 1_28
     elif "12-12" in model_name:
          __SCREAMING_SNAKE_CASE      = 12
          __SCREAMING_SNAKE_CASE      = 12
     elif "14-14" in model_name:
          __SCREAMING_SNAKE_CASE      = 14
          __SCREAMING_SNAKE_CASE      = 14
     elif "16-16" in model_name:
          __SCREAMING_SNAKE_CASE      = 16
          __SCREAMING_SNAKE_CASE      = 16
     else:
          raise ValueError("""Model not supported"""		)

     __SCREAMING_SNAKE_CASE      = """huggingface/label-files"""
     if "speech-commands" in model_name:
          __SCREAMING_SNAKE_CASE      = 35
          __SCREAMING_SNAKE_CASE      = """speech-commands-v2-id2label.json"""
     else:
          __SCREAMING_SNAKE_CASE      = 5_27
          __SCREAMING_SNAKE_CASE      = """audioset-id2label.json"""

     __SCREAMING_SNAKE_CASE      = json.load(open(hf_hub_download(a__ ,       a__ ,       repo_type="""dataset"""		) ,       """r"""		)		)
     __SCREAMING_SNAKE_CASE      = {int(a__		): v for k, v in idalabel.items()}
     __SCREAMING_SNAKE_CASE      = idalabel
     __SCREAMING_SNAKE_CASE      = {v: k for k, v in idalabel.items()}

     return config







def   a__			(    a__		):




     """simple docstring"""


     if "module.v" in name:
          __SCREAMING_SNAKE_CASE      = name.replace("""module.v""" ,       """audio_spectrogram_transformer"""		)
     if "cls_token" in name:
          __SCREAMING_SNAKE_CASE      = name.replace("""cls_token""" ,       """embeddings.cls_token"""		)
     if "dist_token" in name:
          __SCREAMING_SNAKE_CASE      = name.replace("""dist_token""" ,       """embeddings.distillation_token"""		)
     if "pos_embed" in name:
          __SCREAMING_SNAKE_CASE      = name.replace("""pos_embed""" ,       """embeddings.position_embeddings"""		)
     if "patch_embed.proj" in name:
          __SCREAMING_SNAKE_CASE      = name.replace("""patch_embed.proj""" ,       """embeddings.patch_embeddings.projection"""		)
     # transformer blocks
     if "blocks" in name:
          __SCREAMING_SNAKE_CASE      = name.replace("""blocks""" ,       """encoder.layer"""		)
     if "attn.proj" in name:
          __SCREAMING_SNAKE_CASE      = name.replace("""attn.proj""" ,       """attention.output.dense"""		)
     if "attn" in name:
          __SCREAMING_SNAKE_CASE      = name.replace("""attn""" ,       """attention.self"""		)
     if "norm1" in name:
          __SCREAMING_SNAKE_CASE      = name.replace("""norm1""" ,       """layernorm_before"""		)
     if "norm2" in name:
          __SCREAMING_SNAKE_CASE      = name.replace("""norm2""" ,       """layernorm_after"""		)
     if "mlp.fc1" in name:
          __SCREAMING_SNAKE_CASE      = name.replace("""mlp.fc1""" ,       """intermediate.dense"""		)
     if "mlp.fc2" in name:
          __SCREAMING_SNAKE_CASE      = name.replace("""mlp.fc2""" ,       """output.dense"""		)
     # final layernorm
     if "audio_spectrogram_transformer.norm" in name:
          __SCREAMING_SNAKE_CASE      = name.replace("""audio_spectrogram_transformer.norm""" ,       """audio_spectrogram_transformer.layernorm"""		)
     # classifier head
     if "module.mlp_head.0" in name:
          __SCREAMING_SNAKE_CASE      = name.replace("""module.mlp_head.0""" ,       """classifier.layernorm"""		)
     if "module.mlp_head.1" in name:
          __SCREAMING_SNAKE_CASE      = name.replace("""module.mlp_head.1""" ,       """classifier.dense"""		)

     return name







def   a__			(    a__ ,       a__		):




     """simple docstring"""


     for key in orig_state_dict.copy().keys():
          __SCREAMING_SNAKE_CASE      = orig_state_dict.pop(a__		)

          if "qkv" in key:
               __SCREAMING_SNAKE_CASE      = key.split("""."""		)
               __SCREAMING_SNAKE_CASE      = int(key_split[3]		)
               __SCREAMING_SNAKE_CASE      = config.hidden_size
               if "weight" in key:
                    __SCREAMING_SNAKE_CASE      = val[:dim, :]
                    __SCREAMING_SNAKE_CASE      = val[dim : dim * 2, :]
                    __SCREAMING_SNAKE_CASE      = val[-dim:, :]
               else:
                    __SCREAMING_SNAKE_CASE      = val[:dim]
                    __SCREAMING_SNAKE_CASE      = val[dim : dim * 2]
                    __SCREAMING_SNAKE_CASE      = val[-dim:]
          else:
               __SCREAMING_SNAKE_CASE      = val

     return orig_state_dict







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = [
         """module.v.head.weight""",
         """module.v.head.bias""",
         """module.v.head_dist.weight""",
         """module.v.head_dist.bias""",
     ]
     for k in ignore_keys:
          state_dict.pop(a__ ,       a__		)







@torch.no_grad()
def   a__			(    a__ ,       a__ ,       a__=False		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = get_audio_spectrogram_transformer_config(a__		)

     __SCREAMING_SNAKE_CASE      = {
         """ast-finetuned-audioset-10-10-0.4593""": (
             """https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1"""
         ),
         """ast-finetuned-audioset-10-10-0.450""": (
             """https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1"""
         ),
         """ast-finetuned-audioset-10-10-0.448""": (
             """https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1"""
         ),
         """ast-finetuned-audioset-10-10-0.448-v2""": (
             """https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1"""
         ),
         """ast-finetuned-audioset-12-12-0.447""": (
             """https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1"""
         ),
         """ast-finetuned-audioset-14-14-0.443""": (
             """https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1"""
         ),
         """ast-finetuned-audioset-16-16-0.442""": (
             """https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1"""
         ),
         """ast-finetuned-speech-commands-v2""": (
             """https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1"""
         ),
     }

     # load original state_dict
     __SCREAMING_SNAKE_CASE      = model_name_to_url[model_name]
     __SCREAMING_SNAKE_CASE      = torch.hub.load_state_dict_from_url(a__ ,       map_location="""cpu"""		)
     # remove some keys
     remove_keys(a__		)
     # rename some keys
     __SCREAMING_SNAKE_CASE      = convert_state_dict(a__ ,       a__		)

     # load 🤗 model
     __SCREAMING_SNAKE_CASE      = ASTForAudioClassification(a__		)
     model.eval()

     model.load_state_dict(a__		)

     # verify outputs on dummy input
     # source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
     __SCREAMING_SNAKE_CASE      = -4.2_677_393 if """speech-commands""" not in model_name else -6.845_978
     __SCREAMING_SNAKE_CASE      = 4.5_689_974 if """speech-commands""" not in model_name else 5.5_654_526
     __SCREAMING_SNAKE_CASE      = 10_24 if """speech-commands""" not in model_name else 1_28
     __SCREAMING_SNAKE_CASE      = ASTFeatureExtractor(mean=a__ ,       std=a__ ,       max_length=a__		)

     if "speech-commands" in model_name:
          __SCREAMING_SNAKE_CASE      = load_dataset("""speech_commands""" ,       """v0.02""" ,       split="""validation"""		)
          __SCREAMING_SNAKE_CASE      = dataset[0]["""audio"""]["""array"""]
     else:
          __SCREAMING_SNAKE_CASE      = hf_hub_download(
              repo_id="""nielsr/audio-spectogram-transformer-checkpoint""" ,       filename="""sample_audio.flac""" ,       repo_type="""dataset""" ,       )

          __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = torchaudio.load(a__		)
          __SCREAMING_SNAKE_CASE      = waveform.squeeze().numpy()

     __SCREAMING_SNAKE_CASE      = feature_extractor(a__ ,       sampling_rate=1_60_00 ,       return_tensors="""pt"""		)

     # forward pass
     __SCREAMING_SNAKE_CASE      = model(**a__		)
     __SCREAMING_SNAKE_CASE      = outputs.logits

     if model_name == "ast-finetuned-audioset-10-10-0.4593":
          __SCREAMING_SNAKE_CASE      = torch.tensor([-0.8_760, -7.0_042, -8.6_602]		)
     elif model_name == "ast-finetuned-audioset-10-10-0.450":
          __SCREAMING_SNAKE_CASE      = torch.tensor([-1.1_986, -7.0_903, -8.2_718]		)
     elif model_name == "ast-finetuned-audioset-10-10-0.448":
          __SCREAMING_SNAKE_CASE      = torch.tensor([-2.6_128, -8.0_080, -9.4_344]		)
     elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
          __SCREAMING_SNAKE_CASE      = torch.tensor([-1.5_080, -7.4_534, -8.8_917]		)
     elif model_name == "ast-finetuned-audioset-12-12-0.447":
          __SCREAMING_SNAKE_CASE      = torch.tensor([-0.5_050, -6.5_833, -8.0_843]		)
     elif model_name == "ast-finetuned-audioset-14-14-0.443":
          __SCREAMING_SNAKE_CASE      = torch.tensor([-0.3_826, -7.0_336, -8.2_413]		)
     elif model_name == "ast-finetuned-audioset-16-16-0.442":
          __SCREAMING_SNAKE_CASE      = torch.tensor([-1.2_113, -6.9_101, -8.3_470]		)
     elif model_name == "ast-finetuned-speech-commands-v2":
          __SCREAMING_SNAKE_CASE      = torch.tensor([6.1_589, -8.0_566, -8.7_984]		)
     else:
          raise ValueError("""Unknown model name"""		)
     if not torch.allclose(logits[0, :3] ,       a__ ,       atol=1E-4		):
          raise ValueError("""Logits don't match"""		)
     print("""Looks ok!"""		)

     if pytorch_dump_folder_path is not None:
          Path(a__		).mkdir(exist_ok=a__		)
          print(F'Saving model {model_name} to {pytorch_dump_folder_path}'		)
          model.save_pretrained(a__		)
          print(F'Saving feature extractor to {pytorch_dump_folder_path}'		)
          feature_extractor.save_pretrained(a__		)

     if push_to_hub:
          print("""Pushing model and feature extractor to the hub..."""		)
          model.push_to_hub(F'MIT/{model_name}'		)
          feature_extractor.push_to_hub(F'MIT/{model_name}'		)


if __name__ == "__main__":
  UpperCAmelCase					:			Optional[int]       =	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--model_name',
      default='ast-finetuned-audioset-10-10-0.4593',
      type=str,
      help='Name of the Audio Spectrogram Transformer model you\'d like to convert.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
  )
  parser.add_argument(
      '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
  )

  UpperCAmelCase					:			Optional[int]       =	parser.parse_args()
  convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''



import inspect
import unittest

from transformers import MobileNetVaConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MobileNetVaForImageClassification, MobileNetVaModel
  from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import MobileNetVaImageProcessor







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 def  UpperCAmelCase__						(						self   :			List[str]     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.config_class(**self.inputs_dict     )
      self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,				"""tf_padding"""     )     )
      self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,				"""depth_multiplier"""     )     )







class 		lowerCAmelCase__  :



 """simple docstring"""



 def __init__(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			str=13   ,				__SCREAMING_SNAKE_CASE   :			int=3   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=32   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=0.25   ,				__SCREAMING_SNAKE_CASE   :			List[str]=8   ,				__SCREAMING_SNAKE_CASE   :			str=True   ,				__SCREAMING_SNAKE_CASE   :			int=1_024   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=32   ,				__SCREAMING_SNAKE_CASE   :			List[Any]="relu6"   ,				__SCREAMING_SNAKE_CASE   :			str=0.1   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=0.02   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=True   ,				__SCREAMING_SNAKE_CASE   :			Tuple=True   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=10   ,				__SCREAMING_SNAKE_CASE   :			int=None   ,				)    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = parent
      __SCREAMING_SNAKE_CASE      = batch_size
      __SCREAMING_SNAKE_CASE      = num_channels
      __SCREAMING_SNAKE_CASE      = image_size
      __SCREAMING_SNAKE_CASE      = depth_multiplier
      __SCREAMING_SNAKE_CASE      = min_depth
      __SCREAMING_SNAKE_CASE      = tf_padding
      __SCREAMING_SNAKE_CASE      = int(last_hidden_size * depth_multiplier     )
      __SCREAMING_SNAKE_CASE      = output_stride
      __SCREAMING_SNAKE_CASE      = hidden_act
      __SCREAMING_SNAKE_CASE      = classifier_dropout_prob
      __SCREAMING_SNAKE_CASE      = use_labels
      __SCREAMING_SNAKE_CASE      = is_training
      __SCREAMING_SNAKE_CASE      = num_labels
      __SCREAMING_SNAKE_CASE      = initializer_range
      __SCREAMING_SNAKE_CASE      = scope
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

      __SCREAMING_SNAKE_CASE      = self.get_config()

      return config, pixel_values, labels, pixel_labels
 def  UpperCAmelCase__						(						self   :			Any     )    ->					Tuple:


      """simple docstring"""




      return MobileNetVaConfig(
          num_channels=self.num_channels   ,				image_size=self.image_size   ,				depth_multiplier=self.depth_multiplier   ,				min_depth=self.min_depth   ,				tf_padding=self.tf_padding   ,				hidden_act=self.hidden_act   ,				classifier_dropout_prob=self.classifier_dropout_prob   ,				initializer_range=self.initializer_range   ,				)
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			int   ,				__SCREAMING_SNAKE_CASE   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			int     )    ->					Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = MobileNetVaModel(config=__SCREAMING_SNAKE_CASE     )
      model.to(__SCREAMING_SNAKE_CASE     )
      model.eval()
      __SCREAMING_SNAKE_CASE      = model(__SCREAMING_SNAKE_CASE     )
      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   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.num_labels
      __SCREAMING_SNAKE_CASE      = MobileNetVaForImageClassification(__SCREAMING_SNAKE_CASE     )
      model.to(__SCREAMING_SNAKE_CASE     )
      model.eval()
      __SCREAMING_SNAKE_CASE      = model(__SCREAMING_SNAKE_CASE   ,				labels=__SCREAMING_SNAKE_CASE     )
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)     )


 def  UpperCAmelCase__						(						self   :			str     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = config_and_inputs
      __SCREAMING_SNAKE_CASE      = {"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_torch
class 		lowerCAmelCase__  ( a					,   a					,   unittest.TestCase   ):



 """simple docstring"""



 lowerCAmelCase__       = (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
 lowerCAmelCase__       = (
     {"feature-extraction": MobileNetVaModel, "image-classification": MobileNetVaForImageClassification}
     if is_torch_available()
     else {}
 )

 lowerCAmelCase__       = False
 lowerCAmelCase__       = False
 lowerCAmelCase__       = False
 lowerCAmelCase__       = False
 def  UpperCAmelCase__						(						self   :			str     )    ->					Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = MobileNetVaModelTester(self     )
      __SCREAMING_SNAKE_CASE      = MobileNetVaConfigTester(self   ,				config_class=__SCREAMING_SNAKE_CASE   ,				has_text_modality=__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					Any:


      """simple docstring"""




      self.config_tester.run_common_tests()
 @unittest.skip(reason="""MobileNetV1 does not use inputs_embeds"""     )
 def  UpperCAmelCase__						(						self   :			str     )    ->					List[Any]:


      """simple docstring"""




      pass
 @unittest.skip(reason="""MobileNetV1 does not support input and output embeddings"""     )
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					List[Any]:


      """simple docstring"""




      pass
 @unittest.skip(reason="""MobileNetV1 does not output attentions"""     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					Optional[int]:


      """simple docstring"""




      pass
 def  UpperCAmelCase__						(						self   :			int     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

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

           __SCREAMING_SNAKE_CASE      = ["""pixel_values"""]
           self.assertListEqual(arg_names[:1]   ,				__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Any     )    ->					Optional[Any]:


      """simple docstring"""




      def check_hidden_states_output(__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			str     ):
           __SCREAMING_SNAKE_CASE      = model_class(__SCREAMING_SNAKE_CASE     )
           model.to(__SCREAMING_SNAKE_CASE     )
           model.eval()

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

           __SCREAMING_SNAKE_CASE      = outputs.hidden_states

           __SCREAMING_SNAKE_CASE      = 26
           self.assertEqual(len(__SCREAMING_SNAKE_CASE     )   ,				__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

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

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

           check_hidden_states_output(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE     )


 @slow
 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					Optional[Any]:


      """simple docstring"""




      for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __SCREAMING_SNAKE_CASE      = MobileNetVaModel.from_pretrained(__SCREAMING_SNAKE_CASE     )
           self.assertIsNotNone(__SCREAMING_SNAKE_CASE     )







def   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
     return image







@require_torch
@require_vision
class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 @cached_property
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					List[str]:


      """simple docstring"""




      return (
          MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v1_1.0_224"""     ) if is_vision_available() else None
      )


 @slow
 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224"""     ).to(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = self.default_image_processor
      __SCREAMING_SNAKE_CASE      = prepare_img()
      __SCREAMING_SNAKE_CASE      = image_processor(images=__SCREAMING_SNAKE_CASE   ,				return_tensors="""pt"""     ).to(__SCREAMING_SNAKE_CASE     )

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

      # verify the logits
      __SCREAMING_SNAKE_CASE      = torch.Size((1, 1_001)     )
      self.assertEqual(outputs.logits.shape   ,				__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.tensor([-4.1739, -1.1233, 3.1205]     ).to(__SCREAMING_SNAKE_CASE     )

      self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,				__SCREAMING_SNAKE_CASE   ,				atol=1E-4     )     )






