"""simple docstring"""





import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_UpperCamelCase     :		Dict											=					logging.get_logger(__name__)

_UpperCamelCase     :		List[Any]											=					{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
_UpperCamelCase     :		List[str]											=					[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				Optional[Any]     ,				_lowerCAmelCase						:				Tuple  ):





       '''simple docstring'''

       for attribute in key.split('.'  ):
              lowercase__    :	Dict     					=			getattr(_lowerCAmelCase     ,				_lowerCAmelCase  )

       if weight_type is not None:
              lowercase__    :	Optional[int]     					=			getattr(_lowerCAmelCase     ,				_lowerCAmelCase  ).shape
       else:
              lowercase__    :	Optional[int]     					=			hf_pointer.shape

       if hf_shape != value.shape:
              raise ValueError(
                  f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
                  f""" {value.shape} for {full_name}"""  )

       if weight_type == "weight":
              lowercase__    :	Optional[Any]     					=			value
       elif weight_type == "weight_g":
              lowercase__    :	Dict     					=			value
       elif weight_type == "weight_v":
              lowercase__    :	List[str]     					=			value
       elif weight_type == "bias":
              lowercase__    :	Optional[Any]     					=			value
       else:
              lowercase__    :	List[str]     					=			value

       logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""  )





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				Optional[Any]  ):





       '''simple docstring'''

       lowercase__    :	Tuple     					=			[]
       lowercase__    :	List[str]     					=			fairseq_model.state_dict()

       lowercase__    :	Union[str, Any]     					=			hf_model.unispeech_sat.feature_extractor

       for name, value in fairseq_dict.items():
              lowercase__    :	Optional[int]     					=			False
              if "conv_layers" in name:
                     load_conv_layer(
                         _lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				hf_model.config.feat_extract_norm == 'group'     ,				)
                     lowercase__    :	Optional[Any]     					=			True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowercase__    :	List[Any]     					=			'unispeech_sat.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                            if key in name or key.split('w2v_model.'  )[-1] == name.split('.'  )[0]:
                                   if "layer_norm_for_extract" in name and (".".join(name.split('.'  )[:-1]  ) != key):
                                          # special case since naming is very similar
                                          continue
                                   lowercase__    :	int     					=			True
                                   if "*" in mapped_key:
                                          lowercase__    :	Optional[int]     					=			name.split(_lowerCAmelCase  )[0].split('.'  )[-2]
                                          lowercase__    :	List[str]     					=			mapped_key.replace('*'     ,				_lowerCAmelCase  )
                                   if "weight_g" in name:
                                          lowercase__    :	List[Any]     					=			'weight_g'
                                   elif "weight_v" in name:
                                          lowercase__    :	int     					=			'weight_v'
                                   elif "bias" in name:
                                          lowercase__    :	Dict     					=			'bias'
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          lowercase__    :	Union[str, Any]     					=			'weight'
                                   else:
                                          lowercase__    :	int     					=			None
                                   set_recursively(_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )
                            continue
              if not is_used:
                     unused_weights.append(_lowerCAmelCase  )

       logger.warning(f"""Unused weights: {unused_weights}"""  )





def        a_  (   _lowerCAmelCase						:				List[Any]     ,				_lowerCAmelCase						:				Union[str, Any]     ,				_lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				Dict  ):





       '''simple docstring'''

       lowercase__    :	int     					=			full_name.split('conv_layers.'  )[-1]
       lowercase__    :	int     					=			name.split('.'  )
       lowercase__    :	int     					=			int(items[0]  )
       lowercase__    :	Dict     					=			int(items[1]  )

       if type_id == 0:
              if "bias" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                            raise ValueError(
                                f"""{full_name} has size {value.shape}, but"""
                                f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""  )
                     lowercase__    :	Union[str, Any]     					=			value
                     logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""  )
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                            raise ValueError(
                                f"""{full_name} has size {value.shape}, but"""
                                f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""  )
                     lowercase__    :	Optional[int]     					=			value
                     logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""  )
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
              if "bias" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                            raise ValueError(
                                f"""{full_name} has size {value.shape}, but"""
                                f""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."""  )
                     lowercase__    :	List[Any]     					=			value
                     logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                            raise ValueError(
                                f"""{full_name} has size {value.shape}, but"""
                                f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""  )
                     lowercase__    :	int     					=			value
                     logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
       else:
              unused_weights.append(_lowerCAmelCase  )





@torch.no_grad()
def        a_  (   _lowerCAmelCase						:				Union[str, Any]     ,				_lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				List[Any]=None     ,				_lowerCAmelCase						:				str=None     ,				_lowerCAmelCase						:				Tuple=True  ):





       '''simple docstring'''

       if config_path is not None:
              lowercase__    :	Any     					=			UniSpeechSatConfig.from_pretrained(_lowerCAmelCase  )
       else:
              lowercase__    :	Any     					=			UniSpeechSatConfig()

       lowercase__    :	Union[str, Any]     					=			''

       if is_finetuned:
              lowercase__    :	Optional[Any]     					=			UniSpeechSatForCTC(_lowerCAmelCase  )
       else:
              lowercase__    :	List[Any]     					=			UniSpeechSatForPreTraining(_lowerCAmelCase  )

       lowercase__					,   lowercase__					,   lowercase__    :	int     					=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
           [checkpoint_path]     ,				arg_overrides={'data': '/'.join(dict_path.split('/'  )[:-1]  )}  )
       lowercase__    :	Union[str, Any]     					=			model[0].eval()

       recursively_load_weights(_lowerCAmelCase     ,				_lowerCAmelCase  )

       hf_wavavec.save_pretrained(_lowerCAmelCase  )


if __name__ == "__main__":
       _UpperCamelCase     :		Tuple											=					argparse.ArgumentParser()
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
       parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       parser.add_argument(
           "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
       )
       _UpperCamelCase     :		str											=					parser.parse_args()
       convert_unispeech_sat_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )


"""simple docstring"""





import inspect
import unittest

from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import YolosForObjectDetection, YolosModel
       from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor


class   UpperCAmelCase_			:


      def __init__(     self	,    a	,    a=1_3	,    a=[3_0, 3_0]	,    a=2	,    a=3	,    a=True	,    a=True	,    a=3_2	,    a=5	,    a=4	,    a=3_7	,    a="gelu"	,    a=0.1	,    a=0.1	,    a=1_0	,    a=0.02	,    a=3	,    a=None	,    a=8	,    a=1_0	,    ) ->      Any:
             lowercase__    :	List[str]     					=			parent
             lowercase__    :	Optional[Any]     					=			batch_size
             lowercase__    :	Optional[int]     					=			image_size
             lowercase__    :	List[Any]     					=			patch_size
             lowercase__    :	Optional[Any]     					=			num_channels
             lowercase__    :	str     					=			is_training
             lowercase__    :	Optional[Any]     					=			use_labels
             lowercase__    :	Optional[Any]     					=			hidden_size
             lowercase__    :	Dict     					=			num_hidden_layers
             lowercase__    :	Optional[Any]     					=			num_attention_heads
             lowercase__    :	Dict     					=			intermediate_size
             lowercase__    :	List[Any]     					=			hidden_act
             lowercase__    :	List[Any]     					=			hidden_dropout_prob
             lowercase__    :	Any     					=			attention_probs_dropout_prob
             lowercase__    :	Any     					=			type_sequence_label_size
             lowercase__    :	Dict     					=			initializer_range
             lowercase__    :	Union[str, Any]     					=			num_labels
             lowercase__    :	Tuple     					=			scope
             lowercase__    :	Tuple     					=			n_targets
             lowercase__    :	Optional[int]     					=			num_detection_tokens
             # we set the expected sequence length (which is used in several tests)
             # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
             lowercase__    :	Optional[Any]     					=			(image_size[1] // patch_size) * (image_size[0] // patch_size)
             lowercase__    :	Tuple     					=			num_patches + 1 + self.num_detection_tokens


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Union[str, Any]     					=			floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]							)

             lowercase__    :	Tuple     					=			None
             if self.use_labels:
                    # labels is a list of Dict (each Dict being the labels for a given example in the batch)
                    lowercase__    :	int     					=			[]
                    for i in range(self.batch_size							):
                           lowercase__    :	Optional[Any]     					=			{}
                           lowercase__    :	Any     					=			torch.randint(
                               high=self.num_labels	,    size=(self.n_targets,)	,    device=a							)
                           lowercase__    :	List[str]     					=			torch.rand(self.n_targets	,    4	,    device=a							)
                           labels.append(a							)

             lowercase__    :	Tuple     					=			self.get_config()

             return config, pixel_values, labels


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             return YolosConfig(
                 image_size=self.image_size	,    patch_size=self.patch_size	,    num_channels=self.num_channels	,    hidden_size=self.hidden_size	,    num_hidden_layers=self.num_hidden_layers	,    num_attention_heads=self.num_attention_heads	,    intermediate_size=self.intermediate_size	,    hidden_act=self.hidden_act	,    hidden_dropout_prob=self.hidden_dropout_prob	,    attention_probs_dropout_prob=self.attention_probs_dropout_prob	,    is_decoder=a	,    initializer_range=self.initializer_range	,    num_detection_tokens=self.num_detection_tokens	,    num_labels=self.num_labels	,    )


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      int:
             lowercase__    :	List[str]     					=			YolosModel(config=a							)
             model.to(a							)
             model.eval()
             lowercase__    :	List[Any]     					=			model(a							)
             self.parent.assertEqual(
                 result.last_hidden_state.shape	,    (self.batch_size, self.expected_seq_len, self.hidden_size)							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Union[str, Any]:
             lowercase__    :	str     					=			YolosForObjectDetection(a							)
             model.to(a							)
             model.eval()

             lowercase__    :	Dict     					=			model(pixel_values=a							)
             lowercase__    :	Tuple     					=			model(a							)

             self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_detection_tokens, self.num_labels + 1)							)
             self.parent.assertEqual(result.pred_boxes.shape	,    (self.batch_size, self.num_detection_tokens, 4)							)

             lowercase__    :	str     					=			model(pixel_values=a	,    labels=a							)

             self.parent.assertEqual(result.loss.shape	,    ()							)
             self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_detection_tokens, self.num_labels + 1)							)
             self.parent.assertEqual(result.pred_boxes.shape	,    (self.batch_size, self.num_detection_tokens, 4)							)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	int     					=			self.prepare_config_and_inputs()
             lowercase__					,   lowercase__					,   lowercase__    :	Any     					=			config_and_inputs
             lowercase__    :	Any     					=			{'pixel_values': pixel_values}
             return config, inputs_dict





@require_torch
class   UpperCAmelCase_			(					_a				,							_a				,							unittest.TestCase):
      lowerCamelCase__	:					Optional[int]										=						(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
      lowerCamelCase__	:					List[str]										=						(
          {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
      )

      lowerCamelCase__	:					List[Any]										=						False
      lowerCamelCase__	:					Dict										=						False
      lowerCamelCase__	:					Tuple										=						False
      lowerCamelCase__	:					Union[str, Any]										=						False


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a=False							) ->      Dict:
             lowercase__    :	List[str]     					=			super()._prepare_for_class(a	,    a	,    return_labels=a							)

             if return_labels:
                    if model_class.__name__ == "YolosForObjectDetection":
                           lowercase__    :	Optional[Any]     					=			[]
                           for i in range(self.model_tester.batch_size							):
                                  lowercase__    :	Dict     					=			{}
                                  lowercase__    :	Dict     					=			torch.ones(
                                      size=(self.model_tester.n_targets,)	,    device=a	,    dtype=torch.long							)
                                  lowercase__    :	Optional[Any]     					=			torch.ones(
                                      self.model_tester.n_targets	,    4	,    device=a	,    dtype=torch.float							)
                                  labels.append(a							)
                           lowercase__    :	Union[str, Any]     					=			labels

             return inputs_dict


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	Dict     					=			YolosModelTester(self							)
             lowercase__    :	Optional[int]     					=			ConfigTester(self	,    config_class=a	,    has_text_modality=a	,    hidden_size=3_7							)


      def 	_UpperCAmelCase (     self							) ->      str:
             self.config_tester.run_common_tests()


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             # YOLOS does not use inputs_embeds
             pass


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__					,   lowercase__    :	Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__    :	List[str]     					=			model_class(a							)
                    self.assertIsInstance(model.get_input_embeddings()	,    (nn.Module)							)
                    lowercase__    :	List[str]     					=			model.get_output_embeddings()
                    self.assertTrue(x is None or isinstance(a	,    nn.Linear							)							)


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__					,   lowercase__    :	Any     					=			self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__    :	int     					=			model_class(a							)
                    lowercase__    :	Union[str, Any]     					=			inspect.signature(model.forward							)
                    # signature.parameters is an OrderedDict => so arg_names order is deterministic
                    lowercase__    :	Tuple     					=			[*signature.parameters.keys()]

                    lowercase__    :	List[Any]     					=			['pixel_values']
                    self.assertListEqual(arg_names[:1]	,    a							)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Dict     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a							)


      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__					,   lowercase__    :	int     					=			self.model_tester.prepare_config_and_inputs_for_common()
             lowercase__    :	Dict     					=			True

             # in YOLOS, the seq_len is different
             lowercase__    :	Tuple     					=			self.model_tester.expected_seq_len
             for model_class in self.all_model_classes:
                    lowercase__    :	Optional[int]     					=			True
                    lowercase__    :	str     					=			False
                    lowercase__    :	str     					=			True
                    lowercase__    :	List[str]     					=			model_class(a							)
                    model.to(a							)
                    model.eval()
                    with torch.no_grad():
                           lowercase__    :	Any     					=			model(**self._prepare_for_class(a	,    a							)							)
                    lowercase__    :	str     					=			outputs.attentions
                    self.assertEqual(len(a							)	,    self.model_tester.num_hidden_layers							)

                    # check that output_attentions also work using config
                    del inputs_dict["output_attentions"]
                    lowercase__    :	Optional[int]     					=			True
                    lowercase__    :	List[Any]     					=			model_class(a							)
                    model.to(a							)
                    model.eval()
                    with torch.no_grad():
                           lowercase__    :	Union[str, Any]     					=			model(**self._prepare_for_class(a	,    a							)							)
                    lowercase__    :	List[str]     					=			outputs.attentions
                    self.assertEqual(len(a							)	,    self.model_tester.num_hidden_layers							)

                    self.assertListEqual(
                        list(attentions[0].shape[-3:]							)	,    [self.model_tester.num_attention_heads, seq_len, seq_len]	,    )
                    lowercase__    :	Dict     					=			len(a							)

                    # Check attention is always last and order is fine
                    lowercase__    :	Any     					=			True
                    lowercase__    :	int     					=			True
                    lowercase__    :	int     					=			model_class(a							)
                    model.to(a							)
                    model.eval()
                    with torch.no_grad():
                           lowercase__    :	Any     					=			model(**self._prepare_for_class(a	,    a							)							)

                    lowercase__    :	Optional[Any]     					=			1
                    self.assertEqual(out_len + added_hidden_states	,    len(a							)							)

                    lowercase__    :	Tuple     					=			outputs.attentions

                    self.assertEqual(len(a							)	,    self.model_tester.num_hidden_layers							)
                    self.assertListEqual(
                        list(self_attentions[0].shape[-3:]							)	,    [self.model_tester.num_attention_heads, seq_len, seq_len]	,    )


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             def check_hidden_states_output(a	,    a	,    a							):
                    lowercase__    :	str     					=			model_class(a							)
                    model.to(a							)
                    model.eval()

                    with torch.no_grad():
                           lowercase__    :	int     					=			model(**self._prepare_for_class(a	,    a							)							)

                    lowercase__    :	int     					=			outputs.hidden_states

                    lowercase__    :	Any     					=			getattr(
                        self.model_tester	,    'expected_num_hidden_layers'	,    self.model_tester.num_hidden_layers + 1							)
                    self.assertEqual(len(a							)	,    a							)

                    # YOLOS has a different seq_length
                    lowercase__    :	Optional[int]     					=			self.model_tester.expected_seq_len

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

             lowercase__					,   lowercase__    :	List[str]     					=			self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__    :	Any     					=			True
                    check_hidden_states_output(a	,    a	,    a							)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    lowercase__    :	List[Any]     					=			True

                    check_hidden_states_output(a	,    a	,    a							)


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	List[Any]     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_object_detection(*a							)


      @slow
      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__    :	int     					=			YolosModel.from_pretrained(a							)
                    self.assertIsNotNone(a							)





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Optional[int]     					=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
       return image






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


      @cached_property
      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             return AutoImageProcessor.from_pretrained('hustvl/yolos-small'							) if is_vision_available() else None


      @slow
      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Dict     					=			YolosForObjectDetection.from_pretrained('hustvl/yolos-small'							).to(a							)

             lowercase__    :	Tuple     					=			self.default_image_processor
             lowercase__    :	Optional[int]     					=			prepare_img()
             lowercase__    :	int     					=			image_processor(images=a	,    return_tensors='pt'							).to(a							)

             # forward pass
             with torch.no_grad():
                    lowercase__    :	int     					=			model(inputs.pixel_values							)

             # verify outputs
             lowercase__    :	Tuple     					=			torch.Size((1, 1_0_0, 9_2)							)
             self.assertEqual(outputs.logits.shape	,    a							)

             lowercase__    :	Any     					=			torch.tensor(
                 [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]	,    device=a	,    )
             lowercase__    :	List[str]     					=			torch.tensor(
                 [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]	,    device=a							)
             self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]	,    a	,    atol=1e-4							)							)
             self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]	,    a	,    atol=1e-4							)							)

             # verify postprocessing
             lowercase__    :	Optional[Any]     					=			image_processor.post_process_object_detection(
                 a	,    threshold=0.3	,    target_sizes=[image.size[::-1]]							)[0]
             lowercase__    :	str     					=			torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861]							).to(a							)
             lowercase__    :	Any     					=			[7_5, 7_5, 1_7, 6_3, 1_7]
             lowercase__    :	Optional[int]     					=			torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495]							).to(a							)

             self.assertEqual(len(results['scores']							)	,    5							)
             self.assertTrue(torch.allclose(results['scores']	,    a	,    atol=1e-4							)							)
             self.assertSequenceEqual(results['labels'].tolist()	,    a							)
             self.assertTrue(torch.allclose(results['boxes'][0, :]	,    a							)							)

