
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def 							a		(				snake_case__:   Dict		,  snake_case__:   Any		,  snake_case__:   Dict   ):


				'''simple docstring'''
				lowercase_	    =   AutoConfig.from_pretrained(snake_case__   )
				lowercase_	    =   FlaxAutoModelForSeqaSeqLM.from_config(config=snake_case__   )
				lowercase_	    =   checkpoints.load_tax_checkpoint(snake_case__   )

				lowercase_	    =   '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

				if config.model_type == "t5":
								lowercase_	    =   '''SelfAttention'''
				if config.model_type == "longt5" and config.encoder_attention_type == "local":
								lowercase_	    =   '''LocalSelfAttention'''
				elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								lowercase_	    =   '''TransientGlobalSelfAttention'''
				else:
								raise ValueError(
								    '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
								    ''' attribute with a value from [\'local\', \'transient-global].'''   )

				# Encoder
				for layer_index in range(config.num_layers   ):
								lowercase_	    =   F'''layers_{str(snake_case__   )}'''

								# Self-Attention
								lowercase_	    =   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
								lowercase_	    =   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
								lowercase_	    =   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
								lowercase_	    =   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

								# Global input layer norm
								if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												lowercase_	    =   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

								# Layer Normalization
								lowercase_	    =   tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

								if split_mlp_wi:
												lowercase_	    =   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
												lowercase_	    =   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
								else:
												lowercase_	    =   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

								lowercase_	    =   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

								# Layer Normalization
								lowercase_	    =   tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

								# Assigning
								lowercase_	    =   flax_model.params['''encoder''']['''block'''][str(snake_case__   )]['''layer''']
								lowercase_	    =   tax_attention_key
								lowercase_	    =   tax_attention_out
								lowercase_	    =   tax_attention_query
								lowercase_	    =   tax_attention_value

								lowercase_	    =   tax_attention_layer_norm

								# Global input layer norm
								if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												lowercase_	    =   tax_global_layer_norm

								if split_mlp_wi:
												lowercase_	    =   tax_mlp_wi_a
												lowercase_	    =   tax_mlp_wi_a
								else:
												lowercase_	    =   tax_mlp_wi

								lowercase_	    =   tax_mlp_wo
								lowercase_	    =   tax_mlp_layer_norm

								lowercase_	    =   flax_model_encoder_layer_block

				# Only for layer 0:
				lowercase_	    =   tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
				lowercase_	    =   tax_encoder_rel_embedding

				# Side/global relative position_bias + layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								lowercase_	    =   tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
								lowercase_	    =   tax_encoder_global_rel_embedding

				# Assigning
				lowercase_	    =   tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
				lowercase_	    =   tax_encoder_norm

				# Decoder
				for layer_index in range(config.num_layers   ):
								lowercase_	    =   F'''layers_{str(snake_case__   )}'''

								# Self-Attention
								lowercase_	    =   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
								lowercase_	    =   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
								lowercase_	    =   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
								lowercase_	    =   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

								# Layer Normalization
								lowercase_	    =   tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
								    '''scale'''
								]

								# Encoder-Decoder-Attention
								lowercase_	    =   tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
								lowercase_	    =   tax_enc_dec_attention_module['''key''']['''kernel''']
								lowercase_	    =   tax_enc_dec_attention_module['''out''']['''kernel''']
								lowercase_	    =   tax_enc_dec_attention_module['''query''']['''kernel''']
								lowercase_	    =   tax_enc_dec_attention_module['''value''']['''kernel''']

								# Layer Normalization
								lowercase_	    =   tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

								# MLP
								if split_mlp_wi:
												lowercase_	    =   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
												lowercase_	    =   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
								else:
												lowercase_	    =   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

								lowercase_	    =   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

								# Layer Normalization
								lowercase_	    =   tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

								# Assigning
								lowercase_	    =   flax_model.params['''decoder''']['''block'''][str(snake_case__   )]['''layer''']
								lowercase_	    =   tax_attention_key
								lowercase_	    =   tax_attention_out
								lowercase_	    =   tax_attention_query
								lowercase_	    =   tax_attention_value

								lowercase_	    =   tax_pre_attention_layer_norm

								lowercase_	    =   tax_enc_dec_attention_key
								lowercase_	    =   tax_enc_dec_attention_out
								lowercase_	    =   tax_enc_dec_attention_query
								lowercase_	    =   tax_enc_dec_attention_value

								lowercase_	    =   tax_cross_layer_norm

								if split_mlp_wi:
												lowercase_	    =   tax_mlp_wi_a
												lowercase_	    =   tax_mlp_wi_a
								else:
												lowercase_	    =   tax_mlp_wi

								lowercase_	    =   tax_mlp_wo

								lowercase_	    =   txa_mlp_layer_norm

								lowercase_	    =   flax_model_decoder_layer_block

				# Decoder Normalization
				lowercase_	    =   tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
				lowercase_	    =   txa_decoder_norm

				# Only for layer 0:
				lowercase_	    =   tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
				lowercase_	    =   tax_decoder_rel_embedding

				# Token Embeddings
				lowercase_	    =   tax_model['''target''']['''token_embedder''']['''embedding''']
				lowercase_	    =   txa_token_embeddings

				# LM Head (only in v1.1 and LongT5 checkpoints)
				if "logits_dense" in tax_model["target"]["decoder"]:
								lowercase_	    =   tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

				flax_model.save_pretrained(snake_case__   )
				print('''T5X Model was sucessfully converted!'''   )


if __name__ == "__main__":
						__a         =   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
						)
						parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
						parser.add_argument(
						    '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
						)
						__a         =   parser.parse_args()
						convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)











'''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 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=13		,      _UpperCAmelCase=[30, 30]		,      _UpperCAmelCase=2		,      _UpperCAmelCase=3		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=32		,      _UpperCAmelCase=5		,      _UpperCAmelCase=4		,      _UpperCAmelCase=37		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=10		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=3		,      _UpperCAmelCase=None		,      _UpperCAmelCase=8		,      _UpperCAmelCase=10		,      ):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			parent
          __A		:					Tuple						=			batch_size
          __A		:					List[str]						=			image_size
          __A		:					Dict						=			patch_size
          __A		:					Optional[Any]						=			num_channels
          __A		:					Tuple						=			is_training
          __A		:					Dict						=			use_labels
          __A		:					List[Any]						=			hidden_size
          __A		:					Tuple						=			num_hidden_layers
          __A		:					int						=			num_attention_heads
          __A		:					Optional[int]						=			intermediate_size
          __A		:					Tuple						=			hidden_act
          __A		:					Any						=			hidden_dropout_prob
          __A		:					Optional[Any]						=			attention_probs_dropout_prob
          __A		:					List[Any]						=			type_sequence_label_size
          __A		:					List[Any]						=			initializer_range
          __A		:					Optional[int]						=			num_labels
          __A		:					List[Any]						=			scope
          __A		:					Any						=			n_targets
          __A		:					Union[str, Any]						=			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
          __A		:					List[str]						=			(image_size[1] // patch_size) * (image_size[0] // patch_size)
          __A		:					int						=			num_patches + 1 + self.num_detection_tokens


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[str]						=			floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])

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

          __A		:					Any						=			self.get_config()

          return config, pixel_values, labels


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          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=_UpperCAmelCase		,      initializer_range=self.initializer_range		,      num_detection_tokens=self.num_detection_tokens		,      num_labels=self.num_labels		,      )


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Any						=			YolosModel(config=_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A		:					Dict						=			model(_UpperCAmelCase)
          self.parent.assertEqual(
              result.last_hidden_state.shape		,      (self.batch_size, self.expected_seq_len, self.hidden_size))


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Any						=			YolosForObjectDetection(_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()

          __A		:					str						=			model(pixel_values=_UpperCAmelCase)
          __A		:					List[str]						=			model(_UpperCAmelCase)

          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))

          __A		:					Union[str, Any]						=			model(pixel_values=_UpperCAmelCase		,      labels=_UpperCAmelCase)

          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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			self.prepare_config_and_inputs()
          __A					,__A					,__A		:					Tuple						=			config_and_inputs
          __A		:					Tuple						=			{'pixel_values': pixel_values}
          return config, inputs_dict




@require_torch
class 					SCREAMING_SNAKE_CASE       (a__   ,						a__   ,						unittest.TestCase				):
     lowerCAmelCase						     =			(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
     lowerCAmelCase						     =			(
         {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
     )

     lowerCAmelCase						     =			False
     lowerCAmelCase						     =			False
     lowerCAmelCase						     =			False
     lowerCAmelCase						     =			False


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=False):

          '''simple docstring'''






          __A		:					Optional[Any]						=			super()._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase		,      return_labels=_UpperCAmelCase)

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

          return inputs_dict


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			YolosModelTester(self)
          __A		:					Dict						=			ConfigTester(self		,      config_class=_UpperCAmelCase		,      has_text_modality=_UpperCAmelCase		,      hidden_size=37)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          self.config_tester.run_common_tests()


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

          for model_class in self.all_model_classes:
               __A		:					Tuple						=			model_class(_UpperCAmelCase)
               self.assertIsInstance(model.get_input_embeddings()		,      (nn.Module))
               __A		:					Any						=			model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(_UpperCAmelCase		,      nn.Linear))


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					List[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

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

               __A		:					List[str]						=			['pixel_values']
               self.assertListEqual(arg_names[:1]		,      _UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					Dict						=			self.model_tester.prepare_config_and_inputs_for_common()
          __A		:					Optional[int]						=			True

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

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

               self.assertListEqual(
                   list(attentions[0].shape[-3:])		,      [self.model_tester.num_attention_heads, seq_len, seq_len]		,      )
               __A		:					str						=			len(_UpperCAmelCase)

               # Check attention is always last and order is fine
               __A		:					Dict						=			True
               __A		:					Dict						=			True
               __A		:					Dict						=			model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.eval()
               with torch.no_grad():
                    __A		:					List[str]						=			model(**self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))

               __A		:					Union[str, Any]						=			1
               self.assertEqual(out_len + added_hidden_states		,      len(_UpperCAmelCase))

               __A		:					Optional[Any]						=			outputs.attentions

               self.assertEqual(len(_UpperCAmelCase)		,      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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          def check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):
               __A		:					Tuple						=			model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.eval()

               with torch.no_grad():
                    __A		:					List[str]						=			model(**self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))

               __A		:					Optional[Any]						=			outputs.hidden_states

               __A		:					List[str]						=			getattr(
                   self.model_tester		,      'expected_num_hidden_layers'		,      self.model_tester.num_hidden_layers + 1)
               self.assertEqual(len(_UpperCAmelCase)		,      _UpperCAmelCase)

               # YOLOS has a different seq_length
               __A		:					Dict						=			self.model_tester.expected_seq_len

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

          __A					,__A		:					Union[str, Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A		:					List[str]						=			True
               check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __A		:					Optional[int]						=			True

               check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_object_detection(*_UpperCAmelCase)


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __A		:					List[Any]						=			YolosModel.from_pretrained(_UpperCAmelCase)
               self.assertIsNotNone(_UpperCAmelCase)






def 			_lowerCAmelCase     (    )	->	int:
     __A		:					Dict						=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
     return image







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


     @cached_property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return AutoImageProcessor.from_pretrained('hustvl/yolos-small') if is_vision_available() else None


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			YolosForObjectDetection.from_pretrained('hustvl/yolos-small').to(_UpperCAmelCase)

          __A		:					Any						=			self.default_image_processor
          __A		:					str						=			prepare_img()
          __A		:					int						=			image_processor(images=_UpperCAmelCase		,      return_tensors='pt').to(_UpperCAmelCase)

          # forward pass
          with torch.no_grad():
               __A		:					str						=			model(inputs.pixel_values)

          # verify outputs
          __A		:					Tuple						=			torch.Size((1, 100, 92))
          self.assertEqual(outputs.logits.shape		,      _UpperCAmelCase)

          __A		:					Dict						=			torch.tensor(
              [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]		,      device=_UpperCAmelCase		,      )
          __A		:					int						=			torch.tensor(
              [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]		,      device=_UpperCAmelCase)
          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]		,      _UpperCAmelCase		,      atol=1e-4))
          self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]		,      _UpperCAmelCase		,      atol=1e-4))

          # verify postprocessing
          __A		:					List[str]						=			image_processor.post_process_object_detection(
              _UpperCAmelCase		,      threshold=0.3		,      target_sizes=[image.size[::-1]])[0]
          __A		:					Optional[int]						=			torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]).to(_UpperCAmelCase)
          __A		:					Union[str, Any]						=			[75, 75, 17, 63, 17]
          __A		:					Any						=			torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]).to(_UpperCAmelCase)

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