

"""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  lowerCamelCase    :

			def __init__(       self					:    Optional[int]     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    str=1_3     ,					__UpperCAmelCase					:    List[str]=[3_0, 3_0]     ,					__UpperCAmelCase					:    Optional[int]=2     ,					__UpperCAmelCase					:    List[Any]=3     ,					__UpperCAmelCase					:    List[Any]=True     ,					__UpperCAmelCase					:    Tuple=True     ,					__UpperCAmelCase					:    str=3_2     ,					__UpperCAmelCase					:    Union[str, Any]=5     ,					__UpperCAmelCase					:    Any=4     ,					__UpperCAmelCase					:    Any=3_7     ,					__UpperCAmelCase					:    str="gelu"     ,					__UpperCAmelCase					:    List[Any]=0.1     ,					__UpperCAmelCase					:    Any=0.1     ,					__UpperCAmelCase					:    Dict=1_0     ,					__UpperCAmelCase					:    Dict=0.02     ,					__UpperCAmelCase					:    List[Any]=3     ,					__UpperCAmelCase					:    Optional[int]=None     ,					__UpperCAmelCase					:    int=8     ,					__UpperCAmelCase					:    List[str]=1_0     ,					) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   parent
						SCREAMING_SNAKE_CASE__										=   batch_size
						SCREAMING_SNAKE_CASE__										=   image_size
						SCREAMING_SNAKE_CASE__										=   patch_size
						SCREAMING_SNAKE_CASE__										=   num_channels
						SCREAMING_SNAKE_CASE__										=   is_training
						SCREAMING_SNAKE_CASE__										=   use_labels
						SCREAMING_SNAKE_CASE__										=   hidden_size
						SCREAMING_SNAKE_CASE__										=   num_hidden_layers
						SCREAMING_SNAKE_CASE__										=   num_attention_heads
						SCREAMING_SNAKE_CASE__										=   intermediate_size
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE__										=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__										=   type_sequence_label_size
						SCREAMING_SNAKE_CASE__										=   initializer_range
						SCREAMING_SNAKE_CASE__										=   num_labels
						SCREAMING_SNAKE_CASE__										=   scope
						SCREAMING_SNAKE_CASE__										=   n_targets
						SCREAMING_SNAKE_CASE__										=   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
						SCREAMING_SNAKE_CASE__										=   (image_size[1] // patch_size) * (image_size[0] // patch_size)
						SCREAMING_SNAKE_CASE__										=   num_patches + 1 + self.num_detection_tokens

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

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

						SCREAMING_SNAKE_CASE__										=   self.get_config()

						return config, pixel_values, labels

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Union[str, 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        SCREAMING_SNAKE_CASE					(       self					:    Tuple     ,					__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    List[Any]     ,					__UpperCAmelCase					:    List[Any]							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   YolosModel(config=A_							)
						model.to(A_							)
						model.eval()
						SCREAMING_SNAKE_CASE__										=   model(A_							)
						self.parent.assertEqual(
						    result.last_hidden_state.shape     ,					(self.batch_size, self.expected_seq_len, self.hidden_size)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    str     ,					__UpperCAmelCase					:    Optional[Any]     ,					__UpperCAmelCase					:    Union[str, Any]     ,					__UpperCAmelCase					:    List[str]							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   YolosForObjectDetection(A_							)
						model.to(A_							)
						model.eval()

						SCREAMING_SNAKE_CASE__										=   model(pixel_values=A_							)
						SCREAMING_SNAKE_CASE__										=   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)							)

						SCREAMING_SNAKE_CASE__										=   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        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE__										=   config_and_inputs
						SCREAMING_SNAKE_CASE__										=   {'''pixel_values''': pixel_values}
						return config, inputs_dict





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

			lowerCamelCase__ :		Any       		=				False
			lowerCamelCase__ :		Any       		=				False
			lowerCamelCase__ :		Tuple       		=				False
			lowerCamelCase__ :		int       		=				False

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    Optional[Any]=False							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   super()._prepare_for_class(A_     ,					A_     ,					return_labels=A_							)

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

						return inputs_dict

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     Optional[int]:
						SCREAMING_SNAKE_CASE__										=   YolosModelTester(self							)
						SCREAMING_SNAKE_CASE__										=   ConfigTester(self     ,					config_class=A_     ,					has_text_modality=A_     ,					hidden_size=3_7							)

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Union[str, Any]:
						self.config_tester.run_common_tests()

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

			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     int:
						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(A_							)
									self.assertIsInstance(model.get_input_embeddings()     ,					(nn.Module)							)
									SCREAMING_SNAKE_CASE__										=   model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(A_     ,					nn.Linear							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     int:
						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(A_							)
									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]     ,					A_							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A_							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     int:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs_for_common()
						SCREAMING_SNAKE_CASE__										=   True

						# in YOLOS, the seq_len is different
						SCREAMING_SNAKE_CASE__										=   self.model_tester.expected_seq_len
						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__										=   True
									SCREAMING_SNAKE_CASE__										=   False
									SCREAMING_SNAKE_CASE__										=   True
									SCREAMING_SNAKE_CASE__										=   model_class(A_							)
									model.to(A_							)
									model.eval()
									with torch.no_grad():
												SCREAMING_SNAKE_CASE__										=   model(**self._prepare_for_class(A_     ,					A_							)							)
									SCREAMING_SNAKE_CASE__										=   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"]
									SCREAMING_SNAKE_CASE__										=   True
									SCREAMING_SNAKE_CASE__										=   model_class(A_							)
									model.to(A_							)
									model.eval()
									with torch.no_grad():
												SCREAMING_SNAKE_CASE__										=   model(**self._prepare_for_class(A_     ,					A_							)							)
									SCREAMING_SNAKE_CASE__										=   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]     ,					)
									SCREAMING_SNAKE_CASE__										=   len(A_							)

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

									SCREAMING_SNAKE_CASE__										=   1
									self.assertEqual(out_len + added_hidden_states     ,					len(A_							)							)

									SCREAMING_SNAKE_CASE__										=   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        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Optional[Any]:




						def check_hidden_states_output(__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    List[Any]     ,					__UpperCAmelCase					:    List[str]							):
									SCREAMING_SNAKE_CASE__										=   model_class(A_							)
									model.to(A_							)
									model.eval()

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

									SCREAMING_SNAKE_CASE__										=   outputs.hidden_states

									SCREAMING_SNAKE_CASE__										=   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
									SCREAMING_SNAKE_CASE__										=   self.model_tester.expected_seq_len

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

						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(A_     ,					A_     ,					A_							)

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

									check_hidden_states_output(A_     ,					A_     ,					A_							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_object_detection(*A_							)





			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Dict:




						for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE__										=   YolosModel.from_pretrained(A_							)
									self.assertIsNotNone(A_							)

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      ):

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





			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   YolosForObjectDetection.from_pretrained("""hustvl/yolos-small"""							).to(A_							)

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

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

						# verify outputs
						SCREAMING_SNAKE_CASE__										=   torch.Size((1, 1_0_0, 9_2)							)
						self.assertEqual(outputs.logits.shape     ,					A_							)

						SCREAMING_SNAKE_CASE__										=   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_     ,					)
						SCREAMING_SNAKE_CASE__										=   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
						SCREAMING_SNAKE_CASE__										=   image_processor.post_process_object_detection(
						    A_     ,					threshold=0.3     ,					target_sizes=[image.size[::-1]]							)[0]
						SCREAMING_SNAKE_CASE__										=   torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861]							).to(A_							)
						SCREAMING_SNAKE_CASE__										=   [7_5, 7_5, 1_7, 6_3, 1_7]
						SCREAMING_SNAKE_CASE__										=   torch.tensor([3_3_5.0_6_0_9, 79.3_848, 3_7_5.4_2_1_6, 1_8_7.2_4_9_5]							).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_							)							)




"""simple docstring"""




import math
import sys




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  ''''''
  try:
    with open(_UpperCamelCase			,					'''rb'''    ) as binary_file:
      _lowercase:      Dict   			=  binary_file.read()
    for dat in data:
      _lowercase:      List[str]   			=  f'''{dat:08b}'''
      result += curr_byte
    return result
  except OSError:
    print('''File not accessible'''    )
    sys.exit()




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  {'''0''': '''0''', '''1''': '''1'''}
  _lowercase			,					_lowercase:      Optional[int]   			=  '''''', ''''''
  _lowercase:      Dict   			=  len(_UpperCamelCase    )

  for i in range(len(_UpperCamelCase    )    ):
    curr_string += data_bits[i]
    if curr_string not in lexicon:
      continue

    _lowercase:      Any   			=  lexicon[curr_string]
    result += last_match_id
    _lowercase:      Optional[int]   			=  last_match_id + '''0'''

    if math.loga(_UpperCamelCase    ).is_integer():
      _lowercase:      str   			=  {}
      for curr_key in list(_UpperCamelCase    ):
        _lowercase:      List[Any]   			=  lexicon.pop(_UpperCamelCase    )
      _lowercase:      Tuple   			=  new_lex

    _lowercase:      List[Any]   			=  last_match_id + '''1'''
    index += 1
    _lowercase:      List[Any]   			=  ''''''
  return result




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Optional[Any]   			=  8
  try:
    with open(_UpperCamelCase			,					'''wb'''    ) as opened_file:
      _lowercase:      List[Any]   			=  [
          to_write[i : i + byte_length]
          for i in range(0			,					len(_UpperCamelCase    )			,					_UpperCamelCase    )
      ]

      if len(result_byte_array[-1]    ) % byte_length == 0:
        result_byte_array.append('''10000000'''    )
      else:
        result_byte_array[-1] += "1" + "0" * (
            byte_length - len(result_byte_array[-1]    ) - 1
        )

      for elem in result_byte_array[:-1]:
        opened_file.write(int(_UpperCamelCase			,					2    ).to_bytes(1			,					byteorder='''big'''    )    )
  except OSError:
    print('''File not accessible'''    )
    sys.exit()




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  0
  for letter in data_bits:
    if letter == "1":
      break
    counter += 1

  _lowercase:      List[str]   			=  data_bits[counter:]
  _lowercase:      Union[str, Any]   			=  data_bits[counter + 1 :]
  return data_bits




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Tuple   			=  read_file_binary(_UpperCamelCase    )
  _lowercase:      Optional[Any]   			=  remove_prefix(_UpperCamelCase    )
  _lowercase:      Dict   			=  decompress_data(_UpperCamelCase    )
  write_file_binary(_UpperCamelCase			,					_UpperCamelCase    )


if __name__ == "__main__":
  compress(sys.argv[1], sys.argv[2])
