




import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
			import torch

			from transformers import (
			    MraForMaskedLM,
			    MraForMultipleChoice,
			    MraForQuestionAnswering,
			    MraForSequenceClassification,
			    MraForTokenClassification,
			    MraModel,
			)
			from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST





class 	lowerCamelCase_							:


	'''simple docstring'''







	def __init__(							self  :		str    ,    _lowerCAmelCase  :		List[Any]    ,    _lowerCAmelCase  :		Dict=2    ,    _lowerCAmelCase  :		Dict=8    ,    _lowerCAmelCase  :		str=True    ,    _lowerCAmelCase  :		Tuple=True    ,    _lowerCAmelCase  :		Tuple=True    ,    _lowerCAmelCase  :		Optional[Any]=True    ,    _lowerCAmelCase  :		Dict=99    ,    _lowerCAmelCase  :		Union[str, Any]=16    ,    _lowerCAmelCase  :		Dict=5    ,    _lowerCAmelCase  :		Tuple=2    ,    _lowerCAmelCase  :		Optional[Any]=36    ,    _lowerCAmelCase  :		Optional[int]="gelu"    ,    _lowerCAmelCase  :		int=0.0    ,    _lowerCAmelCase  :		int=0.0    ,    _lowerCAmelCase  :		Dict=512    ,    _lowerCAmelCase  :		Tuple=16    ,    _lowerCAmelCase  :		Optional[int]=2    ,    _lowerCAmelCase  :		Optional[int]=0.02    ,    _lowerCAmelCase  :		int=3    ,    _lowerCAmelCase  :		str=4    ,    _lowerCAmelCase  :		Tuple=None    ,    ):
							SCREAMING_SNAKE_CASE_     						=      parent
							SCREAMING_SNAKE_CASE_     						=      batch_size
							SCREAMING_SNAKE_CASE_     						=      seq_length
							SCREAMING_SNAKE_CASE_     						=      is_training
							SCREAMING_SNAKE_CASE_     						=      use_input_mask
							SCREAMING_SNAKE_CASE_     						=      use_token_type_ids
							SCREAMING_SNAKE_CASE_     						=      use_labels
							SCREAMING_SNAKE_CASE_     						=      vocab_size
							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_     						=      max_position_embeddings
							SCREAMING_SNAKE_CASE_     						=      type_vocab_size
							SCREAMING_SNAKE_CASE_     						=      type_sequence_label_size
							SCREAMING_SNAKE_CASE_     						=      initializer_range
							SCREAMING_SNAKE_CASE_     						=      num_labels
							SCREAMING_SNAKE_CASE_     						=      num_choices
							SCREAMING_SNAKE_CASE_     						=      scope



	def 				lowerCAmelCase_  (							self  :		Dict						):
							SCREAMING_SNAKE_CASE_     						=      ids_tensor([self.batch_size, self.seq_length]    ,    self.vocab_size						)

							SCREAMING_SNAKE_CASE_     						=      None
							if self.use_input_mask:
													SCREAMING_SNAKE_CASE_     						=      random_attention_mask([self.batch_size, self.seq_length]						)

							SCREAMING_SNAKE_CASE_     						=      None
							if self.use_token_type_ids:
													SCREAMING_SNAKE_CASE_     						=      ids_tensor([self.batch_size, self.seq_length]    ,    self.type_vocab_size						)

							SCREAMING_SNAKE_CASE_     						=      None
							SCREAMING_SNAKE_CASE_     						=      None
							SCREAMING_SNAKE_CASE_     						=      None
							if self.use_labels:
													SCREAMING_SNAKE_CASE_     						=      ids_tensor([self.batch_size]    ,    self.type_sequence_label_size						)
													SCREAMING_SNAKE_CASE_     						=      ids_tensor([self.batch_size, self.seq_length]    ,    self.num_labels						)
													SCREAMING_SNAKE_CASE_     						=      ids_tensor([self.batch_size]    ,    self.num_choices						)

							SCREAMING_SNAKE_CASE_     						=      self.get_config()

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



	def 				lowerCAmelCase_  (							self  :		List[Any]						):
							return MraConfig(
							    vocab_size=self.vocab_size    ,    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    ,    max_position_embeddings=self.max_position_embeddings    ,    type_vocab_size=self.type_vocab_size    ,    is_decoder=_lowerCAmelCase    ,    initializer_range=self.initializer_range    ,    )



	def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
							SCREAMING_SNAKE_CASE_     						=      self.get_config()
							SCREAMING_SNAKE_CASE_     						=      300
							return config



	def 				lowerCAmelCase_  (							self  :		Dict						):
							(
							    (
							    SCREAMING_SNAKE_CASE_   
							)						,			(
							    SCREAMING_SNAKE_CASE_   
							)						,			(
							    SCREAMING_SNAKE_CASE_   
							)						,			(
							    SCREAMING_SNAKE_CASE_   
							)						,			(
							    SCREAMING_SNAKE_CASE_   
							)						,			(
							    SCREAMING_SNAKE_CASE_   
							)						,			(
							    SCREAMING_SNAKE_CASE_   
							)						,			   
							)     						=      self.prepare_config_and_inputs()

							SCREAMING_SNAKE_CASE_     						=      True
							SCREAMING_SNAKE_CASE_     						=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
							SCREAMING_SNAKE_CASE_     						=      ids_tensor([self.batch_size, self.seq_length]    ,    vocab_size=2						)

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_mask,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)



	def 				lowerCAmelCase_  (							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		List[Any]    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		Optional[int]    ,    _lowerCAmelCase  :		List[Any]						):
							SCREAMING_SNAKE_CASE_     						=      MraModel(config=_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()
							SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase    ,    attention_mask=_lowerCAmelCase    ,    token_type_ids=_lowerCAmelCase						)
							SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase    ,    token_type_ids=_lowerCAmelCase						)
							SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase						)
							self.parent.assertEqual(result.last_hidden_state.shape    ,    (self.batch_size, self.seq_length, self.hidden_size)						)



	def 				lowerCAmelCase_  (							self  :		List[Any]    ,    _lowerCAmelCase  :		Any    ,    _lowerCAmelCase  :		Any    ,    _lowerCAmelCase  :		Optional[int]    ,    _lowerCAmelCase  :		Tuple    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		int    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		List[Any]    ,    ):
							SCREAMING_SNAKE_CASE_     						=      True
							SCREAMING_SNAKE_CASE_     						=      MraModel(_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()
							SCREAMING_SNAKE_CASE_     						=      model(
							    _lowerCAmelCase    ,    attention_mask=_lowerCAmelCase    ,    token_type_ids=_lowerCAmelCase    ,    encoder_hidden_states=_lowerCAmelCase    ,    encoder_attention_mask=_lowerCAmelCase    ,    )
							SCREAMING_SNAKE_CASE_     						=      model(
							    _lowerCAmelCase    ,    attention_mask=_lowerCAmelCase    ,    token_type_ids=_lowerCAmelCase    ,    encoder_hidden_states=_lowerCAmelCase    ,    )
							SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase    ,    attention_mask=_lowerCAmelCase    ,    token_type_ids=_lowerCAmelCase						)
							self.parent.assertEqual(result.last_hidden_state.shape    ,    (self.batch_size, self.seq_length, self.hidden_size)						)



	def 				lowerCAmelCase_  (							self  :		Tuple    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		Optional[int]    ,    _lowerCAmelCase  :		int    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		int    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		str						):
							SCREAMING_SNAKE_CASE_     						=      MraForMaskedLM(config=_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()
							SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase    ,    attention_mask=_lowerCAmelCase    ,    token_type_ids=_lowerCAmelCase    ,    labels=_lowerCAmelCase						)
							self.parent.assertEqual(result.logits.shape    ,    (self.batch_size, self.seq_length, self.vocab_size)						)



	def 				lowerCAmelCase_  (							self  :		Optional[int]    ,    _lowerCAmelCase  :		List[Any]    ,    _lowerCAmelCase  :		List[Any]    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		int    ,    _lowerCAmelCase  :		Any    ,    _lowerCAmelCase  :		Optional[Any]						):
							SCREAMING_SNAKE_CASE_     						=      MraForQuestionAnswering(config=_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()
							SCREAMING_SNAKE_CASE_     						=      model(
							    _lowerCAmelCase    ,    attention_mask=_lowerCAmelCase    ,    token_type_ids=_lowerCAmelCase    ,    start_positions=_lowerCAmelCase    ,    end_positions=_lowerCAmelCase    ,    )
							self.parent.assertEqual(result.start_logits.shape    ,    (self.batch_size, self.seq_length)						)
							self.parent.assertEqual(result.end_logits.shape    ,    (self.batch_size, self.seq_length)						)



	def 				lowerCAmelCase_  (							self  :		Tuple    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		Union[str, Any]    ,    _lowerCAmelCase  :		int    ,    _lowerCAmelCase  :		List[str]    ,    _lowerCAmelCase  :		Any    ,    _lowerCAmelCase  :		Optional[Any]						):
							SCREAMING_SNAKE_CASE_     						=      self.num_labels
							SCREAMING_SNAKE_CASE_     						=      MraForSequenceClassification(_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()
							SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase    ,    attention_mask=_lowerCAmelCase    ,    token_type_ids=_lowerCAmelCase    ,    labels=_lowerCAmelCase						)
							self.parent.assertEqual(result.logits.shape    ,    (self.batch_size, self.num_labels)						)



	def 				lowerCAmelCase_  (							self  :		Dict    ,    _lowerCAmelCase  :		Union[str, Any]    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		Any    ,    _lowerCAmelCase  :		Tuple    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		List[Any]						):
							SCREAMING_SNAKE_CASE_     						=      self.num_labels
							SCREAMING_SNAKE_CASE_     						=      MraForTokenClassification(config=_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()
							SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase    ,    attention_mask=_lowerCAmelCase    ,    token_type_ids=_lowerCAmelCase    ,    labels=_lowerCAmelCase						)
							self.parent.assertEqual(result.logits.shape    ,    (self.batch_size, self.seq_length, self.num_labels)						)



	def 				lowerCAmelCase_  (							self  :		List[str]    ,    _lowerCAmelCase  :		Union[str, Any]    ,    _lowerCAmelCase  :		List[Any]    ,    _lowerCAmelCase  :		Union[str, Any]    ,    _lowerCAmelCase  :		List[str]    ,    _lowerCAmelCase  :		List[str]    ,    _lowerCAmelCase  :		Optional[int]    ,    _lowerCAmelCase  :		Optional[int]						):
							SCREAMING_SNAKE_CASE_     						=      self.num_choices
							SCREAMING_SNAKE_CASE_     						=      MraForMultipleChoice(config=_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()
							SCREAMING_SNAKE_CASE_     						=      input_ids.unsqueeze(1						).expand(-1    ,    self.num_choices    ,    -1						).contiguous()
							SCREAMING_SNAKE_CASE_     						=      token_type_ids.unsqueeze(1						).expand(-1    ,    self.num_choices    ,    -1						).contiguous()
							SCREAMING_SNAKE_CASE_     						=      input_mask.unsqueeze(1						).expand(-1    ,    self.num_choices    ,    -1						).contiguous()
							SCREAMING_SNAKE_CASE_     						=      model(
							    _lowerCAmelCase    ,    attention_mask=_lowerCAmelCase    ,    token_type_ids=_lowerCAmelCase    ,    labels=_lowerCAmelCase    ,    )
							self.parent.assertEqual(result.logits.shape    ,    (self.batch_size, self.num_choices)						)




	def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
							SCREAMING_SNAKE_CASE_     						=      self.prepare_config_and_inputs()
							(
							    (
							    SCREAMING_SNAKE_CASE_   
							)						,			(
							    SCREAMING_SNAKE_CASE_   
							)						,			(
							    SCREAMING_SNAKE_CASE_   
							)						,			(
							    SCREAMING_SNAKE_CASE_   
							)						,			(
							    SCREAMING_SNAKE_CASE_   
							)						,			(
							    SCREAMING_SNAKE_CASE_   
							)						,			(
							    SCREAMING_SNAKE_CASE_   
							)						,			   
							)     						=      config_and_inputs
							SCREAMING_SNAKE_CASE_     						=      {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
							return config, inputs_dict





@require_torch
class 	lowerCamelCase_							(				snake_case_	,       unittest.TestCase ):


	'''simple docstring'''







	lowercase_       =   (
	    (
	        MraModel,
	        MraForMaskedLM,
	        MraForMultipleChoice,
	        MraForQuestionAnswering,
	        MraForSequenceClassification,
	        MraForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	lowercase_       =   False
	lowercase_       =   False
	lowercase_       =   False
	lowercase_       =   False

	lowercase_       =   ()



	def 				lowerCAmelCase_  (							self  :		Any						):
							SCREAMING_SNAKE_CASE_     						=      MraModelTester(self						)
							SCREAMING_SNAKE_CASE_     						=      ConfigTester(self    ,    config_class=_lowerCAmelCase    ,    hidden_size=37						)



	def 				lowerCAmelCase_  (							self  :		List[str]						):
							self.config_tester.run_common_tests()



	def 				lowerCAmelCase_  (							self  :		int						):
							SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_lowerCAmelCase						)



	def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
							SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
													SCREAMING_SNAKE_CASE_     						=      type
													self.model_tester.create_and_check_model(*_lowerCAmelCase						)



	def 				lowerCAmelCase_  (							self  :		Optional[int]						):
							SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*_lowerCAmelCase						)



	def 				lowerCAmelCase_  (							self  :		Tuple						):
							SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*_lowerCAmelCase						)



	def 				lowerCAmelCase_  (							self  :		Any						):
							SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_lowerCAmelCase						)



	def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
							SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_lowerCAmelCase						)



	def 				lowerCAmelCase_  (							self  :		str						):
							SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_lowerCAmelCase						)



	@slow
	def 				lowerCAmelCase_  (							self  :		Any						):
							for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE_     						=      MraModel.from_pretrained(_lowerCAmelCase						)
													self.assertIsNotNone(_lowerCAmelCase						)




	@unittest.skip(reason='MRA does not output attentions'						)
	def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
							return





@require_torch
class 	lowerCamelCase_							(				unittest.TestCase ):


	'''simple docstring'''







	@slow
	def 				lowerCAmelCase_  (							self  :		List[str]						):
							SCREAMING_SNAKE_CASE_     						=      MraModel.from_pretrained('uw-madison/mra-base-512-4'						)
							SCREAMING_SNAKE_CASE_     						=      torch.arange(256						).unsqueeze(0						)

							with torch.no_grad():
													SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase						)[0]

							SCREAMING_SNAKE_CASE_     						=      torch.Size((1, 256, 768)						)
							self.assertEqual(output.shape    ,    _lowerCAmelCase						)

							SCREAMING_SNAKE_CASE_     						=      torch.tensor(
							    [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]]						)

							self.assertTrue(torch.allclose(output[:, :3, :3]    ,    _lowerCAmelCase    ,    atol=1E-4						)						)



	@slow
	def 				lowerCAmelCase_  (							self  :		List[str]						):
							SCREAMING_SNAKE_CASE_     						=      MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4'						)
							SCREAMING_SNAKE_CASE_     						=      torch.arange(256						).unsqueeze(0						)

							with torch.no_grad():
													SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase						)[0]

							SCREAMING_SNAKE_CASE_     						=      50_265

							SCREAMING_SNAKE_CASE_     						=      torch.Size((1, 256, vocab_size)						)
							self.assertEqual(output.shape    ,    _lowerCAmelCase						)

							SCREAMING_SNAKE_CASE_     						=      torch.tensor(
							    [[[9.2595, -3.6038, 11.8_819], [9.3869, -3.2693, 11.0_956], [11.8_524, -3.4938, 13.1_210]]]						)

							self.assertTrue(torch.allclose(output[:, :3, :3]    ,    _lowerCAmelCase    ,    atol=1E-4						)						)




	@slow
	def 				lowerCAmelCase_  (							self  :		List[str]						):
							SCREAMING_SNAKE_CASE_     						=      MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3'						)
							SCREAMING_SNAKE_CASE_     						=      torch.arange(4_096						).unsqueeze(0						)

							with torch.no_grad():
													SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase						)[0]

							SCREAMING_SNAKE_CASE_     						=      50_265

							SCREAMING_SNAKE_CASE_     						=      torch.Size((1, 4_096, vocab_size)						)
							self.assertEqual(output.shape    ,    _lowerCAmelCase						)

							SCREAMING_SNAKE_CASE_     						=      torch.tensor(
							    [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]]						)

							self.assertTrue(torch.allclose(output[:, :3, :3]    ,    _lowerCAmelCase    ,    atol=1E-4						)						)





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import LevitImageProcessor





class 	lowerCamelCase_							(				unittest.TestCase ):


 '''simple docstring'''







 def __init__(							self  :		str    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		Any=7    ,    _lowerCAmelCase  :		Tuple=3    ,    _lowerCAmelCase  :		List[str]=18    ,    _lowerCAmelCase  :		Any=30    ,    _lowerCAmelCase  :		List[Any]=400    ,    _lowerCAmelCase  :		Dict=True    ,    _lowerCAmelCase  :		Tuple=None    ,    _lowerCAmelCase  :		Tuple=True    ,    _lowerCAmelCase  :		Union[str, Any]=None    ,    _lowerCAmelCase  :		Tuple=True    ,    _lowerCAmelCase  :		List[Any]=[0.5, 0.5, 0.5]    ,    _lowerCAmelCase  :		Union[str, Any]=[0.5, 0.5, 0.5]    ,    ):
       SCREAMING_SNAKE_CASE_     						=      size if size is not None else {'shortest_edge': 18}
       SCREAMING_SNAKE_CASE_     						=      crop_size if crop_size is not None else {'height': 18, 'width': 18}
       SCREAMING_SNAKE_CASE_     						=      parent
       SCREAMING_SNAKE_CASE_     						=      batch_size
       SCREAMING_SNAKE_CASE_     						=      num_channels
       SCREAMING_SNAKE_CASE_     						=      image_size
       SCREAMING_SNAKE_CASE_     						=      min_resolution
       SCREAMING_SNAKE_CASE_     						=      max_resolution
       SCREAMING_SNAKE_CASE_     						=      do_resize
       SCREAMING_SNAKE_CASE_     						=      size
       SCREAMING_SNAKE_CASE_     						=      do_center_crop
       SCREAMING_SNAKE_CASE_     						=      crop_size
       SCREAMING_SNAKE_CASE_     						=      do_normalize
       SCREAMING_SNAKE_CASE_     						=      image_mean
       SCREAMING_SNAKE_CASE_     						=      image_std




 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "do_center_crop": self.do_center_crop,
           "size": self.size,
           "crop_size": self.crop_size,
       }





@require_torch
@require_vision
class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE	,       unittest.TestCase ):


 '''simple docstring'''







 lowercase_       =   LevitImageProcessor if is_vision_available() else None



 def 				lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_     						=      LevitImageProcessingTester(self						)



 @property
 def 				lowerCAmelCase_  (							self  :		Dict						):
       return self.image_processor_tester.prepare_image_processor_dict()



 def 				lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'image_mean'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'image_std'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'do_normalize'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'do_resize'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'do_center_crop'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'size'						)						)



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class.from_dict(self.image_processor_dict						)
       self.assertEqual(image_processor.size    ,    {'shortest_edge': 18}						)
       self.assertEqual(image_processor.crop_size    ,    {'height': 18, 'width': 18}						)

       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class.from_dict(self.image_processor_dict    ,    size=42    ,    crop_size=84						)
       self.assertEqual(image_processor.size    ,    {'shortest_edge': 42}						)
       self.assertEqual(image_processor.crop_size    ,    {'height': 84, 'width': 84}						)



 def 				lowerCAmelCase_  (							self  :		Dict						):
       pass



 def 				lowerCAmelCase_  (							self  :		int						):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       SCREAMING_SNAKE_CASE_     						=      prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=_lowerCAmelCase						)
       for image in image_inputs:
             self.assertIsInstance(_lowerCAmelCase    ,    Image.Image						)

       # Test not batched input
       SCREAMING_SNAKE_CASE_     						=      image_processing(image_inputs[0]    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )

       # Test batched
       SCREAMING_SNAKE_CASE_     						=      image_processing(_lowerCAmelCase    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )



 def 				lowerCAmelCase_  (							self  :		str						):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE_     						=      prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=_lowerCAmelCase    ,    numpify=_lowerCAmelCase						)
       for image in image_inputs:
             self.assertIsInstance(_lowerCAmelCase    ,    np.ndarray						)

       # Test not batched input
       SCREAMING_SNAKE_CASE_     						=      image_processing(image_inputs[0]    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )

       # Test batched
       SCREAMING_SNAKE_CASE_     						=      image_processing(_lowerCAmelCase    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )




 def 				lowerCAmelCase_  (							self  :		Tuple						):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE_     						=      prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=_lowerCAmelCase    ,    torchify=_lowerCAmelCase						)
       for image in image_inputs:
             self.assertIsInstance(_lowerCAmelCase    ,    torch.Tensor						)

       # Test not batched input
       SCREAMING_SNAKE_CASE_     						=      image_processing(image_inputs[0]    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )

       # Test batched
       SCREAMING_SNAKE_CASE_     						=      image_processing(_lowerCAmelCase    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )