'''simple docstring'''




import unittest

from transformers import LayoutLMConfig, 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, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      LayoutLMForMaskedLM,
      LayoutLMForQuestionAnswering,
      LayoutLMForSequenceClassification,
      LayoutLMForTokenClassification,
      LayoutLMModel,
  )




class       __UpperCAmelCase							:





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=13       ,	_SCREAMING_SNAKE_CASE=7       ,	_SCREAMING_SNAKE_CASE=True       ,	_SCREAMING_SNAKE_CASE=True       ,	_SCREAMING_SNAKE_CASE=True       ,	_SCREAMING_SNAKE_CASE=True       ,	_SCREAMING_SNAKE_CASE=99       ,	_SCREAMING_SNAKE_CASE=32       ,	_SCREAMING_SNAKE_CASE=5       ,	_SCREAMING_SNAKE_CASE=4       ,	_SCREAMING_SNAKE_CASE=37       ,	_SCREAMING_SNAKE_CASE="gelu"       ,	_SCREAMING_SNAKE_CASE=0.1       ,	_SCREAMING_SNAKE_CASE=0.1       ,	_SCREAMING_SNAKE_CASE=512       ,	_SCREAMING_SNAKE_CASE=16       ,	_SCREAMING_SNAKE_CASE=2       ,	_SCREAMING_SNAKE_CASE=0.02       ,	_SCREAMING_SNAKE_CASE=3       ,	_SCREAMING_SNAKE_CASE=4       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=1000       ,	)						->	Dict:
           A_      =  parent
           A_      =  batch_size
           A_      =  seq_length
           A_      =  is_training
           A_      =  use_input_mask
           A_      =  use_token_type_ids
           A_      =  use_labels
           A_      =  vocab_size
           A_      =  hidden_size
           A_      =  num_hidden_layers
           A_      =  num_attention_heads
           A_      =  intermediate_size
           A_      =  hidden_act
           A_      =  hidden_dropout_prob
           A_      =  attention_probs_dropout_prob
           A_      =  max_position_embeddings
           A_      =  type_vocab_size
           A_      =  type_sequence_label_size
           A_      =  initializer_range
           A_      =  num_labels
           A_      =  num_choices
           A_      =  scope
           A_      =  range_bbox

       def    __A			( self					)						->	List[Any]:
           A_      =  ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size					)

           A_      =  ids_tensor([self.batch_size, self.seq_length, 4]       ,	self.range_bbox					)
           # Ensure that bbox is legal
           for i in range(bbox.shape[0]					):
               for j in range(bbox.shape[1]					):
                   if bbox[i, j, 3] < bbox[i, j, 1]:
                       A_      =  bbox[i, j, 3]
                       A_      =  bbox[i, j, 1]
                       A_      =  t
                   if bbox[i, j, 2] < bbox[i, j, 0]:
                       A_      =  bbox[i, j, 2]
                       A_      =  bbox[i, j, 0]
                       A_      =  t

           A_      =  None
           if self.use_input_mask:
               A_      =  ids_tensor([self.batch_size, self.seq_length]       ,	vocab_size=2					)

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

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

           A_      =  self.get_config()

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

       def    __A			( self					)						->	List[Any]:
           return LayoutLMConfig(
               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       ,	initializer_range=self.initializer_range       ,	)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	int:
           A_      =  LayoutLMModel(config=_SCREAMING_SNAKE_CASE					)
           model.to(_SCREAMING_SNAKE_CASE					)
           model.eval()
           A_      =  model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	attention_mask=_SCREAMING_SNAKE_CASE       ,	token_type_ids=_SCREAMING_SNAKE_CASE					)
           A_      =  model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	token_type_ids=_SCREAMING_SNAKE_CASE					)
           A_      =  model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
           self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.seq_length, self.hidden_size)					)
           self.parent.assertEqual(result.pooler_output.shape       ,	(self.batch_size, self.hidden_size)					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	List[Any]:
           A_      =  LayoutLMForMaskedLM(config=_SCREAMING_SNAKE_CASE					)
           model.to(_SCREAMING_SNAKE_CASE					)
           model.eval()
           A_      =  model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	attention_mask=_SCREAMING_SNAKE_CASE       ,	token_type_ids=_SCREAMING_SNAKE_CASE       ,	labels=_SCREAMING_SNAKE_CASE					)
           self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.vocab_size)					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	Optional[Any]:
           A_      =  self.num_labels
           A_      =  LayoutLMForSequenceClassification(_SCREAMING_SNAKE_CASE					)
           model.to(_SCREAMING_SNAKE_CASE					)
           model.eval()
           A_      =  model(
               _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	attention_mask=_SCREAMING_SNAKE_CASE       ,	token_type_ids=_SCREAMING_SNAKE_CASE       ,	labels=_SCREAMING_SNAKE_CASE					)
           self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_labels)					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	Optional[Any]:
           A_      =  self.num_labels
           A_      =  LayoutLMForTokenClassification(config=_SCREAMING_SNAKE_CASE					)
           model.to(_SCREAMING_SNAKE_CASE					)
           model.eval()
           A_      =  model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	attention_mask=_SCREAMING_SNAKE_CASE       ,	token_type_ids=_SCREAMING_SNAKE_CASE       ,	labels=_SCREAMING_SNAKE_CASE					)
           self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.num_labels)					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	Any:
           A_      =  LayoutLMForQuestionAnswering(config=_SCREAMING_SNAKE_CASE					)
           model.to(_SCREAMING_SNAKE_CASE					)
           model.eval()
           A_      =  model(
               _SCREAMING_SNAKE_CASE       ,	bbox=_SCREAMING_SNAKE_CASE       ,	attention_mask=_SCREAMING_SNAKE_CASE       ,	token_type_ids=_SCREAMING_SNAKE_CASE       ,	start_positions=_SCREAMING_SNAKE_CASE       ,	end_positions=_SCREAMING_SNAKE_CASE       ,	)
           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    __A			( self					)						->	List[Any]:
           A_      =  self.prepare_config_and_inputs()
           (
               (
               A_
           )  ,(
               A_
           )  ,(
               A_
           )  ,(
               A_
           )  ,(
               A_
           )  ,(
               A_
           )  ,(
               A_
           )  ,(
               A_
           )  ,
           )      =  config_and_inputs
           A_      =  {
               '''input_ids''': input_ids,
               '''bbox''': bbox,
               '''token_type_ids''': token_type_ids,
               '''attention_mask''': input_mask,
           }
           return config, inputs_dict




@require_torch
class       __UpperCAmelCase							(							_UpperCamelCase		, _UpperCamelCase		, unittest.TestCase	):





       '''simple docstring'''



       __lowercase   :   List[str]  					=							(
           (
               LayoutLMModel,
               LayoutLMForMaskedLM,
               LayoutLMForSequenceClassification,
               LayoutLMForTokenClassification,
               LayoutLMForQuestionAnswering,
           )
           if is_torch_available()
           else None
       )
       __lowercase   :   List[Any]  					=							(
           {
               "document-question-answering": LayoutLMForQuestionAnswering,
               "feature-extraction": LayoutLMModel,
               "fill-mask": LayoutLMForMaskedLM,
               "text-classification": LayoutLMForSequenceClassification,
               "token-classification": LayoutLMForTokenClassification,
               "zero-shot": LayoutLMForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       __lowercase   :   int  					=							True

       def    __A			( self					)						->	List[str]:
           A_      =  LayoutLMModelTester(self					)
           A_      =  ConfigTester(self       ,	config_class=_SCREAMING_SNAKE_CASE       ,	hidden_size=37					)

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

       def    __A			( self					)						->	Optional[int]:
           A_      =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Any:
           A_      =  self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
               A_      =  type
               self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Optional[int]:
           A_      =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Dict:
           A_      =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Optional[int]:
           A_      =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Optional[int]:
           A_      =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE					)

       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''					)
       def    __A			( self					)						->	Union[str, Any]:
           pass





def 	_UpperCAmelCase       (				)      -> Optional[int]:
    # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
    # fmt: off
    A_      =  torch.tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]],  device=lowercase_  )  # noqa: E231
    A_      =  torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],],  device=lowercase_  )  # noqa: E231
    A_      =  torch.tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]],  device=lowercase_  )  # noqa: E231
    A_      =  torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]],  device=lowercase_  )  # noqa: E231
    # these are sequence labels (i.e. at the token level)
    A_      =  torch.tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]],  device=lowercase_  )  # noqa: E231
    # fmt: on

    return input_ids, attention_mask, bbox, token_type_ids, labels




@require_torch
class       __UpperCAmelCase							(							unittest.TestCase	):





       '''simple docstring'''



       @slow
       def    __A			( self					)						->	Union[str, Any]:
           A_      =  LayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''					).to(_SCREAMING_SNAKE_CASE					)

           A_  ,A_  ,A_  ,A_  ,A_      =  prepare_layoutlm_batch_inputs()

           # forward pass
           A_      =  model(input_ids=_SCREAMING_SNAKE_CASE       ,	bbox=_SCREAMING_SNAKE_CASE       ,	attention_mask=_SCREAMING_SNAKE_CASE       ,	token_type_ids=_SCREAMING_SNAKE_CASE					)

           # test the sequence output on [0, :3, :3]
           A_      =  torch.tensor(
               [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]       ,	device=_SCREAMING_SNAKE_CASE       ,	)

           self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]       ,	_SCREAMING_SNAKE_CASE       ,	atol=1E-3					)					)

           # test the pooled output on [1, :3]
           A_      =  torch.tensor([-0.6_580, -0.0_214, 0.8_552]       ,	device=_SCREAMING_SNAKE_CASE					)

           self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]       ,	_SCREAMING_SNAKE_CASE       ,	atol=1E-3					)					)

       @slow
       def    __A			( self					)						->	str:
           A_      =  LayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''       ,	num_labels=2					).to(
               _SCREAMING_SNAKE_CASE					)

           A_  ,A_  ,A_  ,A_  ,A_      =  prepare_layoutlm_batch_inputs()

           # forward pass
           A_      =  model(
               input_ids=_SCREAMING_SNAKE_CASE       ,	bbox=_SCREAMING_SNAKE_CASE       ,	attention_mask=_SCREAMING_SNAKE_CASE       ,	token_type_ids=_SCREAMING_SNAKE_CASE       ,	labels=torch.tensor([1, 1]       ,	device=_SCREAMING_SNAKE_CASE					)       ,	)

           # test whether we get a loss as a scalar
           A_      =  outputs.loss
           A_      =  torch.Size([]					)
           self.assertEqual(loss.shape       ,	_SCREAMING_SNAKE_CASE					)

           # test the shape of the logits
           A_      =  outputs.logits
           A_      =  torch.Size((2, 2)					)
           self.assertEqual(logits.shape       ,	_SCREAMING_SNAKE_CASE					)

       @slow
       def    __A			( self					)						->	Union[str, Any]:
           A_      =  LayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''       ,	num_labels=13					).to(
               _SCREAMING_SNAKE_CASE					)

           A_  ,A_  ,A_  ,A_  ,A_      =  prepare_layoutlm_batch_inputs()

           # forward pass
           A_      =  model(
               input_ids=_SCREAMING_SNAKE_CASE       ,	bbox=_SCREAMING_SNAKE_CASE       ,	attention_mask=_SCREAMING_SNAKE_CASE       ,	token_type_ids=_SCREAMING_SNAKE_CASE       ,	labels=_SCREAMING_SNAKE_CASE					)

           # test the loss calculation to be around 2.65
           # expected_loss = torch.tensor(2.65, device=torch_device)

           # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
           # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

           # test the shape of the logits
           A_      =  outputs.logits
           A_      =  torch.Size((2, 25, 13)					)
           self.assertEqual(logits.shape       ,	_SCREAMING_SNAKE_CASE					)

       @slow
       def    __A			( self					)						->	int:
           A_      =  LayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''					).to(_SCREAMING_SNAKE_CASE					)

           A_  ,A_  ,A_  ,A_  ,A_      =  prepare_layoutlm_batch_inputs()

           # forward pass
           A_      =  model(input_ids=_SCREAMING_SNAKE_CASE       ,	bbox=_SCREAMING_SNAKE_CASE       ,	attention_mask=_SCREAMING_SNAKE_CASE       ,	token_type_ids=_SCREAMING_SNAKE_CASE					)

           # test the shape of the logits
           A_      =  torch.Size((2, 25)					)
           self.assertEqual(outputs.start_logits.shape       ,	_SCREAMING_SNAKE_CASE					)
           self.assertEqual(outputs.end_logits.shape       ,	_SCREAMING_SNAKE_CASE					)







"""simple docstring"""







import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
     from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import GLPNImageProcessor






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.config_class(**self.inputs_dict	)
      self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'hidden_sizes'	)	)
      self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'num_attention_heads'	)	)
      self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'num_encoder_blocks'	)	)






class      _SCREAMING_SNAKE_CASE							:



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=13 ,				_SCREAMING_SNAKE_CASE=64 ,				_SCREAMING_SNAKE_CASE=3 ,				_SCREAMING_SNAKE_CASE=4 ,				_SCREAMING_SNAKE_CASE=[2, 2, 2, 2] ,				_SCREAMING_SNAKE_CASE=[8, 4, 2, 1] ,				_SCREAMING_SNAKE_CASE=[16, 32, 64, 128] ,				_SCREAMING_SNAKE_CASE=[1, 4, 8, 16] ,				_SCREAMING_SNAKE_CASE=[1, 2, 4, 8] ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE="gelu" ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=0.02 ,				_SCREAMING_SNAKE_CASE=16 ,				_SCREAMING_SNAKE_CASE=3 ,				_SCREAMING_SNAKE_CASE=None ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					parent
      __lowerCamelCase    		=					batch_size
      __lowerCamelCase    		=					image_size
      __lowerCamelCase    		=					num_channels
      __lowerCamelCase    		=					num_encoder_blocks
      __lowerCamelCase    		=					sr_ratios
      __lowerCamelCase    		=					depths
      __lowerCamelCase    		=					hidden_sizes
      __lowerCamelCase    		=					downsampling_rates
      __lowerCamelCase    		=					num_attention_heads
      __lowerCamelCase    		=					is_training
      __lowerCamelCase    		=					use_labels
      __lowerCamelCase    		=					hidden_act
      __lowerCamelCase    		=					hidden_dropout_prob
      __lowerCamelCase    		=					attention_probs_dropout_prob
      __lowerCamelCase    		=					initializer_range
      __lowerCamelCase    		=					decoder_hidden_size
      __lowerCamelCase    		=					num_labels
      __lowerCamelCase    		=					scope







   def    _A				(	self	):


      '''simple docstring'''

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

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

      __lowerCamelCase    		=					self.get_config()
      return config, pixel_values, labels







   def    _A				(	self	):


      '''simple docstring'''

      return GLPNConfig(
          image_size=self.image_size ,				num_channels=self.num_channels ,				num_encoder_blocks=self.num_encoder_blocks ,				depths=self.depths ,				hidden_sizes=self.hidden_sizes ,				num_attention_heads=self.num_attention_heads ,				hidden_act=self.hidden_act ,				hidden_dropout_prob=self.hidden_dropout_prob ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob ,				initializer_range=self.initializer_range ,				decoder_hidden_size=self.decoder_hidden_size ,				)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					GLPNModel(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					__lowerCamelCase    		=					self.image_size // (self.downsampling_rates[-1] * 2)
      self.parent.assertEqual(
          result.last_hidden_state.shape ,				(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.num_labels
      __lowerCamelCase    		=					GLPNForDepthEstimation(_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.predicted_depth.shape ,				(self.batch_size, self.image_size, self.image_size)	)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.predicted_depth.shape ,				(self.batch_size, self.image_size, self.image_size)	)







   def    _A				(	self	):


      '''simple docstring'''

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






@require_torch
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase    ,				UpperCAmelCase    ,				unittest.TestCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Union[str, Any]    =    (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
   __UpperCAmelCase	:							Any    =    (
       {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
   )

   __UpperCAmelCase	:							Optional[Any]    =    False
   __UpperCAmelCase	:							Optional[int]    =    False
   __UpperCAmelCase	:							int    =    False







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					GLPNModelTester(self	)
      __lowerCamelCase    		=					GLPNConfigTester(self ,				config_class=_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      self.config_tester.run_common_tests()







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*_SCREAMING_SNAKE_CASE	)







   @unittest.skip('GLPN does not use inputs_embeds'	)
   def    _A				(	self	):


      '''simple docstring'''

      pass







   @unittest.skip('GLPN does not have get_input_embeddings method and get_output_embeddings methods'	)
   def    _A				(	self	):


      '''simple docstring'''

      pass







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()

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

         __lowerCamelCase    		=					['pixel_values']
         self.assertListEqual(arg_names[:1] ,				_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCamelCase    		=					True

      for model_class in self.all_model_classes:
         __lowerCamelCase    		=					True
         __lowerCamelCase    		=					False
         __lowerCamelCase    		=					True
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.to(_SCREAMING_SNAKE_CASE	)
         model.eval()
         with torch.no_grad():
            __lowerCamelCase    		=					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)	)
         __lowerCamelCase    		=					outputs.attentions

         __lowerCamelCase    		=					sum(self.model_tester.depths	)
         self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)

         # check that output_attentions also work using config
         del inputs_dict["output_attentions"]
         __lowerCamelCase    		=					True
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.to(_SCREAMING_SNAKE_CASE	)
         model.eval()
         with torch.no_grad():
            __lowerCamelCase    		=					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)	)
         __lowerCamelCase    		=					outputs.attentions

         self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)

         # verify the first attentions (first block, first layer)
         __lowerCamelCase    		=					(self.model_tester.image_size // 4) ** 2
         __lowerCamelCase    		=					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
         self.assertListEqual(
             list(attentions[0].shape[-3:]	) ,				[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] ,				)

         # verify the last attentions (last block, last layer)
         __lowerCamelCase    		=					(self.model_tester.image_size // 32) ** 2
         __lowerCamelCase    		=					(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
         self.assertListEqual(
             list(attentions[-1].shape[-3:]	) ,				[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len] ,				)
         __lowerCamelCase    		=					len(_SCREAMING_SNAKE_CASE	)

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

         self.assertEqual(out_len + 1 ,				len(_SCREAMING_SNAKE_CASE	)	)

         __lowerCamelCase    		=					outputs.attentions

         self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)
         # verify the first attentions (first block, first layer)
         __lowerCamelCase    		=					(self.model_tester.image_size // 4) ** 2
         __lowerCamelCase    		=					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
         self.assertListEqual(
             list(self_attentions[0].shape[-3:]	) ,				[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] ,				)







   def    _A				(	self	):


      '''simple docstring'''

      def check_hidden_states_output(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.to(_SCREAMING_SNAKE_CASE	)
         model.eval()

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

         __lowerCamelCase    		=					outputs.hidden_states

         __lowerCamelCase    		=					self.model_tester.num_encoder_blocks
         self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)

         # verify the first hidden states (first block)
         self.assertListEqual(
             list(hidden_states[0].shape[-3:]	) ,				[
                 self.model_tester.hidden_sizes[0],
                 self.model_tester.image_size // 4,
                 self.model_tester.image_size // 4,
             ] ,				)

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()

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

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

         check_hidden_states_output(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      if not self.model_tester.is_training:
         return

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCamelCase    		=					True

      for model_class in self.all_model_classes:
         if model_class in get_values(_SCREAMING_SNAKE_CASE	):
            continue
         # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
         # this can then be incorporated into _prepare_for_class in test_modeling_common.py
         if model_class.__name__ == "GLPNForDepthEstimation":
            __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					inputs_dict['pixel_values'].shape
            __lowerCamelCase    		=					torch.zeros(
                [self.model_tester.batch_size, height, width] ,				device=_SCREAMING_SNAKE_CASE	).long()
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.to(_SCREAMING_SNAKE_CASE	)
         model.train()
         __lowerCamelCase    		=					self._prepare_for_class(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				return_labels=_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase    		=					model(**_SCREAMING_SNAKE_CASE	).loss
         loss.backward()







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __lowerCamelCase    		=					GLPNModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
         self.assertIsNotNone(_SCREAMING_SNAKE_CASE	)



def       A__    (	)  ->							Union[str, Any]:
   __lowerCamelCase    		=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
   return image






@require_torch
@require_vision
@slow
class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
      __lowerCamelCase    		=					GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					prepare_img()
      __lowerCamelCase    		=					image_processor(images=_SCREAMING_SNAKE_CASE ,				return_tensors='pt'	).to(_SCREAMING_SNAKE_CASE	)

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

      # verify the predicted depth
      __lowerCamelCase    		=					torch.Size([1, 480, 640]	)
      self.assertEqual(outputs.predicted_depth.shape ,				_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					torch.tensor(
          [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]	).to(_SCREAMING_SNAKE_CASE	)

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






