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 						snake_case_ (	__A ):






   def    __UpperCamelCase  (   self       :  str						)      ->		Union[str, Any]:
       lowercase__				:     Optional[Any]					      =       self.config_class(**self.inputs_dict						)
       self.parent.assertTrue(hasattr(lowercase_			,		"hidden_sizes"						)						)
       self.parent.assertTrue(hasattr(lowercase_			,		"num_attention_heads"						)						)
       self.parent.assertTrue(hasattr(lowercase_			,		"num_encoder_blocks"						)						)



class 						snake_case_ :






   def __init__(   self       :  List[Any]			,		lowercase_       :  Any			,		lowercase_       :  Any=13			,		lowercase_       :  List[str]=64			,		lowercase_       :  Any=3			,		lowercase_       :  Tuple=4			,		lowercase_       :  str=[2, 2, 2, 2]			,		lowercase_       :  List[str]=[8, 4, 2, 1]			,		lowercase_       :  Tuple=[16, 32, 64, 1_28]			,		lowercase_       :  Tuple=[1, 4, 8, 16]			,		lowercase_       :  Dict=[1, 2, 4, 8]			,		lowercase_       :  Optional[int]=True			,		lowercase_       :  List[str]=True			,		lowercase_       :  Optional[Any]="gelu"			,		lowercase_       :  Dict=0.1			,		lowercase_       :  Dict=0.1			,		lowercase_       :  int=0.02			,		lowercase_       :  int=16			,		lowercase_       :  Tuple=3			,		lowercase_       :  Optional[Any]=None			,		)      ->		Tuple:
       lowercase__				:     Optional[int]					      =       parent
       lowercase__				:     Any					      =       batch_size
       lowercase__				:     Union[str, Any]					      =       image_size
       lowercase__				:     Optional[int]					      =       num_channels
       lowercase__				:     Optional[Any]					      =       num_encoder_blocks
       lowercase__				:     List[Any]					      =       sr_ratios
       lowercase__				:     Optional[int]					      =       depths
       lowercase__				:     Union[str, Any]					      =       hidden_sizes
       lowercase__				:     List[str]					      =       downsampling_rates
       lowercase__				:     Union[str, Any]					      =       num_attention_heads
       lowercase__				:     int					      =       is_training
       lowercase__				:     int					      =       use_labels
       lowercase__				:     str					      =       hidden_act
       lowercase__				:     str					      =       hidden_dropout_prob
       lowercase__				:     List[Any]					      =       attention_probs_dropout_prob
       lowercase__				:     List[Any]					      =       initializer_range
       lowercase__				:     Tuple					      =       decoder_hidden_size
       lowercase__				:     Union[str, Any]					      =       num_labels
       lowercase__				:     Union[str, Any]					      =       scope






   def    __UpperCamelCase  (   self       :  Tuple						)      ->		Tuple:
       lowercase__				:     str					      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       lowercase__				:     List[str]					      =       None
       if self.use_labels:
           lowercase__				:     Any					      =       ids_tensor([self.batch_size, self.image_size, self.image_size]			,		self.num_labels						)

       lowercase__				:     Optional[int]					      =       self.get_config()
       return config, pixel_values, labels






   def    __UpperCamelCase  (   self       :  Optional[Any]						)      ->		List[Any]:
       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    __UpperCamelCase  (   self       :  Tuple			,		lowercase_       :  List[str]			,		lowercase_       :  Optional[Any]			,		lowercase_       :  Optional[int]						)      ->		Union[str, Any]:
       lowercase__				:     Optional[Any]					      =       GLPNModel(config=lowercase_						)
       model.to(lowercase_						)
       model.eval()
       lowercase__				:     Optional[int]					      =       model(lowercase_						)
       lowercase__				:     int					      =       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    __UpperCamelCase  (   self       :  List[Any]			,		lowercase_       :  Optional[Any]			,		lowercase_       :  Optional[int]			,		lowercase_       :  int						)      ->		Optional[Any]:
       lowercase__				:     int					      =       self.num_labels
       lowercase__				:     Dict					      =       GLPNForDepthEstimation(lowercase_						)
       model.to(lowercase_						)
       model.eval()
       lowercase__				:     Union[str, Any]					      =       model(lowercase_						)
       self.parent.assertEqual(result.predicted_depth.shape			,		(self.batch_size, self.image_size, self.image_size)						)
       lowercase__				:     Any					      =       model(lowercase_			,		labels=lowercase_						)
       self.parent.assertEqual(result.predicted_depth.shape			,		(self.batch_size, self.image_size, self.image_size)						)







   def    __UpperCamelCase  (   self       :  Optional[Any]						)      ->		List[Any]:
       lowercase__				:     List[Any]					      =       self.prepare_config_and_inputs()
       lowercase__      ,     lowercase__      ,     lowercase__				:     Union[str, Any]					      =       config_and_inputs
       lowercase__				:     Dict					      =       {"pixel_values": pixel_values}
       return config, inputs_dict



@require_torch
class 						snake_case_ (	__A						,__A						,unittest.TestCase ):
   __A			:	Tuple        =       (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
   __A			:	Union[str, Any]        =       (
       {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
   )

   __A			:	Union[str, Any]        =       False
   __A			:	List[str]        =       False
   __A			:	str        =       False






   def    __UpperCamelCase  (   self       :  Tuple						)      ->		List[str]:
       lowercase__				:     int					      =       GLPNModelTester(self						)
       lowercase__				:     List[Any]					      =       GLPNConfigTester(self			,		config_class=lowercase_						)






   def    __UpperCamelCase  (   self       :  List[Any]						)      ->		Optional[int]:
       self.config_tester.run_common_tests()






   def    __UpperCamelCase  (   self       :  Optional[Any]						)      ->		Union[str, Any]:
       lowercase__				:     Optional[Any]					      =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowercase_						)






   def    __UpperCamelCase  (   self       :  List[Any]						)      ->		List[str]:
       lowercase__				:     Dict					      =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_depth_estimation(*lowercase_						)






   @unittest.skip("GLPN does not use inputs_embeds"						)
   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		Union[str, Any]:
       pass






   @unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods"						)
   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		Optional[Any]:
       pass






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

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

           lowercase__				:     Optional[Any]					      =       ["pixel_values"]
           self.assertListEqual(arg_names[:1]			,		lowercase_						)






   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		Tuple:
       lowercase__      ,     lowercase__				:     int					      =       self.model_tester.prepare_config_and_inputs_for_common()
       lowercase__				:     List[Any]					      =       True

       for model_class in self.all_model_classes:
           lowercase__				:     Optional[int]					      =       True
           lowercase__				:     Union[str, Any]					      =       False
           lowercase__				:     Optional[int]					      =       True
           lowercase__				:     Any					      =       model_class(lowercase_						)
           model.to(lowercase_						)
           model.eval()
           with torch.no_grad():
               lowercase__				:     int					      =       model(**self._prepare_for_class(lowercase_			,		lowercase_						)						)
           lowercase__				:     int					      =       outputs.attentions

           lowercase__				:     int					      =       sum(self.model_tester.depths						)
           self.assertEqual(len(lowercase_						)			,		lowercase_						)

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

           self.assertEqual(len(lowercase_						)			,		lowercase_						)

           # verify the first attentions (first block, first layer)
           lowercase__				:     str					      =       (self.model_tester.image_size // 4) ** 2
           lowercase__				:     Any					      =       (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)
           lowercase__				:     Tuple					      =       (self.model_tester.image_size // 32) ** 2
           lowercase__				:     Any					      =       (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]			,		)
           lowercase__				:     Any					      =       len(lowercase_						)

           # Check attention is always last and order is fine
           lowercase__				:     Optional[int]					      =       True
           lowercase__				:     List[Any]					      =       True
           lowercase__				:     Union[str, Any]					      =       model_class(lowercase_						)
           model.to(lowercase_						)
           model.eval()
           with torch.no_grad():
               lowercase__				:     Optional[int]					      =       model(**self._prepare_for_class(lowercase_			,		lowercase_						)						)

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

           lowercase__				:     Optional[int]					      =       outputs.attentions

           self.assertEqual(len(lowercase_						)			,		lowercase_						)
           # verify the first attentions (first block, first layer)
           lowercase__				:     Any					      =       (self.model_tester.image_size // 4) ** 2
           lowercase__				:     str					      =       (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    __UpperCamelCase  (   self       :  List[str]						)      ->		List[Any]:
       def check_hidden_states_output(lowercase_       :  Dict			,		lowercase_       :  Optional[int]			,		lowercase_       :  str						):
           lowercase__				:     int					      =       model_class(lowercase_						)
           model.to(lowercase_						)
           model.eval()

           with torch.no_grad():
               lowercase__				:     Optional[int]					      =       model(**self._prepare_for_class(lowercase_			,		lowercase_						)						)

           lowercase__				:     str					      =       outputs.hidden_states

           lowercase__				:     Optional[Any]					      =       self.model_tester.num_encoder_blocks
           self.assertEqual(len(lowercase_						)			,		lowercase_						)

           # 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,
               ]			,		)

       lowercase__      ,     lowercase__				:     Optional[Any]					      =       self.model_tester.prepare_config_and_inputs_for_common()

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

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

           check_hidden_states_output(lowercase_			,		lowercase_			,		lowercase_						)






   def    __UpperCamelCase  (   self       :  List[Any]						)      ->		int:
       if not self.model_tester.is_training:
           return

       lowercase__      ,     lowercase__				:     Tuple					      =       self.model_tester.prepare_config_and_inputs_for_common()
       lowercase__				:     Optional[Any]					      =       True

       for model_class in self.all_model_classes:
           if model_class in get_values(lowercase_						):
               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":
               lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__				:     str					      =       inputs_dict["pixel_values"].shape
               lowercase__				:     Optional[Any]					      =       torch.zeros(
                   [self.model_tester.batch_size, height, width]			,		device=lowercase_						).long()
           lowercase__				:     Optional[Any]					      =       model_class(lowercase_						)
           model.to(lowercase_						)
           model.train()
           lowercase__				:     str					      =       self._prepare_for_class(lowercase_			,		lowercase_			,		return_labels=lowercase_						)
           lowercase__				:     Optional[int]					      =       model(**lowercase_						).loss
           loss.backward()







   @slow
   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		Tuple:
       for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowercase__				:     List[str]					      =       GLPNModel.from_pretrained(lowercase_						)
           self.assertIsNotNone(lowercase_						)



def     lowercase_       (    ):
    lowercase__				:     List[Any]					      =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
    return image



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






   @slow
   def    __UpperCamelCase  (   self       :  Dict						)      ->		Tuple:
       lowercase__				:     Dict					      =       GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
       lowercase__				:     Optional[Any]					      =       GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(lowercase_						)

       lowercase__				:     str					      =       prepare_img()
       lowercase__				:     List[str]					      =       image_processor(images=lowercase_			,		return_tensors="pt"						).to(lowercase_						)

       # forward pass
       with torch.no_grad():
           lowercase__				:     List[Any]					      =       model(**lowercase_						)

       # verify the predicted depth
       lowercase__				:     Union[str, Any]					      =       torch.Size([1, 4_80, 6_40]						)
       self.assertEqual(outputs.predicted_depth.shape			,		lowercase_						)

       lowercase__				:     str					      =       torch.tensor(
           [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]						).to(lowercase_						)

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase													=		{
    '''configuration_flava''': [
        '''FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''FlavaConfig''',
        '''FlavaImageCodebookConfig''',
        '''FlavaImageConfig''',
        '''FlavaMultimodalConfig''',
        '''FlavaTextConfig''',
    ],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		['''FlavaFeatureExtractor''']
      UpperCamelCase													=		['''FlavaImageProcessor''']
      UpperCamelCase													=		['''FlavaProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		[
          '''FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''FlavaForPreTraining''',
          '''FlavaImageCodebook''',
          '''FlavaImageModel''',
          '''FlavaModel''',
          '''FlavaMultimodalModel''',
          '''FlavaPreTrainedModel''',
          '''FlavaTextModel''',
      ]

if TYPE_CHECKING:
      from .configuration_flava import (
          FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
          FlavaConfig,
          FlavaImageCodebookConfig,
          FlavaImageConfig,
          FlavaMultimodalConfig,
          FlavaTextConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_flava import FlavaFeatureExtractor
            from .image_processing_flava import FlavaImageProcessor
            from .processing_flava import FlavaProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flava import (
                FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlavaForPreTraining,
                FlavaImageCodebook,
                FlavaImageModel,
                FlavaModel,
                FlavaMultimodalModel,
                FlavaPreTrainedModel,
                FlavaTextModel,
            )

else:
      import sys

      UpperCamelCase													=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



