
"""simple docstring"""



import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
    from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import DPTImageProcessor





class 				a			:



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=2     ,	UpperCamelCase_=32     ,	UpperCamelCase_=16     ,	UpperCamelCase_=3     ,	UpperCamelCase_=True     ,	UpperCamelCase_=True     ,	UpperCamelCase_=32     ,	UpperCamelCase_=4     ,	UpperCamelCase_=[0, 1, 2, 3]     ,	UpperCamelCase_=4     ,	UpperCamelCase_=37     ,	UpperCamelCase_="gelu"     ,	UpperCamelCase_=0.1     ,	UpperCamelCase_=0.1     ,	UpperCamelCase_=0.02     ,	UpperCamelCase_=3     ,	UpperCamelCase_=[1, 384, 24, 24]     ,	UpperCamelCase_=True     ,	UpperCamelCase_=None     ,	):
          UpperCAmelCase__	:      Union[str, Any]     							=					parent
          UpperCAmelCase__	:      List[str]     							=					batch_size
          UpperCAmelCase__	:      Any     							=					image_size
          UpperCAmelCase__	:      int     							=					patch_size
          UpperCAmelCase__	:      str     							=					num_channels
          UpperCAmelCase__	:      Any     							=					is_training
          UpperCAmelCase__	:      str     							=					use_labels
          UpperCAmelCase__	:      Optional[Any]     							=					hidden_size
          UpperCAmelCase__	:      int     							=					num_hidden_layers
          UpperCAmelCase__	:      int     							=					backbone_out_indices
          UpperCAmelCase__	:      Optional[int]     							=					num_attention_heads
          UpperCAmelCase__	:      int     							=					intermediate_size
          UpperCAmelCase__	:      Any     							=					hidden_act
          UpperCAmelCase__	:      List[str]     							=					hidden_dropout_prob
          UpperCAmelCase__	:      Any     							=					attention_probs_dropout_prob
          UpperCAmelCase__	:      List[str]     							=					initializer_range
          UpperCAmelCase__	:      Dict     							=					num_labels
          UpperCAmelCase__	:      Tuple     							=					backbone_featmap_shape
          UpperCAmelCase__	:      Any     							=					scope
          UpperCAmelCase__	:      Optional[Any]     							=					is_hybrid
          # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
          UpperCAmelCase__	:      Optional[int]     							=					(image_size // patch_size) ** 2
          UpperCAmelCase__	:      Optional[Any]     							=					num_patches + 1



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      str     							=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

          UpperCAmelCase__	:      Dict     							=					self.get_config()

          return config, pixel_values, labels



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      str     							=					{
              '''global_padding''': '''same''',
              '''layer_type''': '''bottleneck''',
              '''depths''': [3, 4, 9],
              '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
              '''embedding_dynamic_padding''': True,
              '''hidden_sizes''': [96, 192, 384, 768],
              '''num_groups''': 2,
          }

          return DPTConfig(
              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     ,	backbone_out_indices=self.backbone_out_indices     ,	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     ,	is_hybrid=self.is_hybrid     ,	backbone_config=A_     ,	backbone_featmap_shape=self.backbone_featmap_shape     ,	)



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      Any     							=					DPTModel(config=A_      )
          model.to(A_      )
          model.eval()
          UpperCAmelCase__	:      Any     							=					model(A_      )
          self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					self.num_labels
          UpperCAmelCase__	:      Optional[int]     							=					DPTForDepthEstimation(A_      )
          model.to(A_      )
          model.eval()
          UpperCAmelCase__	:      str     							=					model(A_      )
          self.parent.assertEqual(result.predicted_depth.shape     ,	(self.batch_size, self.image_size, self.image_size)      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      Tuple     							=					self.num_labels
          UpperCAmelCase__	:      List[Any]     							=					DPTForSemanticSegmentation(A_      )
          model.to(A_      )
          model.eval()
          UpperCAmelCase__	:      List[Any]     							=					model(A_     ,	labels=A_      )
          self.parent.assertEqual(
              result.logits.shape     ,	(self.batch_size, self.num_labels, self.image_size, self.image_size)      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[int]     							=					self.prepare_config_and_inputs()
          UpperCAmelCase__	:      Union[str, Any]     							=					config_and_inputs
          UpperCAmelCase__	:      List[Any]     							=					{'''pixel_values''': pixel_values}
          return config, inputs_dict





@require_torch
class 				a			(					SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		unittest.TestCase      ):
      UpperCamelCase							:   Tuple           =     (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
      UpperCamelCase							:   List[Any]           =     (
          {
              """depth-estimation""": DPTForDepthEstimation,
              """feature-extraction""": DPTModel,
              """image-segmentation""": DPTForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      UpperCamelCase							:   List[str]           =     False
      UpperCamelCase							:   str           =     False
      UpperCamelCase							:   str           =     False



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[int]     							=					DPTModelTester(self      )
          UpperCAmelCase__	:      Dict     							=					ConfigTester(self     ,	config_class=A_     ,	has_text_modality=A_     ,	hidden_size=37      )



      def  __snake_case							(   self      ):
          self.config_tester.run_common_tests()



      @unittest.skip(reason='DPT does not use inputs_embeds'      )
      def  __snake_case							(   self      ):
          pass



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              UpperCAmelCase__	:      int     							=					model_class(A_      )
              self.assertIsInstance(model.get_input_embeddings()     ,	(nn.Module)      )
              UpperCAmelCase__	:      str     							=					model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(A_     ,	nn.Linear      )      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[int]     							=					self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCAmelCase__	:      Optional[Any]     							=					['''pixel_values''']
              self.assertListEqual(arg_names[:1]     ,	A_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      int     							=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Any     							=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_depth_estimation(*A_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Tuple     							=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*A_      )



      def  __snake_case							(   self      ):




          for model_class in self.all_model_classes:
              if model_class.__name__ == "DPTForDepthEstimation":
                  continue

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

              if model_class in get_values(A_      ):
                  continue

              UpperCAmelCase__	:      Any     							=					model_class(A_      )
              model.to(A_      )
              model.train()
              UpperCAmelCase__	:      Tuple     							=					self._prepare_for_class(A_     ,	A_     ,	return_labels=A_      )
              UpperCAmelCase__	:      Optional[Any]     							=					model(**A_      ).loss
              loss.backward()



      def  __snake_case							(   self      ):




          for model_class in self.all_model_classes:
              if model_class.__name__ == "DPTForDepthEstimation":
                  continue

              UpperCAmelCase__	:      Optional[Any]     							=					self.model_tester.prepare_config_and_inputs_for_common()
              UpperCAmelCase__	:      List[Any]     							=					False
              UpperCAmelCase__	:      Optional[int]     							=					True

              if model_class in get_values(A_      ) or not model_class.supports_gradient_checkpointing:
                  continue
              UpperCAmelCase__	:      Union[str, Any]     							=					model_class(A_      )
              model.to(A_      )
              model.gradient_checkpointing_enable()
              model.train()
              UpperCAmelCase__	:      Optional[int]     							=					self._prepare_for_class(A_     ,	A_     ,	return_labels=A_      )
              UpperCAmelCase__	:      Union[str, Any]     							=					model(**A_      ).loss
              loss.backward()



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Tuple     							=					self.model_tester.prepare_config_and_inputs_for_common()

          UpperCAmelCase__	:      Optional[Any]     							=					_config_zero_init(A_      )
          for model_class in self.all_model_classes:
              UpperCAmelCase__	:      List[str]     							=					model_class(config=A_      )
              # Skip the check for the backbone
              UpperCAmelCase__	:      str     							=					[]
              for name, module in model.named_modules():
                  if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                      UpperCAmelCase__	:      int     							=					[F'''{name}.{key}''' for key in module.state_dict().keys()]
                      break

              for name, param in model.named_parameters():
                  if param.requires_grad:
                      if name in backbone_params:
                          continue
                      self.assertIn(
                          ((param.data.mean() * 1E9).round() / 1E9).item()     ,	[0.0, 1.0]     ,	msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''     ,	)



      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'      )
      def  __snake_case							(   self      ):
          pass



      @slow
      def  __snake_case							(   self      ):




          for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
              UpperCAmelCase__	:      Dict     							=					DPTModel.from_pretrained(A_      )
              self.assertIsNotNone(A_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      int     							=					self.model_tester.prepare_config_and_inputs_for_common()
          UpperCAmelCase__	:      Tuple     							=					'''add'''
          with self.assertRaises(A_      ):
              UpperCAmelCase__	:      List[Any]     							=					DPTForDepthEstimation(A_      )




def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      Tuple     							=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
    return image





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



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'      )
          UpperCAmelCase__	:      Optional[int]     							=					DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'      ).to(A_      )

          UpperCAmelCase__	:      str     							=					prepare_img()
          UpperCAmelCase__	:      Union[str, Any]     							=					image_processor(images=A_     ,	return_tensors='pt'      ).to(A_      )

          # forward pass
          with torch.no_grad():
              UpperCAmelCase__	:      Dict     							=					model(**A_      )
              UpperCAmelCase__	:      int     							=					outputs.predicted_depth

          # verify the predicted depth
          UpperCAmelCase__	:      str     							=					torch.Size((1, 384, 384)      )
          self.assertEqual(predicted_depth.shape     ,	A_      )

          UpperCAmelCase__	:      Optional[Any]     							=					torch.tensor(
              [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]      ).to(A_      )

          self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100     ,	A_     ,	atol=1E-4      )      )



"""simple docstring"""




from typing import TYPE_CHECKING

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


A__ :       List[str]				= {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__ :       Dict				= [
      'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BioGptForCausalLM',
      'BioGptForTokenClassification',
      'BioGptForSequenceClassification',
      'BioGptModel',
      'BioGptPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

  A__ :       int				= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
