


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  UpperCamelCase__ :






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






      def 	__lowercase(     self					:   Any   )  -> Optional[int]:
           UpperCamelCase__							:  str								=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

           UpperCamelCase__							:  Union[str, Any]								=     self.get_config()

           return config, pixel_values, labels






      def 	__lowercase(     self					:   List[Any]   )  -> List[str]:
           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=__lowerCamelCase,		initializer_range=self.initializer_range,		is_hybrid=self.is_hybrid,		)






      def 	__lowercase(     self					:   Dict,		__lowerCamelCase					:   Any,		__lowerCamelCase					:   Optional[int],		__lowerCamelCase					:   Tuple   )  -> Optional[int]:
           UpperCamelCase__							:  Tuple								=     DPTModel(config=__lowerCamelCase   )
           model.to(__lowerCamelCase   )
           model.eval()
           UpperCamelCase__							:  Tuple								=     model(__lowerCamelCase   )
           self.parent.assertEqual(result.last_hidden_state.shape,		(self.batch_size, self.seq_length, self.hidden_size)   )






      def 	__lowercase(     self					:   Dict,		__lowerCamelCase					:   Any,		__lowerCamelCase					:   List[Any],		__lowerCamelCase					:   List[str]   )  -> List[str]:
           UpperCamelCase__							:  Optional[int]								=     self.num_labels
           UpperCamelCase__							:  List[str]								=     DPTForDepthEstimation(__lowerCamelCase   )
           model.to(__lowerCamelCase   )
           model.eval()
           UpperCamelCase__							:  Dict								=     model(__lowerCamelCase   )
           self.parent.assertEqual(result.predicted_depth.shape,		(self.batch_size, self.image_size, self.image_size)   )






      def 	__lowercase(     self					:   Union[str, Any],		__lowerCamelCase					:   Optional[int],		__lowerCamelCase					:   Union[str, Any],		__lowerCamelCase					:   List[str]   )  -> Optional[int]:
           UpperCamelCase__							:  Any								=     self.num_labels
           UpperCamelCase__							:  Dict								=     DPTForSemanticSegmentation(__lowerCamelCase   )
           model.to(__lowerCamelCase   )
           model.eval()
           UpperCamelCase__							:  Any								=     model(__lowerCamelCase,		labels=__lowerCamelCase   )
           self.parent.assertEqual(
               result.logits.shape,		(self.batch_size, self.num_labels, self.image_size, self.image_size)   )






      def 	__lowercase(     self					:   Union[str, Any]   )  -> Optional[Any]:
           UpperCamelCase__							:  Optional[int]								=     self.prepare_config_and_inputs()
           UpperCamelCase__							:  Optional[int]								=     config_and_inputs
           UpperCamelCase__							:  Tuple								=     {'''pixel_values''': pixel_values}
           return config, inputs_dict

@require_torch
class  UpperCamelCase__ ( __lowerCamelCase							,						__lowerCamelCase							,						unittest.TestCase			):
      a__   :     Union[str, Any]								=						(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
      a__   :     Optional[int]								=						(
          {
              'depth-estimation': DPTForDepthEstimation,
              'feature-extraction': DPTModel,
              'image-segmentation': DPTForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

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






      def 	__lowercase(     self					:   List[Any]   )  -> Optional[int]:
           UpperCamelCase__							:  int								=     DPTModelTester(self   )
           UpperCamelCase__							:  List[str]								=     ConfigTester(self,		config_class=__lowerCamelCase,		has_text_modality=__lowerCamelCase,		hidden_size=37   )






      def 	__lowercase(     self					:   str   )  -> List[Any]:
           self.config_tester.run_common_tests()






      @unittest.skip(reason='''DPT does not use inputs_embeds'''   )
      def 	__lowercase(     self					:   Union[str, Any]   )  -> Optional[int]:
           pass






      def 	__lowercase(     self					:   Optional[Any]   )  -> str:
           UpperCamelCase__							:  Tuple								=     self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                UpperCamelCase__							:  int								=     model_class(__lowerCamelCase   )
                self.assertIsInstance(model.get_input_embeddings(),		(nn.Module)   )
                UpperCamelCase__							:  Union[str, Any]								=     model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(__lowerCamelCase,		nn.Linear   )   )






      def 	__lowercase(     self					:   Union[str, Any]   )  -> List[str]:
           UpperCamelCase__							:  Optional[Any]								=     self.model_tester.prepare_config_and_inputs_for_common()

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

                UpperCamelCase__							:  Union[str, Any]								=     ['''pixel_values''']
                self.assertListEqual(arg_names[:1],		__lowerCamelCase   )






      def 	__lowercase(     self					:   Optional[Any]   )  -> List[str]:
           UpperCamelCase__							:  Dict								=     self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__lowerCamelCase   )






      def 	__lowercase(     self					:   Tuple   )  -> List[Any]:
           UpperCamelCase__							:  Any								=     self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_depth_estimation(*__lowerCamelCase   )






      def 	__lowercase(     self					:   Optional[Any]   )  -> Optional[Any]:
           UpperCamelCase__							:  Tuple								=     self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase   )






      def 	__lowercase(     self					:   str   )  -> str:
           for model_class in self.all_model_classes:
                if model_class.__name__ == "DPTForDepthEstimation":
                     continue

                UpperCamelCase__							:  Dict								=     self.model_tester.prepare_config_and_inputs_for_common()
                UpperCamelCase__							:  Optional[int]								=     True

                if model_class in get_values(__lowerCamelCase   ):
                     continue

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






      def 	__lowercase(     self					:   Optional[int]   )  -> Dict:
           for model_class in self.all_model_classes:
                if model_class.__name__ == "DPTForDepthEstimation":
                     continue

                UpperCamelCase__							:  int								=     self.model_tester.prepare_config_and_inputs_for_common()
                UpperCamelCase__							:  Dict								=     False
                UpperCamelCase__							:  Optional[Any]								=     True

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






      def 	__lowercase(     self					:   str   )  -> Dict:
           UpperCamelCase__							:  Any								=     self.model_tester.prepare_config_and_inputs_for_common()

           UpperCamelCase__							:  List[Any]								=     _config_zero_init(__lowerCamelCase   )
           for model_class in self.all_model_classes:
                UpperCamelCase__							:  Optional[Any]								=     model_class(config=__lowerCamelCase   )
                # Skip the check for the backbone
                UpperCamelCase__							:  List[str]								=     []
                for name, module in model.named_modules():
                     if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                          UpperCamelCase__							:  Optional[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 	__lowercase(     self					:   Any   )  -> List[str]:
           pass






      @slow
      def 	__lowercase(     self					:   str   )  -> str:
           for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCamelCase__							:  Union[str, Any]								=     DPTModel.from_pretrained(__lowerCamelCase   )
                self.assertIsNotNone(__lowerCamelCase   )







def 						_lowercase       (     )  ->   Any:





     '''simple docstring'''





     UpperCamelCase__							:  Union[str, Any]								=     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
     return image







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






      def 	__lowercase(     self					:   int   )  -> List[Any]:
           UpperCamelCase__							:  Any								=     DPTImageProcessor.from_pretrained('''Intel/dpt-large'''   )
           UpperCamelCase__							:  List[Any]								=     DPTForDepthEstimation.from_pretrained('''Intel/dpt-large'''   ).to(__lowerCamelCase   )

           UpperCamelCase__							:  Any								=     prepare_img()
           UpperCamelCase__							:  Dict								=     image_processor(images=__lowerCamelCase,		return_tensors='''pt'''   ).to(__lowerCamelCase   )

           # forward pass
           with torch.no_grad():
                UpperCamelCase__							:  Any								=     model(**__lowerCamelCase   )
                UpperCamelCase__							:  int								=     outputs.predicted_depth

           # verify the predicted depth
           UpperCamelCase__							:  Optional[Any]								=     torch.Size((1, 3_84, 3_84)   )
           self.assertEqual(predicted_depth.shape,		__lowerCamelCase   )

           UpperCamelCase__							:  int								=     torch.tensor(
               [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]   ).to(__lowerCamelCase   )

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






      def 	__lowercase(     self					:   List[Any]   )  -> Dict:
           UpperCamelCase__							:  Optional[int]								=     DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''   )
           UpperCamelCase__							:  List[str]								=     DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''   ).to(__lowerCamelCase   )

           UpperCamelCase__							:  Optional[int]								=     prepare_img()
           UpperCamelCase__							:  Union[str, Any]								=     image_processor(images=__lowerCamelCase,		return_tensors='''pt'''   ).to(__lowerCamelCase   )

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

           # verify the logits
           UpperCamelCase__							:  str								=     torch.Size((1, 1_50, 4_80, 4_80)   )
           self.assertEqual(outputs.logits.shape,		__lowerCamelCase   )

           UpperCamelCase__							:  List[str]								=     torch.tensor(
               [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]   ).to(__lowerCamelCase   )

           self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3],		__lowerCamelCase,		atol=1e-4   )   )






      def 	__lowercase(     self					:   Any   )  -> List[Any]:
           UpperCamelCase__							:  Union[str, Any]								=     DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''   )
           UpperCamelCase__							:  int								=     DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''   ).to(__lowerCamelCase   )

           UpperCamelCase__							:  List[Any]								=     prepare_img()
           UpperCamelCase__							:  List[str]								=     image_processor(images=__lowerCamelCase,		return_tensors='''pt'''   ).to(__lowerCamelCase   )

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

           UpperCamelCase__							:  List[Any]								=     outputs.logits.detach().cpu()

           UpperCamelCase__							:  Dict								=     image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase,		target_sizes=[(5_00, 3_00)]   )
           UpperCamelCase__							:  List[Any]								=     torch.Size((5_00, 3_00)   )
           self.assertEqual(segmentation[0].shape,		__lowerCamelCase   )

           UpperCamelCase__							:  Optional[int]								=     image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase   )
           UpperCamelCase__							:  List[Any]								=     torch.Size((4_80, 4_80)   )
           self.assertEqual(segmentation[0].shape,		__lowerCamelCase   )








import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class  UpperCamelCase__ ( unittest.TestCase			):






      @cached_property
      def 	__lowercase(     self					:   List[Any]   )  -> int:
           return BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''   )






      @cached_property
      def 	__lowercase(     self					:   List[Any]   )  -> Tuple:
           return BlenderbotTokenizerFast.from_pretrained('''facebook/blenderbot-3B'''   )






      def 	__lowercase(     self					:   List[str]   )  -> Optional[int]:
           UpperCamelCase__							:  Optional[Any]								=     self.tokenizer_ab
           UpperCamelCase__							:  str								=     ''' I am a small frog.'''
           UpperCamelCase__							:  Dict								=     tok([src_text],		padding=__lowerCamelCase,		truncation=__lowerCamelCase   )['''input_ids''']
           UpperCamelCase__							:  List[str]								=     tok.batch_decode(__lowerCamelCase,		skip_special_tokens=__lowerCamelCase,		clean_up_tokenization_spaces=__lowerCamelCase   )[0]
           assert src_text == decoded






      def 	__lowercase(     self					:   int   )  -> Any:
           UpperCamelCase__							:  str								=     self.rust_tokenizer_ab
           UpperCamelCase__							:  Optional[int]								=     ''' I am a small frog.'''
           UpperCamelCase__							:  List[str]								=     tok([src_text],		padding=__lowerCamelCase,		truncation=__lowerCamelCase   )['''input_ids''']
           UpperCamelCase__							:  Optional[int]								=     tok.batch_decode(__lowerCamelCase,		skip_special_tokens=__lowerCamelCase,		clean_up_tokenization_spaces=__lowerCamelCase   )[0]
           assert src_text == decoded






      def 	__lowercase(     self					:   Optional[int]   )  -> Optional[Any]:
           assert self.tokenizer_ab.add_prefix_space
           assert self.tokenizer_ab([''' Sam''', '''Sam''']   ).input_ids == [[55_02, 2], [55_02, 2]]






      def 	__lowercase(     self					:   Union[str, Any]   )  -> Optional[int]:
           assert self.rust_tokenizer_ab.add_prefix_space
           assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']   ).input_ids == [[55_02, 2], [55_02, 2]]




