


"""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   lowercase__							:





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





   def 		UpperCamelCase_     (       self)     ->     Optional[Any]:
      _lowerCamelCase  :				Optional[int]      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

      _lowerCamelCase  :				Optional[int]      =		self.get_config()

      return config, pixel_values, labels





   def 		UpperCamelCase_     (       self)     ->     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=SCREAMING_SNAKE_CASE			,      initializer_range=self.initializer_range			,      is_hybrid=self.is_hybrid			,      )





   def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     List[str]:
      _lowerCamelCase  :				Any      =		DPTModel(config=SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()
      _lowerCamelCase  :				Optional[int]      =		model(SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(result.last_hidden_state.shape			,      (self.batch_size, self.seq_length, self.hidden_size))





   def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     List[Any]:
      _lowerCamelCase  :				Union[str, Any]      =		self.num_labels
      _lowerCamelCase  :				Optional[Any]      =		DPTForDepthEstimation(SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()
      _lowerCamelCase  :				Optional[Any]      =		model(SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(result.predicted_depth.shape			,      (self.batch_size, self.image_size, self.image_size))





   def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     str:
      _lowerCamelCase  :				List[str]      =		self.num_labels
      _lowerCamelCase  :				Union[str, Any]      =		DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()
      _lowerCamelCase  :				List[Any]      =		model(SCREAMING_SNAKE_CASE			,      labels=SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(
          result.logits.shape			,      (self.batch_size, self.num_labels, self.image_size, self.image_size))





   def 		UpperCamelCase_     (       self)     ->     List[Any]:
      _lowerCamelCase  :				List[str]      =		self.prepare_config_and_inputs()
      _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase  :				Tuple      =		config_and_inputs
      _lowerCamelCase  :				Any      =		{"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_torch
class   lowercase__							(				A_   ,A_   ,unittest.TestCase							):
   __UpperCAmelCase       					=				(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
   __UpperCAmelCase       					=				(
       {
           '''depth-estimation''': DPTForDepthEstimation,
           '''feature-extraction''': DPTModel,
           '''image-segmentation''': DPTForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   __UpperCAmelCase       					=				False
   __UpperCAmelCase       					=				False
   __UpperCAmelCase       					=				False





   def 		UpperCamelCase_     (       self)     ->     Any:
      _lowerCamelCase  :				Union[str, Any]      =		DPTModelTester(self)
      _lowerCamelCase  :				int      =		ConfigTester(self			,      config_class=SCREAMING_SNAKE_CASE			,      has_text_modality=SCREAMING_SNAKE_CASE			,      hidden_size=37)





   def 		UpperCamelCase_     (       self)     ->     int:
      self.config_tester.run_common_tests()





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





   def 		UpperCamelCase_     (       self)     ->     Dict:
      _lowerCamelCase      ,						_lowerCamelCase  :				Optional[int]      =		self.model_tester.prepare_config_and_inputs_for_common()

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





   def 		UpperCamelCase_     (       self)     ->     Optional[Any]:
      _lowerCamelCase      ,						_lowerCamelCase  :				Union[str, Any]      =		self.model_tester.prepare_config_and_inputs_for_common()

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

         _lowerCamelCase  :				Tuple      =		["""pixel_values"""]
         self.assertListEqual(arg_names[:1]			,      SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_     (       self)     ->     List[Any]:
      _lowerCamelCase  :				Tuple      =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
      _lowerCamelCase  :				List[str]      =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_     (       self)     ->     Tuple:
      _lowerCamelCase  :				List[str]      =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE)





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

         _lowerCamelCase      ,						_lowerCamelCase  :				int      =		self.model_tester.prepare_config_and_inputs_for_common()
         _lowerCamelCase  :				List[str]      =		True

         if model_class in get_values(SCREAMING_SNAKE_CASE):
            continue

         _lowerCamelCase  :				List[str]      =		model_class(SCREAMING_SNAKE_CASE)
         model.to(SCREAMING_SNAKE_CASE)
         model.train()
         _lowerCamelCase  :				Union[str, Any]      =		self._prepare_for_class(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      return_labels=SCREAMING_SNAKE_CASE)
         _lowerCamelCase  :				int      =		model(**SCREAMING_SNAKE_CASE).loss
         loss.backward()





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

         _lowerCamelCase      ,						_lowerCamelCase  :				List[Any]      =		self.model_tester.prepare_config_and_inputs_for_common()
         _lowerCamelCase  :				Tuple      =		False
         _lowerCamelCase  :				Any      =		True

         if model_class in get_values(SCREAMING_SNAKE_CASE) or not model_class.supports_gradient_checkpointing:
            continue
         _lowerCamelCase  :				List[str]      =		model_class(SCREAMING_SNAKE_CASE)
         model.to(SCREAMING_SNAKE_CASE)
         model.gradient_checkpointing_enable()
         model.train()
         _lowerCamelCase  :				Any      =		self._prepare_for_class(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      return_labels=SCREAMING_SNAKE_CASE)
         _lowerCamelCase  :				Tuple      =		model(**SCREAMING_SNAKE_CASE).loss
         loss.backward()





   def 		UpperCamelCase_     (       self)     ->     Dict:
      _lowerCamelCase      ,						_lowerCamelCase  :				Optional[Any]      =		self.model_tester.prepare_config_and_inputs_for_common()

      _lowerCamelCase  :				Any      =		_config_zero_init(SCREAMING_SNAKE_CASE)
      for model_class in self.all_model_classes:
         _lowerCamelCase  :				Tuple      =		model_class(config=SCREAMING_SNAKE_CASE)
         # Skip the check for the backbone
         _lowerCamelCase  :				Union[str, Any]      =		[]
         for name, module in model.named_modules():
            if module.__class__.__name__ == "DPTViTHybridEmbeddings":
               _lowerCamelCase  :				Optional[Any]      =		[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 		UpperCamelCase_     (       self)     ->     Optional[Any]:
      pass





   @slow
   def 		UpperCamelCase_     (       self)     ->     Dict:
      for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCamelCase  :				List[Any]      =		DPTModel.from_pretrained(SCREAMING_SNAKE_CASE)
         self.assertIsNotNone(SCREAMING_SNAKE_CASE)







def 							_snake_case			(      ):




   """simple docstring"""







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





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





   def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
      _lowerCamelCase  :				Optional[int]      =		DPTImageProcessor.from_pretrained("""Intel/dpt-large""")
      _lowerCamelCase  :				str      =		DPTForDepthEstimation.from_pretrained("""Intel/dpt-large""").to(SCREAMING_SNAKE_CASE)

      _lowerCamelCase  :				Dict      =		prepare_img()
      _lowerCamelCase  :				Union[str, Any]      =		image_processor(images=SCREAMING_SNAKE_CASE			,      return_tensors="""pt""").to(SCREAMING_SNAKE_CASE)

      # forward pass
      with torch.no_grad():
         _lowerCamelCase  :				Tuple      =		model(**SCREAMING_SNAKE_CASE)
         _lowerCamelCase  :				Dict      =		outputs.predicted_depth

      # verify the predicted depth
      _lowerCamelCase  :				Tuple      =		torch.Size((1, 384, 384))
      self.assertEqual(predicted_depth.shape			,      SCREAMING_SNAKE_CASE)

      _lowerCamelCase  :				List[str]      =		torch.tensor(
          [[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]).to(SCREAMING_SNAKE_CASE)

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





   def 		UpperCamelCase_     (       self)     ->     int:
      _lowerCamelCase  :				int      =		DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade""")
      _lowerCamelCase  :				int      =		DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade""").to(SCREAMING_SNAKE_CASE)

      _lowerCamelCase  :				Dict      =		prepare_img()
      _lowerCamelCase  :				Union[str, Any]      =		image_processor(images=SCREAMING_SNAKE_CASE			,      return_tensors="""pt""").to(SCREAMING_SNAKE_CASE)

      # forward pass
      with torch.no_grad():
         _lowerCamelCase  :				int      =		model(**SCREAMING_SNAKE_CASE)

      # verify the logits
      _lowerCamelCase  :				Optional[int]      =		torch.Size((1, 150, 480, 480))
      self.assertEqual(outputs.logits.shape			,      SCREAMING_SNAKE_CASE)

      _lowerCamelCase  :				str      =		torch.tensor(
          [[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]).to(SCREAMING_SNAKE_CASE)

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





   def 		UpperCamelCase_     (       self)     ->     List[str]:
      _lowerCamelCase  :				List[str]      =		DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade""")
      _lowerCamelCase  :				List[str]      =		DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade""").to(SCREAMING_SNAKE_CASE)

      _lowerCamelCase  :				Optional[Any]      =		prepare_img()
      _lowerCamelCase  :				Any      =		image_processor(images=SCREAMING_SNAKE_CASE			,      return_tensors="""pt""").to(SCREAMING_SNAKE_CASE)

      # forward pass
      with torch.no_grad():
         _lowerCamelCase  :				Tuple      =		model(**SCREAMING_SNAKE_CASE)

      _lowerCamelCase  :				Any      =		outputs.logits.detach().cpu()

      _lowerCamelCase  :				List[str]      =		image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE			,      target_sizes=[(500, 300)])
      _lowerCamelCase  :				Tuple      =		torch.Size((500, 300))
      self.assertEqual(segmentation[0].shape			,      SCREAMING_SNAKE_CASE)

      _lowerCamelCase  :				Optional[Any]      =		image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE)
      _lowerCamelCase  :				Any      =		torch.Size((480, 480))
      self.assertEqual(segmentation[0].shape			,      SCREAMING_SNAKE_CASE)





"""simple docstring"""
from dataclasses import dataclass

UpperCAmelCase   =	"""0.1"""
UpperCAmelCase   =	"""Lucia Harcekova"""




@dataclass
class   lowercase__							:
   __UpperCAmelCase       					=				42
   __UpperCAmelCase       					=				42
   __UpperCAmelCase       					=				42





   def __repr__(       self)     ->     str:
      return F'({self.offset}, {self.length}, {self.indicator})'







class   lowercase__							:





   def __init__(       self			,      SCREAMING_SNAKE_CASE = 13			,      SCREAMING_SNAKE_CASE = 6)     ->     None:
      _lowerCamelCase  :				Optional[Any]      =		window_size
      _lowerCamelCase  :				List[str]      =		lookahead_buffer_size
      _lowerCamelCase  :				List[Any]      =		self.window_size - self.lookahead_buffer_size





   def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     list[Token]:
      _lowerCamelCase  :				Union[str, Any]      =		[]
      _lowerCamelCase  :				Tuple      =		""""""

      # while there are still characters in text to compress
      while text:
         # find the next encoding phrase
         # - triplet with offset, length, indicator (the next encoding character)
         _lowerCamelCase  :				List[Any]      =		self._find_encoding_token(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)

         # update the search buffer:
         # - add new characters from text into it
         # - check if size exceed the max search buffer size, if so, drop the
         #   oldest elements
         search_buffer += text[: token.length + 1]
         if len(SCREAMING_SNAKE_CASE) > self.search_buffer_size:
            _lowerCamelCase  :				int      =		search_buffer[-self.search_buffer_size :]

         # update the text
         _lowerCamelCase  :				Tuple      =		text[token.length + 1 :]

         # append the token to output
         output.append(SCREAMING_SNAKE_CASE)

      return output





   def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     str:
      _lowerCamelCase  :				str      =		""""""

      for token in tokens:
         for _ in range(token.length):
            output += output[-token.offset]
         output += token.indicator

      return output





   def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     Token:

      if not text:
         raise ValueError("""We need some text to work with.""")

      # Initialise result parameters to default values
      _lowerCamelCase      ,						_lowerCamelCase  :				List[str]      =		0, 0

      if not search_buffer:
         return Token(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      text[length])

      for i, character in enumerate(SCREAMING_SNAKE_CASE):
         _lowerCamelCase  :				Tuple      =		len(SCREAMING_SNAKE_CASE) - i
         if character == text[0]:
            _lowerCamelCase  :				Any      =		self._match_length_from_index(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      0			,      SCREAMING_SNAKE_CASE)
            # if the found length is bigger than the current or if it's equal,
            # which means it's offset is smaller: update offset and length
            if found_length >= length:
               _lowerCamelCase      ,						_lowerCamelCase  :				Optional[int]      =		found_offset, found_length

      return Token(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      text[length])





   def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     int:
      if not text or text[text_index] != window[window_index]:
         return 0
      return 1 + self._match_length_from_index(
          SCREAMING_SNAKE_CASE			,      window + text[text_index]			,      text_index + 1			,      window_index + 1)


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 # Initialize compressor class
 UpperCAmelCase   =	LZaaCompressor(window_size=13, lookahead_buffer_size=6)

 # Example
 UpperCAmelCase   =	"""cabracadabrarrarrad"""
 UpperCAmelCase   =	lzaa_compressor.compress(TEXT)
 print(lzaa_compressor.compress("""ababcbababaa"""))
 UpperCAmelCase   =	lzaa_compressor.decompress(compressed_text)
 assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."

