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 __snake_case				:




   def __init__(    self			:				Union[str, Any]       ,	_lowercase			:				Dict       ,	_lowercase			:				Tuple=2       ,	_lowercase			:				Optional[int]=32       ,	_lowercase			:				Any=16       ,	_lowercase			:				Tuple=3       ,	_lowercase			:				Any=True       ,	_lowercase			:				List[Any]=True       ,	_lowercase			:				Union[str, Any]=32       ,	_lowercase			:				Optional[int]=4       ,	_lowercase			:				str=[0, 1, 2, 3]       ,	_lowercase			:				Optional[Any]=4       ,	_lowercase			:				int=37       ,	_lowercase			:				int="gelu"       ,	_lowercase			:				Tuple=0.1       ,	_lowercase			:				Dict=0.1       ,	_lowercase			:				List[Any]=0.02       ,	_lowercase			:				str=3       ,	_lowercase			:				Dict=[1, 3_84, 24, 24]       ,	_lowercase			:				str=True       ,	_lowercase			:				Any=None       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      parent
     SCREAMING_SNAKE_CASE__										=      batch_size
     SCREAMING_SNAKE_CASE__										=      image_size
     SCREAMING_SNAKE_CASE__										=      patch_size
     SCREAMING_SNAKE_CASE__										=      num_channels
     SCREAMING_SNAKE_CASE__										=      is_training
     SCREAMING_SNAKE_CASE__										=      use_labels
     SCREAMING_SNAKE_CASE__										=      hidden_size
     SCREAMING_SNAKE_CASE__										=      num_hidden_layers
     SCREAMING_SNAKE_CASE__										=      backbone_out_indices
     SCREAMING_SNAKE_CASE__										=      num_attention_heads
     SCREAMING_SNAKE_CASE__										=      intermediate_size
     SCREAMING_SNAKE_CASE__										=      hidden_act
     SCREAMING_SNAKE_CASE__										=      hidden_dropout_prob
     SCREAMING_SNAKE_CASE__										=      attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__										=      initializer_range
     SCREAMING_SNAKE_CASE__										=      num_labels
     SCREAMING_SNAKE_CASE__										=      backbone_featmap_shape
     SCREAMING_SNAKE_CASE__										=      scope
     SCREAMING_SNAKE_CASE__										=      is_hybrid
     # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
     SCREAMING_SNAKE_CASE__										=      (image_size // patch_size) ** 2
     SCREAMING_SNAKE_CASE__										=      num_patches + 1




   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE__										=      self.get_config()

     return config, pixel_values, labels




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      {
         'global_padding': 'same',
         'layer_type': 'bottleneck',
         'depths': [3, 4, 9],
         'out_features': ['stage1', 'stage2', 'stage3'],
         'embedding_dynamic_padding': True,
         'hidden_sizes': [96, 1_92, 3_84, 7_68],
         '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=_UpperCAmelCase       ,	initializer_range=self.initializer_range       ,	is_hybrid=self.is_hybrid       ,	backbone_config=_UpperCAmelCase       ,	backbone_featmap_shape=self.backbone_featmap_shape       ,	)




   def 	__a						(    self			:				Any       ,	_lowercase			:				str       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      DPTModel(config=_UpperCAmelCase      )
     model.to(_UpperCAmelCase      )
     model.eval()
     SCREAMING_SNAKE_CASE__										=      model(_UpperCAmelCase      )
     self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.seq_length, self.hidden_size)      )




   def 	__a						(    self			:				str       ,	_lowercase			:				Dict       ,	_lowercase			:				List[str]       ,	_lowercase			:				Union[str, Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.num_labels
     SCREAMING_SNAKE_CASE__										=      DPTForDepthEstimation(_UpperCAmelCase      )
     model.to(_UpperCAmelCase      )
     model.eval()
     SCREAMING_SNAKE_CASE__										=      model(_UpperCAmelCase      )
     self.parent.assertEqual(result.predicted_depth.shape       ,	(self.batch_size, self.image_size, self.image_size)      )




   def 	__a						(    self			:				str       ,	_lowercase			:				str       ,	_lowercase			:				str       ,	_lowercase			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.num_labels
     SCREAMING_SNAKE_CASE__										=      DPTForSemanticSegmentation(_UpperCAmelCase      )
     model.to(_UpperCAmelCase      )
     model.eval()
     SCREAMING_SNAKE_CASE__										=      model(_UpperCAmelCase       ,	labels=_UpperCAmelCase      )
     self.parent.assertEqual(
         result.logits.shape       ,	(self.batch_size, self.num_labels, self.image_size, self.image_size)      )




   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






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







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

   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							False




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      DPTModelTester(self      )
     SCREAMING_SNAKE_CASE__										=      ConfigTester(self       ,	config_class=_UpperCAmelCase       ,	has_text_modality=_UpperCAmelCase       ,	hidden_size=37      )




   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     self.config_tester.run_common_tests()




   @unittest.skip(reason="""DPT does not use inputs_embeds"""      )
   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     pass




   def 	__a						(    self			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs_for_common()

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




   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs_for_common()

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

       SCREAMING_SNAKE_CASE__										=      ['pixel_values']
       self.assertListEqual(arg_names[:1]       ,	_UpperCAmelCase      )




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_UpperCAmelCase      )




   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_depth_estimation(*_UpperCAmelCase      )




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase      )




   def 	__a						(    self			:				str      ):



     """simple docstring"""






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

       SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__										=      True

       if model_class in get_values(_UpperCAmelCase      ):
         continue

       SCREAMING_SNAKE_CASE__										=      model_class(_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.train()
       SCREAMING_SNAKE_CASE__										=      self._prepare_for_class(_UpperCAmelCase       ,	_UpperCAmelCase       ,	return_labels=_UpperCAmelCase      )
       SCREAMING_SNAKE_CASE__										=      model(**_UpperCAmelCase      ).loss
       loss.backward()




   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






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

       SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__										=      False
       SCREAMING_SNAKE_CASE__										=      True

       if model_class in get_values(_UpperCAmelCase      ) or not model_class.supports_gradient_checkpointing:
         continue
       SCREAMING_SNAKE_CASE__										=      model_class(_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.gradient_checkpointing_enable()
       model.train()
       SCREAMING_SNAKE_CASE__										=      self._prepare_for_class(_UpperCAmelCase       ,	_UpperCAmelCase       ,	return_labels=_UpperCAmelCase      )
       SCREAMING_SNAKE_CASE__										=      model(**_UpperCAmelCase      ).loss
       loss.backward()




   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE__										=      _config_zero_init(_UpperCAmelCase      )
     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE__										=      model_class(config=_UpperCAmelCase      )
       # Skip the check for the backbone
       SCREAMING_SNAKE_CASE__										=      []
       for name, module in model.named_modules():
         if module.__class__.__name__ == "DPTViTHybridEmbeddings":
           SCREAMING_SNAKE_CASE__										=      [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 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     pass




   @slow
   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
       SCREAMING_SNAKE_CASE__										=      DPTModel.from_pretrained(_UpperCAmelCase      )
       self.assertIsNotNone(_UpperCAmelCase      )




   def 	__a						(    self			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE__										=      'add'
     with self.assertRaises(_UpperCAmelCase      ):
       SCREAMING_SNAKE_CASE__										=      DPTForDepthEstimation(_UpperCAmelCase      )





def 	__SCREAMING_SNAKE_CASE  (					)							->					Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
  return image







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




   def 	__a						(    self			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas"""      )
     SCREAMING_SNAKE_CASE__										=      DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas"""      ).to(_UpperCAmelCase      )

     SCREAMING_SNAKE_CASE__										=      prepare_img()
     SCREAMING_SNAKE_CASE__										=      image_processor(images=_UpperCAmelCase       ,	return_tensors="""pt"""      ).to(_UpperCAmelCase      )

     # forward pass
     with torch.no_grad():
       SCREAMING_SNAKE_CASE__										=      model(**_UpperCAmelCase      )
       SCREAMING_SNAKE_CASE__										=      outputs.predicted_depth

     # verify the predicted depth
     SCREAMING_SNAKE_CASE__										=      torch.Size((1, 3_84, 3_84)      )
     self.assertEqual(predicted_depth.shape       ,	_UpperCAmelCase      )

     SCREAMING_SNAKE_CASE__										=      torch.tensor(
         [[[5.64_37, 5.61_46, 5.65_11], [5.43_71, 5.56_49, 5.59_58], [5.52_15, 5.51_84, 5.52_93]]]      ).to(_UpperCAmelCase      )

     self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00       ,	_UpperCAmelCase       ,	atol=1E-4      )      )





'''simple docstring'''

import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    str   				=					LayoutLMTokenizer
							lowerCAmelCase       :    Tuple   				=					LayoutLMTokenizerFast
							lowerCAmelCase       :    List[Any]   				=					True
							lowerCAmelCase       :    int   				=					True







							def 							__lowercase			(							self					:       Dict		):
									super().setUp()

									_a     :      int        =							[
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									_a     :      List[str]        =							os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']		)
									with open(self.vocab_file	,'w'	,encoding='utf-8'		) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)







							def 							__lowercase			(							self					:       Dict	,**_UpperCAmelCase					:       List[str]		):
									return LayoutLMTokenizer.from_pretrained(self.tmpdirname	,**_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       Tuple		):
									_a     :      Optional[int]        =							'UNwant\u00E9d,running'
									_a     :      List[Any]        =							'unwanted, running'
									return input_text, output_text







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Optional[Any]        =							self.tokenizer_class(self.vocab_file		)

									_a     :      Optional[Any]        =							tokenizer.tokenize('UNwant\u00E9d,running'		)
									self.assertListEqual(_UpperCAmelCase	,['un', '##want', '##ed', ',', 'runn', '##ing']		)
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)	,[7, 4, 5, 10, 8, 9]		)







							def 							__lowercase			(							self					:       Optional[int]		):
									pass




