


"""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 			_UpperCAmelCase :






		'''simple docstring'''

		def __init__(self				,       a_				,       a_=2				,       a_=32				,       a_=16				,       a_=3				,       a_=True				,       a_=True				,       a_=32				,       a_=4				,       a_=[0, 1, 2, 3]				,       a_=4				,       a_=37				,       a_="gelu"				,       a_=0.1				,       a_=0.1				,       a_=0.02				,       a_=3				,       a_=[1, 3_84, 24, 24]				,       a_=True				,       a_=None				,       ):





			'''simple docstring'''

			__snake_case	:						Any						=				parent
			__snake_case	:						Optional[int]						=				batch_size
			__snake_case	:						Optional[int]						=				image_size
			__snake_case	:						Union[str, Any]						=				patch_size
			__snake_case	:						List[Any]						=				num_channels
			__snake_case	:						Union[str, Any]						=				is_training
			__snake_case	:						Optional[Any]						=				use_labels
			__snake_case	:						List[Any]						=				hidden_size
			__snake_case	:						str						=				num_hidden_layers
			__snake_case	:						int						=				backbone_out_indices
			__snake_case	:						Optional[Any]						=				num_attention_heads
			__snake_case	:						Any						=				intermediate_size
			__snake_case	:						Union[str, Any]						=				hidden_act
			__snake_case	:						Optional[Any]						=				hidden_dropout_prob
			__snake_case	:						Optional[int]						=				attention_probs_dropout_prob
			__snake_case	:						List[str]						=				initializer_range
			__snake_case	:						int						=				num_labels
			__snake_case	:						Optional[int]						=				backbone_featmap_shape
			__snake_case	:						Optional[int]						=				scope
			__snake_case	:						Optional[int]						=				is_hybrid
			# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
			__snake_case	:						Dict						=				(image_size // patch_size) ** 2
			__snake_case	:						List[str]						=				num_patches + 1



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

			__snake_case	:						Any						=				self.get_config()

			return config, pixel_values, labels



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				{
			    '''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=a_				,       initializer_range=self.initializer_range				,       is_hybrid=self.is_hybrid				,       backbone_config=a_				,       backbone_featmap_shape=self.backbone_featmap_shape				,       )



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Dict						=				DPTModel(config=a_					)
			model.to(a_					)
			model.eval()
			__snake_case	:						Optional[Any]						=				model(a_					)
			self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						str						=				self.num_labels
			__snake_case	:						Any						=				DPTForDepthEstimation(a_					)
			model.to(a_					)
			model.eval()
			__snake_case	:						Any						=				model(a_					)
			self.parent.assertEqual(result.predicted_depth.shape				,       (self.batch_size, self.image_size, self.image_size)					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				self.num_labels
			__snake_case	:						Optional[Any]						=				DPTForSemanticSegmentation(a_					)
			model.to(a_					)
			model.eval()
			__snake_case	:						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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



@require_torch
class 			_UpperCAmelCase (							__snake_case,				__snake_case,				unittest.TestCase				):






		'''simple docstring'''

		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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				DPTModelTester(self					)
			__snake_case	:						Tuple						=				ConfigTester(self				,       config_class=a_				,       has_text_modality=a_				,       hidden_size=37					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self.config_tester.run_common_tests()



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





			'''simple docstring'''

			pass



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	,							__snake_case	:						int						=				self.model_tester.prepare_config_and_inputs_for_common()

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

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_depth_estimation(*a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_semantic_segmentation(*a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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

				__snake_case	,							__snake_case	:						Any						=				self.model_tester.prepare_config_and_inputs_for_common()
				__snake_case	:						List[str]						=				True

				if model_class in get_values(a_					):
					continue

				__snake_case	:						Any						=				model_class(a_					)
				model.to(a_					)
				model.train()
				__snake_case	:						Any						=				self._prepare_for_class(a_				,       a_				,       return_labels=a_					)
				__snake_case	:						str						=				model(**a_					).loss
				loss.backward()



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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

				__snake_case	,							__snake_case	:						Union[str, Any]						=				self.model_tester.prepare_config_and_inputs_for_common()
				__snake_case	:						str						=				False
				__snake_case	:						str						=				True

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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

			__snake_case	:						Dict						=				_config_zero_init(a_					)
			for model_class in self.all_model_classes:
				__snake_case	:						Optional[Any]						=				model_class(config=a_					)
				# Skip the check for the backbone
				__snake_case	:						Any						=				[]
				for name, module in model.named_modules():
					if module.__class__.__name__ == "DPTViTHybridEmbeddings":
						__snake_case	:						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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			pass



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
				__snake_case	:						Optional[Any]						=				DPTModel.from_pretrained(a_					)
				self.assertIsNotNone(a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	,							__snake_case	:						Any						=				self.model_tester.prepare_config_and_inputs_for_common()
			__snake_case	:						str						=				'''add'''
			with self.assertRaises(a_					):
				__snake_case	:						List[Any]						=				DPTForDepthEstimation(a_					)






def 		lowercase							(     )     ->Tuple:
	"""simple docstring"""





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



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






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''					)
			__snake_case	:						Dict						=				DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''					).to(a_					)

			__snake_case	:						str						=				prepare_img()
			__snake_case	:						Optional[Any]						=				image_processor(images=a_				,       return_tensors='''pt'''					).to(a_					)

			# forward pass
			with torch.no_grad():
				__snake_case	:						int						=				model(**a_					)
				__snake_case	:						int						=				outputs.predicted_depth

			# verify the predicted depth
			__snake_case	:						Any						=				torch.Size((1, 3_84, 3_84)					)
			self.assertEqual(predicted_depth.shape				,       a_					)

			__snake_case	:						str						=				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] / 1_00				,       a_				,       atol=1E-4					)					)










import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


A__								=						'''http://www.mocksite.com/file1.txt'''
A__								=						'''"text": ["foo", "foo"]'''
A__								=						'''6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'''


class       a							:
		__lowerCAmelCase	:    Optional[int]											=					2_00
		__lowerCAmelCase	:    List[str]											=					{"""Content-Length""": """100"""}
		__lowerCAmelCase	:    Dict											=					{}



		def 	__lowerCamelCase			(     self     :Dict   ,**__lowercase     :List[Any]  ):
						return [bytes(__lowercase   ,'''utf-8'''  )]



def 			_lowerCAmelCase       (					*__lowerCAmelCase  ,  **__lowerCAmelCase      )   ->					Optional[Any]:


				"""simple docstring"""

				return MockResponse()



@pytest.mark.parametrize('''urls_type'''  ,  [str, list, dict]      )
def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )   ->					List[str]:


				"""simple docstring"""

				import requests

				monkeypatch.setattr(__lowerCAmelCase  ,  '''request'''  ,  __lowerCAmelCase      )

				snake_case__	:      Union[str, Any]     		=	URL
				if issubclass(__lowerCAmelCase  ,  __lowerCAmelCase      ):
								snake_case__	:      Optional[Any]     		=	url
				elif issubclass(__lowerCAmelCase  ,  __lowerCAmelCase      ):
								snake_case__	:      int     		=	[url]
				elif issubclass(__lowerCAmelCase  ,  __lowerCAmelCase      ):
								snake_case__	:      int     		=	{'''train''': url}
				snake_case__	:      Dict     		=	'''dummy'''
				snake_case__	:      Any     		=	'''downloads'''
				snake_case__	:      int     		=	tmp_path
				snake_case__	:      Any     		=	DownloadConfig(
				    cache_dir=os.path.join(__lowerCAmelCase  ,  __lowerCAmelCase      )  ,  use_etag=__lowerCAmelCase  ,  )
				snake_case__	:      Tuple     		=	DownloadManager(dataset_name=__lowerCAmelCase  ,  download_config=__lowerCAmelCase      )
				snake_case__	:      List[Any]     		=	dl_manager.download(__lowerCAmelCase      )
				snake_case__	:      Union[str, Any]     		=	urls
				for downloaded_paths in [downloaded_paths]:
								if isinstance(__lowerCAmelCase  ,  __lowerCAmelCase      ):
												snake_case__	:      Optional[int]     		=	[downloaded_paths]
												snake_case__	:      Dict     		=	[urls]
								elif isinstance(__lowerCAmelCase  ,  __lowerCAmelCase      ):
												assert "train" in downloaded_paths.keys()
												snake_case__	:      str     		=	downloaded_paths.values()
												snake_case__	:      List[str]     		=	urls.values()
								assert downloaded_paths
								for downloaded_path, input_url in zip(__lowerCAmelCase  ,  __lowerCAmelCase      ):
												assert downloaded_path == dl_manager.downloaded_paths[input_url]
												snake_case__	:      List[Any]     		=	Path(__lowerCAmelCase      )
												snake_case__	:      Any     		=	downloaded_path.parts
												assert parts[-1] == HASH
												assert parts[-2] == cache_subdir
												assert downloaded_path.exists()
												snake_case__	:      List[str]     		=	downloaded_path.read_text()
												assert content == CONTENT
												snake_case__	:      List[str]     		=	downloaded_path.with_suffix('''.json'''      )
												assert metadata_downloaded_path.exists()
												snake_case__	:      str     		=	json.loads(metadata_downloaded_path.read_text()      )
												assert metadata_content == {"url": URL, "etag": None}



@pytest.mark.parametrize('''paths_type'''  ,  [str, list, dict]      )
def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )   ->					Any:


				"""simple docstring"""

				snake_case__	:      Any     		=	str(__lowerCAmelCase      )
				if issubclass(__lowerCAmelCase  ,  __lowerCAmelCase      ):
								snake_case__	:      Tuple     		=	filename
				elif issubclass(__lowerCAmelCase  ,  __lowerCAmelCase      ):
								snake_case__	:      Dict     		=	[filename]
				elif issubclass(__lowerCAmelCase  ,  __lowerCAmelCase      ):
								snake_case__	:      Dict     		=	{'''train''': filename}
				snake_case__	:      Any     		=	'''dummy'''
				snake_case__	:      Any     		=	xz_file.parent
				snake_case__	:      List[str]     		=	'''extracted'''
				snake_case__	:      Dict     		=	DownloadConfig(
				    cache_dir=__lowerCAmelCase  ,  use_etag=__lowerCAmelCase  ,  )
				snake_case__	:      Dict     		=	DownloadManager(dataset_name=__lowerCAmelCase  ,  download_config=__lowerCAmelCase      )
				snake_case__	:      str     		=	dl_manager.extract(__lowerCAmelCase      )
				snake_case__	:      int     		=	paths
				for extracted_paths in [extracted_paths]:
								if isinstance(__lowerCAmelCase  ,  __lowerCAmelCase      ):
												snake_case__	:      Dict     		=	[extracted_paths]
												snake_case__	:      Optional[Any]     		=	[paths]
								elif isinstance(__lowerCAmelCase  ,  __lowerCAmelCase      ):
												assert "train" in extracted_paths.keys()
												snake_case__	:      int     		=	extracted_paths.values()
												snake_case__	:      int     		=	paths.values()
								assert extracted_paths
								for extracted_path, input_path in zip(__lowerCAmelCase  ,  __lowerCAmelCase      ):
												assert extracted_path == dl_manager.extracted_paths[input_path]
												snake_case__	:      Optional[int]     		=	Path(__lowerCAmelCase      )
												snake_case__	:      int     		=	extracted_path.parts
												assert parts[-1] == hash_url_to_filename(__lowerCAmelCase  ,  etag=__lowerCAmelCase      )
												assert parts[-2] == extracted_subdir
												assert extracted_path.exists()
												snake_case__	:      List[Any]     		=	extracted_path.read_text()
												snake_case__	:      List[str]     		=	text_file.read_text()
												assert extracted_file_content == expected_file_content



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					List[Any]:


				"""simple docstring"""

				assert path.endswith('''.jsonl'''      )
				for num_items, line in enumerate(__lowerCAmelCase  ,  start=1      ):
								snake_case__	:      Any     		=	json.loads(line.decode('''utf-8'''      )      )
								assert item.keys() == {"col_1", "col_2", "col_3"}
				assert num_items == 4



@pytest.mark.parametrize('''archive_jsonl'''  ,  ['''tar_jsonl_path''', '''zip_jsonl_path''']      )
def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					Union[str, Any]:


				"""simple docstring"""

				snake_case__	:      Any     		=	request.getfixturevalue(__lowerCAmelCase      )
				snake_case__	:      Union[str, Any]     		=	DownloadManager()
				for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(__lowerCAmelCase      )  ,  start=1      ):
								_test_jsonl(__lowerCAmelCase  ,  __lowerCAmelCase      )
				assert num_jsonl == 2



@pytest.mark.parametrize('''archive_nested_jsonl'''  ,  ['''tar_nested_jsonl_path''', '''zip_nested_jsonl_path''']      )
def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					List[str]:


				"""simple docstring"""

				snake_case__	:      Union[str, Any]     		=	request.getfixturevalue(__lowerCAmelCase      )
				snake_case__	:      Optional[int]     		=	DownloadManager()
				for num_tar, (path, file) in enumerate(dl_manager.iter_archive(__lowerCAmelCase      )  ,  start=1      ):
								for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(__lowerCAmelCase      )  ,  start=1      ):
												_test_jsonl(__lowerCAmelCase  ,  __lowerCAmelCase      )
				assert num_tar == 1
				assert num_jsonl == 2





def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				snake_case__	:      Any     		=	DownloadManager()
				for num_file, file in enumerate(dl_manager.iter_files(__lowerCAmelCase      )  ,  start=1      ):
								assert os.path.basename(__lowerCAmelCase      ) == ("test.txt" if num_file == 1 else "train.txt")
				assert num_file == 2

