






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   lowerCamelCase      :







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase=2				,		_UpperCamelCase=3_2				,		_UpperCamelCase=1_6				,		_UpperCamelCase=3				,		_UpperCamelCase=True				,		_UpperCamelCase=True				,		_UpperCamelCase=3_2				,		_UpperCamelCase=4				,		_UpperCamelCase=[0, 1, 2, 3]				,		_UpperCamelCase=4				,		_UpperCamelCase=3_7				,		_UpperCamelCase="gelu"				,		_UpperCamelCase=0.1				,		_UpperCamelCase=0.1				,		_UpperCamelCase=0.02				,		_UpperCamelCase=3				,		_UpperCamelCase=[1, 3_8_4, 2_4, 2_4]				,		_UpperCamelCase=True				,		_UpperCamelCase=None				,		)	->							List[str]:
							UpperCAmelCase_    :      Any            =						parent
							UpperCAmelCase_    :      Dict            =						batch_size
							UpperCAmelCase_    :      Optional[Any]            =						image_size
							UpperCAmelCase_    :      Union[str, Any]            =						patch_size
							UpperCAmelCase_    :      List[Any]            =						num_channels
							UpperCAmelCase_    :      int            =						is_training
							UpperCAmelCase_    :      List[str]            =						use_labels
							UpperCAmelCase_    :      Optional[int]            =						hidden_size
							UpperCAmelCase_    :      Dict            =						num_hidden_layers
							UpperCAmelCase_    :      Dict            =						backbone_out_indices
							UpperCAmelCase_    :      Tuple            =						num_attention_heads
							UpperCAmelCase_    :      List[str]            =						intermediate_size
							UpperCAmelCase_    :      Dict            =						hidden_act
							UpperCAmelCase_    :      List[str]            =						hidden_dropout_prob
							UpperCAmelCase_    :      int            =						attention_probs_dropout_prob
							UpperCAmelCase_    :      int            =						initializer_range
							UpperCAmelCase_    :      Dict            =						num_labels
							UpperCAmelCase_    :      List[Any]            =						backbone_featmap_shape
							UpperCAmelCase_    :      Tuple            =						scope
							UpperCAmelCase_    :      int            =						is_hybrid
							# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
							UpperCAmelCase_    :      List[Any]            =						(image_size // patch_size) ** 2
							UpperCAmelCase_    :      Tuple            =						num_patches + 1



		def 					__UpperCAmelCase			(							self   )	->							int:
							UpperCAmelCase_    :      Optional[int]            =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

							UpperCAmelCase_    :      Optional[Any]            =						self.get_config()

							return config, pixel_values, labels



		def 					__UpperCAmelCase			(							self   )	->							Any:
							UpperCAmelCase_    :      str            =						{
							    'global_padding': 'same',
							    'layer_type': 'bottleneck',
							    'depths': [3, 4, 9],
							    'out_features': ['stage1', 'stage2', 'stage3'],
							    'embedding_dynamic_padding': True,
							    'hidden_sizes': [9_6, 1_9_2, 3_8_4, 7_6_8],
							    '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 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Tuple:
							UpperCAmelCase_    :      Tuple            =						DPTModel(config=_UpperCamelCase   )
							model.to(_UpperCamelCase   )
							model.eval()
							UpperCAmelCase_    :      Optional[int]            =						model(_UpperCamelCase   )
							self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size)   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Tuple:
							UpperCAmelCase_    :      Optional[Any]            =						self.num_labels
							UpperCAmelCase_    :      Any            =						DPTForDepthEstimation(_UpperCamelCase   )
							model.to(_UpperCamelCase   )
							model.eval()
							UpperCAmelCase_    :      str            =						model(_UpperCamelCase   )
							self.parent.assertEqual(result.predicted_depth.shape				,		(self.batch_size, self.image_size, self.image_size)   )



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



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





@require_torch
class   lowerCamelCase      (_snake_case ,  _snake_case ,  unittest.TestCase			):







		'''simple docstring'''



		_snake_case    :    int       						=  (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
		_snake_case    :    List[str]       						=  (
		    {
		        '''depth-estimation''': DPTForDepthEstimation,
		        '''feature-extraction''': DPTModel,
		        '''image-segmentation''': DPTForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		_snake_case    :    Optional[int]       						=  False
		_snake_case    :    List[str]       						=  False
		_snake_case    :    Union[str, Any]       						=  False



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							UpperCAmelCase_    :      List[Any]            =						DPTModelTester(self   )
							UpperCAmelCase_    :      Any            =						ConfigTester(self				,		config_class=_UpperCamelCase				,		has_text_modality=_UpperCamelCase				,		hidden_size=3_7   )



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							self.config_tester.run_common_tests()



		@unittest.skip(reason='DPT does not use inputs_embeds'   )
		def 					__UpperCAmelCase			(							self   )	->							str:
							pass



		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							UpperCAmelCase_       ,  UpperCAmelCase_    :      List[str]            =						self.model_tester.prepare_config_and_inputs_for_common()

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



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

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

												UpperCAmelCase_    :      Optional[int]            =						['pixel_values']
												self.assertListEqual(arg_names[:1]				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							UpperCAmelCase_    :      Optional[Any]            =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							int:
							UpperCAmelCase_    :      Dict            =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							UpperCAmelCase_    :      List[Any]            =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCamelCase   )



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

												UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						self.model_tester.prepare_config_and_inputs_for_common()
												UpperCAmelCase_    :      str            =						True

												if model_class in get_values(_UpperCamelCase   ):
																	continue

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



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

												UpperCAmelCase_       ,  UpperCAmelCase_    :      List[Any]            =						self.model_tester.prepare_config_and_inputs_for_common()
												UpperCAmelCase_    :      List[str]            =						False
												UpperCAmelCase_    :      int            =						True

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



		def 					__UpperCAmelCase			(							self   )	->							List[Any]:
							UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						self.model_tester.prepare_config_and_inputs_for_common()

							UpperCAmelCase_    :      int            =						_config_zero_init(_UpperCamelCase   )
							for model_class in self.all_model_classes:
												UpperCAmelCase_    :      Tuple            =						model_class(config=_UpperCamelCase   )
												# Skip the check for the backbone
												UpperCAmelCase_    :      Optional[int]            =						[]
												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 					__UpperCAmelCase			(							self   )	->							int:
							pass



		@slow
		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
												UpperCAmelCase_    :      Tuple            =						DPTModel.from_pretrained(_UpperCamelCase   )
												self.assertIsNotNone(_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							str:
							# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
							UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						self.model_tester.prepare_config_and_inputs_for_common()
							UpperCAmelCase_    :      Union[str, Any]            =						'add'
							with self.assertRaises(_UpperCamelCase   ):
												UpperCAmelCase_    :      Any            =						DPTForDepthEstimation(_UpperCamelCase   )





def      lowercase__      (     ):


					'''simple docstring'''


					UpperCAmelCase_    :      Optional[Any]            =						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
					return image






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







		'''simple docstring'''



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							UpperCAmelCase_    :      int            =						DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'   )
							UpperCAmelCase_    :      int            =						DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'   ).to(_UpperCamelCase   )

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

							# forward pass
							with torch.no_grad():
												UpperCAmelCase_    :      Optional[Any]            =						model(**_UpperCamelCase   )
												UpperCAmelCase_    :      Dict            =						outputs.predicted_depth

							# verify the predicted depth
							UpperCAmelCase_    :      Optional[int]            =						torch.Size((1, 3_8_4, 3_8_4)   )
							self.assertEqual(predicted_depth.shape				,		_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[int]            =						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_0_0				,		_UpperCamelCase				,		atol=1E-4   )   )







"""simple docstring"""




import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


__lowerCamelCase							 =     "\\n@inproceedings{popovic-2015-chrf,\n    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n    month = sep,\n    year = \"2015\",\n    address = \"Lisbon, Portugal\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W15-3049\",\n    doi = \"10.18653/v1/W15-3049\",\n    pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n    title = \"chr{F}++: words helping character n-grams\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W17-4770\",\n    doi = \"10.18653/v1/W17-4770\",\n    pages = \"612--618\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

__lowerCamelCase							 =     "\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"

__lowerCamelCase							 =     "\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    'score' (float): The chrF (chrF++) score,\n    'char_order' (int): The character n-gram order,\n    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,  _KWARGS_DESCRIPTION       )
class 			UpperCamelCase__(	datasets.Metric       ):





						def  snake_case__		(				self   )    -> Tuple:
													if version.parse(scb.__version__   ) < version.parse('1.4.12'   ):
																				raise ImportWarning(
																				    'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
																				    'You can install it with `pip install "sacrebleu>=1.4.12"`.'   )
													return datasets.MetricInfo(
													    description=_DESCRIPTION						,citation=_CITATION						,homepage='https://github.com/mjpost/sacreBLEU#chrf--chrf'						,inputs_description=_KWARGS_DESCRIPTION						,features=datasets.Features(
													        {
													            'predictions': datasets.Value('string'						,id='sequence'   ),
													            'references': datasets.Sequence(datasets.Value('string'						,id='sequence'   )						,id='references'   ),
													        }   )						,codebase_urls=['https://github.com/mjpost/sacreBLEU#chrf--chrf']						,reference_urls=[
													        'https://github.com/m-popovic/chrF',
													    ]						,)





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = CHRF.CHAR_ORDER						,__UpperCAmelCase = CHRF.WORD_ORDER						,__UpperCAmelCase = CHRF.BETA						,__UpperCAmelCase = False						,__UpperCAmelCase = False						,__UpperCAmelCase = False						,)    -> Union[str, Any]:
													A__		   =							len(references[0]   )
													if any(len(__UpperCAmelCase   ) != references_per_prediction for refs in references   ):
																				raise ValueError('Sacrebleu requires the same number of references for each prediction'   )
													A__		   =							[[refs[i] for refs in references] for i in range(__UpperCAmelCase   )]

													A__		   =							CHRF(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )
													A__		   =							sb_chrf.corpus_score(__UpperCAmelCase						,__UpperCAmelCase   )

													return {
													    "score": output.score,
													    "char_order": output.char_order,
													    "word_order": output.word_order,
													    "beta": output.beta,
													}





