



import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


__snake_case     :List[str]			       ='\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",\n    author = "Lin, Chin-Yew  and\n      Och, Franz Josef",\n    booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",\n    month = "aug 23{--}aug 27",\n    year = "2004",\n    address = "Geneva, Switzerland",\n    publisher = "COLING",\n    url = "https://www.aclweb.org/anthology/C04-1072",\n    pages = "501--507",\n}\n'

__snake_case     :Union[str, Any]			       ='\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,\nthe better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n'

__snake_case     :Optional[Any]			       ='\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    \'bleu\': bleu score,\n    \'precisions\': geometric mean of n-gram precisions,\n    \'brevity_penalty\': brevity penalty,\n    \'length_ratio\': ratio of lengths,\n    \'translation_length\': translation_length,\n    \'reference_length\': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     ["hello", "there", "general", "kenobi"],                             # tokenized prediction of the first sample\n    ...     ["foo", "bar", "foobar"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]],  # tokenized references for the first sample (2 references)\n    ...     [["foo", "bar", "foobar"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric("bleu")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results["bleu"])\n    1.0\n'



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,				_KWARGS_DESCRIPTION       )
class        lowerCAmelCase__					(  datasets.Metric       ):


				def __UpperCamelCase						(							self      :  List[str]       )		->     Any:
											return datasets.MetricInfo(
											    description=_DESCRIPTION						, citation=_CITATION						, inputs_description=_KWARGS_DESCRIPTION						, features=datasets.Features(
											        {
											            'predictions': datasets.Sequence(datasets.Value('string'						, id='token'       )						, id='sequence'       ),
											            'references': datasets.Sequence(
											                datasets.Sequence(datasets.Value('string'						, id='token'       )						, id='sequence'       )						, id='references'       ),
											        }       )						, codebase_urls=['https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py']						, reference_urls=[
											        'https://en.wikipedia.org/wiki/BLEU',
											        'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
											    ]						, )


				def __UpperCamelCase						(							self      :  List[str]						, __UpperCamelCase      :  Tuple						, __UpperCamelCase      :  str						, __UpperCamelCase      :  Any=4						, __UpperCamelCase      :  Tuple=False       )		->     str:
											A      						=    compute_bleu(
											    reference_corpus=__UpperCamelCase						, translation_corpus=__UpperCamelCase						, max_order=__UpperCamelCase						, smooth=__UpperCamelCase       )
											((A)						,   (A)						,   (A)						,   (A)						,   (A)						,   (A))      						=    score
											return {
											    "bleu": bleu,
											    "precisions": precisions,
											    "brevity_penalty": bp,
											    "length_ratio": ratio,
											    "translation_length": translation_length,
											    "reference_length": reference_length,
											}

'''simple docstring'''




import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import YolosForObjectDetection, YolosModel
      from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor





class 					A							:





     def __init__(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE=13		,     SCREAMING_SNAKE_CASE=[30, 30]		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=32		,     SCREAMING_SNAKE_CASE=5		,     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=10		,     SCREAMING_SNAKE_CASE=0.02		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=10		,     )					->							Dict:






         """simple docstring"""






         A			:  List[Any]   =		parent
         A			:  Optional[Any]   =		batch_size
         A			:  int   =		image_size
         A			:  Optional[Any]   =		patch_size
         A			:  Union[str, Any]   =		num_channels
         A			:  Optional[int]   =		is_training
         A			:  List[str]   =		use_labels
         A			:  Any   =		hidden_size
         A			:  Dict   =		num_hidden_layers
         A			:  List[str]   =		num_attention_heads
         A			:  List[Any]   =		intermediate_size
         A			:  List[Any]   =		hidden_act
         A			:  Union[str, Any]   =		hidden_dropout_prob
         A			:  Union[str, Any]   =		attention_probs_dropout_prob
         A			:  Optional[Any]   =		type_sequence_label_size
         A			:  Optional[Any]   =		initializer_range
         A			:  Optional[Any]   =		num_labels
         A			:  Union[str, Any]   =		scope
         A			:  Optional[Any]   =		n_targets
         A			:  str   =		num_detection_tokens
         # we set the expected sequence length (which is used in several tests)
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
         A			:  Dict   =		(image_size[1] // patch_size) * (image_size[0] // patch_size)
         A			:  str   =		num_patches + 1 + self.num_detection_tokens





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A			:  Tuple   =		floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]      )

         A			:  Dict   =		None
         if self.use_labels:
             # labels is a list of Dict (each Dict being the labels for a given example in the batch)
             A			:  int   =		[]
             for i in range(self.batch_size      ):
                 A			:  str   =		{}
                 A			:  Union[str, Any]   =		torch.randint(
                     high=self.num_labels		,     size=(self.n_targets,)		,     device=SCREAMING_SNAKE_CASE      )
                 A			:  Optional[Any]   =		torch.rand(self.n_targets		,     4		,     device=SCREAMING_SNAKE_CASE      )
                 labels.append(SCREAMING_SNAKE_CASE      )

         A			:  Any   =		self.get_config()

         return config, pixel_values, labels





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         return YolosConfig(
             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		,     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		,     num_detection_tokens=self.num_detection_tokens		,     num_labels=self.num_labels		,     )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         A			:  int   =		YolosModel(config=SCREAMING_SNAKE_CASE      )
         model.to(SCREAMING_SNAKE_CASE      )
         model.eval()
         A			:  Optional[int]   =		model(SCREAMING_SNAKE_CASE      )
         self.parent.assertEqual(
             result.last_hidden_state.shape		,     (self.batch_size, self.expected_seq_len, self.hidden_size)      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         A			:  int   =		YolosForObjectDetection(SCREAMING_SNAKE_CASE      )
         model.to(SCREAMING_SNAKE_CASE      )
         model.eval()

         A			:  Any   =		model(pixel_values=SCREAMING_SNAKE_CASE      )
         A			:  Tuple   =		model(SCREAMING_SNAKE_CASE      )

         self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_detection_tokens, self.num_labels + 1)      )
         self.parent.assertEqual(result.pred_boxes.shape		,     (self.batch_size, self.num_detection_tokens, 4)      )

         A			:  List[str]   =		model(pixel_values=SCREAMING_SNAKE_CASE		,     labels=SCREAMING_SNAKE_CASE      )

         self.parent.assertEqual(result.loss.shape		,     ()      )
         self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_detection_tokens, self.num_labels + 1)      )
         self.parent.assertEqual(result.pred_boxes.shape		,     (self.batch_size, self.num_detection_tokens, 4)      )





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A			:  Any   =		self.prepare_config_and_inputs()
         A,    A,    A			:  int   =		config_and_inputs
         A			:  List[Any]   =		{'''pixel_values''': pixel_values}
         return config, inputs_dict





@require_torch
class 					A							(				__snake_case       ,     __snake_case       ,     unittest.TestCase		):
     __magic_name__          =						(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
     __magic_name__          =						(
         {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
     )

     __magic_name__          =						False
     __magic_name__          =						False
     __magic_name__          =						False
     __magic_name__          =						False





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE=False      )					->							Union[str, Any]:






         """simple docstring"""






         A			:  Union[str, Any]   =		super()._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     return_labels=SCREAMING_SNAKE_CASE      )

         if return_labels:
             if model_class.__name__ == "YolosForObjectDetection":
                 A			:  str   =		[]
                 for i in range(self.model_tester.batch_size      ):
                     A			:  Optional[Any]   =		{}
                     A			:  int   =		torch.ones(
                         size=(self.model_tester.n_targets,)		,     device=SCREAMING_SNAKE_CASE		,     dtype=torch.long      )
                     A			:  Any   =		torch.ones(
                         self.model_tester.n_targets		,     4		,     device=SCREAMING_SNAKE_CASE		,     dtype=torch.float      )
                     labels.append(SCREAMING_SNAKE_CASE      )
                 A			:  Optional[int]   =		labels

         return inputs_dict





     def 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






         A			:  Tuple   =		YolosModelTester(self      )
         A			:  Any   =		ConfigTester(self		,     config_class=SCREAMING_SNAKE_CASE		,     has_text_modality=SCREAMING_SNAKE_CASE		,     hidden_size=37      )





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         self.config_tester.run_common_tests()





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         pass





     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         A,    A			:  Tuple   =		self.model_tester.prepare_config_and_inputs_for_common()

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





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






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

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

             A			:  Dict   =		['''pixel_values''']
             self.assertListEqual(arg_names[:1]		,     SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         A			:  Optional[int]   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






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

         # in YOLOS, the seq_len is different
         A			:  Optional[int]   =		self.model_tester.expected_seq_len
         for model_class in self.all_model_classes:
             A			:  Optional[int]   =		True
             A			:  int   =		False
             A			:  List[str]   =		True
             A			:  str   =		model_class(SCREAMING_SNAKE_CASE      )
             model.to(SCREAMING_SNAKE_CASE      )
             model.eval()
             with torch.no_grad():
                 A			:  Optional[Any]   =		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )      )
             A			:  int   =		outputs.attentions
             self.assertEqual(len(SCREAMING_SNAKE_CASE      )		,     self.model_tester.num_hidden_layers      )

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             A			:  int   =		True
             A			:  Optional[int]   =		model_class(SCREAMING_SNAKE_CASE      )
             model.to(SCREAMING_SNAKE_CASE      )
             model.eval()
             with torch.no_grad():
                 A			:  List[str]   =		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )      )
             A			:  List[Any]   =		outputs.attentions
             self.assertEqual(len(SCREAMING_SNAKE_CASE      )		,     self.model_tester.num_hidden_layers      )

             self.assertListEqual(
                 list(attentions[0].shape[-3:]      )		,     [self.model_tester.num_attention_heads, seq_len, seq_len]		,     )
             A			:  Any   =		len(SCREAMING_SNAKE_CASE      )

             # Check attention is always last and order is fine
             A			:  Optional[Any]   =		True
             A			:  Union[str, Any]   =		True
             A			:  Optional[int]   =		model_class(SCREAMING_SNAKE_CASE      )
             model.to(SCREAMING_SNAKE_CASE      )
             model.eval()
             with torch.no_grad():
                 A			:  Tuple   =		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )      )

             A			:  Optional[int]   =		1
             self.assertEqual(out_len + added_hidden_states		,     len(SCREAMING_SNAKE_CASE      )      )

             A			:  List[str]   =		outputs.attentions

             self.assertEqual(len(SCREAMING_SNAKE_CASE      )		,     self.model_tester.num_hidden_layers      )
             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]      )		,     [self.model_tester.num_attention_heads, seq_len, seq_len]		,     )





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         def check_hidden_states_output(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      ):
             A			:  List[Any]   =		model_class(SCREAMING_SNAKE_CASE      )
             model.to(SCREAMING_SNAKE_CASE      )
             model.eval()

             with torch.no_grad():
                 A			:  List[Any]   =		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )      )

             A			:  Dict   =		outputs.hidden_states

             A			:  List[Any]   =		getattr(
                 self.model_tester		,     '''expected_num_hidden_layers'''		,     self.model_tester.num_hidden_layers + 1      )
             self.assertEqual(len(SCREAMING_SNAKE_CASE      )		,     SCREAMING_SNAKE_CASE      )

             # YOLOS has a different seq_length
             A			:  int   =		self.model_tester.expected_seq_len

             self.assertListEqual(
                 list(hidden_states[0].shape[-2:]      )		,     [seq_length, self.model_tester.hidden_size]		,     )

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

         for model_class in self.all_model_classes:
             A			:  List[Any]   =		True
             check_hidden_states_output(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             A			:  Optional[Any]   =		True

             check_hidden_states_output(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






         A			:  Dict   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_object_detection(*SCREAMING_SNAKE_CASE      )





     @slow
     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             A			:  int   =		YolosModel.from_pretrained(SCREAMING_SNAKE_CASE      )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE      )







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    A			:  List[str]   =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
    return image







@require_torch
@require_vision
class 					A							(				unittest.TestCase		):





     @cached_property
     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''      ) if is_vision_available() else None





     @slow
     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  Union[str, Any]   =		YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''      ).to(SCREAMING_SNAKE_CASE      )

         A			:  str   =		self.default_image_processor
         A			:  Any   =		prepare_img()
         A			:  Any   =		image_processor(images=SCREAMING_SNAKE_CASE		,     return_tensors='''pt'''      ).to(SCREAMING_SNAKE_CASE      )

         # forward pass
         with torch.no_grad():
             A			:  Optional[Any]   =		model(inputs.pixel_values      )

         # verify outputs
         A			:  List[Any]   =		torch.Size((1, 100, 92)      )
         self.assertEqual(outputs.logits.shape		,     SCREAMING_SNAKE_CASE      )

         A			:  Optional[Any]   =		torch.tensor(
             [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]		,     device=SCREAMING_SNAKE_CASE		,     )
         A			:  str   =		torch.tensor(
             [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]		,     device=SCREAMING_SNAKE_CASE      )
         self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]		,     SCREAMING_SNAKE_CASE		,     atol=1e-4      )      )
         self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]		,     SCREAMING_SNAKE_CASE		,     atol=1e-4      )      )

         # verify postprocessing
         A			:  int   =		image_processor.post_process_object_detection(
             SCREAMING_SNAKE_CASE		,     threshold=0.3		,     target_sizes=[image.size[::-1]]      )[0]
         A			:  str   =		torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861]      ).to(SCREAMING_SNAKE_CASE      )
         A			:  Optional[Any]   =		[75, 75, 17, 63, 17]
         A			:  Union[str, Any]   =		torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495]      ).to(SCREAMING_SNAKE_CASE      )

         self.assertEqual(len(results['''scores''']      )		,     5      )
         self.assertTrue(torch.allclose(results['''scores''']		,     SCREAMING_SNAKE_CASE		,     atol=1e-4      )      )
         self.assertSequenceEqual(results['''labels'''].tolist()		,     SCREAMING_SNAKE_CASE      )
         self.assertTrue(torch.allclose(results['''boxes'''][0, :]		,     SCREAMING_SNAKE_CASE      )      )




