
"""simple docstring"""




import coval  # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator

import datasets


a		:       Tuple     						=       datasets.logging.get_logger(__name__)


a		:       str     						=       '\\n@InProceedings{moosavi2019minimum,\n    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},\n    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},\n    year = {2019},\n    booktitle = {Proceedings of the 57th Annual Meeting of\n        the Association for Computational Linguistics (Volume 1: Long Papers)},\n    publisher = {Association for Computational Linguistics},\n    address = {Florence, Italy},\n}\n\n@inproceedings{10.3115/1072399.1072405,\nauthor = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},\ntitle = {A Model-Theoretic Coreference Scoring Scheme},\nyear = {1995},\nisbn = {1558604022},\npublisher = {Association for Computational Linguistics},\naddress = {USA},\nurl = {https://doi.org/10.3115/1072399.1072405},\ndoi = {10.3115/1072399.1072405},\nbooktitle = {Proceedings of the 6th Conference on Message Understanding},\npages = {45–52},\nnumpages = {8},\nlocation = {Columbia, Maryland},\nseries = {MUC6 ’95}\n}\n\n@INPROCEEDINGS{Bagga98algorithmsfor,\n    author = {Amit Bagga and Breck Baldwin},\n    title = {Algorithms for Scoring Coreference Chains},\n    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},\n    year = {1998},\n    pages = {563--566}\n}\n\n@INPROCEEDINGS{Luo05oncoreference,\n    author = {Xiaoqiang Luo},\n    title = {On coreference resolution performance metrics},\n    booktitle = {In Proc. of HLT/EMNLP},\n    year = {2005},\n    pages = {25--32},\n    publisher = {URL}\n}\n\n@inproceedings{moosavi-strube-2016-coreference,\n    title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",\n    author = "Moosavi, Nafise Sadat  and\n      Strube, Michael",\n    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",\n    month = aug,\n    year = "2016",\n    address = "Berlin, Germany",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/P16-1060",\n    doi = "10.18653/v1/P16-1060",\n    pages = "632--642",\n}\n\n'

a		:       str     						=       '\\nCoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which\nimplements of the common evaluation metrics including MUC [Vilain et al, 1995],\nB-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],\nLEA [Moosavi and Strube, 2016] and the averaged CoNLL score\n(the average of the F1 values of MUC, B-cubed and CEAFe)\n[Denis and Baldridge, 2009a; Pradhan et al., 2011].\n\nThis wrapper of CoVal currently only work with CoNLL line format:\nThe CoNLL format has one word per line with all the annotation for this word in column separated by spaces:\nColumn	Type	Description\n1	Document ID	This is a variation on the document filename\n2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.\n3	Word number\n4	Word itself	This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.\n5	Part-of-Speech\n6	Parse bit	This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column.\n7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-"\n8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.\n9	Word sense	This is the word sense of the word in Column 3.\n10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.\n11	Named Entities	These columns identifies the spans representing various named entities.\n12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.\nN	Coreference	Coreference chain information encoded in a parenthesis structure.\nMore informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html\n\nDetails on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md\n\nCoVal code was written by @ns-moosavi.\nSome parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py\nThe test suite is taken from https://github.com/conll/reference-coreference-scorers/\nMention evaluation and the test suite are added by @andreasvc.\nParsing CoNLL files is developed by Leo Born.\n'

a		:       Dict     						=       '\nCalculates coreference evaluation metrics.\nArgs:\n    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.\n        Each prediction is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.\n        Each reference is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    keep_singletons: After extracting all mentions of key or system files,\n        mentions whose corresponding coreference chain is of size one,\n        are considered as singletons. The default evaluation mode will include\n        singletons in evaluations if they are included in the key or the system files.\n        By setting \'keep_singletons=False\', all singletons in the key and system files\n        will be excluded from the evaluation.\n    NP_only: Most of the recent coreference resolvers only resolve NP mentions and\n        leave out the resolution of VPs. By setting the \'NP_only\' option, the scorer will only evaluate the resolution of NPs.\n    min_span: By setting \'min_span\', the scorer reports the results based on automatically detected minimum spans.\n        Minimum spans are determined using the MINA algorithm.\n\nReturns:\n    \'mentions\': mentions\n    \'muc\': MUC metric [Vilain et al, 1995]\n    \'bcub\': B-cubed [Bagga and Baldwin, 1998]\n    \'ceafe\': CEAFe [Luo et al., 2005]\n    \'lea\': LEA [Moosavi and Strube, 2016]\n    \'conll_score\': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)\n\nExamples:\n\n    >>> coval = datasets.load_metric(\'coval\')\n    >>> words = [\'bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -\',\n    ... \'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)\',\n    ... \'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)\',\n    ... \'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -\',\n    ... \'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -\',\n    ... \'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -\']\n    >>> references = [words]\n    >>> predictions = [words]\n    >>> results = coval.compute(predictions=predictions, references=references)\n    >>> print(results) # doctest:+ELLIPSIS\n    {\'mentions/recall\': 1.0,[...] \'conll_score\': 100.0}\n'



def     __magic_name__		(					UpperCamelCase     :		List[Any]							,		UpperCamelCase     :		List[Any]							,		UpperCamelCase     :		Tuple=False							,		UpperCamelCase     :		Union[str, Any]=False							,		UpperCamelCase     :		Dict=True							,		UpperCamelCase     :		List[str]=False							,		UpperCamelCase     :		Dict="dummy_doc"     )     ->						List[str]:
    a__		=			{doc: key_lines}
    a__		=			{doc: sys_lines}

    a__		=			{}

    a__		=			0
    a__		=			0
    a__		=			0
    a__		=			0
    a__		=			0
    a__		=			0

    a__	,     a__		=			reader.get_doc_mentions(UpperCamelCase							,		key_doc_lines[doc]							,		UpperCamelCase     )
    key_singletons_num += singletons_num

    if NP_only or min_span:
        a__		=			reader.set_annotated_parse_trees(UpperCamelCase							,		key_doc_lines[doc]							,		UpperCamelCase							,		UpperCamelCase     )

    a__	,     a__		=			reader.get_doc_mentions(UpperCamelCase							,		sys_doc_lines[doc]							,		UpperCamelCase     )
    sys_singletons_num += singletons_num

    if NP_only or min_span:
        a__		=			reader.set_annotated_parse_trees(UpperCamelCase							,		key_doc_lines[doc]							,		UpperCamelCase							,		UpperCamelCase     )

    if remove_nested:
        a__	,     a__		=			reader.remove_nested_coref_mentions(UpperCamelCase							,		UpperCamelCase     )
        key_nested_coref_num += nested_mentions
        key_removed_nested_clusters += removed_clusters

        a__	,     a__		=			reader.remove_nested_coref_mentions(UpperCamelCase							,		UpperCamelCase     )
        sys_nested_coref_num += nested_mentions
        sys_removed_nested_clusters += removed_clusters

    a__		=			reader.get_mention_assignments(UpperCamelCase							,		UpperCamelCase     )
    a__		=			reader.get_mention_assignments(UpperCamelCase							,		UpperCamelCase     )

    a__		=			(key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

    if remove_nested:
        logger.info(
            'Number of removed nested coreferring mentions in the key '
            f'annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}'     )
        logger.info(
            'Number of resulting singleton clusters in the key '
            f'annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}'     )

    if not keep_singletons:
        logger.info(
            f'{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system '
            'files, respectively'     )

    return doc_coref_infos



def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		Dict							,		UpperCamelCase     :		Optional[Any]							,		UpperCamelCase     :		Optional[Any]							,		UpperCamelCase     :		List[str]							,		UpperCamelCase     :		List[str]							,		UpperCamelCase     :		Optional[int]     )     ->						List[Any]:
    a__		=			get_coref_infos(UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase     )

    a__		=			{}
    a__		=			0
    a__		=			0

    for name, metric in metrics:
        a__	,     a__	,     a__		=			evaluator.evaluate_documents(UpperCamelCase							,		UpperCamelCase							,		beta=1     )
        if name in ["muc", "bcub", "ceafe"]:
            conll += fa
            conll_subparts_num += 1
        output_scores.update({f'{name}/recall': recall, f'{name}/precision': precision, f'{name}/f1': fa}     )

        logger.info(
            name.ljust(10     )							,		f'Recall: {recall * 100:.2f}'							,		f' Precision: {precision * 100:.2f}'							,		f' F1: {fa * 100:.2f}'							,		)

    if conll_subparts_num == 3:
        a__		=			(conll / 3) * 100
        logger.info(f'CoNLL score: {conll:.2f}'     )
        output_scores.update({'conll_score': conll}     )

    return output_scores



def     __magic_name__		(					UpperCamelCase     :		str     )     ->						Any:
    a__		=			False
    for line in key_lines:
        if not line.startswith('#'     ):
            if len(line.split()     ) > 6:
                a__		=			line.split()[5]
                if not parse_col == "-":
                    a__		=			True
                    break
                else:
                    break
    return has_gold_parse







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



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







         """simple docstring"""



         return datasets.MetricInfo(
             description=_DESCRIPTION     ,				citation=_CITATION     ,				inputs_description=_KWARGS_DESCRIPTION     ,				features=datasets.Features(
                 {
                     'predictions': datasets.Sequence(datasets.Value('string'   )   ),
                     'references': datasets.Sequence(datasets.Value('string'   )   ),
                 }   )     ,				codebase_urls=['https://github.com/ns-moosavi/coval']     ,				reference_urls=[
                 'https://github.com/ns-moosavi/coval',
                 'https://www.aclweb.org/anthology/P16-1060',
                 'http://www.conll.cemantix.org/2012/data.html',
             ]     ,				)




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=True     ,				__SCREAMING_SNAKE_CASE=False     ,				__SCREAMING_SNAKE_CASE=False     ,				__SCREAMING_SNAKE_CASE=False   )		->     Optional[Any]:







         """simple docstring"""



         a__		=			[
             ('mentions', evaluator.mentions),
             ('muc', evaluator.muc),
             ('bcub', evaluator.b_cubed),
             ('ceafe', evaluator.ceafe),
             ('lea', evaluator.lea),
         ]

         if min_span:
             a__		=			util.check_gold_parse_annotation(__SCREAMING_SNAKE_CASE   )
             if not has_gold_parse:
                 raise NotImplementedError('References should have gold parse annotation to use \'min_span\'.'   )
                 # util.parse_key_file(key_file)
                 # key_file = key_file + ".parsed"

         a__		=			evaluate(
             key_lines=__SCREAMING_SNAKE_CASE     ,				sys_lines=__SCREAMING_SNAKE_CASE     ,				metrics=__SCREAMING_SNAKE_CASE     ,				NP_only=__SCREAMING_SNAKE_CASE     ,				remove_nested=__SCREAMING_SNAKE_CASE     ,				keep_singletons=__SCREAMING_SNAKE_CASE     ,				min_span=__SCREAMING_SNAKE_CASE     ,				)

         return score




"""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     lowercase:



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=1_3     ,				__SCREAMING_SNAKE_CASE=[3_0, 3_0]     ,				__SCREAMING_SNAKE_CASE=2     ,				__SCREAMING_SNAKE_CASE=3     ,				__SCREAMING_SNAKE_CASE=True     ,				__SCREAMING_SNAKE_CASE=True     ,				__SCREAMING_SNAKE_CASE=3_2     ,				__SCREAMING_SNAKE_CASE=5     ,				__SCREAMING_SNAKE_CASE=4     ,				__SCREAMING_SNAKE_CASE=3_7     ,				__SCREAMING_SNAKE_CASE="gelu"     ,				__SCREAMING_SNAKE_CASE=0.1     ,				__SCREAMING_SNAKE_CASE=0.1     ,				__SCREAMING_SNAKE_CASE=1_0     ,				__SCREAMING_SNAKE_CASE=0.02     ,				__SCREAMING_SNAKE_CASE=3     ,				__SCREAMING_SNAKE_CASE=None     ,				__SCREAMING_SNAKE_CASE=8     ,				__SCREAMING_SNAKE_CASE=1_0     ,				)		->     str:







         """simple docstring"""



         a__		=			parent
         a__		=			batch_size
         a__		=			image_size
         a__		=			patch_size
         a__		=			num_channels
         a__		=			is_training
         a__		=			use_labels
         a__		=			hidden_size
         a__		=			num_hidden_layers
         a__		=			num_attention_heads
         a__		=			intermediate_size
         a__		=			hidden_act
         a__		=			hidden_dropout_prob
         a__		=			attention_probs_dropout_prob
         a__		=			type_sequence_label_size
         a__		=			initializer_range
         a__		=			num_labels
         a__		=			scope
         a__		=			n_targets
         a__		=			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__		=			(image_size[1] // patch_size) * (image_size[0] // patch_size)
         a__		=			num_patches + 1 + self.num_detection_tokens



     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         a__		=			floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]   )

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

         a__		=			self.get_config()

         return config, pixel_values, labels



     def 			lowercase__  (							self   )		->     Tuple:







         """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 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



         a__		=			YolosModel(config=__SCREAMING_SNAKE_CASE   )
         model.to(__SCREAMING_SNAKE_CASE   )
         model.eval()
         a__		=			model(__SCREAMING_SNAKE_CASE   )
         self.parent.assertEqual(
             result.last_hidden_state.shape     ,				(self.batch_size, self.expected_seq_len, self.hidden_size)   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     Any:







         """simple docstring"""



         a__		=			YolosForObjectDetection(__SCREAMING_SNAKE_CASE   )
         model.to(__SCREAMING_SNAKE_CASE   )
         model.eval()

         a__		=			model(pixel_values=__SCREAMING_SNAKE_CASE   )
         a__		=			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__		=			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 			lowercase__  (							self   )		->     int:







         """simple docstring"""



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







@require_torch
class     lowercase(_lowercase    ,						_lowercase    ,						unittest.TestCase       ):
     __snake_case:	List[str]						=     (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
     __snake_case:	Tuple						=     (
         {'feature-extraction': YolosModel, 'object-detection': YolosForObjectDetection} if is_torch_available() else {}
     )

     __snake_case:	Any						=     False
     __snake_case:	List[Any]						=     False
     __snake_case:	Optional[Any]						=     False
     __snake_case:	int						=     False



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=False   )		->     Optional[int]:







         """simple docstring"""



         a__		=			super()._prepare_for_class(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				return_labels=__SCREAMING_SNAKE_CASE   )

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

         return inputs_dict



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







         """simple docstring"""



         a__		=			YolosModelTester(self   )
         a__		=			ConfigTester(self     ,				config_class=__SCREAMING_SNAKE_CASE     ,				has_text_modality=__SCREAMING_SNAKE_CASE     ,				hidden_size=3_7   )



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         self.config_tester.run_common_tests()



     def 			lowercase__  (							self   )		->     int:







         """simple docstring"""



         pass



     def 			lowercase__  (							self   )		->     str:







         """simple docstring"""



         a__	,     a__		=			self.model_tester.prepare_config_and_inputs_for_common()

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



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







         """simple docstring"""



         a__	,     a__		=			self.model_tester.prepare_config_and_inputs_for_common()

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

             a__		=			['pixel_values']
             self.assertListEqual(arg_names[:1]     ,				__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__		=			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         a__	,     a__		=			self.model_tester.prepare_config_and_inputs_for_common()
         a__		=			True

         # in YOLOS, the seq_len is different
         a__		=			self.model_tester.expected_seq_len
         for model_class in self.all_model_classes:
             a__		=			True
             a__		=			False
             a__		=			True
             a__		=			model_class(__SCREAMING_SNAKE_CASE   )
             model.to(__SCREAMING_SNAKE_CASE   )
             model.eval()
             with torch.no_grad():
                 a__		=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )   )
             a__		=			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__		=			True
             a__		=			model_class(__SCREAMING_SNAKE_CASE   )
             model.to(__SCREAMING_SNAKE_CASE   )
             model.eval()
             with torch.no_grad():
                 a__		=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )   )
             a__		=			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__		=			len(__SCREAMING_SNAKE_CASE   )

             # Check attention is always last and order is fine
             a__		=			True
             a__		=			True
             a__		=			model_class(__SCREAMING_SNAKE_CASE   )
             model.to(__SCREAMING_SNAKE_CASE   )
             model.eval()
             with torch.no_grad():
                 a__		=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )   )

             a__		=			1
             self.assertEqual(out_len + added_hidden_states     ,				len(__SCREAMING_SNAKE_CASE   )   )

             a__		=			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 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         def check_hidden_states_output(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   ):
             a__		=			model_class(__SCREAMING_SNAKE_CASE   )
             model.to(__SCREAMING_SNAKE_CASE   )
             model.eval()

             with torch.no_grad():
                 a__		=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )   )

             a__		=			outputs.hidden_states

             a__		=			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__		=			self.model_tester.expected_seq_len

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

         a__	,     a__		=			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             a__		=			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__		=			True

             check_hidden_states_output(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         a__		=			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_object_detection(*__SCREAMING_SNAKE_CASE   )




     @slow
     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             a__		=			YolosModel.from_pretrained(__SCREAMING_SNAKE_CASE   )
             self.assertIsNotNone(__SCREAMING_SNAKE_CASE   )




def     __magic_name__		(					)     ->						Union[str, Any]:
    a__		=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
    return image







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



     @cached_property
     def 			lowercase__  (							self   )		->     List[Any]:







         """simple docstring"""



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




     @slow
     def 			lowercase__  (							self   )		->     int:







         """simple docstring"""



         a__		=			YolosForObjectDetection.from_pretrained('hustvl/yolos-small'   ).to(__SCREAMING_SNAKE_CASE   )

         a__		=			self.default_image_processor
         a__		=			prepare_img()
         a__		=			image_processor(images=__SCREAMING_SNAKE_CASE     ,				return_tensors='pt'   ).to(__SCREAMING_SNAKE_CASE   )

         # forward pass
         with torch.no_grad():
             a__		=			model(inputs.pixel_values   )

         # verify outputs
         a__		=			torch.Size((1, 1_0_0, 9_2)   )
         self.assertEqual(outputs.logits.shape     ,				__SCREAMING_SNAKE_CASE   )

         a__		=			torch.tensor(
             [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]     ,				device=__SCREAMING_SNAKE_CASE     ,				)
         a__		=			torch.tensor(
             [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]     ,				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__		=			image_processor.post_process_object_detection(
             __SCREAMING_SNAKE_CASE     ,				threshold=0.3     ,				target_sizes=[image.size[::-1]]   )[0]
         a__		=			torch.tensor([0.99_94, 0.97_90, 0.99_64, 0.99_72, 0.98_61]   ).to(__SCREAMING_SNAKE_CASE   )
         a__		=			[7_5, 7_5, 1_7, 6_3, 1_7]
         a__		=			torch.tensor([3_35.06_09, 79.38_48, 3_75.42_16, 1_87.24_95]   ).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   )   )


