


import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


lowerCAmelCase__			:      Any								='\\n@inproceedings{snover-etal-2006-study,\n    title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n    author = "Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John",\n    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",\n    month = aug # " 8-12",\n    year = "2006",\n    address = "Cambridge, Massachusetts, USA",\n    publisher = "Association for Machine Translation in the Americas",\n    url = "https://aclanthology.org/2006.amta-papers.25",\n    pages = "223--231",\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__			:      List[Any]								='\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\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#ter for more information.\n'

lowerCAmelCase__			:      int								='\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)\n    \'num_edits\' (int): The cumulative number of edits\n    \'ref_length\' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?",\n        ...                     "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}\n\n    Example 2:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}\n\n    Example 3:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}\n\n    Example 4:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}\n\n    Example 5:\n        >>> predictions = ["does this sentence match??",\n        ...                    "what about this sentence?",\n        ...                    "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}\n'







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





							"""simple docstring"""







							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										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='http://www.cs.umd.edu/~snover/tercom/'			,						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#ter']			,						reference_urls=[
										        'https://github.com/jhclark/tercom',
										    ]			,						)






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					len(references[0]       )
										if any(len(lowerCAmelCase__       ) != references_per_prediction for refs in references       ):
													raise ValueError('Sacrebleu requires the same number of references for each prediction'       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					[[refs[i] for refs in references] for i in range(lowerCAmelCase__       )]

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					TER(
										    normalized=lowerCAmelCase__			,						no_punct=lowerCAmelCase__			,						asian_support=lowerCAmelCase__			,						case_sensitive=lowerCAmelCase__			,						)
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					sb_ter.corpus_score(lowerCAmelCase__			,						lowerCAmelCase__       )

										return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}









import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import SwiftFormerForImageClassification, SwiftFormerModel
				from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor







class 	__lowercase    :





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__=1_3			,						lowerCAmelCase__=3			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=2_2_4			,						lowerCAmelCase__=1_0_0_0			,						lowerCAmelCase__=[3, 3, 6, 4]			,						lowerCAmelCase__=[4_8, 5_6, 1_1_2, 2_2_0]			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					parent
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					batch_size
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					num_channels
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					is_training
										SCREAMING_SNAKE_CASE_		:     str					=					use_labels
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					hidden_dropout_prob
										SCREAMING_SNAKE_CASE_		:     Tuple					=					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_		:     int					=					num_labels
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					image_size
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					layer_depths
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					embed_dims






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					None
										if self.use_labels:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					ids_tensor([self.batch_size]			,						self.num_labels       )

										SCREAMING_SNAKE_CASE_		:     Dict					=					self.get_config()

										return config, pixel_values, labels






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return SwiftFormerConfig(
										    depths=self.layer_depths			,						embed_dims=self.embed_dims			,						mlp_ratio=4			,						downsamples=[True, True, True, True]			,						hidden_act='gelu'			,						num_labels=self.num_labels			,						down_patch_size=3			,						down_stride=2			,						down_pad=1			,						drop_rate=0.0			,						drop_path_rate=0.0			,						use_layer_scale=lowerCAmelCase__			,						layer_scale_init_value=1E-5			,						)






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					SwiftFormerModel(config=lowerCAmelCase__       )
										model.to(lowerCAmelCase__       )
										model.eval()
										SCREAMING_SNAKE_CASE_		:     int					=					model(lowerCAmelCase__       )
										self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.embed_dims[-1], 7, 7)       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.num_labels
										SCREAMING_SNAKE_CASE_		:     str					=					SwiftFormerForImageClassification(lowerCAmelCase__       )
										model.to(lowerCAmelCase__       )
										model.eval()
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					model(lowerCAmelCase__			,						labels=lowerCAmelCase__       )
										self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					SwiftFormerForImageClassification(lowerCAmelCase__       )
										model.to(lowerCAmelCase__       )
										model.eval()

										SCREAMING_SNAKE_CASE_		:     str					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
										SCREAMING_SNAKE_CASE_		:     str					=					model(lowerCAmelCase__       )
										self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										((SCREAMING_SNAKE_CASE_)		,      (SCREAMING_SNAKE_CASE_)		,      (SCREAMING_SNAKE_CASE_))		:     Optional[Any]					=					self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE_		:     str					=					{'pixel_values': pixel_values}
										return config, inputs_dict







@require_torch
class 	__lowercase    (__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
							_UpperCAmelCase      =						(
							    {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase      =						False
							_UpperCAmelCase      =						False
							_UpperCAmelCase      =						False
							_UpperCAmelCase      =						False
							_UpperCAmelCase      =						False






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					SwiftFormerModelTester(self       )
										SCREAMING_SNAKE_CASE_		:     str					=					ConfigTester(
										    self			,						config_class=lowerCAmelCase__			,						has_text_modality=lowerCAmelCase__			,						hidden_size=3_7			,						num_attention_heads=1_2			,						num_hidden_layers=1_2			,						)






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										self.config_tester.run_common_tests()






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





										"""simple docstring"""




										pass






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[str]					=					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:     str					=					model_class(lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     Dict					=					model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(lowerCAmelCase__			,						nn.Linear       )       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:     int					=					model_class(lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					inspect.signature(model.forward       )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													SCREAMING_SNAKE_CASE_		:     int					=					[*signature.parameters.keys()]

													SCREAMING_SNAKE_CASE_		:     Tuple					=					['pixel_values']
													self.assertListEqual(arg_names[:1]			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__       )






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					SwiftFormerModel.from_pretrained(lowerCAmelCase__       )
													self.assertIsNotNone(lowerCAmelCase__       )






							@unittest.skip(reason='SwiftFormer does not output attentions'       )
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										pass






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										def check_hidden_states_output(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):
													SCREAMING_SNAKE_CASE_		:     Tuple					=					model_class(lowerCAmelCase__       )
													model.to(lowerCAmelCase__       )
													model.eval()

													with torch.no_grad():
																SCREAMING_SNAKE_CASE_		:     str					=					model(**self._prepare_for_class(lowerCAmelCase__			,						lowerCAmelCase__       )       )

													SCREAMING_SNAKE_CASE_		:     List[str]					=					outputs.hidden_states

													SCREAMING_SNAKE_CASE_		:     Any					=					8
													self.assertEqual(len(lowerCAmelCase__       )			,						lowerCAmelCase__       )  # TODO

													# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
													# with the width and height being successively divided by 2, after every 2 blocks
													for i in range(len(lowerCAmelCase__       )       ):
																self.assertEqual(
																    hidden_states[i].shape			,						torch.Size(
																        [
																            self.model_tester.batch_size,
																            self.model_tester.embed_dims[i // 2],
																            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																        ]       )			,						)

										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:     str					=					True
													check_hidden_states_output(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													SCREAMING_SNAKE_CASE_		:     Dict					=					True

													check_hidden_states_output(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										def _config_zero_init(lowerCAmelCase__       ):
													SCREAMING_SNAKE_CASE_		:     List[str]					=					copy.deepcopy(lowerCAmelCase__       )
													for key in configs_no_init.__dict__.keys():
																if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																			setattr(lowerCAmelCase__			,						lowerCAmelCase__			,						1E-10       )
																if isinstance(getattr(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       )			,						lowerCAmelCase__       ):
																			SCREAMING_SNAKE_CASE_		:     int					=					_config_zero_init(getattr(lowerCAmelCase__			,						lowerCAmelCase__       )       )
																			setattr(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       )
													return configs_no_init

										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.model_tester.prepare_config_and_inputs_for_common()

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					_config_zero_init(lowerCAmelCase__       )
										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:     str					=					model_class(config=lowerCAmelCase__       )
													for name, param in model.named_parameters():
																if param.requires_grad:
																			self.assertIn(
																			    ((param.data.mean() * 1E9) / 1E9).round().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       ):





										"""simple docstring"""




										pass







def 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     Dict					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
			return image







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





							"""simple docstring"""







							@cached_property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'       ) if is_vision_available() else None






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'       ).to(lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					self.default_image_processor
										SCREAMING_SNAKE_CASE_		:     Any					=					prepare_img()
										SCREAMING_SNAKE_CASE_		:     List[str]					=					image_processor(images=lowerCAmelCase__			,						return_tensors='pt'       ).to(lowerCAmelCase__       )

										# forward pass
										with torch.no_grad():
													SCREAMING_SNAKE_CASE_		:     List[str]					=					model(**lowerCAmelCase__       )

										# verify the logits
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch.Size((1, 1_0_0_0)       )
										self.assertEqual(outputs.logits.shape			,						lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     str					=					torch.tensor([[-2.1_703E00, 2.1_107E00, -2.0_811E00]]       ).to(lowerCAmelCase__       )
										self.assertTrue(torch.allclose(outputs.logits[0, :3]			,						lowerCAmelCase__			,						atol=1E-4       )       )





