





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        _a:







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


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   parent
							_snake_case       :					List[str]				       =   batch_size
							_snake_case       :					Any				       =   num_channels
							_snake_case       :					Dict				       =   is_training
							_snake_case       :					Tuple				       =   use_labels
							_snake_case       :					str				       =   hidden_dropout_prob
							_snake_case       :					Tuple				       =   attention_probs_dropout_prob
							_snake_case       :					Optional[Any]				       =   num_labels
							_snake_case       :					List[str]				       =   image_size
							_snake_case       :					Optional[Any]				       =   layer_depths
							_snake_case       :					List[Any]				       =   embed_dims







		def        lowercase       (    self				)      ->      str:


							'''simple docstring'''

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

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

							_snake_case       :					int				       =   self.get_config()

							return config, pixel_values, labels







		def        lowercase       (    self				)      ->      int:


							'''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=__snake_case ,						layer_scale_init_value=1E-5 ,						)







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					List[str]				       =   SwiftFormerModel(config=__snake_case				)
							model.to(__snake_case				)
							model.eval()
							_snake_case       :					Dict				       =   model(__snake_case				)
							self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.embed_dims[-1], 7, 7)				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      Optional[Any]:


							'''simple docstring'''

							_snake_case       :					Tuple				       =   self.num_labels
							_snake_case       :					Dict				       =   SwiftFormerForImageClassification(__snake_case				)
							model.to(__snake_case				)
							model.eval()
							_snake_case       :					int				       =   model(__snake_case ,						labels=__snake_case				)
							self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels)				)

							_snake_case       :					Union[str, Any]				       =   SwiftFormerForImageClassification(__snake_case				)
							model.to(__snake_case				)
							model.eval()

							_snake_case       :					Optional[Any]				       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)
							_snake_case       :					Dict				       =   model(__snake_case				)
							self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels)				)







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


							'''simple docstring'''

							((_snake_case)       , (_snake_case)       , (_snake_case))       :					int				       =   self.prepare_config_and_inputs()
							_snake_case       :					List[Any]				       =   {"pixel_values": pixel_values}
							return config, inputs_dict






@require_torch
class        _a(    __A	,   __A	,   unittest.TestCase    ):
		lowerCamelCase__     :Optional[int]   				=		(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
		lowerCamelCase__     :Optional[Any]   				=		(
		    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
		    if is_torch_available()
		    else {}
		)

		lowerCamelCase__     :str   				=		False
		lowerCamelCase__     :Optional[int]   				=		False
		lowerCamelCase__     :Optional[Any]   				=		False
		lowerCamelCase__     :int   				=		False
		lowerCamelCase__     :Union[str, Any]   				=		False







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


							'''simple docstring'''

							_snake_case       :					Any				       =   SwiftFormerModelTester(self				)
							_snake_case       :					List[Any]				       =   ConfigTester(
							    self ,						config_class=__snake_case ,						has_text_modality=__snake_case ,						hidden_size=3_7 ,						num_attention_heads=1_2 ,						num_hidden_layers=1_2 ,						)







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


							'''simple docstring'''

							self.config_tester.run_common_tests()







		@unittest.skip(reason="SwiftFormer does not use inputs_embeds"				)
		def        lowercase       (    self				)      ->      Any:


							'''simple docstring'''

							pass







		def        lowercase       (    self				)      ->      Any:


							'''simple docstring'''

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

							for model_class in self.all_model_classes:
												_snake_case       :					Optional[Any]				       =   model_class(__snake_case				)
												_snake_case       :					Dict				       =   model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(__snake_case ,						nn.Linear				)				)







		def        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

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

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

												_snake_case       :					str				       =   ["pixel_values"]
												self.assertListEqual(arg_names[:1] ,						__snake_case				)







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


							'''simple docstring'''

							_snake_case       :					Tuple				       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__snake_case				)







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


							'''simple docstring'''

							_snake_case       :					Tuple				       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__snake_case				)







		@slow
		def        lowercase       (    self				)      ->      Optional[int]:


							'''simple docstring'''

							for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_snake_case       :					Any				       =   SwiftFormerModel.from_pretrained(__snake_case				)
												self.assertIsNotNone(__snake_case				)







		@unittest.skip(reason="SwiftFormer does not output attentions"				)
		def        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							pass







		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							def check_hidden_states_output(__snake_case ,						__snake_case ,						__snake_case				):
												_snake_case       :					Union[str, Any]				       =   model_class(__snake_case				)
												model.to(__snake_case				)
												model.eval()

												with torch.no_grad():
																	_snake_case       :					List[str]				       =   model(**self._prepare_for_class(__snake_case ,						__snake_case				)				)

												_snake_case       :					List[Any]				       =   outputs.hidden_states

												_snake_case       :					List[str]				       =   8
												self.assertEqual(len(__snake_case				) ,						__snake_case				)  # 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(__snake_case				)				):
																	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),
																	        ]				) ,						)

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

							for model_class in self.all_model_classes:
												_snake_case       :					Optional[Any]				       =   True
												check_hidden_states_output(__snake_case ,						__snake_case ,						__snake_case				)

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

												check_hidden_states_output(__snake_case ,						__snake_case ,						__snake_case				)







		def        lowercase       (    self				)      ->      str:


							'''simple docstring'''

							def _config_zero_init(__snake_case				):
												_snake_case       :					List[Any]				       =   copy.deepcopy(__snake_case				)
												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(__snake_case ,						__snake_case ,						1E-10				)
																	if isinstance(getattr(__snake_case ,						__snake_case ,						__snake_case				) ,						__snake_case				):
																						_snake_case       :					Union[str, Any]				       =   _config_zero_init(getattr(__snake_case ,						__snake_case				)				)
																						setattr(__snake_case ,						__snake_case ,						__snake_case				)
												return configs_no_init

							_snake_case       , _snake_case       :					Dict				       =   self.model_tester.prepare_config_and_inputs_for_common()

							_snake_case       :					Tuple				       =   _config_zero_init(__snake_case				)
							for model_class in self.all_model_classes:
												_snake_case       :					List[Any]				       =   model_class(config=__snake_case				)
												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        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							pass







def    A				(							):
					_snake_case       :					int				       =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
					return image






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







		@cached_property
		def        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

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







		@slow
		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					List[str]				       =   SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"				).to(__snake_case				)

							_snake_case       :					List[Any]				       =   self.default_image_processor
							_snake_case       :					Tuple				       =   prepare_img()
							_snake_case       :					int				       =   image_processor(images=__snake_case ,						return_tensors="pt"				).to(__snake_case				)

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

							# verify the logits
							_snake_case       :					Any				       =   torch.Size((1, 1_0_0_0)				)
							self.assertEqual(outputs.logits.shape ,						__snake_case				)

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






import datasets

from .evaluate import evaluate


__lowerCAmelCase :Any     =    '\\n@article{hendrycks2021cuad,\n      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n      journal={arXiv preprint arXiv:2103.06268},\n      year={2021}\n}\n'

__lowerCAmelCase :Union[str, Any]     =    '\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n'

__lowerCAmelCase :Optional[Any]     =    '\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair as given in the references (see below)\n        - \'prediction_text\': list of possible texts for the answer, as a list of strings\n        depending on a threshold on the confidence probability of each prediction.\n    references: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair (see above),\n        - \'answers\': a Dict in the CUAD dataset format\n            {\n                \'text\': list of possible texts for the answer, as a list of strings\n                \'answer_start\': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\n    \'aupr\': Area Under the Precision-Recall curve\n    \'prec_at_80_recall\': Precision at 80% recall\n    \'prec_at_90_recall\': Precision at 90% recall\nExamples:\n    >>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n    >>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n    >>> cuad_metric = datasets.load_metric("cuad")\n    >>> results = cuad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}\n'






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







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


							'''simple docstring'''

							return datasets.MetricInfo(
							    description=_DESCRIPTION ,						citation=_CITATION ,						inputs_description=_KWARGS_DESCRIPTION ,						features=datasets.Features(
							        {
							            "predictions": {
							                "id": datasets.Value("string"				),
							                "prediction_text": datasets.features.Sequence(datasets.Value("string"				)				),
							            },
							            "references": {
							                "id": datasets.Value("string"				),
							                "answers": datasets.features.Sequence(
							                    {
							                        "text": datasets.Value("string"				),
							                        "answer_start": datasets.Value("int32"				),
							                    }				),
							            },
							        }				) ,						codebase_urls=["https://www.atticusprojectai.org/cuad"] ,						reference_urls=["https://www.atticusprojectai.org/cuad"] ,						)







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


							'''simple docstring'''

							_snake_case       :					str				       =   {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
							_snake_case       :					Union[str, Any]				       =   [
							    {
							        "paragraphs": [
							            {
							                "qas": [
							                    {
							                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
							                        "id": ref["id"],
							                    }
							                    for ref in references
							                ]
							            }
							        ]
							    }
							]
							_snake_case       :					Union[str, Any]				       =   evaluate(dataset=__snake_case ,						predictions=__snake_case				)
							return score