



'''simple docstring'''

from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor

class      __magic_name__						:







       def __init__(							self :	str     ,_UpperCAmelCase :	Optional[Any]     ,_UpperCAmelCase :	List[str]=13     ,_UpperCAmelCase :	str=30     ,_UpperCAmelCase :	Tuple=2     ,_UpperCAmelCase :	Dict=3     ,_UpperCAmelCase :	str=True     ,_UpperCAmelCase :	int=True     ,_UpperCAmelCase :	Optional[int]=32     ,_UpperCAmelCase :	Dict=2     ,_UpperCAmelCase :	Any=4     ,_UpperCAmelCase :	Tuple=37     ,_UpperCAmelCase :	List[Any]="gelu"     ,_UpperCAmelCase :	str=0.1     ,_UpperCAmelCase :	Tuple=0.1     ,_UpperCAmelCase :	Union[str, Any]=10     ,_UpperCAmelCase :	List[Any]=0.02     ,_UpperCAmelCase :	Dict=3     ,_UpperCAmelCase :	Dict=None     ,):
         _a					:				Dict				=							parent
         _a					:				List[Any]				=							batch_size
         _a					:				Dict				=							image_size
         _a					:				List[Any]				=							patch_size
         _a					:				List[str]				=							num_channels
         _a					:				Optional[int]				=							is_training
         _a					:				str				=							use_labels
         _a					:				str				=							hidden_size
         _a					:				Optional[Any]				=							num_hidden_layers
         _a					:				Dict				=							num_attention_heads
         _a					:				List[str]				=							intermediate_size
         _a					:				Any				=							hidden_act
         _a					:				List[str]				=							hidden_dropout_prob
         _a					:				Any				=							attention_probs_dropout_prob
         _a					:				List[Any]				=							type_sequence_label_size
         _a					:				List[Any]				=							initializer_range
         _a					:				Tuple				=							scope

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         _a					:				Optional[int]				=							(image_size // patch_size) ** 2
         _a					:				str				=							num_patches + 1







       def 							__lowercase      (							self :	Optional[int]		):
         _a					:				Dict				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

         _a					:				str				=							None
         if self.use_labels:
           _a					:				List[Any]				=							ids_tensor([self.batch_size]     ,self.type_sequence_label_size		)

         _a					:				Optional[int]				=							self.get_config()

         return config, pixel_values, labels







       def 							__lowercase      (							self :	Dict		):
         return ViTConfig(
             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=_snake_case     ,initializer_range=self.initializer_range     ,)







       def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	Union[str, Any]		):
         _a					:				List[Any]				=							TFViTModel(config=_snake_case		)
         _a					:				List[Any]				=							model(_snake_case     ,training=_snake_case		)
         self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)		)

         # Test with an image with different size than the one specified in config.
         _a					:				List[Any]				=							self.image_size // 2
         _a					:				str				=							pixel_values[:, :, :image_size, :image_size]
         _a					:				Tuple				=							model(_snake_case     ,interpolate_pos_encoding=_snake_case     ,training=_snake_case		)
         _a					:				Tuple				=							(image_size // self.patch_size) ** 2 + 1
         self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, seq_length, self.hidden_size)		)







       def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Optional[Any]     ,_UpperCAmelCase :	Optional[Any]		):
         _a					:				str				=							self.type_sequence_label_size
         _a					:				List[str]				=							TFViTForImageClassification(_snake_case		)
         _a					:				Union[str, Any]				=							model(_snake_case     ,labels=_snake_case     ,training=_snake_case		)
         self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)		)

         # Test with an image with different size than the one specified in config.
         _a					:				int				=							self.image_size // 2
         _a					:				List[str]				=							pixel_values[:, :, :image_size, :image_size]
         _a					:				int				=							model(_snake_case     ,interpolate_pos_encoding=_snake_case     ,training=_snake_case		)
         self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)		)

         # test greyscale images
         _a					:				Optional[int]				=							1
         _a					:				Tuple				=							TFViTForImageClassification(_snake_case		)
         _a					:				int				=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
         _a					:				str				=							model(_snake_case		)
         self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)		)







       def 							__lowercase      (							self :	Optional[int]		):
         _a					:				List[str]				=							self.prepare_config_and_inputs()
         _a	, _a	, _a					:				Union[str, Any]				=							config_and_inputs
         _a					:				Optional[int]				=							{'pixel_values': pixel_values}
         return config, inputs_dict

@require_tf
class      __magic_name__						(   _UpperCamelCase      ,						_UpperCamelCase      ,						unittest.TestCase       ):
       lowerCAmelCase	:   str           = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
       lowerCAmelCase	:   str           = (
           {'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
           if is_tf_available()
           else {}
       )

       lowerCAmelCase	:   str           = False
       lowerCAmelCase	:   List[str]           = False
       lowerCAmelCase	:   Optional[int]           = False







       def 							__lowercase      (							self :	Optional[Any]		):
         _a					:				Any				=							TFViTModelTester(self		)
         _a					:				Any				=							ConfigTester(self     ,config_class=_snake_case     ,has_text_modality=_snake_case     ,hidden_size=37		)







       def 							__lowercase      (							self :	str		):
         self.config_tester.run_common_tests()







       @unittest.skip(reason='ViT does not use inputs_embeds'		)
       def 							__lowercase      (							self :	Optional[int]		):
         pass







       @unittest.skip(reason='ViT does not use inputs_embeds'		)
       def 							__lowercase      (							self :	Any		):
         pass







       def 							__lowercase      (							self :	int		):
         _a	, _a					:				str				=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           _a					:				Optional[int]				=							model_class(_snake_case		)
           self.assertIsInstance(model.get_input_embeddings()     ,(tf.keras.layers.Layer)		)
           _a					:				Any				=							model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(_snake_case     ,tf.keras.layers.Layer		)		)







       def 							__lowercase      (							self :	Optional[Any]		):
         _a	, _a					:				int				=							self.model_tester.prepare_config_and_inputs_for_common()

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

           _a					:				Optional[Any]				=							['pixel_values']
           self.assertListEqual(arg_names[:1]     ,_snake_case		)







       def 							__lowercase      (							self :	Tuple		):
         _a					:				str				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_snake_case		)







       def 							__lowercase      (							self :	Any		):
         _a					:				int				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_snake_case		)







       @slow
       def 							__lowercase      (							self :	List[Any]		):
         _a					:				Optional[int]				=							TFViTModel.from_pretrained('google/vit-base-patch16-224'		)
         self.assertIsNotNone(_snake_case		)



def 					__lowerCamelCase					(				)      ->				Optional[int]:
  _a					:				List[Any]				=							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
  return image

@require_tf
@require_vision
class      __magic_name__						(   unittest.TestCase       ):







       @cached_property
       def 							__lowercase      (							self :	List[str]		):
         return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'		) if is_vision_available() else None







       @slow
       def 							__lowercase      (							self :	Tuple		):
         _a					:				List[str]				=							TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224'		)

         _a					:				Optional[Any]				=							self.default_image_processor
         _a					:				str				=							prepare_img()
         _a					:				int				=							image_processor(images=_snake_case     ,return_tensors='tf'		)

         # forward pass
         _a					:				List[str]				=							model(**_snake_case		)

         # verify the logits
         _a					:				Union[str, Any]				=							tf.TensorShape((1, 1000)		)
         self.assertEqual(outputs.logits.shape     ,_snake_case		)

         _a					:				Tuple				=							tf.constant([-0.27_44, 0.82_15, -0.08_36]		)

         tf.debugging.assert_near(outputs.logits[0, :3]     ,_snake_case     ,atol=1E-4		)









'''simple docstring'''

import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
__lowerCAmelCase						      =       pytest.mark.integration


__lowerCAmelCase						      =       {'''comet'''}
__lowerCAmelCase						      =       importlib.util.find_spec('''fairseq''') is not None

__lowerCAmelCase						      =       {'''code_eval'''}
__lowerCAmelCase						      =       os.name == '''nt'''

__lowerCAmelCase						      =       {'''bertscore''', '''frugalscore''', '''perplexity'''}
__lowerCAmelCase						      =       importlib.util.find_spec('''transformers''') is not None



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Any:
		@wraps(lowerCAmelCase_  )
		def wrapper(self						,      lowerCAmelCase_  ):
				if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
						self.skipTest('"test requires Fairseq"'  )
				else:
						test_case(self						,      lowerCAmelCase_  )

		return wrapper



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Union[str, Any]:
		@wraps(lowerCAmelCase_  )
		def wrapper(self						,      lowerCAmelCase_  ):
				if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
						self.skipTest('"test requires transformers"'  )
				else:
						test_case(self						,      lowerCAmelCase_  )

		return wrapper



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				int:
		@wraps(lowerCAmelCase_  )
		def wrapper(self						,      lowerCAmelCase_  ):
				if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
						self.skipTest('"test not supported on Windows"'  )
				else:
						test_case(self						,      lowerCAmelCase_  )

		return wrapper



def 					__lowerCamelCase					(				)      ->				Tuple:
		_a					:				Optional[int]				=							[metric_dir.split(os.sep  )[-2] for metric_dir in glob.glob('./metrics/*/'  )]
		return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished

@parameterized.named_parameters(get_local_metric_names()       )
@for_all_test_methods(
    _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase       )
@local
class      __magic_name__						(   parameterized.TestCase       ):
							lowerCAmelCase	:   List[str]           = {}
							lowerCAmelCase	:   Optional[int]           = None







							@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'		)
							@pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'		)
							def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	Optional[Any]		):
									_a					:				Tuple				=							'[...]'
									_a					:				Dict				=							importlib.import_module(
									    datasets.load.metric_module_factory(os.path.join('metrics'     ,_UpperCAmelCase		)		).module_path		)
									_a					:				Optional[int]				=							datasets.load.import_main_class(metric_module.__name__     ,dataset=_UpperCAmelCase		)
									# check parameters
									_a					:				Optional[int]				=							inspect.signature(metric._compute		).parameters
									self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()		)		)  # no **kwargs
									# run doctest
									with self.patch_intensive_calls(_UpperCAmelCase     ,metric_module.__name__		):
											with self.use_local_metrics():
													try:
															_a					:				Optional[int]				=							doctest.testmod(_UpperCAmelCase     ,verbose=_UpperCAmelCase     ,raise_on_error=_UpperCAmelCase		)
													except doctest.UnexpectedException as e:
															raise e.exc_info[1]  # raise the exception that doctest caught
									self.assertEqual(results.failed     ,0		)
									self.assertGreater(results.attempted     ,1		)







							@slow
							def 							__lowercase      (							self :	Tuple     ,_UpperCAmelCase :	Dict		):
									_a					:				Tuple				=							'[...]'
									_a					:				Optional[Any]				=							importlib.import_module(
									    datasets.load.metric_module_factory(os.path.join('metrics'     ,_UpperCAmelCase		)		).module_path		)
									# run doctest
									with self.use_local_metrics():
											_a					:				int				=							doctest.testmod(_UpperCAmelCase     ,verbose=_UpperCAmelCase     ,raise_on_error=_UpperCAmelCase		)
									self.assertEqual(results.failed     ,0		)
									self.assertGreater(results.attempted     ,1		)







							@contextmanager
							def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	List[str]		):
									if metric_name in self.INTENSIVE_CALLS_PATCHER:
											with self.INTENSIVE_CALLS_PATCHER[metric_name](_UpperCAmelCase		):
													yield
									else:
											yield







							@contextmanager
							def 							__lowercase      (							self :	Optional[int]		):
									def load_local_metric(_UpperCAmelCase :	Tuple     ,*_UpperCAmelCase :	Dict     ,**_UpperCAmelCase :	Tuple		):
											return load_metric(os.path.join('metrics'     ,_UpperCAmelCase		)     ,*_UpperCAmelCase     ,**_UpperCAmelCase		)

									with patch('datasets.load_metric'		) as mock_load_metric:
											_a					:				Any				=							load_local_metric
											yield







							@classmethod
							def 							__lowercase      (							cls :	str     ,_UpperCAmelCase :	List[str]		):
									def wrapper(_UpperCAmelCase :	int		):
											_a					:				Optional[Any]				=							contextmanager(_UpperCAmelCase		)
											_a					:				Optional[int]				=							patcher
											return patcher

									return wrapper



@LocalMetricTest.register_intensive_calls_patcher('bleurt'  )
def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				List[str]:
		import tensorflow.compat.va as tf
		from bleurt.score import Predictor

		tf.flags.DEFINE_string('sv'						,      ''						,      ''  )  # handle pytest cli flags

		class      __magic_name__						(   _UpperCamelCase       ):







									def 							__lowercase      (							self :	int     ,_UpperCAmelCase :	Union[str, Any]		):
											assert len(input_dict['input_ids']		) == 2
											return np.array([1.03, 1.04]		)

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
		with patch('bleurt.score._create_predictor'  ) as mock_create_predictor:
				_a					:				int				=							MockedPredictor()
				yield



@LocalMetricTest.register_intensive_calls_patcher('bertscore'  )
def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Union[str, Any]:
		import torch

		def bert_cos_score_idf(lowerCAmelCase_						,      lowerCAmelCase_						,      *lowerCAmelCase_						,      **lowerCAmelCase_  ):
				return torch.tensor([[1.0, 1.0, 1.0]] * len(lowerCAmelCase_  )  )

		# mock get_model which is supposed to do download a bert model
		# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
		with patch('bert_score.scorer.get_model'  ), patch(
		    'bert_score.scorer.bert_cos_score_idf'  ) as mock_bert_cos_score_idf:
				_a					:				Any				=							bert_cos_score_idf
				yield



@LocalMetricTest.register_intensive_calls_patcher('comet'  )
def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Dict:
		def load_from_checkpoint(lowerCAmelCase_  ):

				class      __magic_name__						:







											def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	Dict     ,*_UpperCAmelCase :	int     ,**_UpperCAmelCase :	str		):
													assert len(_UpperCAmelCase		) == 2
													_a					:				Dict				=							[0.19, 0.92]
													return scores, sum(_UpperCAmelCase		) / len(_UpperCAmelCase		)
				return Model()

		# mock load_from_checkpoint which is supposed to do download a bert model
		# mock load_from_checkpoint which is supposed to do download a bert model
		with patch('comet.download_model'  ) as mock_download_model:
				_a					:				Any				=							None
				with patch('comet.load_from_checkpoint'  ) as mock_load_from_checkpoint:
						_a					:				Optional[Any]				=							load_from_checkpoint
						yield



def 					__lowerCamelCase					(				)      ->				Tuple:
		_a					:				Dict				=							load_metric(os.path.join('metrics'						,      'seqeval'  )  )
		_a					:				Optional[int]				=							'ERROR'
		_a					:				Optional[Any]				=							f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
		with pytest.raises(lowerCAmelCase_						,      match=re.escape(lowerCAmelCase_  )  ):
				metric.compute(predictions=[]						,      references=[]						,      scheme=lowerCAmelCase_  )




