"""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    :							Union[str, Any]													=						pytest.mark.integration


__lowerCAmelCase    :							List[str]													=						{"comet"}
__lowerCAmelCase    :							List[Any]													=						importlib.util.find_spec("fairseq") is not None

__lowerCAmelCase    :							Dict													=						{"code_eval"}
__lowerCAmelCase    :							List[str]													=						os.name == "nt"

__lowerCAmelCase    :							Tuple													=						{"bertscore", "frugalscore", "perplexity"}
__lowerCAmelCase    :							List[Any]													=						importlib.util.find_spec("transformers") is not None







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		@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 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		@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 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		@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 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		lowerCAmelCase__        =   [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       a_      (     parameterized.TestCase			):
					UpperCamelCase_ :	Union[str, Any]			=    {}
					UpperCamelCase_ :	Dict			=    None




					@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""     )
					@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning"""     )
					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     Union[str, Any]     ):
							lowerCAmelCase__        =   """[...]"""
							lowerCAmelCase__        =   importlib.import_module(
							    datasets.load.metric_module_factory(os.path.join("""metrics"""  ,						snake_case__     )     ).module_path     )
							lowerCAmelCase__        =   datasets.load.import_main_class(metric_module.__name__  ,						dataset=snake_case__     )
							# check parameters
							lowerCAmelCase__        =   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(snake_case__  ,						metric_module.__name__     ):
									with self.use_local_metrics():
											try:
													lowerCAmelCase__        =   doctest.testmod(snake_case__  ,						verbose=snake_case__  ,						raise_on_error=snake_case__     )
											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 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     int     ):
							lowerCAmelCase__        =   """[...]"""
							lowerCAmelCase__        =   importlib.import_module(
							    datasets.load.metric_module_factory(os.path.join("""metrics"""  ,						snake_case__     )     ).module_path     )
							# run doctest
							with self.use_local_metrics():
									lowerCAmelCase__        =   doctest.testmod(snake_case__  ,						verbose=snake_case__  ,						raise_on_error=snake_case__     )
							self.assertEqual(results.failed  ,						0     )
							self.assertGreater(results.attempted  ,						1     )




					@contextmanager
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     int     ):
							if metric_name in self.INTENSIVE_CALLS_PATCHER:
									with self.INTENSIVE_CALLS_PATCHER[metric_name](snake_case__     ):
											yield
							else:
									yield




					@contextmanager
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							def load_local_metric(snake_case__						:     Dict  ,						*snake_case__						:     List[str]  ,						**snake_case__						:     int     ):
									return load_metric(os.path.join("""metrics"""  ,						snake_case__     )  ,						*snake_case__  ,						**snake_case__     )

							with patch("""datasets.load_metric"""     ) as mock_load_metric:
									lowerCAmelCase__        =   load_local_metric
									yield




					@classmethod
					def 						_SCREAMING_SNAKE_CASE						(	cls						:     Union[str, Any]  ,						snake_case__						:     int     ):
							def wrapper(snake_case__						:     str     ):
									lowerCAmelCase__        =   contextmanager(snake_case__     )
									lowerCAmelCase__        =   patcher
									return patcher

							return wrapper







@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""     )
def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		import tensorflow.compat.va as tf
		from bleurt.score import Predictor

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

		class       a_      (     __UpperCamelCase			):




							def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     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:
				lowerCAmelCase__        =   MockedPredictor()
				yield







@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""     )
def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		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:
				lowerCAmelCase__        =   bert_cos_score_idf
				yield







@LocalMetricTest.register_intensive_calls_patcher("""comet"""     )
def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		def load_from_checkpoint(lowerCamelCase__     ):

				class       a_      :




									def 						_SCREAMING_SNAKE_CASE						(	self						:     Any  ,						snake_case__						:     List[str]  ,						*snake_case__						:     Union[str, Any]  ,						**snake_case__						:     List[str]     ):
											assert len(snake_case__     ) == 2
											lowerCAmelCase__        =   [0.19, 0.92]
											return scores, sum(snake_case__     ) / len(snake_case__     )
				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:
				lowerCAmelCase__        =   None
				with patch("""comet.load_from_checkpoint"""     ) as mock_load_from_checkpoint:
						lowerCAmelCase__        =   load_from_checkpoint
						yield







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		lowerCAmelCase__        =   load_metric(os.path.join("""metrics"""			,  """seqeval"""     )     )
		lowerCAmelCase__        =   """ERROR"""
		lowerCAmelCase__        =   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__     )





"""simple docstring"""




from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
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 (
			    TFDeiTForImageClassification,
			    TFDeiTForImageClassificationWithTeacher,
			    TFDeiTForMaskedImageModeling,
			    TFDeiTModel,
			)
			from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import DeiTImageProcessor

class       a_      :




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

							# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
							lowerCAmelCase__        =   (image_size // patch_size) ** 2
							lowerCAmelCase__        =   num_patches + 2




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							lowerCAmelCase__        =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

							lowerCAmelCase__        =   None
							if self.use_labels:
									lowerCAmelCase__        =   ids_tensor([self.batch_size]  ,						self.type_sequence_label_size     )

							lowerCAmelCase__        =   self.get_config()

							return config, pixel_values, labels




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							return DeiTConfig(
							    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  ,						encoder_stride=self.encoder_stride  ,						)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     int  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     List[str]     ):
							lowerCAmelCase__        =   TFDeiTModel(config=snake_case__     )
							lowerCAmelCase__        =   model(snake_case__     )
							self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     Dict  ,						snake_case__						:     Dict  ,						snake_case__						:     Dict     ):
							lowerCAmelCase__        =   TFDeiTForMaskedImageModeling(config=snake_case__     )
							lowerCAmelCase__        =   model(snake_case__     )
							self.parent.assertEqual(
							    result.reconstruction.shape  ,						(self.batch_size, self.num_channels, self.image_size, self.image_size)     )

							# test greyscale images
							lowerCAmelCase__        =   1
							lowerCAmelCase__        =   TFDeiTForMaskedImageModeling(snake_case__     )

							lowerCAmelCase__        =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
							lowerCAmelCase__        =   model(snake_case__     )
							self.parent.assertEqual(result.reconstruction.shape  ,						(self.batch_size, 1, self.image_size, self.image_size)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Any  ,						snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   self.type_sequence_label_size
							lowerCAmelCase__        =   TFDeiTForImageClassification(snake_case__     )
							lowerCAmelCase__        =   model(snake_case__  ,						labels=snake_case__     )
							self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size)     )

							# test greyscale images
							lowerCAmelCase__        =   1
							lowerCAmelCase__        =   TFDeiTForImageClassification(snake_case__     )

							lowerCAmelCase__        =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
							lowerCAmelCase__        =   model(snake_case__  ,						labels=snake_case__     )
							self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   self.prepare_config_and_inputs()
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   config_and_inputs
							lowerCAmelCase__        =   {"""pixel_values""": pixel_values}
							return config, inputs_dict

@require_tf
class       a_      (     __UpperCamelCase ,		__UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	Optional[Any]			=    (
					    (
					        TFDeiTModel,
					        TFDeiTForImageClassification,
					        TFDeiTForImageClassificationWithTeacher,
					        TFDeiTForMaskedImageModeling,
					    )
					    if is_tf_available()
					    else ()
					)
					UpperCamelCase_ :	Any			=    (
					    {
					        "feature-extraction": TFDeiTModel,
					        "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
					    }
					    if is_tf_available()
					    else {}
					)

					UpperCamelCase_ :	Optional[Any]			=    False
					UpperCamelCase_ :	Optional[Any]			=    False
					UpperCamelCase_ :	Optional[int]			=    False
					UpperCamelCase_ :	int			=    False




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   TFDeiTModelTester(self     )
							lowerCAmelCase__        =   ConfigTester(self  ,						config_class=snake_case__  ,						has_text_modality=snake_case__  ,						hidden_size=37     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							self.config_tester.run_common_tests()




					@unittest.skip(reason="""DeiT does not use inputs_embeds"""     )
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							pass




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowerCAmelCase__        =   model_class(snake_case__     )
									self.assertIsInstance(model.get_input_embeddings()  ,						(tf.keras.layers.Layer)     )
									lowerCAmelCase__        =   model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(snake_case__  ,						tf.keras.layers.Dense     )     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs_for_common()

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

									lowerCAmelCase__        =   ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     int  ,						snake_case__						:     Union[str, Any]=False     ):
							lowerCAmelCase__        =   super()._prepare_for_class(snake_case__  ,						snake_case__  ,						return_labels=snake_case__     )

							if return_labels:
									if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call     ).parameters:
											del inputs_dict["labels"]

							return inputs_dict




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCAmelCase__        =   TFDeiTModel.from_pretrained(snake_case__     )
									self.assertIsNotNone(snake_case__     )







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		lowerCAmelCase__        =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
		return image






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




					@cached_property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							return (
							    DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""     )
							    if is_vision_available()
							    else None
							)




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""     )

							lowerCAmelCase__        =   self.default_image_processor
							lowerCAmelCase__        =   prepare_img()
							lowerCAmelCase__        =   image_processor(images=snake_case__  ,						return_tensors="""tf"""     )

							# forward pass
							lowerCAmelCase__        =   model(**snake_case__     )

							# verify the logits
							lowerCAmelCase__        =   tf.TensorShape((1, 1000)     )
							self.assertEqual(outputs.logits.shape  ,						snake_case__     )

							lowerCAmelCase__        =   tf.constant([-1.0266, 0.1912, -1.2861]     )

							self.assertTrue(np.allclose(outputs.logits[0, :3]  ,						snake_case__  ,						atol=1E-4     )     )




