




'''simple docstring'''




from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
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 TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
							from PIL import Image

							from transformers import ConvNextImageProcessor






class      UpperCamelCase_  :







						"""simple docstring"""


						def __init__(  self   :							Any				,			_lowerCamelCase   :							int				,			_lowerCamelCase   :							List[Any]=13				,			_lowerCamelCase   :							int=32				,			_lowerCamelCase   :							Tuple=3				,			_lowerCamelCase   :							int=4				,			_lowerCamelCase   :							List[str]=[10, 20, 30, 40]				,			_lowerCamelCase   :							Optional[Any]=[2, 2, 3, 2]				,			_lowerCamelCase   :							Any=True				,			_lowerCamelCase   :							List[Any]=True				,			_lowerCamelCase   :							Optional[Any]=37				,			_lowerCamelCase   :							Tuple="gelu"				,			_lowerCamelCase   :							List[str]=10				,			_lowerCamelCase   :							Any=0.02				,			_lowerCamelCase   :							List[str]=3				,			_lowerCamelCase   :							Any=None				,			):





											"""simple docstring"""







											A_			:  Tuple			=				parent
											A_			:  Union[str, Any]			=				batch_size
											A_			:  Dict			=				image_size
											A_			:  List[Any]			=				num_channels
											A_			:  int			=				num_stages
											A_			:  List[str]			=				hidden_sizes
											A_			:  List[str]			=				depths
											A_			:  Any			=				is_training
											A_			:  str			=				use_labels
											A_			:  Dict			=				intermediate_size
											A_			:  Optional[Any]			=				hidden_act
											A_			:  Optional[Any]			=				type_sequence_label_size
											A_			:  Union[str, Any]			=				initializer_range
											A_			:  Any			=				scope







						def    _a     (  self   :							Optional[Any]     ):





											"""simple docstring"""







											A_			:  Union[str, Any]			=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

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

											return config, pixel_values, labels







						def    _a     (  self   :							List[str]     ):





											"""simple docstring"""







											return ConvNextConfig(
											    num_channels=self.num_channels				,			hidden_sizes=self.hidden_sizes				,			depths=self.depths				,			num_stages=self.num_stages				,			hidden_act=self.hidden_act				,			is_decoder=_lowerCamelCase				,			initializer_range=self.initializer_range				,			)







						def    _a     (  self   :							Optional[int]				,			_lowerCamelCase   :							Any				,			_lowerCamelCase   :							int				,			_lowerCamelCase   :							Optional[Any]     ):





											"""simple docstring"""







											A_			:  Optional[int]			=				TFConvNextModel(config=_lowerCamelCase     )
											A_			:  Any			=				model(_lowerCamelCase				,			training=_lowerCamelCase     )
											# expected last hidden states: B, C, H // 32, W // 32
											self.parent.assertEqual(
											    result.last_hidden_state.shape				,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)				,			)







						def    _a     (  self   :							Tuple				,			_lowerCamelCase   :							Union[str, Any]				,			_lowerCamelCase   :							Union[str, Any]				,			_lowerCamelCase   :							Dict     ):





											"""simple docstring"""







											A_			:  Any			=				self.type_sequence_label_size
											A_			:  List[str]			=				TFConvNextForImageClassification(_lowerCamelCase     )
											A_			:  Optional[int]			=				model(_lowerCamelCase				,			labels=_lowerCamelCase				,			training=_lowerCamelCase     )
											self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.type_sequence_label_size)     )







						def    _a     (  self   :							int     ):





											"""simple docstring"""







											A_			:  List[Any]			=				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      UpperCamelCase_  (a__,   a__,   unittest.TestCase  ):







						"""simple docstring"""


						_lowerCAmelCase         =       (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
						_lowerCAmelCase         =       (
						    {'feature-extraction': TFConvNextModel, 'image-classification': TFConvNextForImageClassification}
						    if is_tf_available()
						    else {}
						)

						_lowerCAmelCase         =       False
						_lowerCAmelCase         =       False
						_lowerCAmelCase         =       False
						_lowerCAmelCase         =       False
						_lowerCAmelCase         =       False







						def    _a     (  self   :							Optional[int]     ):





											"""simple docstring"""







											A_			:  List[str]			=				TFConvNextModelTester(self     )
											A_			:  Union[str, Any]			=				ConfigTester(
											    self				,			config_class=_lowerCamelCase				,			has_text_modality=_lowerCamelCase				,			hidden_size=37				,			)







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





											"""simple docstring"""







											pass







						@unittest.skipIf(
						    not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''     )     ) == 0				,			reason='''TF does not support backprop for grouped convolutions on CPU.'''				,			)
						@slow
						def    _a     (  self   :							Union[str, Any]     ):





											"""simple docstring"""







											super().test_keras_fit()







						@unittest.skip(reason='''ConvNext does not support input and output embeddings'''     )
						def    _a     (  self   :							Union[str, Any]     ):





											"""simple docstring"""







											pass







						def    _a     (  self   :							int     ):





											"""simple docstring"""







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

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

																A_			:  Any			=				['''pixel_values''']
																self.assertListEqual(arg_names[:1]				,			_lowerCamelCase     )







						def    _a     (  self   :							Optional[int]     ):





											"""simple docstring"""







											A_			:  Optional[int]			=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_lowerCamelCase     )







						@unittest.skipIf(
						    not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''     )     ) == 0				,			reason='''TF does not support backprop for grouped convolutions on CPU.'''				,			)
						def    _a     (  self   :							Optional[int]     ):





											"""simple docstring"""







											super().test_dataset_conversion()







						def    _a     (  self   :							Optional[int]     ):





											"""simple docstring"""







											def check_hidden_states_output(_lowerCamelCase   :							Union[str, Any]				,			_lowerCamelCase   :							List[str]				,			_lowerCamelCase   :							List[str]     ):
																A_			:  Union[str, Any]			=				model_class(_lowerCamelCase     )

																A_			:  List[Any]			=				model(**self._prepare_for_class(_lowerCamelCase				,			_lowerCamelCase     )     )
																A_			:  List[Any]			=				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																A_			:  List[str]			=				self.model_tester.num_stages
																self.assertEqual(len(_lowerCamelCase     )				,			expected_num_stages + 1     )

																# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
																self.assertListEqual(
																    list(hidden_states[0].shape[-2:]     )				,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4]				,			)

											A_			,A_			:  str			=				self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																A_			:  Dict			=				True
																check_hidden_states_output(_lowerCamelCase				,			_lowerCamelCase				,			_lowerCamelCase     )

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

																check_hidden_states_output(_lowerCamelCase				,			_lowerCamelCase				,			_lowerCamelCase     )







						def    _a     (  self   :							Optional[int]     ):





											"""simple docstring"""







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

											def check_equivalence(_lowerCamelCase   :							Tuple				,			_lowerCamelCase   :							Any				,			_lowerCamelCase   :							Dict				,			_lowerCamelCase   :							Tuple={}     ):
																A_			:  Union[str, Any]			=				model(_lowerCamelCase				,			return_dict=_lowerCamelCase				,			**_lowerCamelCase     )
																A_			:  List[Any]			=				model(_lowerCamelCase				,			return_dict=_lowerCamelCase				,			**_lowerCamelCase     ).to_tuple()

																def recursive_check(_lowerCamelCase   :							Any				,			_lowerCamelCase   :							str     ):
																					if isinstance(_lowerCamelCase				,			(List, Tuple)     ):
																										for tuple_iterable_value, dict_iterable_value in zip(_lowerCamelCase				,			_lowerCamelCase     ):
																															recursive_check(_lowerCamelCase				,			_lowerCamelCase     )
																					elif tuple_object is None:
																										return
																					else:
																										self.assertTrue(
																										    all(tf.equal(_lowerCamelCase				,			_lowerCamelCase     )     )				,			msg=(
																										        '''Tuple and dict output are not equal. Difference:'''
																										        f' {tf.math.reduce_max(tf.abs(tuple_object - dict_object     )     )}'
																										    )				,			)

																					recursive_check(_lowerCamelCase				,			_lowerCamelCase     )

											for model_class in self.all_model_classes:
																A_			:  Tuple			=				model_class(_lowerCamelCase     )

																A_			:  List[str]			=				self._prepare_for_class(_lowerCamelCase				,			_lowerCamelCase     )
																A_			:  int			=				self._prepare_for_class(_lowerCamelCase				,			_lowerCamelCase     )
																check_equivalence(_lowerCamelCase				,			_lowerCamelCase				,			_lowerCamelCase     )

																A_			:  List[Any]			=				self._prepare_for_class(_lowerCamelCase				,			_lowerCamelCase				,			return_labels=_lowerCamelCase     )
																A_			:  str			=				self._prepare_for_class(_lowerCamelCase				,			_lowerCamelCase				,			return_labels=_lowerCamelCase     )
																check_equivalence(_lowerCamelCase				,			_lowerCamelCase				,			_lowerCamelCase     )

																A_			:  Union[str, Any]			=				self._prepare_for_class(_lowerCamelCase				,			_lowerCamelCase     )
																A_			:  List[Any]			=				self._prepare_for_class(_lowerCamelCase				,			_lowerCamelCase     )
																check_equivalence(_lowerCamelCase				,			_lowerCamelCase				,			_lowerCamelCase				,			{'''output_hidden_states''': True}     )

																A_			:  Any			=				self._prepare_for_class(_lowerCamelCase				,			_lowerCamelCase				,			return_labels=_lowerCamelCase     )
																A_			:  int			=				self._prepare_for_class(_lowerCamelCase				,			_lowerCamelCase				,			return_labels=_lowerCamelCase     )
																check_equivalence(_lowerCamelCase				,			_lowerCamelCase				,			_lowerCamelCase				,			{'''output_hidden_states''': True}     )







						def    _a     (  self   :							str     ):





											"""simple docstring"""







											A_			:  str			=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase     )







						@slow
						def    _a     (  self   :							Tuple     ):





											"""simple docstring"""







											A_			:  Optional[int]			=				TFConvNextModel.from_pretrained('''facebook/convnext-tiny-224'''     )
											self.assertIsNotNone(_lowerCamelCase     )






def     snake_case__     (			) -> Optional[int]:
					A_			:  str			=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
					return image






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







						"""simple docstring"""


						@cached_property
						def    _a     (  self   :							Optional[int]     ):





											"""simple docstring"""







											return ConvNextImageProcessor.from_pretrained('''facebook/convnext-tiny-224'''     ) if is_vision_available() else None







						@slow
						def    _a     (  self   :							str     ):





											"""simple docstring"""







											A_			:  Optional[int]			=				TFConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224'''     )

											A_			:  Union[str, Any]			=				self.default_image_processor
											A_			:  Tuple			=				prepare_img()
											A_			:  str			=				image_processor(images=_lowerCamelCase				,			return_tensors='''tf'''     )

											# forward pass
											A_			:  List[str]			=				model(**_lowerCamelCase     )

											# verify the logits
											A_			:  Optional[int]			=				tf.TensorShape((1, 1000)     )
											self.assertEqual(outputs.logits.shape				,			_lowerCamelCase     )

											A_			:  Tuple			=				tf.constant([-0.02_60, -0.47_39, 0.19_11]     )

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









'''simple docstring'''




import io
import json
import subprocess


snake_case__ 							=    [
    ["""en""", """ru"""],
    ["""ru""", """en"""],
    ["""en""", """de"""],
    ["""de""", """en"""],
]

snake_case__ 							=    8




def     snake_case__     (			lowerCamelCase__					: Union[str, Any]      ,		lowerCamelCase__					: Optional[int]      ) -> List[Any]:
					A_			:  Tuple			=				{}
					for src, tgt in pairs:
										A_			:  List[str]			=				f'{src}-{tgt}'
										A_			:  Optional[int]			=				f'sacrebleu -t wmt19 -l {pair} --echo src'.split()
										A_			:  int			=				subprocess.run(lowerCamelCase__      ,		stdout=subprocess.PIPE      ).stdout.decode('''utf-8'''      ).splitlines()
										A_			:  List[str]			=				f'sacrebleu -t wmt19 -l {pair} --echo ref'.split()
										A_			:  Any			=				subprocess.run(lowerCamelCase__      ,		stdout=subprocess.PIPE      ).stdout.decode('''utf-8'''      ).splitlines()
										A_			:  List[Any]			=				{'''src''': src_lines[:n_objs], '''tgt''': tgt_lines[:n_objs]}
					return text


snake_case__ 							=    get_all_data(pairs, n_objs)
snake_case__ 							=    """./fsmt_val_data.json"""
with io.open(filename, """w""", encoding="""utf-8""") as f:
							snake_case__ 							=    json.dump(text, f, indent=2, ensure_ascii=False)



