
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 		__lowerCamelCase     :



							def __init__(     self	,				__snake_case	,				__snake_case=1_3	,				__snake_case=3_2	,				__snake_case=3	,				__snake_case=4	,				__snake_case=[1_0, 2_0, 3_0, 4_0]	,				__snake_case=[2, 2, 3, 2]	,				__snake_case=True	,				__snake_case=True	,				__snake_case=3_7	,				__snake_case="gelu"	,				__snake_case=1_0	,				__snake_case=0.02	,				__snake_case=3	,				__snake_case=None	,				)						->     List[Any]:






								"""simple docstring"""



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



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






								"""simple docstring"""



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

								UpperCAmelCase:					int 						=     None
								if self.use_labels:
									UpperCAmelCase:					Tuple 						=     ids_tensor([self.batch_size]	,				self.type_sequence_label_size							)

								UpperCAmelCase:					List[Any] 						=     self.get_config()

								return config, pixel_values, labels



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






								"""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=__snake_case	,				initializer_range=self.initializer_range	,				)



							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case							)						->     List[Any]:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     TFConvNextModel(config=__snake_case							)
								UpperCAmelCase:					Any 						=     model(__snake_case	,				training=__snake_case							)
								# 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 // 3_2, self.image_size // 3_2)	,				)



							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     self.type_sequence_label_size
								UpperCAmelCase:					str 						=     TFConvNextForImageClassification(__snake_case							)
								UpperCAmelCase:					Dict 						=     model(__snake_case	,				labels=__snake_case	,				training=__snake_case							)
								self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.type_sequence_label_size)							)




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






								"""simple docstring"""



								UpperCAmelCase:					str 						=     self.prepare_config_and_inputs()
								UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					List[str] 						=     config_and_inputs
								UpperCAmelCase:					Union[str, Any] 						=     {"pixel_values": pixel_values}
								return config, inputs_dict





@require_tf
class 		__lowerCamelCase     (   lowercase							, lowercase							, unittest.TestCase		):
							lowerCamelCase__:				str      =							(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
							lowerCamelCase__:				Dict      =							(
							    {'''feature-extraction''': TFConvNextModel, '''image-classification''': TFConvNextForImageClassification}
							    if is_tf_available()
							    else {}
							)

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



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






								"""simple docstring"""



								UpperCAmelCase:					List[Any] 						=     TFConvNextModelTester(self							)
								UpperCAmelCase:					str 						=     ConfigTester(
								    self	,				config_class=__snake_case	,				has_text_modality=__snake_case	,				hidden_size=3_7	,				)



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






								"""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							)						->     Any:






								"""simple docstring"""



								super().test_keras_fit()



							@unittest.skip(reason="ConvNext does not support input and output embeddings"							)
							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								pass



							def     A__ (     self							)						->     str:






								"""simple docstring"""



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

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

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



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






								"""simple docstring"""



								UpperCAmelCase:					List[Any] 						=     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__snake_case							)



							@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							)						->     List[str]:






								"""simple docstring"""



								super().test_dataset_conversion()



							def     A__ (     self							)						->     str:






								"""simple docstring"""



								def check_hidden_states_output(__snake_case	,				__snake_case	,				__snake_case							):
									UpperCAmelCase:					str 						=     model_class(__snake_case							)

									UpperCAmelCase:					Union[str, Any] 						=     model(**self._prepare_for_class(__snake_case	,				__snake_case							)							)
									UpperCAmelCase:					Optional[Any] 						=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									UpperCAmelCase:					Union[str, Any] 						=     self.model_tester.num_stages
									self.assertEqual(len(__snake_case							)	,				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]	,				)

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

								for model_class in self.all_model_classes:
									UpperCAmelCase:					List[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"]
									UpperCAmelCase:					Tuple 						=     True

									check_hidden_states_output(__snake_case	,				__snake_case	,				__snake_case							)



							def     A__ (     self							)						->     str:






								"""simple docstring"""



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

								def check_equivalence(__snake_case	,				__snake_case	,				__snake_case	,				__snake_case={}							):
									UpperCAmelCase:					Optional[int] 						=     model(__snake_case	,				return_dict=__snake_case	,				**__snake_case							)
									UpperCAmelCase:					str 						=     model(__snake_case	,				return_dict=__snake_case	,				**__snake_case							).to_tuple()

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

										recursive_check(__snake_case	,				__snake_case							)

								for model_class in self.all_model_classes:
									UpperCAmelCase:					Dict 						=     model_class(__snake_case							)

									UpperCAmelCase:					List[Any] 						=     self._prepare_for_class(__snake_case	,				__snake_case							)
									UpperCAmelCase:					int 						=     self._prepare_for_class(__snake_case	,				__snake_case							)
									check_equivalence(__snake_case	,				__snake_case	,				__snake_case							)

									UpperCAmelCase:					str 						=     self._prepare_for_class(__snake_case	,				__snake_case	,				return_labels=__snake_case							)
									UpperCAmelCase:					Tuple 						=     self._prepare_for_class(__snake_case	,				__snake_case	,				return_labels=__snake_case							)
									check_equivalence(__snake_case	,				__snake_case	,				__snake_case							)

									UpperCAmelCase:					Dict 						=     self._prepare_for_class(__snake_case	,				__snake_case							)
									UpperCAmelCase:					Optional[Any] 						=     self._prepare_for_class(__snake_case	,				__snake_case							)
									check_equivalence(__snake_case	,				__snake_case	,				__snake_case	,				{"output_hidden_states": True}							)

									UpperCAmelCase:					int 						=     self._prepare_for_class(__snake_case	,				__snake_case	,				return_labels=__snake_case							)
									UpperCAmelCase:					Dict 						=     self._prepare_for_class(__snake_case	,				__snake_case	,				return_labels=__snake_case							)
									check_equivalence(__snake_case	,				__snake_case	,				__snake_case	,				{"output_hidden_states": True}							)



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






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__snake_case							)




							@slow
							def     A__ (     self							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"							)
								self.assertIsNotNone(__snake_case							)



def      __UpperCAmelCase    (  ):



	'''simple docstring'''



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





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



							@cached_property
							def     A__ (     self							)						->     Dict:






								"""simple docstring"""



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




							@slow
							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					Dict 						=     TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"							)

								UpperCAmelCase:					Dict 						=     self.default_image_processor
								UpperCAmelCase:					Optional[Any] 						=     prepare_img()
								UpperCAmelCase:					Union[str, Any] 						=     image_processor(images=__snake_case	,				return_tensors="tf"							)

								# forward pass
								UpperCAmelCase:					int 						=     model(**__snake_case							)

								# verify the logits
								UpperCAmelCase:					Tuple 						=     tf.TensorShape((1, 1_0_0_0)							)
								self.assertEqual(outputs.logits.shape	,				__snake_case							)

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

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


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_      :					Optional[int]				      =     {
    'configuration_splinter': ['SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SplinterConfig'],
    'tokenization_splinter': ['SplinterTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Dict				      =     ['SplinterTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Optional[Any]				      =     [
	    'SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SplinterForQuestionAnswering',
	    'SplinterForPreTraining',
	    'SplinterLayer',
	    'SplinterModel',
	    'SplinterPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
	from .tokenization_splinter import SplinterTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_splinter_fast import SplinterTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_splinter import (
		    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SplinterForPreTraining,
		    SplinterForQuestionAnswering,
		    SplinterLayer,
		    SplinterModel,
		    SplinterPreTrainedModel,
		)


else:
	import sys

	snake_case_      :					Dict				      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
