



'''simple docstring'''
from typing import TYPE_CHECKING

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


_UpperCamelCase									=      {
    '''configuration_swin2sr''': ['''SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swin2SRConfig'''],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      [
					    '''SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''Swin2SRForImageSuperResolution''',
					    '''Swin2SRModel''',
					    '''Swin2SRPreTrainedModel''',
					]


try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      ['''Swin2SRImageProcessor''']


if TYPE_CHECKING:
					from .configuration_swinasr import SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinaSRConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swinasr import (
										    SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwinaSRForImageSuperResolution,
										    SwinaSRModel,
										    SwinaSRPreTrainedModel,
										)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_swinasr import SwinaSRImageProcessor


else:
					import sys

					_UpperCamelCase									=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









'''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 _A	:





		def __init__(  self			,							__UpperCAmelCase			,							__UpperCAmelCase=13			,							__UpperCAmelCase=32			,							__UpperCAmelCase=3			,							__UpperCAmelCase=4			,							__UpperCAmelCase=[10, 20, 30, 40]			,							__UpperCAmelCase=[2, 2, 3, 2]			,							__UpperCAmelCase=True			,							__UpperCAmelCase=True			,							__UpperCAmelCase=37			,							__UpperCAmelCase="gelu"			,							__UpperCAmelCase=10			,							__UpperCAmelCase=0.02			,							__UpperCAmelCase=3			,							__UpperCAmelCase=None			,							)							->  Any:

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





		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase :   List[Any]    					=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

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

					return config, pixel_values, labels





		def 				__A							(  self					)							->  Optional[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=__UpperCAmelCase			,							initializer_range=self.initializer_range			,							)





		def 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					)							->  List[Any]:

					'''simple docstring'''
					__UpperCAmelCase :   List[Any]    					=  TFConvNextModel(config=__UpperCAmelCase					)
					__UpperCAmelCase :   List[Any]    					=  model(__UpperCAmelCase			,							training=__UpperCAmelCase					)
					# 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			,							__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					)							->  Optional[int]:

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





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

					'''simple docstring'''
					__UpperCAmelCase :   List[str]    					=  self.prepare_config_and_inputs()
					__UpperCAmelCase					,	__UpperCAmelCase					,	__UpperCAmelCase :   Optional[int]    					=  config_and_inputs
					__UpperCAmelCase :   Union[str, Any]    					=  {"""pixel_values""": pixel_values}
					return config, inputs_dict



@require_tf
class _A	(     __SCREAMING_SNAKE_CASE						,					__SCREAMING_SNAKE_CASE						,					unittest.TestCase     ):
		_SCREAMING_SNAKE_CASE       :     Dict       =      (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
		_SCREAMING_SNAKE_CASE       :     Optional[Any]       =      (
		    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
		    if is_tf_available()
		    else {}
		)

		_SCREAMING_SNAKE_CASE       :     Tuple       =      False
		_SCREAMING_SNAKE_CASE       :     Tuple       =      False
		_SCREAMING_SNAKE_CASE       :     Union[str, Any]       =      False
		_SCREAMING_SNAKE_CASE       :     Optional[Any]       =      False
		_SCREAMING_SNAKE_CASE       :     str       =      False





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

					'''simple docstring'''
					__UpperCAmelCase :   Dict    					=  TFConvNextModelTester(self					)
					__UpperCAmelCase :   Optional[int]    					=  ConfigTester(
					    self			,							config_class=__UpperCAmelCase			,							has_text_modality=__UpperCAmelCase			,							hidden_size=37			,							)





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

					'''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					)							->  Optional[int]:

					'''simple docstring'''
					super().test_keras_fit()





		@unittest.skip(reason="""ConvNext does not support input and output embeddings"""					)
		def 				__A							(  self					)							->  Dict:

					'''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 :   List[Any]    					=  model_class(__UpperCAmelCase					)
								__UpperCAmelCase :   str    					=  inspect.signature(model.call					)
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								__UpperCAmelCase :   Tuple    					=  [*signature.parameters.keys()]

								__UpperCAmelCase :   Tuple    					=  ["""pixel_values"""]
								self.assertListEqual(arg_names[:1]			,							__UpperCAmelCase					)





		def 				__A							(  self					)							->  Dict:

					'''simple docstring'''
					__UpperCAmelCase :   Dict    					=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__UpperCAmelCase					)





		@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					)							->  Union[str, Any]:

					'''simple docstring'''
					super().test_dataset_conversion()





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

					'''simple docstring'''
					def check_hidden_states_output(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					):
								__UpperCAmelCase :   Optional[int]    					=  model_class(__UpperCAmelCase					)

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

								__UpperCAmelCase :   Optional[int]    					=  self.model_tester.num_stages
								self.assertEqual(len(__UpperCAmelCase					)			,							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 :   Union[str, Any]    					=  self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								__UpperCAmelCase :   List[str]    					=  True
								check_hidden_states_output(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					)

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

								check_hidden_states_output(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					)





		def 				__A							(  self					)							->  Tuple:

					'''simple docstring'''
					__UpperCAmelCase					,	__UpperCAmelCase :   Any    					=  self.model_tester.prepare_config_and_inputs_for_common()

					def check_equivalence(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase={}					):
								__UpperCAmelCase :   Optional[Any]    					=  model(__UpperCAmelCase			,							return_dict=__UpperCAmelCase			,							**__UpperCAmelCase					)
								__UpperCAmelCase :   Optional[int]    					=  model(__UpperCAmelCase			,							return_dict=__UpperCAmelCase			,							**__UpperCAmelCase					).to_tuple()

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

											recursive_check(__UpperCAmelCase			,							__UpperCAmelCase					)

					for model_class in self.all_model_classes:
								__UpperCAmelCase :   Dict    					=  model_class(__UpperCAmelCase					)

								__UpperCAmelCase :   Optional[int]    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase					)
								__UpperCAmelCase :   Tuple    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase					)
								check_equivalence(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					)

								__UpperCAmelCase :   Union[str, Any]    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase			,							return_labels=__UpperCAmelCase					)
								__UpperCAmelCase :   Union[str, Any]    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase			,							return_labels=__UpperCAmelCase					)
								check_equivalence(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					)

								__UpperCAmelCase :   Optional[Any]    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase					)
								__UpperCAmelCase :   List[str]    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase					)
								check_equivalence(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase			,							{"""output_hidden_states""": True}					)

								__UpperCAmelCase :   List[Any]    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase			,							return_labels=__UpperCAmelCase					)
								__UpperCAmelCase :   Dict    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase			,							return_labels=__UpperCAmelCase					)
								check_equivalence(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase			,							{"""output_hidden_states""": True}					)





		def 				__A							(  self					)							->  Tuple:

					'''simple docstring'''
					__UpperCAmelCase :   int    					=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase					)





		@slow
		def 				__A							(  self					)							->  Tuple:

					'''simple docstring'''
					__UpperCAmelCase :   Union[str, Any]    					=  TFConvNextModel.from_pretrained("""facebook/convnext-tiny-224"""					)
					self.assertIsNotNone(__UpperCAmelCase					)


def 	lowercase_						( ):

			"""simple docstring"""





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






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





		@cached_property
		def 				__A							(  self					)							->  Any:

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





		@slow
		def 				__A							(  self					)							->  int:

					'''simple docstring'''
					__UpperCAmelCase :   Tuple    					=  TFConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""					)

					__UpperCAmelCase :   List[Any]    					=  self.default_image_processor
					__UpperCAmelCase :   int    					=  prepare_img()
					__UpperCAmelCase :   Optional[Any]    					=  image_processor(images=__UpperCAmelCase			,							return_tensors="""tf"""					)

					# forward pass
					__UpperCAmelCase :   List[Any]    					=  model(**__UpperCAmelCase					)

					# verify the logits
					__UpperCAmelCase :   List[str]    					=  tf.TensorShape((1, 1_000)					)
					self.assertEqual(outputs.logits.shape			,							__UpperCAmelCase					)

					__UpperCAmelCase :   str    					=  tf.constant([-0.0260, -0.4739, 0.1911]					)

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




