



"""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_     :



						"""simple docstring"""






						def __init__(   self					:str  ,    lowercase_					:Optional[Any]  ,    lowercase_					:Tuple=13  ,    lowercase_					:Any=32  ,    lowercase_					:Dict=3  ,    lowercase_					:Union[str, Any]=4  ,    lowercase_					:Union[str, Any]=[10, 20, 30, 40]  ,    lowercase_					:Dict=[2, 2, 3, 2]  ,    lowercase_					:int=True  ,    lowercase_					:List[str]=True  ,    lowercase_					:Optional[int]=37  ,    lowercase_					:Tuple="gelu"  ,    lowercase_					:int=10  ,    lowercase_					:Union[str, Any]=0.02  ,    lowercase_					:Dict=3  ,    lowercase_					:Tuple=None  ,    )							->     int:
												UpperCAmelCase			 =					parent
												UpperCAmelCase			 =					batch_size
												UpperCAmelCase			 =					image_size
												UpperCAmelCase			 =					num_channels
												UpperCAmelCase			 =					num_stages
												UpperCAmelCase			 =					hidden_sizes
												UpperCAmelCase			 =					depths
												UpperCAmelCase			 =					is_training
												UpperCAmelCase			 =					use_labels
												UpperCAmelCase			 =					intermediate_size
												UpperCAmelCase			 =					hidden_act
												UpperCAmelCase			 =					type_sequence_label_size
												UpperCAmelCase			 =					initializer_range
												UpperCAmelCase			 =					scope






						def       UpperCAmelCase__	(   self					:str   )							->     Optional[int]:
												UpperCAmelCase			 =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

												UpperCAmelCase			 =					self.get_config()

												return config, pixel_values, labels






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     str:
												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=lowercase_  ,    initializer_range=self.initializer_range  ,    )






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:Dict  ,    lowercase_					:Tuple  ,    lowercase_					:Optional[Any]   )							->     Any:
												UpperCAmelCase			 =					TFConvNextModel(config=lowercase_   )
												UpperCAmelCase			 =					model(lowercase_  ,    training=lowercase_   )
												# 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       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:Optional[int]  ,    lowercase_					:List[Any]  ,    lowercase_					:Optional[Any]   )							->     Optional[int]:
												UpperCAmelCase			 =					self.type_sequence_label_size
												UpperCAmelCase			 =					TFConvNextForImageClassification(lowercase_   )
												UpperCAmelCase			 =					model(lowercase_  ,    labels=lowercase_  ,    training=lowercase_   )
												self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.type_sequence_label_size)   )






						def       UpperCAmelCase__	(   self					:int   )							->     Dict:
												UpperCAmelCase			 =					self.prepare_config_and_inputs()
												UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					config_and_inputs
												UpperCAmelCase			 =					{'pixel_values': pixel_values}
												return config, inputs_dict







@require_tf
class  A_     (	SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_      ,  unittest.TestCase							):



						"""simple docstring"""
						__UpperCamelCase													=				(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
						__UpperCamelCase													=				(
						    {"""feature-extraction""": TFConvNextModel, """image-classification""": TFConvNextForImageClassification}
						    if is_tf_available()
						    else {}
						)

						__UpperCamelCase													=				False
						__UpperCamelCase													=				False
						__UpperCamelCase													=				False
						__UpperCamelCase													=				False
						__UpperCamelCase													=				False






						def       UpperCAmelCase__	(   self					:List[Any]   )							->     str:
												UpperCAmelCase			 =					TFConvNextModelTester(self   )
												UpperCAmelCase			 =					ConfigTester(
												    self  ,    config_class=lowercase_  ,    has_text_modality=lowercase_  ,    hidden_size=37  ,    )






						@unittest.skip(reason='ConvNext does not use inputs_embeds'   )
						def       UpperCAmelCase__	(   self					:Any   )							->     Optional[int]:
												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       UpperCAmelCase__	(   self					:Any   )							->     Optional[int]:
												super().test_keras_fit()






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






						def       UpperCAmelCase__	(   self					:str   )							->     Optional[int]:
												UpperCAmelCase ,       UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs_for_common()

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

																		UpperCAmelCase			 =					['pixel_values']
																		self.assertListEqual(arg_names[:1]  ,    lowercase_   )






						def       UpperCAmelCase__	(   self					:List[Any]   )							->     Dict:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*lowercase_   )






						@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       UpperCAmelCase__	(   self					:List[str]   )							->     str:
												super().test_dataset_conversion()






						def       UpperCAmelCase__	(   self					:Dict   )							->     Any:
												def check_hidden_states_output(lowercase_					:Dict  ,    lowercase_					:str  ,    lowercase_					:Optional[Any]   ):
																		UpperCAmelCase			 =					model_class(lowercase_   )

																		UpperCAmelCase			 =					model(**self._prepare_for_class(lowercase_  ,    lowercase_   )   )
																		UpperCAmelCase			 =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																		UpperCAmelCase			 =					self.model_tester.num_stages
																		self.assertEqual(len(lowercase_   )  ,    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			 =					self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		UpperCAmelCase			 =					True
																		check_hidden_states_output(lowercase_  ,    lowercase_  ,    lowercase_   )

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		UpperCAmelCase			 =					True

																		check_hidden_states_output(lowercase_  ,    lowercase_  ,    lowercase_   )






						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     Optional[int]:
												UpperCAmelCase ,       UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs_for_common()

												def check_equivalence(lowercase_					:Union[str, Any]  ,    lowercase_					:str  ,    lowercase_					:Optional[Any]  ,    lowercase_					:Tuple={}   ):
																		UpperCAmelCase			 =					model(lowercase_  ,    return_dict=lowercase_  ,    **lowercase_   )
																		UpperCAmelCase			 =					model(lowercase_  ,    return_dict=lowercase_  ,    **lowercase_   ).to_tuple()

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

																								recursive_check(lowercase_  ,    lowercase_   )

												for model_class in self.all_model_classes:
																		UpperCAmelCase			 =					model_class(lowercase_   )

																		UpperCAmelCase			 =					self._prepare_for_class(lowercase_  ,    lowercase_   )
																		UpperCAmelCase			 =					self._prepare_for_class(lowercase_  ,    lowercase_   )
																		check_equivalence(lowercase_  ,    lowercase_  ,    lowercase_   )

																		UpperCAmelCase			 =					self._prepare_for_class(lowercase_  ,    lowercase_  ,    return_labels=lowercase_   )
																		UpperCAmelCase			 =					self._prepare_for_class(lowercase_  ,    lowercase_  ,    return_labels=lowercase_   )
																		check_equivalence(lowercase_  ,    lowercase_  ,    lowercase_   )

																		UpperCAmelCase			 =					self._prepare_for_class(lowercase_  ,    lowercase_   )
																		UpperCAmelCase			 =					self._prepare_for_class(lowercase_  ,    lowercase_   )
																		check_equivalence(lowercase_  ,    lowercase_  ,    lowercase_  ,    {'output_hidden_states': True}   )

																		UpperCAmelCase			 =					self._prepare_for_class(lowercase_  ,    lowercase_  ,    return_labels=lowercase_   )
																		UpperCAmelCase			 =					self._prepare_for_class(lowercase_  ,    lowercase_  ,    return_labels=lowercase_   )
																		check_equivalence(lowercase_  ,    lowercase_  ,    lowercase_  ,    {'output_hidden_states': True}   )






						def       UpperCAmelCase__	(   self					:Tuple   )							->     List[str]:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*lowercase_   )






						@slow
						def       UpperCAmelCase__	(   self					:Tuple   )							->     Union[str, Any]:
												UpperCAmelCase			 =					TFConvNextModel.from_pretrained('facebook/convnext-tiny-224'   )
												self.assertIsNotNone(lowercase_   )


def 					_lowerCAmelCase (  ):
						UpperCAmelCase			 =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
						return image







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



						"""simple docstring"""






						@cached_property
						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     Dict:
												return ConvNextImageProcessor.from_pretrained('facebook/convnext-tiny-224'   ) if is_vision_available() else None






						@slow
						def       UpperCAmelCase__	(   self					:Any   )							->     Optional[Any]:
												UpperCAmelCase			 =					TFConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224'   )

												UpperCAmelCase			 =					self.default_image_processor
												UpperCAmelCase			 =					prepare_img()
												UpperCAmelCase			 =					image_processor(images=lowercase_  ,    return_tensors='tf'   )

												# forward pass
												UpperCAmelCase			 =					model(**lowercase_   )

												# verify the logits
												UpperCAmelCase			 =					tf.TensorShape((1, 10_00)   )
												self.assertEqual(outputs.logits.shape  ,    lowercase_   )

												UpperCAmelCase			 =					tf.constant([-0.0260, -0.4739, 0.1911]   )

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











'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__ 							=    logging.get_logger(__name__)

snake_case__ 							=    {
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}






class      UpperCamelCase_  (a__  ):







						"""simple docstring"""


						_lowerCAmelCase         =       'nystromformer'







						def __init__(  self   :							Optional[Any]				,			_lowerCamelCase   :							str=30000				,			_lowerCamelCase   :							Optional[Any]=768				,			_lowerCamelCase   :							Optional[Any]=12				,			_lowerCamelCase   :							str=12				,			_lowerCamelCase   :							List[str]=3072				,			_lowerCamelCase   :							Any="gelu_new"				,			_lowerCamelCase   :							List[Any]=0.1				,			_lowerCamelCase   :							List[str]=0.1				,			_lowerCamelCase   :							List[str]=510				,			_lowerCamelCase   :							Optional[Any]=2				,			_lowerCamelCase   :							List[Any]=64				,			_lowerCamelCase   :							str=64				,			_lowerCamelCase   :							str=65				,			_lowerCamelCase   :							List[Any]=False				,			_lowerCamelCase   :							List[Any]=0.02				,			_lowerCamelCase   :							List[Any]=1E-5				,			_lowerCamelCase   :							Dict=1				,			_lowerCamelCase   :							Tuple=0				,			_lowerCamelCase   :							List[str]=2				,			**_lowerCamelCase   :							List[str]				,			):





											"""simple docstring"""







											A_			:  List[Any]			=				vocab_size
											A_			:  Tuple			=				max_position_embeddings
											A_			:  Dict			=				hidden_size
											A_			:  Optional[Any]			=				num_hidden_layers
											A_			:  Dict			=				num_attention_heads
											A_			:  Tuple			=				intermediate_size
											A_			:  Dict			=				hidden_act
											A_			:  List[Any]			=				hidden_dropout_prob
											A_			:  str			=				attention_probs_dropout_prob
											A_			:  Optional[Any]			=				initializer_range
											A_			:  List[str]			=				type_vocab_size
											A_			:  List[Any]			=				segment_means_seq_len
											A_			:  Dict			=				num_landmarks
											A_			:  int			=				conv_kernel_size
											A_			:  Optional[Any]			=				inv_coeff_init_option
											A_			:  str			=				layer_norm_eps
											super().__init__(pad_token_id=_lowerCamelCase				,			bos_token_id=_lowerCamelCase				,			eos_token_id=_lowerCamelCase				,			**_lowerCamelCase     )



