

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							:	List[Any] , a							:	str , a							:	List[str]=13 , a							:	Optional[Any]=32 , a							:	Union[str, Any]=3 , a							:	Optional[int]=4 , a							:	str=[10, 20, 30, 40] , a							:	List[Any]=[2, 2, 3, 2] , a							:	List[str]=True , a							:	List[str]=True , a							:	Tuple=37 , a							:	int="gelu" , a							:	str=10 , a							:	Union[str, Any]=0.02 , a							:	int=3 , a							:	List[Any]=None , )							->    Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[Any]			       =    parent
				SCREAMING_SNAKE_CASE				: str			       =    batch_size
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    image_size
				SCREAMING_SNAKE_CASE				: Any			       =    num_channels
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    num_stages
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    hidden_sizes
				SCREAMING_SNAKE_CASE				: List[str]			       =    depths
				SCREAMING_SNAKE_CASE				: List[str]			       =    is_training
				SCREAMING_SNAKE_CASE				: Any			       =    use_labels
				SCREAMING_SNAKE_CASE				: Dict			       =    intermediate_size
				SCREAMING_SNAKE_CASE				: Any			       =    hidden_act
				SCREAMING_SNAKE_CASE				: List[str]			       =    type_sequence_label_size
				SCREAMING_SNAKE_CASE				: List[str]			       =    initializer_range
				SCREAMING_SNAKE_CASE				: int			       =    scope
			def 							__UpperCamelCase	(     self							:	Dict		)							->    Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Dict			       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

				SCREAMING_SNAKE_CASE				: Any			       =    self.get_config()

				return config, pixel_values, labels
			def 							__UpperCamelCase	(     self							:	Union[str, Any]		)							->    List[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=a , initializer_range=self.initializer_range , )
			def 							__UpperCamelCase	(     self							:	int , a							:	Tuple , a							:	int , a							:	int		)							->    Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Dict			       =    TFConvNextModel(config=a		)
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    model(a , training=a		)
				# 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] , a							:	Dict , a							:	List[Any] , a							:	str		)							->    Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.type_sequence_label_size
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    TFConvNextForImageClassification(a		)
				SCREAMING_SNAKE_CASE				: Dict			       =    model(a , labels=a , training=a		)
				self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size)		)






			def 							__UpperCamelCase	(     self							:	Optional[int]		)							->    List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Any			       =    self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: str			       =    config_and_inputs
				SCREAMING_SNAKE_CASE				: Dict			       =    {"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 							__UpperCamelCase	(     self							:	Optional[int]		)							->    Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[Any]			       =    TFConvNextModelTester(self		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    ConfigTester(
				    self , config_class=a , has_text_modality=a , hidden_size=37 , )
			@unittest.skip(reason="ConvNext does not use inputs_embeds"		)
			def 							__UpperCamelCase	(     self							:	Union[str, Any]		)							->    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 							__UpperCamelCase	(     self							:	Optional[int]		)							->    List[Any]:

				"""simple docstring"""

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

				"""simple docstring"""

				pass
			def 							__UpperCamelCase	(     self							:	Dict		)							->    str:

				"""simple docstring"""

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

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE				: List[Any]			       =    model_class(a		)
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    inspect.signature(model.call		)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					SCREAMING_SNAKE_CASE				: List[str]			       =    [*signature.parameters.keys()]

					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    ["pixel_values"]
					self.assertListEqual(arg_names[:1] , a		)
			def 							__UpperCamelCase	(     self							:	Dict		)							->    Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Tuple			       =    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*a		)
			@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[Any]		)							->    List[str]:

				"""simple docstring"""

				super().test_dataset_conversion()
			def 							__UpperCamelCase	(     self							:	Union[str, Any]		)							->    Optional[int]:

				"""simple docstring"""

				def check_hidden_states_output(a							:	Tuple , a							:	List[Any] , a							:	Optional[Any]		):
					SCREAMING_SNAKE_CASE				: Optional[int]			       =    model_class(a		)

					SCREAMING_SNAKE_CASE				: int			       =    model(**self._prepare_for_class(a , a		)		)
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

					SCREAMING_SNAKE_CASE				: Any			       =    self.model_tester.num_stages
					self.assertEqual(len(a		) , 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] , )

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

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE				: str			       =    True
					check_hidden_states_output(a , a , a		)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    True

					check_hidden_states_output(a , a , a		)
			def 							__UpperCamelCase	(     self							:	str		)							->    str:

				"""simple docstring"""

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

				def check_equivalence(a							:	str , a							:	int , a							:	str , a							:	Tuple={}		):
					SCREAMING_SNAKE_CASE				: Optional[int]			       =    model(a , return_dict=a , **a		)
					SCREAMING_SNAKE_CASE				: Dict			       =    model(a , return_dict=a , **a		).to_tuple()

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

						recursive_check(a , a		)

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE				: str			       =    model_class(a		)

					SCREAMING_SNAKE_CASE				: str			       =    self._prepare_for_class(a , a		)
					SCREAMING_SNAKE_CASE				: str			       =    self._prepare_for_class(a , a		)
					check_equivalence(a , a , a		)

					SCREAMING_SNAKE_CASE				: Optional[Any]			       =    self._prepare_for_class(a , a , return_labels=a		)
					SCREAMING_SNAKE_CASE				: str			       =    self._prepare_for_class(a , a , return_labels=a		)
					check_equivalence(a , a , a		)

					SCREAMING_SNAKE_CASE				: Any			       =    self._prepare_for_class(a , a		)
					SCREAMING_SNAKE_CASE				: Any			       =    self._prepare_for_class(a , a		)
					check_equivalence(a , a , a , {"output_hidden_states": True}		)

					SCREAMING_SNAKE_CASE				: Dict			       =    self._prepare_for_class(a , a , return_labels=a		)
					SCREAMING_SNAKE_CASE				: Tuple			       =    self._prepare_for_class(a , a , return_labels=a		)
					check_equivalence(a , a , a , {"output_hidden_states": True}		)
			def 							__UpperCamelCase	(     self							:	List[str]		)							->    str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*a		)






			@slow
			def 							__UpperCamelCase	(     self							:	Any		)							->    Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"		)
				self.assertIsNotNone(a		)

def 	lowerCamelCase__	(   ):
	SCREAMING_SNAKE_CASE				: Tuple			       =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
	return image






@require_tf
@require_vision
class _UpperCamelCase							(							unittest.TestCase				):
			'''simple docstring'''







			@cached_property
			def 							__UpperCamelCase	(     self							:	Optional[Any]		)							->    int:

				"""simple docstring"""

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






			@slow
			def 							__UpperCamelCase	(     self							:	Optional[int]		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: str			       =    TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"		)

				SCREAMING_SNAKE_CASE				: Any			       =    self.default_image_processor
				SCREAMING_SNAKE_CASE				: Dict			       =    prepare_img()
				SCREAMING_SNAKE_CASE				: Tuple			       =    image_processor(images=a , return_tensors="tf"		)

				# forward pass
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    model(**a		)

				# verify the logits
				SCREAMING_SNAKE_CASE				: Any			       =    tf.TensorShape((1, 1000)		)
				self.assertEqual(outputs.logits.shape , a		)

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

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


from __future__ import annotations

def 	lowerCamelCase__	(   _a							,     _a							,     _a):
	if len(_a) == 0:
		raise ValueError("find_min() arg is an empty sequence")
	if (
	    left >= len(_a)
	    or left < -len(_a)
	    or right >= len(_a)
	    or right < -len(_a)
	):
		raise IndexError("list index out of range")
	if left == right:
		return nums[left]
	SCREAMING_SNAKE_CASE				: List[str]			       =    (left + right) >> 1  # the middle
	SCREAMING_SNAKE_CASE				: Tuple			       =    find_min(_a							,     _a							,     _a)  # find min in range[left, mid]
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    find_min(_a							,     mid + 1							,     _a)  # find min in range[mid + 1, right]

	return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)