from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
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 TFResNetForImageClassification, TFResNetModel
					from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor


class     lowercase :



		def __init__(			self    ,				A_    ,				A_=3    ,				A_=32    ,				A_=3    ,				A_=10    ,				A_=[10, 20, 30, 40]    ,				A_=[1, 1, 2, 1]    ,				A_=True    ,				A_=True    ,				A_="relu"    ,				A_=3    ,				A_=None    ,				)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					parent
				UpperCamelCase          =					batch_size
				UpperCamelCase          =					image_size
				UpperCamelCase          =					num_channels
				UpperCamelCase          =					embeddings_size
				UpperCamelCase          =					hidden_sizes
				UpperCamelCase          =					depths
				UpperCamelCase          =					is_training
				UpperCamelCase          =					use_labels
				UpperCamelCase          =					hidden_act
				UpperCamelCase          =					num_labels
				UpperCamelCase          =					scope
				UpperCamelCase          =					len(_SCREAMING_SNAKE_CASE	)



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				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.num_labels	)

				UpperCamelCase          =					self.get_config()

				return config, pixel_values, labels



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				return ResNetConfig(
				    num_channels=self.num_channels    ,				embeddings_size=self.embeddings_size    ,				hidden_sizes=self.hidden_sizes    ,				depths=self.depths    ,				hidden_act=self.hidden_act    ,				num_labels=self.num_labels    ,				image_size=self.image_size    ,				)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					TFResNetModel(config=_SCREAMING_SNAKE_CASE	)
				UpperCamelCase          =					model(_SCREAMING_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 // 32, self.image_size // 32)    ,				)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					self.num_labels
				UpperCamelCase          =					TFResNetForImageClassification(_SCREAMING_SNAKE_CASE	)
				UpperCamelCase          =					model(_SCREAMING_SNAKE_CASE    ,				labels=_SCREAMING_SNAKE_CASE	)
				self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_labels)	)



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					self.prepare_config_and_inputs()
				UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					config_and_inputs
				UpperCamelCase          =					{'pixel_values': pixel_values}
				return config, inputs_dict






@require_tf
class     lowercase (		_UpperCamelCase  ,       _UpperCamelCase  ,       unittest.TestCase       ):
		__lowercase	:	Optional[Any]     					=    (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
		__lowercase	:	List[str]     					=    (
		    {'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification}
		    if is_tf_available()
		    else {}
		)

		__lowercase	:	str     					=    False
		__lowercase	:	Union[str, Any]     					=    False
		__lowercase	:	str     					=    False
		__lowercase	:	Optional[int]     					=    False
		__lowercase	:	Any     					=    False



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					TFResNetModelTester(self	)
				UpperCamelCase          =					ConfigTester(self    ,				config_class=_SCREAMING_SNAKE_CASE    ,				has_text_modality=_SCREAMING_SNAKE_CASE	)



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				self.create_and_test_config_common_properties()
				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				return



		@unittest.skip(reason='ResNet does not use inputs_embeds'	)
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				pass



		@unittest.skip(reason='ResNet does not support input and output embeddings'	)
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				pass



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase          =					model_class(_SCREAMING_SNAKE_CASE	)
						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]    ,				_SCREAMING_SNAKE_CASE	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				def check_hidden_states_output(A_    ,				A_    ,				A_	):
						UpperCamelCase          =					model_class(_SCREAMING_SNAKE_CASE	)
						UpperCamelCase          =					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,				_SCREAMING_SNAKE_CASE	)	)

						UpperCamelCase          =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

						UpperCamelCase          =					self.model_tester.num_stages
						self.assertEqual(len(_SCREAMING_SNAKE_CASE	)    ,				expected_num_stages + 1	)

						# ResNet'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()
				UpperCamelCase          =					['basic', 'bottleneck']
				for model_class in self.all_model_classes:
						for layer_type in layers_type:
								UpperCamelCase          =					layer_type
								UpperCamelCase          =					True
								check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,				_SCREAMING_SNAKE_CASE    ,				_SCREAMING_SNAKE_CASE	)

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

								check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,				_SCREAMING_SNAKE_CASE    ,				_SCREAMING_SNAKE_CASE	)



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE	)



		@slow
		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase          =					TFResNetModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
						self.assertIsNotNone(_SCREAMING_SNAKE_CASE	)






def 				A	( )       ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase          =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
		return image


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



		@cached_property
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				return (
				    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
				    if is_vision_available()
				    else None
				)



		@slow
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)

				UpperCamelCase          =					self.default_image_processor
				UpperCamelCase          =					prepare_img()
				UpperCamelCase          =					image_processor(images=_SCREAMING_SNAKE_CASE    ,				return_tensors='tf'	)

				# forward pass
				UpperCamelCase          =					model(**_SCREAMING_SNAKE_CASE	)

				# verify the logits
				UpperCamelCase          =					tf.TensorShape((1, 1_000)	)
				self.assertEqual(outputs.logits.shape    ,				_SCREAMING_SNAKE_CASE	)

				UpperCamelCase          =					tf.constant([-11.1069, -9.7877, -8.3777]	)

				self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()    ,				_SCREAMING_SNAKE_CASE    ,				atol=1e-4	)	)


from __future__ import annotations


def 				A	( lowercase   ,	lowercase			)       ->  tuple[int, int]:
		'''simple docstring'''

		if b == 0:
				return (1, 0)
		((UpperCamelCase)     ,  (UpperCamelCase))          =					extended_euclid(lowercase   ,	a % b			)
		UpperCamelCase          =					a // b
		return (y, x - k * y)


def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)       ->  int:
		'''simple docstring'''

		((UpperCamelCase)     ,  (UpperCamelCase))          =					extended_euclid(lowercase   ,	lowercase			)
		UpperCamelCase          =					na * na
		UpperCamelCase          =					ra * x * na + ra * y * na
		return (n % m + m) % m


def 				A	( lowercase   ,	lowercase			)       ->  int:
		'''simple docstring'''

		((UpperCamelCase)     ,  (UpperCamelCase))          =					extended_euclid(lowercase   ,	lowercase			)
		if b < 0:
				UpperCamelCase          =					(b % n + n) % n
		return b


def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)       ->  int:
		'''simple docstring'''

		UpperCamelCase     ,  UpperCamelCase          =					invert_modulo(lowercase   ,	lowercase			), invert_modulo(lowercase   ,	lowercase			)
		UpperCamelCase          =					na * na
		UpperCamelCase          =					ra * x * na + ra * y * na
		return (n % m + m) % m


if __name__ == "__main__":
					from doctest import testmod

					testmod(name="chinese_remainder_theorem", verbose=True)
					testmod(name="chinese_remainder_theorem2", verbose=True)
					testmod(name="invert_modulo", verbose=True)
					testmod(name="extended_euclid", verbose=True)
