




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  A		:



		def __init__(     self,	UpperCamelCase__,	UpperCamelCase__=3,	UpperCamelCase__=32,	UpperCamelCase__=3,	UpperCamelCase__=10,	UpperCamelCase__=[10, 20, 30, 40],	UpperCamelCase__=[1, 1, 2, 1],	UpperCamelCase__=True,	UpperCamelCase__=True,	UpperCamelCase__="relu",	UpperCamelCase__=3,	UpperCamelCase__=None,	):







								"""simple docstring"""



								lowerCAmelCase_      			=  parent
								lowerCAmelCase_      			=  batch_size
								lowerCAmelCase_      			=  image_size
								lowerCAmelCase_      			=  num_channels
								lowerCAmelCase_      			=  embeddings_size
								lowerCAmelCase_      			=  hidden_sizes
								lowerCAmelCase_      			=  depths
								lowerCAmelCase_      			=  is_training
								lowerCAmelCase_      			=  use_labels
								lowerCAmelCase_      			=  hidden_act
								lowerCAmelCase_      			=  num_labels
								lowerCAmelCase_      			=  scope
								lowerCAmelCase_      			=  len(UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

								lowerCAmelCase_      			=  None
								if self.use_labels:
														lowerCAmelCase_      			=  ids_tensor([self.batch_size],	self.num_labels   )

								lowerCAmelCase_      			=  self.get_config()

								return config, pixel_values, labels



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""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        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  TFResNetModel(config=UpperCamelCase__   )
								lowerCAmelCase_      			=  model(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        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.num_labels
								lowerCAmelCase_      			=  TFResNetForImageClassification(UpperCamelCase__   )
								lowerCAmelCase_      			=  model(UpperCamelCase__,	labels=UpperCamelCase__   )
								self.parent.assertEqual(result.logits.shape,	(self.batch_size, self.num_labels)   )







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.prepare_config_and_inputs()
								lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_      			=  config_and_inputs
								lowerCAmelCase_      			=  {'''pixel_values''': pixel_values}
								return config, inputs_dict







@require_tf
class  A		(  __UpperCAmelCase      ,				__UpperCAmelCase      ,				unittest.TestCase     ):
		__snake_case									=   (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
		__snake_case									=   (
		    {'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification}
		    if is_tf_available()
		    else {}
		)

		__snake_case									=   False
		__snake_case									=   False
		__snake_case									=   False
		__snake_case									=   False
		__snake_case									=   False



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  TFResNetModelTester(self   )
								lowerCAmelCase_      			=  ConfigTester(self,	config_class=UpperCamelCase__,	has_text_modality=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return



		@unittest.skip(reason='''ResNet does not use inputs_embeds'''   )
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass



		@unittest.skip(reason='''ResNet does not support input and output embeddings'''   )
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_			,					lowerCAmelCase_      			=  self.model_tester.prepare_config_and_inputs_for_common()

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

														lowerCAmelCase_      			=  ['''pixel_values''']
														self.assertListEqual(arg_names[:1],	UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								def check_hidden_states_output(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):
														lowerCAmelCase_      			=  model_class(UpperCamelCase__   )
														lowerCAmelCase_      			=  model(**self._prepare_for_class(UpperCamelCase__,	UpperCamelCase__   )   )

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

														lowerCAmelCase_      			=  self.model_tester.num_stages
														self.assertEqual(len(UpperCamelCase__   ),	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],	)

								lowerCAmelCase_			,					lowerCAmelCase_      			=  self.model_tester.prepare_config_and_inputs_for_common()
								lowerCAmelCase_      			=  ['''basic''', '''bottleneck''']
								for model_class in self.all_model_classes:
														for layer_type in layers_type:
																				lowerCAmelCase_      			=  layer_type
																				lowerCAmelCase_      			=  True
																				check_hidden_states_output(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   )

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

																				check_hidden_states_output(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__   )







		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase_      			=  TFResNetModel.from_pretrained(UpperCamelCase__   )
														self.assertIsNotNone(UpperCamelCase__   )







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






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



		@cached_property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return (
								    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
								    if is_vision_available()
								    else None
								)







		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )

								lowerCAmelCase_      			=  self.default_image_processor
								lowerCAmelCase_      			=  prepare_img()
								lowerCAmelCase_      			=  image_processor(images=UpperCamelCase__,	return_tensors='''tf'''   )

								# forward pass
								lowerCAmelCase_      			=  model(**UpperCamelCase__   )

								# verify the logits
								lowerCAmelCase_      			=  tf.TensorShape((1, 1000)   )
								self.assertEqual(outputs.logits.shape,	UpperCamelCase__   )

								lowerCAmelCase_      			=  tf.constant([-11.1_069, -9.7_877, -8.3_777]   )

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










def __UpperCamelCase			(       _A  ):
						if not numbers:
												return 0

						if not isinstance(_A ,  (list, tuple)  ) or not all(
						    isinstance(_A ,  _A  ) for number in numbers  ):
												raise ValueError('''numbers must be an iterable of integers'''  )

						lowerCAmelCase_      			=  lowerCAmelCase_      			=  lowerCAmelCase_      			=  numbers[0]

						for i in range(1 ,  len(_A  )  ):
												# update the maximum and minimum subarray products
												lowerCAmelCase_      			=  numbers[i]
												if number < 0:
																		lowerCAmelCase_			,					lowerCAmelCase_      			=  min_till_now, max_till_now
												lowerCAmelCase_      			=  max(_A ,  max_till_now * number  )
												lowerCAmelCase_      			=  min(_A ,  min_till_now * number  )

												# update the maximum product found till now
												lowerCAmelCase_      			=  max(_A ,  _A  )

						return max_prod




