


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						:




							"""simple docstring"""


							def __init__(				self			:    int       ,      lowerCamelCase			:    List[Any]       ,      lowerCamelCase			:    List[Any]=3       ,      lowerCamelCase			:    Dict=32       ,      lowerCamelCase			:    List[Any]=3       ,      lowerCamelCase			:    List[str]=10       ,      lowerCamelCase			:    List[str]=[10, 20, 30, 40]       ,      lowerCamelCase			:    Optional[Any]=[1, 1, 2, 1]       ,      lowerCamelCase			:    Union[str, Any]=True       ,      lowerCamelCase			:    Tuple=True       ,      lowerCamelCase			:    int="relu"       ,      lowerCamelCase			:    int=3       ,      lowerCamelCase			:    str=None       ,      )      ->       Optional[int]:
														__snake_case						:     str									=  parent
														__snake_case						:     List[Any]									=  batch_size
														__snake_case						:     int									=  image_size
														__snake_case						:     Optional[Any]									=  num_channels
														__snake_case						:     Any									=  embeddings_size
														__snake_case						:     Dict									=  hidden_sizes
														__snake_case						:     Tuple									=  depths
														__snake_case						:     List[str]									=  is_training
														__snake_case						:     int									=  use_labels
														__snake_case						:     str									=  hidden_act
														__snake_case						:     Tuple									=  num_labels
														__snake_case						:     Optional[int]									=  scope
														__snake_case						:     int									=  len(lowerCamelCase							)

							def        __snake_case   (				self			:    Tuple							)      ->       Optional[int]:
														__snake_case						:     int									=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

														__snake_case						:     str									=  None
														if self.use_labels:
																					__snake_case						:     int									=  ids_tensor([self.batch_size]       ,      self.num_labels							)

														__snake_case						:     Any									=  self.get_config()

														return config, pixel_values, labels

							def        __snake_case   (				self			:    List[Any]							)      ->       Optional[int]:
														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        __snake_case   (				self			:    str       ,      lowerCamelCase			:    List[str]       ,      lowerCamelCase			:    Tuple       ,      lowerCamelCase			:    Optional[Any]							)      ->       Tuple:
														__snake_case						:     Dict									=  TFResNetModel(config=lowerCamelCase							)
														__snake_case						:     Dict									=  model(lowerCamelCase							)
														# 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        __snake_case   (				self			:    List[Any]       ,      lowerCamelCase			:    Tuple       ,      lowerCamelCase			:    Union[str, Any]       ,      lowerCamelCase			:    Optional[int]							)      ->       Optional[int]:
														__snake_case						:     Optional[int]									=  self.num_labels
														__snake_case						:     List[Any]									=  TFResNetForImageClassification(lowerCamelCase							)
														__snake_case						:     List[Any]									=  model(lowerCamelCase       ,      labels=lowerCamelCase							)
														self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)							)






							def        __snake_case   (				self			:    int							)      ->       Dict:
														__snake_case						:     str									=  self.prepare_config_and_inputs()
														__snake_case					,     __snake_case					,     __snake_case						:     Tuple									=  config_and_inputs
														__snake_case						:     Optional[int]									=  {"pixel_values": pixel_values}
														return config, inputs_dict

@require_tf
class 							a						(_lowerCAmelCase	,							_lowerCAmelCase	,							unittest.TestCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Optional[Any]	    =    (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
							__UpperCAmelCase       :				Optional[int]	    =    (
							    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
							    if is_tf_available()
							    else {}
							)

							__UpperCAmelCase       :				Tuple	    =    False
							__UpperCAmelCase       :				Tuple	    =    False
							__UpperCAmelCase       :				Optional[int]	    =    False
							__UpperCAmelCase       :				str	    =    False
							__UpperCAmelCase       :				List[str]	    =    False

							def        __snake_case   (				self			:    Optional[Any]							)      ->       Union[str, Any]:
														__snake_case						:     Any									=  TFResNetModelTester(self							)
														__snake_case						:     List[str]									=  ConfigTester(self       ,      config_class=lowerCamelCase       ,      has_text_modality=lowerCamelCase							)

							def        __snake_case   (				self			:    List[Any]							)      ->       str:
														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        __snake_case   (				self			:    List[str]							)      ->       Optional[int]:
														return

							@unittest.skip(reason="ResNet does not use inputs_embeds"							)
							def        __snake_case   (				self			:    Union[str, Any]							)      ->       List[Any]:
														pass

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

							def        __snake_case   (				self			:    Optional[Any]							)      ->       Tuple:
														__snake_case					,     __snake_case						:     Tuple									=  self.model_tester.prepare_config_and_inputs_for_common()

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

																					__snake_case						:     int									=  ["pixel_values"]
																					self.assertListEqual(arg_names[:1]       ,      lowerCamelCase							)

							def        __snake_case   (				self			:    List[Any]							)      ->       str:
														__snake_case						:     int									=  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase							)

							def        __snake_case   (				self			:    Tuple							)      ->       Optional[int]:
														def check_hidden_states_output(lowerCamelCase			:    int       ,      lowerCamelCase			:    Optional[Any]       ,      lowerCamelCase			:    Tuple							):
																					__snake_case						:     List[Any]									=  model_class(lowerCamelCase							)
																					__snake_case						:     Tuple									=  model(**self._prepare_for_class(lowerCamelCase       ,      lowerCamelCase							)							)

																					__snake_case						:     Optional[Any]									=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																					__snake_case						:     Optional[int]									=  self.model_tester.num_stages
																					self.assertEqual(len(lowerCamelCase							)       ,      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]       ,      )

														__snake_case					,     __snake_case						:     str									=  self.model_tester.prepare_config_and_inputs_for_common()
														__snake_case						:     Optional[int]									=  ["basic", "bottleneck"]
														for model_class in self.all_model_classes:
																					for layer_type in layers_type:
																												__snake_case						:     Tuple									=  layer_type
																												__snake_case						:     Union[str, Any]									=  True
																												check_hidden_states_output(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							)

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

																												check_hidden_states_output(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							)

							def        __snake_case   (				self			:    Any							)      ->       int:
														__snake_case						:     Optional[int]									=  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*lowerCamelCase							)






							@slow
							def        __snake_case   (				self			:    List[str]							)      ->       Optional[int]:
														for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					__snake_case						:     int									=  TFResNetModel.from_pretrained(lowerCamelCase							)
																					self.assertIsNotNone(lowerCamelCase							)







def 							lowerCAmelCase_	(   ):
							__snake_case						:     Any									=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
							return image

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




							"""simple docstring"""


							@cached_property
							def        __snake_case   (				self			:    Dict							)      ->       str:
														return (
														    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
														    if is_vision_available()
														    else None
														)






							@slow
							def        __snake_case   (				self			:    Optional[int]							)      ->       Tuple:
														__snake_case						:     Optional[int]									=  TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)

														__snake_case						:     List[Any]									=  self.default_image_processor
														__snake_case						:     Union[str, Any]									=  prepare_img()
														__snake_case						:     str									=  image_processor(images=lowerCamelCase       ,      return_tensors="tf"							)

														# forward pass
														__snake_case						:     Dict									=  model(**lowerCamelCase							)

														# verify the logits
														__snake_case						:     Union[str, Any]									=  tf.TensorShape((1, 1000)							)
														self.assertEqual(outputs.logits.shape       ,      lowerCamelCase							)

														__snake_case						:     List[str]									=  tf.constant([-11.10_69, -9.78_77, -8.37_77]							)

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








def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case = False							)		->    str:

		if not isinstance(snake_case						,   snake_case							):
				__lowercase											=     F"Expected string as input, found {type(snake_case							)}"
				raise ValueError(snake_case							)
		if not isinstance(snake_case						,   snake_case							):
				__lowercase											=     F"Expected boolean as use_pascal parameter, found {type(snake_case							)}"
				raise ValueError(snake_case							)

		__lowercase											=     input_str.split('_'							)

		__lowercase											=     0 if use_pascal else 1

		__lowercase											=     words[start_index:]

		__lowercase											=     [word[0].upper() + word[1:] for word in words_to_capitalize]

		__lowercase											=     '' if use_pascal else words[0]

		return "".join([initial_word, *capitalized_words]							)


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

		testmod()




