
"""simple docstring"""





from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor






class    _UpperCAmelCase:



		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    ,		)	->	Union[str, Any]:






							'''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(__snake_case)



		def 						UpperCAmelCase							( self)	->	List[str]:






							'''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)	->	Dict:






							'''simple docstring'''
							return RegNetConfig(
							    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    ,		)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  TFRegNetModel(config=__snake_case)
							_UpperCamelCase										=  model(__snake_case    ,		training=__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)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  self.num_labels
							_UpperCamelCase										=  TFRegNetForImageClassification(__snake_case)
							_UpperCamelCase										=  model(__snake_case    ,		labels=__snake_case    ,		training=__snake_case)
							self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels))




		def 						UpperCAmelCase							( self)	->	List[str]:






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






@require_tf
class    _UpperCAmelCase(   lowerCamelCase__				,					lowerCamelCase__				,					unittest.TestCase     ):
		lowercase__      =				(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
		lowercase__      =				(
		    {'feature-extraction': TFRegNetModel, 'image-classification': TFRegNetForImageClassification}
		    if is_tf_available()
		    else {}
		)

		lowercase__      =				False
		lowercase__      =				False
		lowercase__      =				False
		lowercase__      =				False
		lowercase__      =				False



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  TFRegNetModelTester(self)
							_UpperCamelCase										=  ConfigTester(self    ,		config_class=__snake_case    ,		has_text_modality=__snake_case)



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return



		@unittest.skip(reason='''RegNet does not use inputs_embeds''')
		def 						UpperCAmelCase							( self)	->	List[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)	->	str:






							'''simple docstring'''
							super().test_keras_fit()



		@unittest.skip(reason='''RegNet does not support input and output embeddings''')
		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							pass



		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												_UpperCamelCase										=  model_class(__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]    ,		__snake_case)



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__snake_case)



		def 						UpperCAmelCase							( self)	->	Union[str, Any]:






							'''simple docstring'''
							def check_hidden_states_output(__a    ,		__a    ,		__a):
												_UpperCamelCase										=  model_class(__snake_case)
												_UpperCamelCase										=  model(**self._prepare_for_class(__snake_case    ,		__snake_case)    ,		training=__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(__snake_case)    ,		expected_num_stages + 1)

												# RegNet'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 // 2, self.model_tester.image_size // 2]    ,		)

							_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(__snake_case    ,		__snake_case    ,		__snake_case)

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

																	check_hidden_states_output(__snake_case    ,		__snake_case    ,		__snake_case)



		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

							def check_equivalence(__a    ,		__a    ,		__a    ,		__a={}):
												_UpperCamelCase										=  model(__snake_case    ,		return_dict=__snake_case    ,		**__snake_case)
												_UpperCamelCase										=  model(__snake_case    ,		return_dict=__snake_case    ,		**__snake_case).to_tuple()

												def recursive_check(__a    ,		__a):
																	if isinstance(__snake_case    ,		(List, Tuple)):
																						for tuple_iterable_value, dict_iterable_value in zip(__snake_case    ,		__snake_case):
																											recursive_check(__snake_case    ,		__snake_case)
																	elif tuple_object is None:
																						return
																	else:
																						self.assertTrue(
																						    all(tf.equal(__snake_case    ,		__snake_case))    ,		msg=(
																						        '''Tuple and dict output are not equal. Difference:'''
																						        F''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}'''
																						    )    ,		)

																	recursive_check(__snake_case    ,		__snake_case)

							for model_class in self.all_model_classes:
												_UpperCamelCase										=  model_class(__snake_case)

												_UpperCamelCase										=  self._prepare_for_class(__snake_case    ,		__snake_case)
												_UpperCamelCase										=  self._prepare_for_class(__snake_case    ,		__snake_case)
												check_equivalence(__snake_case    ,		__snake_case    ,		__snake_case)

												_UpperCamelCase										=  self._prepare_for_class(__snake_case    ,		__snake_case    ,		return_labels=__snake_case)
												_UpperCamelCase										=  self._prepare_for_class(__snake_case    ,		__snake_case    ,		return_labels=__snake_case)
												check_equivalence(__snake_case    ,		__snake_case    ,		__snake_case)

												_UpperCamelCase										=  self._prepare_for_class(__snake_case    ,		__snake_case)
												_UpperCamelCase										=  self._prepare_for_class(__snake_case    ,		__snake_case)
												check_equivalence(__snake_case    ,		__snake_case    ,		__snake_case    ,		{'''output_hidden_states''': True})

												_UpperCamelCase										=  self._prepare_for_class(__snake_case    ,		__snake_case    ,		return_labels=__snake_case)
												_UpperCamelCase										=  self._prepare_for_class(__snake_case    ,		__snake_case    ,		return_labels=__snake_case)
												check_equivalence(__snake_case    ,		__snake_case    ,		__snake_case    ,		{'''output_hidden_states''': True})



		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__snake_case)




		@slow
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase										=  TFRegNetModel.from_pretrained(__snake_case)
												self.assertIsNotNone(__snake_case)





def 				lowerCamelCase__		(      )							->		List[str]:


					"""simple docstring"""





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






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



		@cached_property
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return (
							    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
							    if is_vision_available()
							    else None
							)




		@slow
		def 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])

							_UpperCamelCase										=  self.default_image_processor
							_UpperCamelCase										=  prepare_img()
							_UpperCamelCase										=  image_processor(images=__snake_case    ,		return_tensors='''tf''')

							# forward pass
							_UpperCamelCase										=  model(**__snake_case    ,		training=__snake_case)

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

							_UpperCamelCase										=  tf.constant([-0.4180, -1.5051, -3.4836])

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




"""simple docstring"""





import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    """The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionInpaintPipeline` instead."""
)






