
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    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	def __init__(  self	:    Dict			,  lowercase	:    Optional[Any]			,  lowercase	:    Any=3			,  lowercase	:    Dict=32			,  lowercase	:    List[str]=3			,  lowercase	:    str=10			,  lowercase	:    List[Any]=[10, 20, 30, 40]			,  lowercase	:    List[str]=[1, 1, 2, 1]			,  lowercase	:    Any=True			,  lowercase	:    str=True			,  lowercase	:    Optional[int]="relu"			,  lowercase	:    Tuple=3			,  lowercase	:    int=None			,  ):


				'''simple docstring'''


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



	def  A							(  self	:    List[str]	):


				'''simple docstring'''


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

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

				_snake_case		=			self.get_config()
				return config, pixel_values, labels



	def  A							(  self	:    List[Any]	):


				'''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  A							(  self	:    Union[str, Any]			,  lowercase	:    List[str]			,  lowercase	:    str			,  lowercase	:    str	):


				'''simple docstring'''


				_snake_case		=			TFRegNetModel(config=lowercase	)
				_snake_case		=			model(lowercase			,  training=lowercase	)
				# 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  A							(  self	:    Optional[int]			,  lowercase	:    Dict			,  lowercase	:    Any			,  lowercase	:    Dict	):


				'''simple docstring'''


				_snake_case		=			self.num_labels
				_snake_case		=			TFRegNetForImageClassification(lowercase	)
				_snake_case		=			model(lowercase			,  labels=lowercase			,  training=lowercase	)
				self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.num_labels)	)





	def  A							(  self	:    Any	):


				'''simple docstring'''


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




@require_tf
class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase							,UpperCAmelCase							,unittest.TestCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		List[str]       =					(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
	_UpperCAmelCase       :		List[Any]       =					(
	    {"feature-extraction": TFRegNetModel, "image-classification": TFRegNetForImageClassification}
	    if is_tf_available()
	    else {}
	)

	_UpperCAmelCase       :		List[Any]       =					False
	_UpperCAmelCase       :		int       =					False
	_UpperCAmelCase       :		Union[str, Any]       =					False
	_UpperCAmelCase       :		Any       =					False
	_UpperCAmelCase       :		Any       =					False



	def  A							(  self	:    str	):


				'''simple docstring'''


				_snake_case		=			TFRegNetModelTester(self	)
				_snake_case		=			ConfigTester(self			,  config_class=lowercase			,  has_text_modality=lowercase	)



	def  A							(  self	:    Dict	):


				'''simple docstring'''


				return



	@unittest.skip(reason='RegNet does not use inputs_embeds'	)
	def  A							(  self	:    str	):


				'''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  A							(  self	:    Dict	):


				'''simple docstring'''


				super().test_keras_fit()



	@unittest.skip(reason='RegNet does not support input and output embeddings'	)
	def  A							(  self	:    Dict	):


				'''simple docstring'''


				pass



	def  A							(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case	,					_snake_case		=			self.model_tester.prepare_config_and_inputs_for_common()

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

							_snake_case		=			['pixel_values']
							self.assertListEqual(arg_names[:1]			,  lowercase	)



	def  A							(  self	:    str	):


				'''simple docstring'''


				_snake_case		=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase	)



	def  A							(  self	:    Union[str, Any]	):


				'''simple docstring'''


				def check_hidden_states_output(lowercase	:    str			,  lowercase	:    Dict			,  lowercase	:    List[str]	):
							_snake_case		=			model_class(lowercase	)
							_snake_case		=			model(**self._prepare_for_class(lowercase			,  lowercase	)			,  training=lowercase	)

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

							_snake_case		=			self.model_tester.num_stages
							self.assertEqual(len(lowercase	)			,  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]			,  )

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

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

										check_hidden_states_output(lowercase			,  lowercase			,  lowercase	)



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				_snake_case	,					_snake_case		=			self.model_tester.prepare_config_and_inputs_for_common()

				def check_equivalence(lowercase	:    Tuple			,  lowercase	:    List[str]			,  lowercase	:    Any			,  lowercase	:    Union[str, Any]={}	):
							_snake_case		=			model(lowercase			,  return_dict=lowercase			,  **lowercase	)
							_snake_case		=			model(lowercase			,  return_dict=lowercase			,  **lowercase	).to_tuple()

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

										recursive_check(lowercase			,  lowercase	)

				for model_class in self.all_model_classes:
							_snake_case		=			model_class(lowercase	)

							_snake_case		=			self._prepare_for_class(lowercase			,  lowercase	)
							_snake_case		=			self._prepare_for_class(lowercase			,  lowercase	)
							check_equivalence(lowercase			,  lowercase			,  lowercase	)

							_snake_case		=			self._prepare_for_class(lowercase			,  lowercase			,  return_labels=lowercase	)
							_snake_case		=			self._prepare_for_class(lowercase			,  lowercase			,  return_labels=lowercase	)
							check_equivalence(lowercase			,  lowercase			,  lowercase	)

							_snake_case		=			self._prepare_for_class(lowercase			,  lowercase	)
							_snake_case		=			self._prepare_for_class(lowercase			,  lowercase	)
							check_equivalence(lowercase			,  lowercase			,  lowercase			,  {'output_hidden_states': True}	)

							_snake_case		=			self._prepare_for_class(lowercase			,  lowercase			,  return_labels=lowercase	)
							_snake_case		=			self._prepare_for_class(lowercase			,  lowercase			,  return_labels=lowercase	)
							check_equivalence(lowercase			,  lowercase			,  lowercase			,  {'output_hidden_states': True}	)



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowercase	)





	@slow
	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							_snake_case		=			TFRegNetModel.from_pretrained(lowercase	)
							self.assertIsNotNone(lowercase	)






def        a_		(       ) ->  List[Any]:
			_snake_case		=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
			return image




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





	'''simple docstring'''



	@cached_property
	def  A							(  self	:    Tuple	):


				'''simple docstring'''


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





	@slow
	def  A							(  self	:    Any	):


				'''simple docstring'''


				_snake_case		=			TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)

				_snake_case		=			self.default_image_processor
				_snake_case		=			prepare_img()
				_snake_case		=			image_processor(images=lowercase			,  return_tensors='tf'	)

				# forward pass
				_snake_case		=			model(**lowercase			,  training=lowercase	)

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

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

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

import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_lowerCamelCase       :				int       =			False




class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    Union[str, Any]			,  lowercase	:    Optional[int]=32	):


				'''simple docstring'''


				set_seed(0	)
				_snake_case		=			UNetaDModel(sample_size=lowercase			,  in_channels=3			,  out_channels=3	)
				_snake_case		=			torch.optim.SGD(model.parameters()			,  lr=0.0001	)
				return model, optimizer





	@slow
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				_snake_case		=			'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				_snake_case		=			DDPMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  beta_end=0.02			,  beta_schedule='linear'			,  clip_sample=lowercase			,  )
				_snake_case		=			DDIMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  beta_end=0.02			,  beta_schedule='linear'			,  clip_sample=lowercase			,  )

				assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

				# shared batches for DDPM and DDIM
				set_seed(0	)
				_snake_case		=			[torch.randn((4, 3, 32, 32)	).clip(-1			,  1	).to(lowercase	) for _ in range(4	)]
				_snake_case		=			[torch.randn((4, 3, 32, 32)	).to(lowercase	) for _ in range(4	)]
				_snake_case		=			[torch.randint(0			,  1_000			,  (4,)	).long().to(lowercase	) for _ in range(4	)]

				# train with a DDPM scheduler
				_snake_case	,					_snake_case		=			self.get_model_optimizer(resolution=32	)
				model.train().to(lowercase	)
				for i in range(4	):
							optimizer.zero_grad()
							_snake_case		=			ddpm_scheduler.add_noise(clean_images[i]			,  noise[i]			,  timesteps[i]	)
							_snake_case		=			model(lowercase			,  timesteps[i]	).sample
							_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  noise[i]	)
							loss.backward()
							optimizer.step()
				del model, optimizer

				# recreate the model and optimizer, and retry with DDIM
				_snake_case	,					_snake_case		=			self.get_model_optimizer(resolution=32	)
				model.train().to(lowercase	)
				for i in range(4	):
							optimizer.zero_grad()
							_snake_case		=			ddim_scheduler.add_noise(clean_images[i]			,  noise[i]			,  timesteps[i]	)
							_snake_case		=			model(lowercase			,  timesteps[i]	).sample
							_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  noise[i]	)
							loss.backward()
							optimizer.step()
				del model, optimizer

				self.assertTrue(torch.allclose(lowercase			,  lowercase			,  atol=1E-5	)	)
				self.assertTrue(torch.allclose(lowercase			,  lowercase			,  atol=1E-5	)	)