
import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import RegNetForImageClassification, RegNetModel
				from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor






class        SCREAMING_SNAKE_CASE__    :

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=10     ,	SCREAMING_SNAKE_CASE_=[10, 20, 30, 40]     ,	SCREAMING_SNAKE_CASE_=[1, 1, 2, 1]     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_="relu"     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=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(SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   Tuple:





													'''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     A__		(  self			)->   Tuple:





													'''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     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       =			RegNetModel(config=SCREAMING_SNAKE_CASE_			)
													model.to(SCREAMING_SNAKE_CASE_			)
													model.eval()
													__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     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			self.num_labels
													__UpperCamelCase       =			RegNetForImageClassification(SCREAMING_SNAKE_CASE_			)
													model.to(SCREAMING_SNAKE_CASE_			)
													model.eval()
													__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_     ,	labels=SCREAMING_SNAKE_CASE_			)
													self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_labels)			)



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






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






@require_torch
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_   ,		unittest.TestCase    ):

							"""simple docstring"""






							_snake_case     		=  (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
							_snake_case     		=  (
							    {'feature-extraction': RegNetModel, 'image-classification': RegNetForImageClassification}
							    if is_torch_available()
							    else {}
							)

							_snake_case     		=  False
							_snake_case     		=  False
							_snake_case     		=  False
							_snake_case     		=  False



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





													'''simple docstring'''






													__UpperCamelCase       =			RegNetModelTester(self			)
													__UpperCamelCase       =			ConfigTester(self     ,	config_class=SCREAMING_SNAKE_CASE_     ,	has_text_modality=SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   Optional[int]:





													'''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     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													return



							@unittest.skip(reason='''RegNet does not use inputs_embeds'''			)
							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													pass



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





													'''simple docstring'''






													pass



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





													'''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.forward			)
																			# 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     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   Any:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase       =			model_class(config=SCREAMING_SNAKE_CASE_			)
																			for name, module in model.named_modules():
																									if isinstance(SCREAMING_SNAKE_CASE_     ,	(nn.BatchNormad, nn.GroupNorm)			):
																															self.assertTrue(
																															    torch.all(module.weight == 1			)     ,	msg=F"Parameter {name} of model {model_class} seems not properly initialized"     ,	)
																															self.assertTrue(
																															    torch.all(module.bias == 0			)     ,	msg=F"Parameter {name} of model {model_class} seems not properly initialized"     ,	)



							def     A__		(  self			)->   int:





													'''simple docstring'''






													def check_hidden_states_output(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			):
																			__UpperCamelCase       =			model_class(SCREAMING_SNAKE_CASE_			)
																			model.to(SCREAMING_SNAKE_CASE_			)
																			model.eval()

																			with torch.no_grad():
																									__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			)

																			# 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  ,							__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     A__		(  self			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_			)



							@slow
							def     A__		(  self			)->   Any:





													'''simple docstring'''






													for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__UpperCamelCase       =			RegNetModel.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_torch
@require_vision
class        SCREAMING_SNAKE_CASE__    (							unittest.TestCase    ):

							"""simple docstring"""






							@cached_property
							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






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



							@slow
							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       =			RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			self.default_image_processor
													__UpperCamelCase       =			prepare_img()
													__UpperCamelCase       =			image_processor(images=SCREAMING_SNAKE_CASE_     ,	return_tensors='''pt'''			).to(SCREAMING_SNAKE_CASE_			)

													# forward pass
													with torch.no_grad():
																			__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_			)

													# verify the logits
													__UpperCamelCase       =			torch.Size((1, 1000)			)
													self.assertEqual(outputs.logits.shape     ,	SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]			).to(SCREAMING_SNAKE_CASE_			)

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






import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  1



							@register_to_config
							def __init__(  self     ,	SCREAMING_SNAKE_CASE_ = 1000     ,	SCREAMING_SNAKE_CASE_ = None			)->   Tuple:





													'''simple docstring'''






													self.set_timesteps(SCREAMING_SNAKE_CASE_			)

													# standard deviation of the initial noise distribution
													__UpperCamelCase       =			1.0

													# For now we only support F-PNDM, i.e. the runge-kutta method
													# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
													# mainly at formula (9), (12), (13) and the Algorithm 2.
													__UpperCamelCase       =			4

													# running values
													__UpperCamelCase       =			[]



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			num_inference_steps
													__UpperCamelCase       =			torch.linspace(1     ,	0     ,	num_inference_steps + 1			)[:-1]
													__UpperCamelCase       =			torch.cat([steps, torch.tensor([0.0]			)]			)

													if self.config.trained_betas is not None:
																			__UpperCamelCase       =			torch.tensor(self.config.trained_betas     ,	dtype=torch.floataa			)
													else:
																			__UpperCamelCase       =			torch.sin(steps * math.pi / 2			) ** 2

													__UpperCamelCase       =			(1.0 - self.betas**2) ** 0.5

													__UpperCamelCase       =			(torch.atana(self.betas     ,	self.alphas			) / math.pi * 2)[:-1]
													__UpperCamelCase       =			timesteps.to(SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			[]



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = True     ,	)->   Union[SchedulerOutput, Tuple]:





													'''simple docstring'''






													if self.num_inference_steps is None:
																			raise ValueError(
																			    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''			)

													__UpperCamelCase       =			(self.timesteps == timestep).nonzero().item()
													__UpperCamelCase       =			timestep_index + 1

													__UpperCamelCase       =			sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
													self.ets.append(SCREAMING_SNAKE_CASE_			)

													if len(self.ets			) == 1:
																			__UpperCamelCase       =			self.ets[-1]
													elif len(self.ets			) == 2:
																			__UpperCamelCase       =			(3 * self.ets[-1] - self.ets[-2]) / 2
													elif len(self.ets			) == 3:
																			__UpperCamelCase       =			(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
													else:
																			__UpperCamelCase       =			(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

													__UpperCamelCase       =			self._get_prev_sample(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)

													if not return_dict:
																			return (prev_sample,)

													return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)->   torch.FloatTensor:





													'''simple docstring'''






													return sample



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			self.alphas[timestep_index]
													__UpperCamelCase       =			self.betas[timestep_index]

													__UpperCamelCase       =			self.alphas[prev_timestep_index]
													__UpperCamelCase       =			self.betas[prev_timestep_index]

													__UpperCamelCase       =			(sample - sigma * ets) / max(SCREAMING_SNAKE_CASE_     ,	1E-8			)
													__UpperCamelCase       =			next_alpha * pred + ets * next_sigma

													return prev_sample



							def __len__(  self			)->   Optional[int]:





													'''simple docstring'''






													return self.config.num_train_timesteps




