





"""simple docstring"""






from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


A       	=     8

def 			__A					(	a_  :str      ,				a_  :Any=BITS)						->						Optional[Any]:
				__a       :  List[Any]					=     x.device

				__a       :  List[Any]					=     (x * 2_55).int().clamp(0      ,				2_55)

				__a       :  List[Any]					=     2 ** torch.arange(bits - 1      ,				-1      ,				-1      ,				device=__lowerCamelCase)
				__a       :  Tuple					=     rearrange(__lowerCamelCase      ,				'''d -> d 1 1''')
				__a       :  Optional[int]					=     rearrange(__lowerCamelCase      ,				'''b c h w -> b c 1 h w''')

				__a       :  Tuple					=     ((x & mask) != 0).float()
				__a       :  Dict					=     rearrange(__lowerCamelCase      ,				'''b c d h w -> b (c d) h w''')
				__a       :  Optional[Any]					=     bits * 2 - 1
				return bits

def 			__A					(	a_  :Dict      ,				a_  :str=BITS)						->						List[Any]:
				__a       :  str					=     x.device

				__a       :  Tuple					=     (x > 0).int()
				__a       :  Optional[Any]					=     2 ** torch.arange(bits - 1      ,				-1      ,				-1      ,				device=__lowerCamelCase      ,				dtype=torch.intaa)

				__a       :  List[Any]					=     rearrange(__lowerCamelCase      ,				'''d -> d 1 1''')
				__a       :  Tuple					=     rearrange(__lowerCamelCase      ,				'''b (c d) h w -> b c d h w'''      ,				d=8)
				__a       :  Union[str, Any]					=     reduce(x * mask      ,				'''b c d h w -> b c h w'''      ,				'''sum''')
				return (dec / 2_55).clamp(0.0      ,				1.0)

def 			__A					(	self  :str      ,				a_  :Union[str, Any]      ,				a_  :Optional[int]      ,				a_  :Dict      ,				a_  :Any = 0.0      ,				a_  :Optional[Any] = True      ,				a_  :Any=None      ,				a_  :int = True      ,				)						->						Union[DDIMSchedulerOutput, Tuple]:
				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''')

				# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
				# Ideally, read DDIM paper in-detail understanding

				# Notation (<variable name> -> <name in paper>
				# - pred_noise_t -> e_theta(x_t, t)
				# - pred_original_sample -> f_theta(x_t, t) or x_0
				# - std_dev_t -> sigma_t
				# - eta -> η
				# - pred_sample_direction -> "direction pointing to x_t"
				# - pred_prev_sample -> "x_t-1"

				# 1. get previous step value (=t-1)
				__a       :  Union[str, Any]					=     timestep - self.config.num_train_timesteps // self.num_inference_steps

				# 2. compute alphas, betas
				__a       :  Tuple					=     self.alphas_cumprod[timestep]
				__a       :  Tuple					=     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

				__a       :  str					=     1 - alpha_prod_t

				# 3. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				__a       :  Optional[int]					=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

				# 4. Clip "predicted x_0"
				__a       :  Dict					=     self.bit_scale
				if self.config.clip_sample:
								__a       :  Union[str, Any]					=     torch.clamp(__lowerCamelCase      ,				-scale      ,				__lowerCamelCase)

				# 5. compute variance: "sigma_t(η)" -> see formula (16)
				# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
				__a       :  Optional[Any]					=     self._get_variance(__lowerCamelCase      ,				__lowerCamelCase)
				__a       :  Optional[int]					=     eta * variance ** 0.5

				if use_clipped_model_output:
								# the model_output is always re-derived from the clipped x_0 in Glide
								__a       :  Any					=     (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

				# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				__a       :  Dict					=     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

				# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				__a       :  Tuple					=     alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

				if eta > 0:
								# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
								__a       :  Union[str, Any]					=     model_output.device if torch.is_tensor(__lowerCamelCase) else '''cpu'''
								__a       :  Union[str, Any]					=     torch.randn(model_output.shape      ,				dtype=model_output.dtype      ,				generator=__lowerCamelCase).to(__lowerCamelCase)
								__a       :  Dict					=     self._get_variance(__lowerCamelCase      ,				__lowerCamelCase) ** 0.5 * eta * noise

								__a       :  List[Any]					=     prev_sample + variance

				if not return_dict:
								return (prev_sample,)

				return DDIMSchedulerOutput(prev_sample=__lowerCamelCase      ,				pred_original_sample=__lowerCamelCase)

def 			__A					(	self  :int      ,				a_  :str      ,				a_  :Union[str, Any]      ,				a_  :Any      ,				a_  :Dict="epsilon"      ,				a_  :Union[str, Any]=None      ,				a_  :str = True      ,				)						->						Union[DDPMSchedulerOutput, Tuple]:
				__a       :  List[str]					=     timestep

				if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
								__a       :  Optional[Any]					=     torch.split(__lowerCamelCase      ,				sample.shape[1]      ,				dim=1)
				else:
								__a       :  Optional[int]					=     None

				# 1. compute alphas, betas
				__a       :  Tuple					=     self.alphas_cumprod[t]
				__a       :  Tuple					=     self.alphas_cumprod[t - 1] if t > 0 else self.one
				__a       :  Optional[Any]					=     1 - alpha_prod_t
				__a       :  Union[str, Any]					=     1 - alpha_prod_t_prev

				# 2. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
				if prediction_type == "epsilon":
								__a       :  Dict					=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif prediction_type == "sample":
								__a       :  Optional[int]					=     model_output
				else:
								raise ValueError(F"""Unsupported prediction_type {prediction_type}.""")

				# 3. Clip "predicted x_0"
				__a       :  Tuple					=     self.bit_scale
				if self.config.clip_sample:
								__a       :  Union[str, Any]					=     torch.clamp(__lowerCamelCase      ,				-scale      ,				__lowerCamelCase)

				# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				__a       :  Any					=     (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
				__a       :  int					=     self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

				# 5. Compute predicted previous sample µ_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				__a       :  List[str]					=     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				__a       :  Any					=     0
				if t > 0:
								__a       :  List[Any]					=     torch.randn(
								    model_output.size()      ,				dtype=model_output.dtype      ,				layout=model_output.layout      ,				generator=__lowerCamelCase).to(model_output.device)
								__a       :  Dict					=     (self._get_variance(__lowerCamelCase      ,				predicted_variance=__lowerCamelCase) ** 0.5) * noise

				__a       :  str					=     pred_prev_sample + variance

				if not return_dict:
								return (pred_prev_sample,)

				return DDPMSchedulerOutput(prev_sample=__lowerCamelCase      ,				pred_original_sample=__lowerCamelCase)






class  __lowercase	(	A_							):







					'''simple docstring'''




					def __init__(  self		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase = 1.0		,	):
									super().__init__()
									__a       :  Optional[Any]					=     bit_scale
									__a       :  Dict					=     (
									    ddim_bit_scheduler_step if isinstance(_snake_case		,	_snake_case   ) else ddpm_bit_scheduler_step
									)

									self.register_modules(unet=_snake_case		,	scheduler=_snake_case   )







					@torch.no_grad()
					def __call__(  self		,	_UpperCAmelCase = 256		,	_UpperCAmelCase = 256		,	_UpperCAmelCase = 50		,	_UpperCAmelCase = None		,	_UpperCAmelCase = 1		,	_UpperCAmelCase = "pil"		,	_UpperCAmelCase = True		,	**_UpperCAmelCase		,	):
									__a       :  Dict					=     torch.randn(
									    (batch_size, self.unet.config.in_channels, height, width)		,	generator=_snake_case		,	)
									__a       :  Dict					=     decimal_to_bits(_snake_case   ) * self.bit_scale
									__a       :  Optional[Any]					=     latents.to(self.device   )

									self.scheduler.set_timesteps(_snake_case   )

									for t in self.progress_bar(self.scheduler.timesteps   ):
													# predict the noise residual
													__a       :  List[Any]					=     self.unet(_snake_case		,	_snake_case   ).sample

													# compute the previous noisy sample x_t -> x_t-1
													__a       :  Any					=     self.scheduler.step(_snake_case		,	_snake_case		,	_snake_case   ).prev_sample

									__a       :  str					=     bits_to_decimal(_snake_case   )

									if output_type == "pil":
													__a       :  str					=     self.numpy_to_pil(_snake_case   )

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=_snake_case   )




"""simple docstring"""







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						:  str			,_snake_case						:  List[Any]			,_snake_case						:  Optional[int]=3			,_snake_case						:  Optional[int]=32			,_snake_case						:  Union[str, Any]=3			,_snake_case						:  int=10			,_snake_case						:  List[str]=[10, 20, 30, 40]			,_snake_case						:  Any=[1, 1, 2, 1]			,_snake_case						:  int=True			,_snake_case						:  Optional[Any]=True			,_snake_case						:  Union[str, Any]="relu"			,_snake_case						:  Dict=3			,_snake_case						:  Any=None			,)					->		str:






				"""simple docstring"""



				lowercase__   :    int				=			parent
				lowercase__   :    Optional[Any]				=			batch_size
				lowercase__   :    Optional[Any]				=			image_size
				lowercase__   :    Optional[Any]				=			num_channels
				lowercase__   :    Optional[Any]				=			embeddings_size
				lowercase__   :    Optional[Any]				=			hidden_sizes
				lowercase__   :    str				=			depths
				lowercase__   :    Tuple				=			is_training
				lowercase__   :    List[Any]				=			use_labels
				lowercase__   :    Union[str, Any]				=			hidden_act
				lowercase__   :    Union[str, Any]				=			num_labels
				lowercase__   :    Tuple				=			scope
				lowercase__   :    Optional[Any]				=			len(_snake_case						)







		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Tuple:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

				lowercase__   :    Tuple				=			None
				if self.use_labels:
						lowercase__   :    Dict				=			ids_tensor([self.batch_size]			,self.num_labels						)

				lowercase__   :    int				=			self.get_config()

				return config, pixel_values, labels







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Optional[Any]:






				"""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 					UpperCAmelCase    (	self						:  List[str]			,_snake_case						:  Optional[int]			,_snake_case						:  int			,_snake_case						:  Tuple						)					->		List[Any]:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			TFResNetModel(config=_snake_case						)
				lowercase__   :    List[str]				=			model(_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						:  Optional[int]			,_snake_case						:  Optional[Any]			,_snake_case						:  int			,_snake_case						:  Any						)					->		Tuple:






				"""simple docstring"""



				lowercase__   :    Tuple				=			self.num_labels
				lowercase__   :    Union[str, Any]				=			TFResNetForImageClassification(_snake_case						)
				lowercase__   :    List[str]				=			model(_snake_case			,labels=_snake_case						)
				self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.num_labels)						)







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		str:






				"""simple docstring"""



				lowercase__   :    Dict				=			self.prepare_config_and_inputs()
				lowercase__		,			lowercase__		,			lowercase__   :    Union[str, Any]				=			config_and_inputs
				lowercase__   :    Dict				=			{'''pixel_values''': pixel_values}
				return config, inputs_dict





@require_tf
class        __A       (	A_     ,A_     ,unittest.TestCase    ):







		'''simple docstring'''





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

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







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






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			TFResNetModelTester(self						)
				lowercase__   :    int				=			ConfigTester(self			,config_class=_snake_case			,has_text_modality=_snake_case						)







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






				"""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 					UpperCAmelCase    (	self						:  List[Any]						)					->		List[str]:






				"""simple docstring"""



				return







		@unittest.skip(reason='''ResNet does not use inputs_embeds'''						)
		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Dict:






				"""simple docstring"""



				pass







		@unittest.skip(reason='''ResNet does not support input and output embeddings'''						)
		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Optional[Any]:






				"""simple docstring"""



				pass







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






				"""simple docstring"""



				lowercase__		,			lowercase__   :    Dict				=			self.model_tester.prepare_config_and_inputs_for_common()

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

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







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Any:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_snake_case						)







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






				"""simple docstring"""



				def check_hidden_states_output(_snake_case						:  Optional[int]			,_snake_case						:  List[str]			,_snake_case						:  Optional[Any]						):
						lowercase__   :    str				=			model_class(_snake_case						)
						lowercase__   :    Union[str, Any]				=			model(**self._prepare_for_class(_snake_case			,_snake_case						)						)

						lowercase__   :    List[Any]				=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

						lowercase__   :    Tuple				=			self.model_tester.num_stages
						self.assertEqual(len(_snake_case						)			,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]			,)

				lowercase__		,			lowercase__   :    int				=			self.model_tester.prepare_config_and_inputs_for_common()
				lowercase__   :    List[Any]				=			['''basic''', '''bottleneck''']
				for model_class in self.all_model_classes:
						for layer_type in layers_type:
								lowercase__   :    List[Any]				=			layer_type
								lowercase__   :    Dict				=			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"]
								lowercase__   :    Dict				=			True

								check_hidden_states_output(_snake_case			,_snake_case			,_snake_case						)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		Any:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_snake_case						)







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






				"""simple docstring"""



				for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    Optional[Any]				=			TFResNetModel.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)





def      __UpperCAmelCase      (    )							->      Dict:
		lowercase__   :    List[str]				=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
		return image





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







		'''simple docstring'''





		@cached_property
		def 					UpperCAmelCase    (	self						:  str						)					->		Any:






				"""simple docstring"""



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







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






				"""simple docstring"""



				lowercase__   :    Tuple				=			TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)

				lowercase__   :    Any				=			self.default_image_processor
				lowercase__   :    int				=			prepare_img()
				lowercase__   :    Tuple				=			image_processor(images=_snake_case			,return_tensors='''tf'''						)

				# forward pass
				lowercase__   :    Dict				=			model(**_snake_case						)

				# verify the logits
				lowercase__   :    List[str]				=			tf.TensorShape((1, 1_000)						)
				self.assertEqual(outputs.logits.shape			,_snake_case						)

				lowercase__   :    Any				=			tf.constant([-11.1069, -9.7877, -8.3777]						)

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