

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class     snake_case_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       =42
							__UpperCAmelCase       =None


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=0.999		,					UpperCAmelCase__="cosine"		,					):

					"""simple docstring"""






					if alpha_transform_type == "cosine":

										def alpha_bar_fn(UpperCAmelCase__      ):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2      ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(UpperCAmelCase__      ):
															return math.exp(t * -12.0      )

					else:
										raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""      )

					__lowerCAmelCase					=							[]
					for i in range(UpperCAmelCase__      ):
										__lowerCAmelCase					=							i / num_diffusion_timesteps
										__lowerCAmelCase					=							(i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(UpperCAmelCase__      ) / alpha_bar_fn(UpperCAmelCase__      )		,					UpperCAmelCase__      )      )
					return torch.tensor(UpperCAmelCase__		,					dtype=torch.floataa      )





class     snake_case_			(				_a			, _a							):


							"""simple docstring"""





							@register_to_config
							def __init__( self					,						_A = 1_0_0_0					,						_A = "fixed_small_log"					,						_A = True					,						_A = 1.0					,						_A = "epsilon"					,						_A = "squaredcos_cap_v2"					,						):
												if beta_schedule != "squaredcos_cap_v2":
																	raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''							)

												__lowerCAmelCase					=							betas_for_alpha_bar(_A							)

												__lowerCAmelCase					=							1.0 - self.betas
												__lowerCAmelCase					=							torch.cumprod(self.alphas					,						dim=0							)
												__lowerCAmelCase					=							torch.tensor(1.0							)

												# standard deviation of the initial noise distribution
												__lowerCAmelCase					=							1.0

												# setable values
												__lowerCAmelCase					=							None
												__lowerCAmelCase					=							torch.from_numpy(np.arange(0					,						_A							)[::-1].copy()							)

												__lowerCAmelCase					=							variance_type




							def        A__  ( self					,						_A					,						_A = None							):
												return sample




							def        A__  ( self					,						_A					,						_A = None							):
												__lowerCAmelCase					=							num_inference_steps
												__lowerCAmelCase					=							(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
												__lowerCAmelCase					=							(np.arange(0					,						_A							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
												__lowerCAmelCase					=							torch.from_numpy(_A							).to(_A							)




							def        A__  ( self					,						_A					,						_A=None					,						_A=None					,						_A=None							):
												if prev_timestep is None:
																	__lowerCAmelCase					=							t - 1

												__lowerCAmelCase					=							self.alphas_cumprod[t]
												__lowerCAmelCase					=							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
												__lowerCAmelCase					=							1 - alpha_prod_t
												__lowerCAmelCase					=							1 - alpha_prod_t_prev

												if prev_timestep == t - 1:
																	__lowerCAmelCase					=							self.betas[t]
												else:
																	__lowerCAmelCase					=							1 - alpha_prod_t / alpha_prod_t_prev

												# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
												# and sample from it to get previous sample
												# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
												__lowerCAmelCase					=							beta_prod_t_prev / beta_prod_t * beta

												if variance_type is None:
																	__lowerCAmelCase					=							self.config.variance_type

												# hacks - were probably added for training stability
												if variance_type == "fixed_small_log":
																	__lowerCAmelCase					=							torch.log(torch.clamp(_A					,						min=1e-2_0							)							)
																	__lowerCAmelCase					=							torch.exp(0.5 * variance							)
												elif variance_type == "learned_range":
																	# NOTE difference with DDPM scheduler
																	__lowerCAmelCase					=							variance.log()
																	__lowerCAmelCase					=							beta.log()

																	__lowerCAmelCase					=							(predicted_variance + 1) / 2
																	__lowerCAmelCase					=							frac * max_log + (1 - frac) * min_log

												return variance




							def        A__  ( self					,						_A					,						_A					,						_A					,						_A = None					,						_A=None					,						_A = True					,						):
												__lowerCAmelCase					=							timestep

												if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
																	__lowerCAmelCase,	__lowerCAmelCase					=							torch.split(_A					,						sample.shape[1]					,						dim=1							)
												else:
																	__lowerCAmelCase					=							None

												# 1. compute alphas, betas
												if prev_timestep is None:
																	__lowerCAmelCase					=							t - 1

												__lowerCAmelCase					=							self.alphas_cumprod[t]
												__lowerCAmelCase					=							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
												__lowerCAmelCase					=							1 - alpha_prod_t
												__lowerCAmelCase					=							1 - alpha_prod_t_prev

												if prev_timestep == t - 1:
																	__lowerCAmelCase					=							self.betas[t]
																	__lowerCAmelCase					=							self.alphas[t]
												else:
																	__lowerCAmelCase					=							1 - alpha_prod_t / alpha_prod_t_prev
																	__lowerCAmelCase					=							1 - beta

												# 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 self.config.prediction_type == "epsilon":
																	__lowerCAmelCase					=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
												elif self.config.prediction_type == "sample":
																	__lowerCAmelCase					=							model_output
												else:
																	raise ValueError(
																	    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
																	    ' for the UnCLIPScheduler.'							)

												# 3. Clip "predicted x_0"
												if self.config.clip_sample:
																	__lowerCAmelCase					=							torch.clamp(
																	    _A					,						-self.config.clip_sample_range					,						self.config.clip_sample_range							)

												# 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
												__lowerCAmelCase					=							(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
												__lowerCAmelCase					=							alpha ** 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
												__lowerCAmelCase					=							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

												# 6. Add noise
												__lowerCAmelCase					=							0
												if t > 0:
																	__lowerCAmelCase					=							randn_tensor(
																	    model_output.shape					,						dtype=model_output.dtype					,						generator=_A					,						device=model_output.device							)

																	__lowerCAmelCase					=							self._get_variance(
																	    _A					,						predicted_variance=_A					,						prev_timestep=_A					,						)

																	if self.variance_type == "fixed_small_log":
																						__lowerCAmelCase					=							variance
																	elif self.variance_type == "learned_range":
																						__lowerCAmelCase					=							(0.5 * variance).exp()
																	else:
																						raise ValueError(
																						    F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
																						    ' for the UnCLIPScheduler.'							)

																	__lowerCAmelCase					=							variance * variance_noise

												__lowerCAmelCase					=							pred_prev_sample + variance

												if not return_dict:
																	return (pred_prev_sample,)

												return UnCLIPSchedulerOutput(prev_sample=_A					,						pred_original_sample=_A							)




							def        A__  ( self					,						_A					,						_A					,						_A					,						):
												# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
												__lowerCAmelCase					=							self.alphas_cumprod.to(device=original_samples.device					,						dtype=original_samples.dtype							)
												__lowerCAmelCase					=							timesteps.to(original_samples.device							)

												__lowerCAmelCase					=							alphas_cumprod[timesteps] ** 0.5
												__lowerCAmelCase					=							sqrt_alpha_prod.flatten()
												while len(sqrt_alpha_prod.shape							) < len(original_samples.shape							):
																	__lowerCAmelCase					=							sqrt_alpha_prod.unsqueeze(-1							)

												__lowerCAmelCase					=							(1 - alphas_cumprod[timesteps]) ** 0.5
												__lowerCAmelCase					=							sqrt_one_minus_alpha_prod.flatten()
												while len(sqrt_one_minus_alpha_prod.shape							) < len(original_samples.shape							):
																	__lowerCAmelCase					=							sqrt_one_minus_alpha_prod.unsqueeze(-1							)

												__lowerCAmelCase					=							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
												return noisy_samples


import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
		import torch

		from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=None					,						_A=None					,						_A=None					,						_A="resnet50"					,						_A=3					,						_A=3_2					,						_A=3					,						_A=True					,						_A=True					,						):
												__lowerCAmelCase					=							parent
												__lowerCAmelCase					=							out_indices if out_indices is not None else [4]
												__lowerCAmelCase					=							stage_names
												__lowerCAmelCase					=							out_features
												__lowerCAmelCase					=							backbone
												__lowerCAmelCase					=							batch_size
												__lowerCAmelCase					=							image_size
												__lowerCAmelCase					=							num_channels
												__lowerCAmelCase					=							use_pretrained_backbone
												__lowerCAmelCase					=							is_training




							def        A__  ( self							):
												__lowerCAmelCase					=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
												__lowerCAmelCase					=							self.get_config()

												return config, pixel_values




							def        A__  ( self							):
												return TimmBackboneConfig(
												    image_size=self.image_size					,						num_channels=self.num_channels					,						out_features=self.out_features					,						out_indices=self.out_indices					,						stage_names=self.stage_names					,						use_pretrained_backbone=self.use_pretrained_backbone					,						backbone=self.backbone					,						)




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							TimmBackbone(config=_A							)
												model.to(_A							)
												model.eval()
												with torch.no_grad():
																	__lowerCAmelCase					=							model(_A							)
												self.parent.assertEqual(
												    result.feature_map[-1].shape					,						(self.batch_size, model.channels[-1], 1_4, 1_4)					,						)




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





@require_torch
@require_timm
class     snake_case_			(				_a			, _a			, _a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =(TimmBackbone,) if is_torch_available() else ()
							__UpperCAmelCase       ={"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False




							def        A__  ( self							):
												__lowerCAmelCase					=							TimmBackboneModelTester(self							)
												__lowerCAmelCase					=							ConfigTester(self					,						config_class=_A					,						has_text_modality=_A							)




							def        A__  ( self							):
												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							):
												__lowerCAmelCase					=							'resnet18'
												__lowerCAmelCase					=							'microsoft/resnet-18'

												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A					,						use_timm_backbone=_A							)
												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A							)

												self.assertEqual(len(timm_model.out_features							)					,						len(transformers_model.out_features							)							)
												self.assertEqual(len(timm_model.stage_names							)					,						len(transformers_model.stage_names							)							)
												self.assertEqual(timm_model.channels					,						transformers_model.channels							)
												# Out indices are set to the last layer by default. For timm models, we don't know
												# the number of layers in advance, so we set it to (-1,), whereas for transformers
												# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
												self.assertEqual(timm_model.out_indices					,						(-1,)							)
												self.assertEqual(transformers_model.out_indices					,						[len(timm_model.stage_names							) - 1]							)

												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A					,						use_timm_backbone=_A					,						out_indices=[1, 2, 3]							)
												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A					,						out_indices=[1, 2, 3]							)

												self.assertEqual(timm_model.out_indices					,						transformers_model.out_indices							)
												self.assertEqual(len(timm_model.out_features							)					,						len(transformers_model.out_features							)							)
												self.assertEqual(timm_model.channels					,						transformers_model.channels							)




							@unittest.skip('TimmBackbone doesn\'t support feed forward chunking'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone initialization is managed on the timm side'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'							)
							def        A__  ( self							):
												pass




							@unittest.skip('model weights aren\'t tied in TimmBackbone.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('model weights aren\'t tied in TimmBackbone.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone doesn\'t support output_attentions.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Safetensors is not supported by timm.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'							)
							def        A__  ( self							):
												pass




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

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

																	__lowerCAmelCase					=							['pixel_values']
																	self.assertListEqual(arg_names[:1]					,						_A							)




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()
												__lowerCAmelCase					=							True
												__lowerCAmelCase					=							self.has_attentions

												# no need to test all models as different heads yield the same functionality
												__lowerCAmelCase					=							self.all_model_classes[0]
												__lowerCAmelCase					=							model_class(_A							)
												model.to(_A							)

												__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A							)
												__lowerCAmelCase					=							model(**_A							)
												__lowerCAmelCase					=							outputs[0][-1]

												# Encoder-/Decoder-only models
												__lowerCAmelCase					=							outputs.hidden_states[0]
												hidden_states.retain_grad()

												if self.has_attentions:
																	__lowerCAmelCase					=							outputs.attentions[0]
																	attentions.retain_grad()

												output.flatten()[0].backward(retain_graph=_A							)

												self.assertIsNotNone(hidden_states.grad							)

												if self.has_attentions:
																	self.assertIsNotNone(attentions.grad							)




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	__lowerCAmelCase					=							model_class(_A							)
																	model.to(_A							)
																	model.eval()
																	__lowerCAmelCase					=							model(**_A							)

																	self.assertEqual(len(result.feature_maps							)					,						len(config.out_indices							)							)
																	self.assertEqual(len(model.channels							)					,						len(config.out_indices							)							)

																	# Check output of last stage is taken if out_features=None, out_indices=None
																	__lowerCAmelCase					=							copy.deepcopy(_A							)
																	__lowerCAmelCase					=							None
																	__lowerCAmelCase					=							model_class(_A							)
																	model.to(_A							)
																	model.eval()
																	__lowerCAmelCase					=							model(**_A							)

																	self.assertEqual(len(result.feature_maps							)					,						1							)
																	self.assertEqual(len(model.channels							)					,						1							)

																	# Check backbone can be initialized with fresh weights
																	__lowerCAmelCase					=							copy.deepcopy(_A							)
																	__lowerCAmelCase					=							False
																	__lowerCAmelCase					=							model_class(_A							)
																	model.to(_A							)
																	model.eval()
																	__lowerCAmelCase					=							model(**_A							)






