






import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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 transformers import (
		    MODEL_MAPPING,
		    SegformerForImageClassification,
		    SegformerForSemanticSegmentation,
		    SegformerModel,
		)
		from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import SegformerImageProcessor





class 	snake_case__ (							_lowerCAmelCase				):
	def       __magic_name__    (    self			)							->				Optional[int]:
							__magic_name__      :       Optional[int]       						= self.config_class(**self.inputs_dict			)
							self.parent.assertTrue(hasattr(lowerCAmelCase__      ,       """hidden_sizes"""			)			)
							self.parent.assertTrue(hasattr(lowerCAmelCase__      ,       """num_attention_heads"""			)			)
							self.parent.assertTrue(hasattr(lowerCAmelCase__      ,       """num_encoder_blocks"""			)			)





class 	snake_case__ :
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=13      ,       lowerCAmelCase__=64      ,       lowerCAmelCase__=3      ,       lowerCAmelCase__=4      ,       lowerCAmelCase__=[2, 2, 2, 2]      ,       lowerCAmelCase__=[8, 4, 2, 1]      ,       lowerCAmelCase__=[16, 32, 64, 1_28]      ,       lowerCAmelCase__=[1, 4, 8, 16]      ,       lowerCAmelCase__=[1, 2, 4, 8]      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__="gelu"      ,       lowerCAmelCase__=0.1      ,       lowerCAmelCase__=0.1      ,       lowerCAmelCase__=0.0_2      ,       lowerCAmelCase__=3      ,       lowerCAmelCase__=None      ,       )							->				str:
							__magic_name__      :       Union[str, Any]       						= parent
							__magic_name__      :       List[Any]       						= batch_size
							__magic_name__      :       Optional[Any]       						= image_size
							__magic_name__      :       str       						= num_channels
							__magic_name__      :       Tuple       						= num_encoder_blocks
							__magic_name__      :       Dict       						= sr_ratios
							__magic_name__      :       Union[str, Any]       						= depths
							__magic_name__      :       Tuple       						= hidden_sizes
							__magic_name__      :       Dict       						= downsampling_rates
							__magic_name__      :       Dict       						= num_attention_heads
							__magic_name__      :       str       						= is_training
							__magic_name__      :       List[str]       						= use_labels
							__magic_name__      :       Tuple       						= hidden_act
							__magic_name__      :       Optional[Any]       						= hidden_dropout_prob
							__magic_name__      :       List[str]       						= attention_probs_dropout_prob
							__magic_name__      :       Any       						= initializer_range
							__magic_name__      :       Dict       						= num_labels
							__magic_name__      :       Union[str, Any]       						= scope
	def       __magic_name__    (    self			)							->				List[str]:
							__magic_name__      :       Optional[int]       						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

							__magic_name__      :       Union[str, Any]       						= None
							if self.use_labels:
													__magic_name__      :       Dict       						= ids_tensor([self.batch_size, self.image_size, self.image_size]      ,       self.num_labels			)

							__magic_name__      :       Any       						= self.get_config()
							return config, pixel_values, labels
	def       __magic_name__    (    self			)							->				str:
							return SegformerConfig(
							    image_size=self.image_size      ,       num_channels=self.num_channels      ,       num_encoder_blocks=self.num_encoder_blocks      ,       depths=self.depths      ,       hidden_sizes=self.hidden_sizes      ,       num_attention_heads=self.num_attention_heads      ,       hidden_act=self.hidden_act      ,       hidden_dropout_prob=self.hidden_dropout_prob      ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,       initializer_range=self.initializer_range      ,       )
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Dict:
							__magic_name__      :       str       						= SegformerModel(config=lowerCAmelCase__			)
							model.to(lowerCAmelCase__			)
							model.eval()
							__magic_name__      :       List[str]       						= model(lowerCAmelCase__			)
							__magic_name__      :       Any       						= self.image_size // (self.downsampling_rates[-1] * 2)
							self.parent.assertEqual(
							    result.last_hidden_state.shape      ,       (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Optional[Any]:
							__magic_name__      :       List[Any]       						= self.num_labels
							__magic_name__      :       Any       						= SegformerForSemanticSegmentation(lowerCAmelCase__			)
							model.to(lowerCAmelCase__			)
							model.eval()
							__magic_name__      :       Optional[int]       						= model(lowerCAmelCase__			)
							self.parent.assertEqual(
							    result.logits.shape      ,       (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)			)
							__magic_name__      :       int       						= model(lowerCAmelCase__      ,       labels=lowerCAmelCase__			)
							self.parent.assertEqual(
							    result.logits.shape      ,       (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)			)
							self.parent.assertGreater(result.loss      ,       0.0			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Any:
							__magic_name__      :       Tuple       						= 1
							__magic_name__      :       Union[str, Any]       						= SegformerForSemanticSegmentation(config=lowerCAmelCase__			)
							model.to(lowerCAmelCase__			)
							model.eval()
							__magic_name__      :       Optional[Any]       						= torch.randint(0      ,       1      ,       (self.batch_size, self.image_size, self.image_size)			).to(lowerCAmelCase__			)
							__magic_name__      :       Union[str, Any]       						= model(lowerCAmelCase__      ,       labels=lowerCAmelCase__			)
							self.parent.assertGreater(result.loss      ,       0.0			)







	def       __magic_name__    (    self			)							->				Any:
							__magic_name__      :       Any       						= self.prepare_config_and_inputs()
							__magic_name__      :       int       						= config_and_inputs
							__magic_name__      :       Dict       						= {"""pixel_values""": pixel_values}
							return config, inputs_dict





@require_torch
class 	snake_case__ (							_lowerCAmelCase						,  _lowerCAmelCase						,  unittest.TestCase				):
	lowercase__						:					Optional[int]											=   (
	    (
	        SegformerModel,
	        SegformerForSemanticSegmentation,
	        SegformerForImageClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	lowercase__						:					Tuple											=   (
	    {
	        '''feature-extraction''': SegformerModel,
	        '''image-classification''': SegformerForImageClassification,
	        '''image-segmentation''': SegformerForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	lowercase__						:					Any											=   True
	lowercase__						:					Any											=   False
	lowercase__						:					Dict											=   False
	lowercase__						:					int											=   False
	def       __magic_name__    (    self			)							->				str:
							__magic_name__      :       Optional[Any]       						= SegformerModelTester(self			)
							__magic_name__      :       str       						= SegformerConfigTester(self      ,       config_class=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Any:
							self.config_tester.run_common_tests()
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       Dict       						= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       Union[str, Any]       						= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_binary_image_segmentation(*lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       Optional[Any]       						= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_segmentation(*lowerCAmelCase__			)
	@unittest.skip("""SegFormer does not use inputs_embeds"""			)
	def       __magic_name__    (    self			)							->				List[str]:
							pass
	@unittest.skip("""SegFormer does not have get_input_embeddings method and get_output_embeddings methods"""			)
	def       __magic_name__    (    self			)							->				Optional[int]:
							pass
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__      :       List[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													__magic_name__      :       Dict       						= model_class(lowerCAmelCase__			)
													__magic_name__      :       List[Any]       						= inspect.signature(model.forward			)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													__magic_name__      :       Tuple       						= [*signature.parameters.keys()]

													__magic_name__      :       Optional[int]       						= ["""pixel_values"""]
													self.assertListEqual(arg_names[:1]      ,       lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				str:
							__magic_name__      :       Optional[int]       						= self.model_tester.prepare_config_and_inputs_for_common()
							__magic_name__      :       Dict       						= True

							for model_class in self.all_model_classes:
													__magic_name__      :       Union[str, Any]       						= True
													__magic_name__      :       Optional[int]       						= False
													__magic_name__      :       int       						= True
													__magic_name__      :       List[Any]       						= model_class(lowerCAmelCase__			)
													model.to(lowerCAmelCase__			)
													model.eval()
													with torch.no_grad():
																			__magic_name__      :       List[Any]       						= model(**self._prepare_for_class(lowerCAmelCase__      ,       lowerCAmelCase__			)			)
													__magic_name__      :       List[str]       						= outputs.attentions

													__magic_name__      :       str       						= sum(self.model_tester.depths			)
													self.assertEqual(len(lowerCAmelCase__			)      ,       lowerCAmelCase__			)

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													__magic_name__      :       List[Any]       						= True
													__magic_name__      :       Dict       						= model_class(lowerCAmelCase__			)
													model.to(lowerCAmelCase__			)
													model.eval()
													with torch.no_grad():
																			__magic_name__      :       Any       						= model(**self._prepare_for_class(lowerCAmelCase__      ,       lowerCAmelCase__			)			)
													__magic_name__      :       List[Any]       						= outputs.attentions

													self.assertEqual(len(lowerCAmelCase__			)      ,       lowerCAmelCase__			)

													# verify the first attentions (first block, first layer)
													__magic_name__      :       str       						= (self.model_tester.image_size // 4) ** 2
													__magic_name__      :       List[Any]       						= (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(attentions[0].shape[-3:]			)      ,       [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]      ,       )

													# verify the last attentions (last block, last layer)
													__magic_name__      :       str       						= (self.model_tester.image_size // 32) ** 2
													__magic_name__      :       int       						= (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
													self.assertListEqual(
													    list(attentions[-1].shape[-3:]			)      ,       [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]      ,       )
													__magic_name__      :       Tuple       						= len(lowerCAmelCase__			)

													# Check attention is always last and order is fine
													__magic_name__      :       List[Any]       						= True
													__magic_name__      :       Optional[int]       						= True
													__magic_name__      :       Union[str, Any]       						= model_class(lowerCAmelCase__			)
													model.to(lowerCAmelCase__			)
													model.eval()
													with torch.no_grad():
																			__magic_name__      :       Optional[Any]       						= model(**self._prepare_for_class(lowerCAmelCase__      ,       lowerCAmelCase__			)			)

													self.assertEqual(out_len + 1      ,       len(lowerCAmelCase__			)			)

													__magic_name__      :       Dict       						= outputs.attentions

													self.assertEqual(len(lowerCAmelCase__			)      ,       lowerCAmelCase__			)
													# verify the first attentions (first block, first layer)
													__magic_name__      :       Tuple       						= (self.model_tester.image_size // 4) ** 2
													__magic_name__      :       Dict       						= (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]			)      ,       [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]      ,       )
	def       __magic_name__    (    self			)							->				Dict:
							def check_hidden_states_output(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			):
													__magic_name__      :       str       						= model_class(lowerCAmelCase__			)
													model.to(lowerCAmelCase__			)
													model.eval()

													with torch.no_grad():
																			__magic_name__      :       Union[str, Any]       						= model(**self._prepare_for_class(lowerCAmelCase__      ,       lowerCAmelCase__			)			)

													__magic_name__      :       List[str]       						= outputs.hidden_states

													__magic_name__      :       Tuple       						= self.model_tester.num_encoder_blocks
													self.assertEqual(len(lowerCAmelCase__			)      ,       lowerCAmelCase__			)

													# verify the first hidden states (first block)
													self.assertListEqual(
													    list(hidden_states[0].shape[-3:]			)      ,       [
													        self.model_tester.hidden_sizes[0],
													        self.model_tester.image_size // 4,
													        self.model_tester.image_size // 4,
													    ]      ,       )

							__magic_name__      :       Dict       						= self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													__magic_name__      :       Optional[int]       						= True
													check_hidden_states_output(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													__magic_name__      :       str       						= True

													check_hidden_states_output(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Optional[int]:
							if not self.model_tester.is_training:
													return

							__magic_name__      :       Optional[int]       						= self.model_tester.prepare_config_and_inputs_for_common()
							__magic_name__      :       List[Any]       						= True

							for model_class in self.all_model_classes:
													if model_class in get_values(lowerCAmelCase__			):
																			continue

													__magic_name__      :       int       						= model_class(lowerCAmelCase__			)
													model.to(lowerCAmelCase__			)
													model.train()
													__magic_name__      :       int       						= self._prepare_for_class(lowerCAmelCase__      ,       lowerCAmelCase__      ,       return_labels=lowerCAmelCase__			)
													__magic_name__      :       Union[str, Any]       						= model(**lowerCAmelCase__			).loss
													loss.backward()
	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""			)
	def       __magic_name__    (    self			)							->				Any:
							pass







	@slow
	def       __magic_name__    (    self			)							->				List[Any]:
							for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__magic_name__      :       int       						= SegformerModel.from_pretrained(lowerCAmelCase__			)
													self.assertIsNotNone(lowerCAmelCase__			)



def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       int       						= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
						return image





@require_torch
class 	snake_case__ (							unittest.TestCase				):
	@slow
	def       __magic_name__    (    self			)							->				Any:
							# only resize + normalize
							__magic_name__      :       Tuple       						= SegformerImageProcessor(
							    image_scale=(5_12, 5_12)      ,       keep_ratio=lowerCAmelCase__      ,       align=lowerCAmelCase__      ,       do_random_crop=lowerCAmelCase__			)
							__magic_name__      :       List[str]       						= SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512"""			).to(
							    lowerCAmelCase__			)

							__magic_name__      :       List[str]       						= prepare_img()
							__magic_name__      :       Any       						= image_processor(images=lowerCAmelCase__      ,       return_tensors="""pt"""			)
							__magic_name__      :       Tuple       						= encoded_inputs.pixel_values.to(lowerCAmelCase__			)

							with torch.no_grad():
													__magic_name__      :       List[Any]       						= model(lowerCAmelCase__			)

							__magic_name__      :       int       						= torch.Size((1, model.config.num_labels, 1_28, 1_28)			)
							self.assertEqual(outputs.logits.shape      ,       lowerCAmelCase__			)

							__magic_name__      :       List[str]       						= torch.tensor(
							    [
							        [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
							        [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
							        [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
							    ]			).to(lowerCAmelCase__			)
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]      ,       lowerCAmelCase__      ,       atol=1e-4			)			)
	@slow
	def       __magic_name__    (    self			)							->				Optional[Any]:
							# only resize + normalize
							__magic_name__      :       List[str]       						= SegformerImageProcessor(
							    image_scale=(5_12, 5_12)      ,       keep_ratio=lowerCAmelCase__      ,       align=lowerCAmelCase__      ,       do_random_crop=lowerCAmelCase__			)
							__magic_name__      :       Any       						= SegformerForSemanticSegmentation.from_pretrained(
							    """nvidia/segformer-b1-finetuned-cityscapes-1024-1024"""			).to(lowerCAmelCase__			)

							__magic_name__      :       Union[str, Any]       						= prepare_img()
							__magic_name__      :       Tuple       						= image_processor(images=lowerCAmelCase__      ,       return_tensors="""pt"""			)
							__magic_name__      :       int       						= encoded_inputs.pixel_values.to(lowerCAmelCase__			)

							with torch.no_grad():
													__magic_name__      :       Tuple       						= model(lowerCAmelCase__			)

							__magic_name__      :       Union[str, Any]       						= torch.Size((1, model.config.num_labels, 1_28, 1_28)			)
							self.assertEqual(outputs.logits.shape      ,       lowerCAmelCase__			)

							__magic_name__      :       List[Any]       						= torch.tensor(
							    [
							        [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
							        [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
							        [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
							    ]			).to(lowerCAmelCase__			)
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]      ,       lowerCAmelCase__      ,       atol=1e-1			)			)







	@slow
	def       __magic_name__    (    self			)							->				List[Any]:
							# only resize + normalize
							__magic_name__      :       Optional[Any]       						= SegformerImageProcessor(
							    image_scale=(5_12, 5_12)      ,       keep_ratio=lowerCAmelCase__      ,       align=lowerCAmelCase__      ,       do_random_crop=lowerCAmelCase__			)
							__magic_name__      :       List[Any]       						= SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512"""			).to(
							    lowerCAmelCase__			)

							__magic_name__      :       Any       						= prepare_img()
							__magic_name__      :       List[str]       						= image_processor(images=lowerCAmelCase__      ,       return_tensors="""pt"""			)
							__magic_name__      :       Union[str, Any]       						= encoded_inputs.pixel_values.to(lowerCAmelCase__			)

							with torch.no_grad():
													__magic_name__      :       List[str]       						= model(lowerCAmelCase__			)

							__magic_name__      :       Optional[int]       						= outputs.logits.detach().cpu()

							__magic_name__      :       Union[str, Any]       						= image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase__      ,       target_sizes=[(5_00, 3_00)]			)
							__magic_name__      :       Tuple       						= torch.Size((5_00, 3_00)			)
							self.assertEqual(segmentation[0].shape      ,       lowerCAmelCase__			)

							__magic_name__      :       Optional[int]       						= image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase__			)
							__magic_name__      :       List[str]       						= torch.Size((1_28, 1_28)			)
							self.assertEqual(segmentation[0].shape      ,       lowerCAmelCase__			)








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__ (							_lowerCAmelCase				):
	lowercase__						:					torch.FloatTensor
	lowercase__						:					Optional[torch.FloatTensor]											=   None



def    UpperCamelCase						(	_A,   _A=0.999,   _A="cosine",   ):






						"""simple docstring"""







						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

						else:
												raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'			)

						__magic_name__      :       Optional[Any]       						= []
						for i in range(_A			):
												__magic_name__      :       Dict       						= i / num_diffusion_timesteps
												__magic_name__      :       Any       						= (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(_A			) / alpha_bar_fn(_A			),   _A			)			)
						return torch.tensor(_A,   dtype=torch.floataa			)





class 	snake_case__ (							_lowerCAmelCase						,  _lowerCAmelCase				):
	@register_to_config
	def __init__(    self      ,       lowerCAmelCase__ = 10_00      ,       lowerCAmelCase__ = "fixed_small_log"      ,       lowerCAmelCase__ = True      ,       lowerCAmelCase__ = 1.0      ,       lowerCAmelCase__ = "epsilon"      ,       lowerCAmelCase__ = "squaredcos_cap_v2"      ,       )							->				Union[str, Any]:
							if beta_schedule != "squaredcos_cap_v2":
													raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""			)

							__magic_name__      :       Tuple       						= betas_for_alpha_bar(lowerCAmelCase__			)

							__magic_name__      :       Union[str, Any]       						= 1.0 - self.betas
							__magic_name__      :       str       						= torch.cumprod(self.alphas      ,       dim=0			)
							__magic_name__      :       Any       						= torch.tensor(1.0			)

							# standard deviation of the initial noise distribution
							__magic_name__      :       Tuple       						= 1.0

							# setable values
							__magic_name__      :       List[Any]       						= None
							__magic_name__      :       int       						= torch.from_numpy(np.arange(0      ,       lowerCAmelCase__			)[::-1].copy()			)

							__magic_name__      :       List[Any]       						= variance_type
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				torch.FloatTensor:
							return sample
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				str:
							__magic_name__      :       List[Any]       						= num_inference_steps
							__magic_name__      :       Union[str, Any]       						= (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
							__magic_name__      :       List[Any]       						= (np.arange(0      ,       lowerCAmelCase__			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
							__magic_name__      :       Dict       						= torch.from_numpy(lowerCAmelCase__			).to(lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None			)							->				Tuple:
							if prev_timestep is None:
													__magic_name__      :       int       						= t - 1

							__magic_name__      :       Optional[Any]       						= self.alphas_cumprod[t]
							__magic_name__      :       Any       						= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
							__magic_name__      :       Tuple       						= 1 - alpha_prod_t
							__magic_name__      :       int       						= 1 - alpha_prod_t_prev

							if prev_timestep == t - 1:
													__magic_name__      :       List[str]       						= self.betas[t]
							else:
													__magic_name__      :       List[Any]       						= 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
							__magic_name__      :       Dict       						= beta_prod_t_prev / beta_prod_t * beta

							if variance_type is None:
													__magic_name__      :       str       						= self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small_log":
													__magic_name__      :       str       						= torch.log(torch.clamp(lowerCAmelCase__      ,       min=1e-2_0			)			)
													__magic_name__      :       Optional[Any]       						= torch.exp(0.5 * variance			)
							elif variance_type == "learned_range":
													# NOTE difference with DDPM scheduler
													__magic_name__      :       List[str]       						= variance.log()
													__magic_name__      :       Optional[int]       						= beta.log()

													__magic_name__      :       Any       						= (predicted_variance + 1) / 2
													__magic_name__      :       Any       						= frac * max_log + (1 - frac) * min_log

							return variance
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__ = True      ,       )							->				Union[UnCLIPSchedulerOutput, Tuple]:
							__magic_name__      :       List[Any]       						= timestep

							if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
													__magic_name__						,__magic_name__      :       List[Any]       						= torch.split(lowerCAmelCase__      ,       sample.shape[1]      ,       dim=1			)
							else:
													__magic_name__      :       List[str]       						= None

							# 1. compute alphas, betas
							if prev_timestep is None:
													__magic_name__      :       Union[str, Any]       						= t - 1

							__magic_name__      :       List[str]       						= self.alphas_cumprod[t]
							__magic_name__      :       Dict       						= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
							__magic_name__      :       Any       						= 1 - alpha_prod_t
							__magic_name__      :       Dict       						= 1 - alpha_prod_t_prev

							if prev_timestep == t - 1:
													__magic_name__      :       Union[str, Any]       						= self.betas[t]
													__magic_name__      :       int       						= self.alphas[t]
							else:
													__magic_name__      :       Tuple       						= 1 - alpha_prod_t / alpha_prod_t_prev
													__magic_name__      :       Tuple       						= 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":
													__magic_name__      :       Optional[int]       						= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
													__magic_name__      :       Tuple       						= 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:
													__magic_name__      :       Tuple       						= torch.clamp(
													    lowerCAmelCase__      ,       -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
							__magic_name__      :       List[Any]       						= (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
							__magic_name__      :       Dict       						= 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
							__magic_name__      :       str       						= pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							__magic_name__      :       Tuple       						= 0
							if t > 0:
													__magic_name__      :       Any       						= randn_tensor(
													    model_output.shape      ,       dtype=model_output.dtype      ,       generator=lowerCAmelCase__      ,       device=model_output.device			)

													__magic_name__      :       Tuple       						= self._get_variance(
													    lowerCAmelCase__      ,       predicted_variance=lowerCAmelCase__      ,       prev_timestep=lowerCAmelCase__      ,       )

													if self.variance_type == "fixed_small_log":
																			__magic_name__      :       Tuple       						= variance
													elif self.variance_type == "learned_range":
																			__magic_name__      :       int       						= (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."""			)

													__magic_name__      :       Tuple       						= variance * variance_noise

							__magic_name__      :       List[str]       						= pred_prev_sample + variance

							if not return_dict:
													return (pred_prev_sample,)

							return UnCLIPSchedulerOutput(prev_sample=lowerCAmelCase__      ,       pred_original_sample=lowerCAmelCase__			)







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       )							->				torch.FloatTensor:
							# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
							__magic_name__      :       List[str]       						= self.alphas_cumprod.to(device=original_samples.device      ,       dtype=original_samples.dtype			)
							__magic_name__      :       Any       						= timesteps.to(original_samples.device			)

							__magic_name__      :       int       						= alphas_cumprod[timesteps] ** 0.5
							__magic_name__      :       Union[str, Any]       						= sqrt_alpha_prod.flatten()
							while len(sqrt_alpha_prod.shape			) < len(original_samples.shape			):
													__magic_name__      :       int       						= sqrt_alpha_prod.unsqueeze(-1			)

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

							__magic_name__      :       Any       						= sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples
