





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class   A							(						__lowercase    ):
      _snake_case				  =42
      _snake_case				  =42



      def __init__(						self:    int		,	_lowerCAmelCase:    UNetaDModel		,	_lowerCAmelCase:    KarrasVeScheduler							)    ->     int:
         '''simple docstring'''

         super().__init__()
         self.register_modules(unet=_lowerCAmelCase		,	scheduler=_lowerCAmelCase							)






      @torch.no_grad()
      def __call__(						self:    int		,	_lowerCAmelCase:    int = 1		,	_lowerCAmelCase:    int = 50		,	_lowerCAmelCase:    Optional[Union[torch.Generator, List[torch.Generator]]] = None		,	_lowerCAmelCase:    Optional[str] = "pil"		,	_lowerCAmelCase:    bool = True		,	**_lowerCAmelCase:    Union[str, Any]		,	)    ->     Union[Tuple, ImagePipelineOutput]:
         '''simple docstring'''

         UpperCAmelCase_           =self.unet.config.sample_size
         UpperCAmelCase_           =(batch_size, 3, img_size, img_size)

         UpperCAmelCase_           =self.unet

         # sample x_0 ~ N(0, sigma_0^2 * I)
         UpperCAmelCase_           =randn_tensor(_lowerCAmelCase		,	generator=_lowerCAmelCase		,	device=self.device							) * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(_lowerCAmelCase							)

         for t in self.progress_bar(self.scheduler.timesteps							):
            # here sigma_t == t_i from the paper
            UpperCAmelCase_           =self.scheduler.schedule[t]
            UpperCAmelCase_           =self.scheduler.schedule[t - 1] if t > 0 else 0

            # 1. Select temporarily increased noise level sigma_hat
            # 2. Add new noise to move from sample_i to sample_hat
            UpperCAmelCase_    ,							UpperCAmelCase_           =self.scheduler.add_noise_to_input(_lowerCAmelCase		,	_lowerCAmelCase		,	generator=_lowerCAmelCase							)

            # 3. Predict the noise residual given the noise magnitude `sigma_hat`
            # The model inputs and output are adjusted by following eq. (213) in [1].
            UpperCAmelCase_           =(sigma_hat / 2) * model((sample_hat + 1) / 2		,	sigma_hat / 2							).sample

            # 4. Evaluate dx/dt at sigma_hat
            # 5. Take Euler step from sigma to sigma_prev
            UpperCAmelCase_           =self.scheduler.step(_lowerCAmelCase		,	_lowerCAmelCase		,	_lowerCAmelCase		,	_lowerCAmelCase							)

            if sigma_prev != 0:
               # 6. Apply 2nd order correction
               # The model inputs and output are adjusted by following eq. (213) in [1].
               UpperCAmelCase_           =(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2		,	sigma_prev / 2							).sample
               UpperCAmelCase_           =self.scheduler.step_correct(
                   _lowerCAmelCase		,	_lowerCAmelCase		,	_lowerCAmelCase		,	_lowerCAmelCase		,	step_output.prev_sample		,	step_output["derivative"]		,	)
            UpperCAmelCase_           =step_output.prev_sample

         UpperCAmelCase_           =(sample / 2 + 0.5).clamp(0		,	1							)
         UpperCAmelCase_           =sample.cpu().permute(0		,	2		,	3		,	1							).numpy()
         if output_type == "pil":
            UpperCAmelCase_           =self.numpy_to_pil(_lowerCAmelCase							)

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=_lowerCAmelCase							)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__ :   Optional[int]						=							{
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :   Tuple						=							['''BloomTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :   Any						=							[
						    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''BloomForCausalLM''',
						    '''BloomModel''',
						    '''BloomPreTrainedModel''',
						    '''BloomForSequenceClassification''',
						    '''BloomForTokenClassification''',
						    '''BloomForQuestionAnswering''',
						]

if TYPE_CHECKING:
						from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bloom_fast import BloomTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bloom import (
												    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BloomForCausalLM,
												    BloomForQuestionAnswering,
												    BloomForSequenceClassification,
												    BloomForTokenClassification,
												    BloomModel,
												    BloomPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__ :   Tuple						=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



