






"""simple docstring"""



from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 			a	(  SCREAMING_SNAKE_CASE__      ):







    def __init__(     self				,						_lowerCamelCase				,						_lowerCamelCase			):
      super().__init__()
      self.register_modules(unet=_lowerCamelCase				,						scheduler=_lowerCamelCase			)







    @torch.no_grad()
    def __call__(     self				,						_lowerCamelCase = 1				,						_lowerCamelCase = None				,						_lowerCamelCase = 5_0				,						_lowerCamelCase = "pil"				,						_lowerCamelCase = True				,						**_lowerCamelCase				,						):
      lowercase				       =			torch.randn(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)				,						generator=_lowerCamelCase				,						)
      lowercase				       =			image.to(self.device			)

      # set step values
      self.scheduler.set_timesteps(_lowerCamelCase			)

      for t in self.progress_bar(self.scheduler.timesteps			):
        # 1. predict noise model_output
        lowercase				       =			self.unet(_lowerCamelCase				,						_lowerCamelCase			).sample

        # 2. predict previous mean of image x_t-1 and add variance depending on eta
        # eta corresponds to η in paper and should be between [0, 1]
        # do x_t -> x_t-1
        lowercase				       =			self.scheduler.step(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			).prev_sample

      lowercase				       =			(image / 2 + 0.5).clamp(0				,						1			)
      lowercase				       =			image.cpu().permute(0				,						2				,						3				,						1			).numpy()
      if output_type == "pil":
        lowercase				       =			self.numpy_to_pil(_lowerCamelCase			)

      if not return_dict:
        return (image,), "This is a local test"

      return ImagePipelineOutput(images=_lowerCamelCase			), "This is a local test"







"""simple docstring"""




from typing import TYPE_CHECKING

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


_UpperCamelCase:					Optional[Any]     				=	{
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase:					Union[str, Any]     				=	['CodeGenTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase:					str     				=	[
         'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
         'CodeGenForCausalLM',
         'CodeGenModel',
         'CodeGenPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
     from .tokenization_codegen import CodeGenTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_codegen_fast import CodeGenTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_codegen import (
              CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
              CodeGenForCausalLM,
              CodeGenModel,
              CodeGenPreTrainedModel,
          )

else:
     import sys

     _UpperCamelCase:					int     				=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
