


'''simple docstring'''



import torch

from diffusers import DiffusionPipeline






class  UpperCamelCase__(     lowerCAmelCase ):




				def __init__(	self    :				Tuple   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				Dict   )->  int:

										"""simple docstring"""




										super().__init__()

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





				def __call__(	self    :				Optional[Any]   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			torch.randn(
										    (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,					)
										UpperCAmelCase         =			1

										UpperCAmelCase         =			self.unet(lowerCAmelCase   ,					lowerCAmelCase   ).sample
										UpperCAmelCase         =			self.scheduler.step(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ).prev_sample

										UpperCAmelCase         =			scheduler_output - scheduler_output + torch.ones_like(lowerCAmelCase   )

										return result









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowercase    :    Any			     =  {
    """configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Optional[int]			     =  [
	    """GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTBigCodeForSequenceClassification""",
	    """GPTBigCodeForTokenClassification""",
	    """GPTBigCodeForCausalLM""",
	    """GPTBigCodeModel""",
	    """GPTBigCodePreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_bigcode import (
		    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTBigCodeForCausalLM,
		    GPTBigCodeForSequenceClassification,
		    GPTBigCodeForTokenClassification,
		    GPTBigCodeModel,
		    GPTBigCodePreTrainedModel,
		)


else:
	import sys

	_lowercase    :    Tuple			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





