


"""simple docstring"""






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	List[Any]:







								'''simple docstring'''




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







							@torch.no_grad()
							def __call__(						self	,					lowerCamelCase__ = 1	,					lowerCamelCase__ = None	,					lowerCamelCase__ = 50	,					lowerCamelCase__ = "pil"	,					lowerCamelCase__ = True	,					**lowerCamelCase__	,					)   ->	Union[ImagePipelineOutput, Tuple]:







								'''simple docstring'''




								snake_case__  :						Any		=      torch.randn(
								    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)	,					generator=lowerCamelCase__	,					)
								snake_case__  :						Optional[int]		=      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
									snake_case__  :						List[str]		=      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
									snake_case__  :						List[Any]		=      self.scheduler.step(lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__).prev_sample

								snake_case__  :						Optional[int]		=      (image / 2 + 0.5).clamp(0	,					1)
								snake_case__  :						List[Any]		=      image.cpu().permute(0	,					2	,					3	,					1).numpy()
								if output_type == "pil":
									snake_case__  :						Any		=      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_tf_available, is_torch_available


lowercase  		=			{
    """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
    """tokenization_flaubert""": ["""FlaubertTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """FlaubertForMultipleChoice""",
					    """FlaubertForQuestionAnswering""",
					    """FlaubertForQuestionAnsweringSimple""",
					    """FlaubertForSequenceClassification""",
					    """FlaubertForTokenClassification""",
					    """FlaubertModel""",
					    """FlaubertWithLMHeadModel""",
					    """FlaubertPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFFlaubertForMultipleChoice""",
					    """TFFlaubertForQuestionAnsweringSimple""",
					    """TFFlaubertForSequenceClassification""",
					    """TFFlaubertForTokenClassification""",
					    """TFFlaubertModel""",
					    """TFFlaubertPreTrainedModel""",
					    """TFFlaubertWithLMHeadModel""",
					]


if TYPE_CHECKING:
					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
					from .tokenization_flaubert import FlaubertTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flaubert import (
										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaubertForMultipleChoice,
										    FlaubertForQuestionAnswering,
										    FlaubertForQuestionAnsweringSimple,
										    FlaubertForSequenceClassification,
										    FlaubertForTokenClassification,
										    FlaubertModel,
										    FlaubertPreTrainedModel,
										    FlaubertWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_flaubert import (
										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFlaubertForMultipleChoice,
										    TFFlaubertForQuestionAnsweringSimple,
										    TFFlaubertForSequenceClassification,
										    TFFlaubertForTokenClassification,
										    TFFlaubertModel,
										    TFFlaubertPreTrainedModel,
										    TFFlaubertWithLMHeadModel,
										)

else:
					import sys

					lowercase  		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





