

# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 						lowercase     (					_lowercase				):


		"""simple docstring"""




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






		@torch.no_grad()
		def __call__(					self						,							__snake_case = 1						,							__snake_case = None						,							__snake_case = 50						,							__snake_case = "pil"						,							__snake_case = True						,							**__snake_case						,							):
			_UpperCamelCase :		List[str]								=    torch.randn(
			    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,							generator=__snake_case						,							)
			_UpperCamelCase :		Dict								=    image.to(self.device)

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

			for t in self.progress_bar(self.scheduler.timesteps):
				# 1. predict noise model_output
				_UpperCamelCase :		Optional[Any]								=    self.unet(__snake_case						,							__snake_case).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
				_UpperCamelCase :		List[Any]								=    self.scheduler.step(__snake_case						,							__snake_case						,							__snake_case).prev_sample

			_UpperCamelCase :		int								=    (image / 2 + 0.5).clamp(0						,							1)
			_UpperCamelCase :		Dict								=    image.cpu().permute(0						,							2						,							3						,							1).numpy()
			if output_type == "pil":
				_UpperCamelCase :		List[str]								=    self.numpy_to_pil(__snake_case)

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

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







from typing import TYPE_CHECKING

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


lowerCAmelCase__             =		{
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__             =		["""MarkupLMTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__             =		[
							    """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MarkupLMForQuestionAnswering""",
							    """MarkupLMForSequenceClassification""",
							    """MarkupLMForTokenClassification""",
							    """MarkupLMModel""",
							    """MarkupLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
							from .feature_extraction_markuplm import MarkupLMFeatureExtractor
							from .processing_markuplm import MarkupLMProcessor
							from .tokenization_markuplm import MarkupLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_markuplm_fast import MarkupLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_markuplm import (
														    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MarkupLMForQuestionAnswering,
														    MarkupLMForSequenceClassification,
														    MarkupLMForTokenClassification,
														    MarkupLMModel,
														    MarkupLMPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase__             =		_LazyModule(__name__, globals()["""__file__"""], _import_structure)




