



from typing import TYPE_CHECKING

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


UpperCAmelCase							       =    {
    '''configuration_layoutlm''': ['''LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMConfig''', '''LayoutLMOnnxConfig'''],
    '''tokenization_layoutlm''': ['''LayoutLMTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							       =    ['''LayoutLMTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							       =    [
	    '''LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''LayoutLMForMaskedLM''',
	    '''LayoutLMForSequenceClassification''',
	    '''LayoutLMForTokenClassification''',
	    '''LayoutLMForQuestionAnswering''',
	    '''LayoutLMModel''',
	    '''LayoutLMPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							       =    [
	    '''TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFLayoutLMForMaskedLM''',
	    '''TFLayoutLMForSequenceClassification''',
	    '''TFLayoutLMForTokenClassification''',
	    '''TFLayoutLMForQuestionAnswering''',
	    '''TFLayoutLMMainLayer''',
	    '''TFLayoutLMModel''',
	    '''TFLayoutLMPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
	from .tokenization_layoutlm import LayoutLMTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_layoutlm import (
		    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    LayoutLMForMaskedLM,
		    LayoutLMForQuestionAnswering,
		    LayoutLMForSequenceClassification,
		    LayoutLMForTokenClassification,
		    LayoutLMModel,
		    LayoutLMPreTrainedModel,
		)
	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_layoutlm import (
		    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFLayoutLMForMaskedLM,
		    TFLayoutLMForQuestionAnswering,
		    TFLayoutLMForSequenceClassification,
		    TFLayoutLMForTokenClassification,
		    TFLayoutLMMainLayer,
		    TFLayoutLMModel,
		    TFLayoutLMPreTrainedModel,
		)

else:
	import sys

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





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class   A__							(						UpperCamelCase__     ):




						def __init__(				self    :					Any ,			_a    :					Optional[Any] ,			_a    :					List[str]     )							->   Tuple:


										"""simple docstring"""





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




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =torch.randn(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,			generator=_a ,			)
										_SCREAMING_SNAKE_CASE	     =image.to(self.device     )

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

										for t in self.progress_bar(self.scheduler.timesteps     ):
														# 1. predict noise model_output
														_SCREAMING_SNAKE_CASE	     =self.unet(_a ,			_a     ).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
														_SCREAMING_SNAKE_CASE	     =self.scheduler.step(_a ,			_a ,			_a     ).prev_sample

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

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

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