


"""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"










import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()




def    UpperCAmelCase					(  lowercase__						:					int				,					lowercase__						:					str				,					lowercase__						:					Union[str, Any]				,					lowercase__						:					Optional[int]				,					lowercase__						:					Optional[Any]	):



			'''simple docstring'''





			a__					=  BertConfig.from_pretrained(
			    """bert-large-cased"""				,					vocab_size=lowercase__				,					max_position_embeddings=512				,					is_decoder=lowercase__				,					add_cross_attention=lowercase__				,					)
			a__					=  bert_config.to_dict()
			del bert_config_dict["type_vocab_size"]
			a__					=  BertGenerationConfig(**lowercase__	)
			if is_encoder:
						a__					=  BertGenerationEncoder(lowercase__	)
			else:
						a__					=  BertGenerationDecoder(lowercase__	)
			print(f'Building PyTorch model from configuration: {config}'	)

			# Load weights from tf checkpoint
			load_tf_weights_in_bert_generation(
			    lowercase__				,					lowercase__				,					model_class="""bert"""				,					is_encoder_named_decoder=lowercase__				,					is_encoder=lowercase__				,					)

			# Save pytorch-model
			print(f'Save PyTorch model and config to {pytorch_dump_path}'	)
			model.save_pretrained(lowercase__	)


if __name__ == "__main__":
	_lowercase				:					str     					=argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--tf_hub_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
	)
	parser.add_argument(
	    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	parser.add_argument(
	    """--is_encoder_named_decoder""",
	    action="""store_true""",
	    help="""If decoder has to be renamed to encoder in PyTorch model.""",
	)
	parser.add_argument("""--is_encoder""", action="""store_true""", help="""If model is an encoder.""")
	parser.add_argument("""--vocab_size""", default=5_03_58, type=int, help="""Vocab size of model""")
	_lowercase				:					List[str]     					=parser.parse_args()
	convert_tf_checkpoint_to_pytorch(
	    args.tf_hub_path,
	    args.pytorch_dump_path,
	    args.is_encoder_named_decoder,
	    args.vocab_size,
	    is_encoder=args.is_encoder,
	)

