


from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class      __lowercase  (_lowerCamelCase   ):





							"""simple docstring"""







							def __init__(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




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






							@torch.no_grad()
							def __call__(	self       ,		lowerCAmelCase__ = 1       ,		lowerCAmelCase__ = None       ,		lowerCAmelCase__ = 1_0_0_0       ,		lowerCAmelCase__ = "pil"       ,		lowerCAmelCase__ = True       ,		):





										"""simple docstring"""




										if isinstance(self.unet.config.sample_size       ,		A__    ):
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= (
													    batch_size,
													    self.unet.config.in_channels,
													    self.unet.config.sample_size,
													    self.unet.config.sample_size,
													)
										else:
													SCREAMING_SNAKE_CASE_						:			int       						= (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

										if self.device.type == "mps":
													# randn does not work reproducibly on mps
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= randn_tensor(A__       ,		generator=A__    )
													SCREAMING_SNAKE_CASE_						:			Any       						= image.to(self.device    )
										else:
													SCREAMING_SNAKE_CASE_						:			Dict       						= randn_tensor(A__       ,		generator=A__       ,		device=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_						:			Optional[Any]       						= self.unet(A__       ,		A__    ).sample

													# 2. compute previous image: x_t -> x_t-1
													SCREAMING_SNAKE_CASE_						:			List[str]       						= self.scheduler.step(A__       ,		A__       ,		A__       ,		generator=A__    ).prev_sample

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

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=A__    )







'''simple docstring'''



import argparse

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


logging.set_verbosity_info()
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Any:




   """simple docstring"""
   # Initialise PyTorch model
   _SCREAMING_SNAKE_CASE          = BertConfig.from_pretrained(
       """bert-large-cased"""			,					vocab_size=SCREAMING_SNAKE_CASE_			,					max_position_embeddings=5_12			,					is_decoder=SCREAMING_SNAKE_CASE_			,					add_cross_attention=SCREAMING_SNAKE_CASE_			,					)
   _SCREAMING_SNAKE_CASE          = bert_config.to_dict()
   del bert_config_dict["type_vocab_size"]
   _SCREAMING_SNAKE_CASE          = BertGenerationConfig(**SCREAMING_SNAKE_CASE_  )
   if is_encoder:
      _SCREAMING_SNAKE_CASE          = BertGenerationEncoder(SCREAMING_SNAKE_CASE_  )
   else:
      _SCREAMING_SNAKE_CASE          = BertGenerationDecoder(SCREAMING_SNAKE_CASE_  )
   print(F"Building PyTorch model from configuration: {config}"  )

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

   # Save pytorch-model
   print(F"Save PyTorch model and config to {pytorch_dump_path}"  )
   model.save_pretrained(SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
     UpperCamelCase__					:						int											=     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=50_358, type=int, help="Vocab size of model")
     UpperCamelCase__					:						int											=     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,
     )






