


# 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       :					Union[str, Any]     ,						SCREAMING_SNAKE_CASE_       :					Optional[int]     ,						SCREAMING_SNAKE_CASE_       :					Union[str, Any]				):
        super().__init__()
        self.register_modules(unet=SCREAMING_SNAKE_CASE_     ,						scheduler=SCREAMING_SNAKE_CASE_				)







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

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

        for t in self.progress_bar(self.scheduler.timesteps				):
           # 1. predict noise model_output
           __lowerCamelCase:					Any	 =						self.unet(SCREAMING_SNAKE_CASE_     ,						SCREAMING_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
           __lowerCamelCase:					Dict	 =						self.scheduler.step(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				).prev_sample

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

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

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






import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def      __lowerCAmelCase    (				snake_case						:     Union[str, Any]    ,   snake_case						:     Optional[int]			)							->				List[Any]:
   # Initialize PyTorch model
   __lowerCamelCase:					str	 =						CanineConfig()
   __lowerCamelCase:					Union[str, Any]	 =						CanineModel(snake_case			)
   model.eval()

   print(f'Building PyTorch model from configuration: {config}'			)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(snake_case    ,   snake_case    ,   snake_case			)

   # Save pytorch-model (weights and configuration)
   print(f'Save PyTorch model to {pytorch_dump_path}'			)
   model.save_pretrained(snake_case			)

   # Save tokenizer files
   __lowerCamelCase:					Optional[int]	 =						CanineTokenizer()
   print(f'Save tokenizer files to {pytorch_dump_path}'			)
   tokenizer.save_pretrained(snake_case			)


if __name__ == "__main__":
   _A		:					int          =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''',
       default=None,
       type=str,
       required=True,
       help='''Path to the TensorFlow checkpoint. Should end with model.ckpt''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''',
       default=None,
       type=str,
       required=True,
       help='''Path to a folder where the PyTorch model will be placed.''',
   )
   _A		:					int          =							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)


