



import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

_lowercase: Any      			=		logging.get_logger(__name__)


_lowercase: Dict      			=		{name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}






def     a(      A       :    List[Any] ,     A       :    Tuple ,     A       :    Union[str, Any] ,     A       :    Dict       )		->       Optional[Any]:
					"""simple docstring"""





					if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
										raise ValueError(f'''Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()       )}.'''       )

					if tokenizer_name is None:
										a					     =   TOKENIZER_CLASSES
					else:
										a					     =   {tokenizer_name: getattr(A ,     tokenizer_name + "Fast"       )}

					logger.info(f'''Loading tokenizer classes: {tokenizer_names}'''       )

					for tokenizer_name in tokenizer_names:
										a					     =   TOKENIZER_CLASSES[tokenizer_name]

										a					     =   True
										if checkpoint_name is None:
															a					     =   list(tokenizer_class.max_model_input_sizes.keys()       )
										else:
															a					     =   [checkpoint_name]

										logger.info(f'''For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}'''       )

										for checkpoint in checkpoint_names:
															logger.info(f'''Loading {tokenizer_class.__class__.__name__} {checkpoint}'''       )

															# Load tokenizer
															a					     =   tokenizer_class.from_pretrained(A ,     force_download=A       )

															# Save fast tokenizer
															logger.info(f'''Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}'''       )

															# For organization names we create sub-directories
															if "/" in checkpoint:
																				a     ,				a					     =   checkpoint.split("/"       )
																				a					     =   os.path.join(A ,     A       )
															elif add_prefix:
																				a					     =   checkpoint
																				a					     =   dump_path
															else:
																				a					     =   None
																				a					     =   dump_path

															logger.info(f'''=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'''       )

															if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()       )[0]:
																				a					     =   list(tokenizer.pretrained_vocab_files_map.values()       )[0][checkpoint]
																				a					     =   file_path.split(A       )[-1][0]
																				if next_char == "/":
																									a					     =   os.path.join(A ,     A       )
																									a					     =   None

																				logger.info(f'''=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'''       )

															a					     =   tokenizer.save_pretrained(
															    A ,     legacy_format=A ,     filename_prefix=A       )
															logger.info(f'''=> File names {file_names}'''       )

															for file_name in file_names:
																				if not file_name.endswith("tokenizer.json"       ):
																									os.remove(A       )
																									logger.info(f'''=> removing {file_name}'''       )


if __name__ == "__main__":
				_lowercase: Optional[int]      			=		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--dump_path", default=None, type=str, required=True, help="Path to output generated fast tokenizer files."
				)
				parser.add_argument(
				    "--tokenizer_name",
				    default=None,
				    type=str,
				    help=(
				        F"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
				        "download and convert all the checkpoints from AWS."
				    ),
				)
				parser.add_argument(
				    "--checkpoint_name",
				    default=None,
				    type=str,
				    help="Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.",
				)
				parser.add_argument(
				    "--force_download",
				    action="store_true",
				    help="Re-download checkpoints.",
				)
				_lowercase: Any      			=		parser.parse_args()

				convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""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_		,       ):
						"""simple docstring"""

						a					     =   torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,       generator=lowerCamelCase_		,       )
						a					     =   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
											a					     =   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
											a					     =   self.scheduler.step(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		).prev_sample

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