


import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

_lowerCamelCase     =  logging.get_logger(__name__)


_lowerCamelCase     =  {name: getattr(transformers, name + 'Fast') for name in SLOW_TO_FAST_CONVERTERS}


def __lowercase     (      lowerCamelCase_  :      List[str]						,					lowerCamelCase_  :      str						,					lowerCamelCase_  :      Optional[int]						,					lowerCamelCase_  :      Tuple  ):
				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:
								SCREAMING_SNAKE_CASE__     							=				TOKENIZER_CLASSES
				else:
								SCREAMING_SNAKE_CASE__     							=				{tokenizer_name: getattr(lowerCamelCase_						,					tokenizer_name + "Fast"  )}

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

				for tokenizer_name in tokenizer_names:
								SCREAMING_SNAKE_CASE__     							=				TOKENIZER_CLASSES[tokenizer_name]

								SCREAMING_SNAKE_CASE__     							=				True
								if checkpoint_name is None:
												SCREAMING_SNAKE_CASE__     							=				list(tokenizer_class.max_model_input_sizes.keys()  )
								else:
												SCREAMING_SNAKE_CASE__     							=				[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
												SCREAMING_SNAKE_CASE__     							=				tokenizer_class.from_pretrained(lowerCamelCase_						,					force_download=lowerCamelCase_  )

												# 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:
																SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				checkpoint.split("/"  )
																SCREAMING_SNAKE_CASE__     							=				os.path.join(lowerCamelCase_						,					lowerCamelCase_  )
												elif add_prefix:
																SCREAMING_SNAKE_CASE__     							=				checkpoint
																SCREAMING_SNAKE_CASE__     							=				dump_path
												else:
																SCREAMING_SNAKE_CASE__     							=				None
																SCREAMING_SNAKE_CASE__     							=				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]:
																SCREAMING_SNAKE_CASE__     							=				list(tokenizer.pretrained_vocab_files_map.values()  )[0][checkpoint]
																SCREAMING_SNAKE_CASE__     							=				file_path.split(lowerCamelCase_  )[-1][0]
																if next_char == "/":
																				SCREAMING_SNAKE_CASE__     							=				os.path.join(lowerCamelCase_						,					lowerCamelCase_  )
																				SCREAMING_SNAKE_CASE__     							=				None

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

												SCREAMING_SNAKE_CASE__     							=				tokenizer.save_pretrained(
												    lowerCamelCase_						,					legacy_format=lowerCamelCase_						,					filename_prefix=lowerCamelCase_  )
												logger.info(F'''=> File names {file_names}'''  )

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


if __name__ == "__main__":
	_lowerCamelCase     =  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.',
	)
	_lowerCamelCase     =  parser.parse_args()

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




"""simple docstring"""






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin


class 			lowerCamelCase_		(			lowercase       ,  lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      List[Any]      							=					1



				@register_to_config
				def __init__(						self       ,		UpperCAmelCase__=2000       ,		UpperCAmelCase__=0.1       ,		UpperCAmelCase__=20       ,		UpperCAmelCase__=1e-3     ):
								SCREAMING_SNAKE_CASE__     							=				None
								SCREAMING_SNAKE_CASE__     							=				None
								SCREAMING_SNAKE_CASE__     							=				None



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None     ):
								SCREAMING_SNAKE_CASE__     							=				torch.linspace(1       ,		self.config.sampling_eps       ,		UpperCAmelCase__       ,		device=UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__=None     ):
								if self.timesteps is None:
												raise ValueError(
												    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"     )

								# TODO(Patrick) better comments + non-PyTorch
								# postprocess model score
								SCREAMING_SNAKE_CASE__     							=				(
								    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
								)
								SCREAMING_SNAKE_CASE__     							=				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff     )     )
								SCREAMING_SNAKE_CASE__     							=				std.flatten()
								while len(std.shape     ) < len(score.shape     ):
												SCREAMING_SNAKE_CASE__     							=				std.unsqueeze(-1     )
								SCREAMING_SNAKE_CASE__     							=				-score / std

								# compute
								SCREAMING_SNAKE_CASE__     							=				-1.0 / len(self.timesteps     )

								SCREAMING_SNAKE_CASE__     							=				self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
								SCREAMING_SNAKE_CASE__     							=				beta_t.flatten()
								while len(beta_t.shape     ) < len(x.shape     ):
												SCREAMING_SNAKE_CASE__     							=				beta_t.unsqueeze(-1     )
								SCREAMING_SNAKE_CASE__     							=				-0.5 * beta_t * x

								SCREAMING_SNAKE_CASE__     							=				torch.sqrt(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				drift - diffusion**2 * score
								SCREAMING_SNAKE_CASE__     							=				x + drift * dt

								# add noise
								SCREAMING_SNAKE_CASE__     							=				randn_tensor(x.shape       ,		layout=x.layout       ,		generator=UpperCAmelCase__       ,		device=x.device       ,		dtype=x.dtype     )
								SCREAMING_SNAKE_CASE__     							=				x_mean + diffusion * math.sqrt(-dt     ) * noise

								return x, x_mean






				def __len__(						self     ):
								return self.config.num_train_timesteps
