


'''simple docstring'''


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				(DPMSolverSinglestepScheduler,)
     lowerCamelCase__       						=				(('num_inference_steps', 25),)


     def lowerCAmelCase__  (   self			,     **lowerCamelCase		):


          '''simple docstring'''





          __A	:       List[Any]      =							{
              "num_train_timesteps": 1000,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
              "solver_order": 2,
              "prediction_type": "epsilon",
              "thresholding": False,
              "sample_max_value": 1.0,
              "algorithm_type": "dpmsolver++",
              "solver_type": "midpoint",
              "lambda_min_clipped": -float("inf"		),
              "variance_type": None,
          }

          config.update(**lowerCamelCase		)
          return config


     def lowerCAmelCase__  (   self			,     lowerCamelCase=0			,     **lowerCamelCase		):


          '''simple docstring'''





          __A	:       Dict      =							dict(self.forward_default_kwargs		)
          __A	:       Optional[int]      =							kwargs.pop("num_inference_steps"			,     lowerCamelCase		)
          __A	:       Optional[int]      =							self.dummy_sample
          __A	:       List[Any]      =							0.1 * sample
          __A	:       List[Any]      =							[residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
               __A	:       Dict      =							self.get_scheduler_config(**lowerCamelCase		)
               __A	:       Optional[Any]      =							scheduler_class(**lowerCamelCase		)
               scheduler.set_timesteps(lowerCamelCase		)
               # copy over dummy past residuals
               __A	:       Dict      =							dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCamelCase		)
                    __A	:       List[Any]      =							scheduler_class.from_pretrained(lowerCamelCase		)
                    new_scheduler.set_timesteps(lowerCamelCase		)
                    # copy over dummy past residuals
                    __A	:       int      =							dummy_past_residuals[: new_scheduler.config.solver_order]

               __A  ,__A	:       str      =							sample, sample
               for t in range(lowerCamelCase			,     time_step + scheduler.config.solver_order + 1		):
                    __A	:       Dict      =							scheduler.step(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     **lowerCamelCase		).prev_sample
                    __A	:       Union[str, Any]      =							new_scheduler.step(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     **lowerCamelCase		).prev_sample

                    assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          pass


     def lowerCAmelCase__  (   self			,     lowerCamelCase=0			,     **lowerCamelCase		):


          '''simple docstring'''





          __A	:       Dict      =							dict(self.forward_default_kwargs		)
          __A	:       str      =							kwargs.pop("num_inference_steps"			,     lowerCamelCase		)
          __A	:       str      =							self.dummy_sample
          __A	:       Dict      =							0.1 * sample
          __A	:       Tuple      =							[residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
               __A	:       Any      =							self.get_scheduler_config()
               __A	:       str      =							scheduler_class(**lowerCamelCase		)
               scheduler.set_timesteps(lowerCamelCase		)

               # copy over dummy past residuals (must be after setting timesteps)
               __A	:       Dict      =							dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCamelCase		)
                    __A	:       List[str]      =							scheduler_class.from_pretrained(lowerCamelCase		)
                    # copy over dummy past residuals
                    new_scheduler.set_timesteps(lowerCamelCase		)

                    # copy over dummy past residual (must be after setting timesteps)
                    __A	:       Dict      =							dummy_past_residuals[: new_scheduler.config.solver_order]

               __A	:       Dict      =							scheduler.step(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     **lowerCamelCase		).prev_sample
               __A	:       Union[str, Any]      =							new_scheduler.step(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     **lowerCamelCase		).prev_sample

               assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"


     def lowerCAmelCase__  (   self			,     lowerCamelCase=None			,     **lowerCamelCase		):


          '''simple docstring'''





          if scheduler is None:
               __A	:       Any      =							self.scheduler_classes[0]
               __A	:       Any      =							self.get_scheduler_config(**lowerCamelCase		)
               __A	:       Any      =							scheduler_class(**lowerCamelCase		)

          __A	:       Optional[int]      =							self.scheduler_classes[0]
          __A	:       Optional[Any]      =							self.get_scheduler_config(**lowerCamelCase		)
          __A	:       List[Any]      =							scheduler_class(**lowerCamelCase		)

          __A	:       Optional[Any]      =							10
          __A	:       Optional[int]      =							self.dummy_model()
          __A	:       Any      =							self.dummy_sample_deter
          scheduler.set_timesteps(lowerCamelCase		)

          for i, t in enumerate(scheduler.timesteps		):
               __A	:       Optional[Any]      =							model(lowerCamelCase			,     lowerCamelCase		)
               __A	:       int      =							scheduler.step(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		).prev_sample

          return sample


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       int      =							DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
          __A	:       Dict      =							50
          __A	:       Union[str, Any]      =							self.dummy_model()
          __A	:       str      =							self.dummy_sample_deter
          scheduler.set_timesteps(lowerCamelCase		)

          # make sure that the first t is uneven
          for i, t in enumerate(scheduler.timesteps[3:]		):
               __A	:       List[str]      =							model(lowerCamelCase			,     lowerCamelCase		)
               __A	:       Optional[int]      =							scheduler.step(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		).prev_sample

          __A	:       Dict      =							torch.mean(torch.abs(lowerCamelCase		)		)

          assert abs(result_mean.item() - 0.2574		) < 1E-3


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          for timesteps in [25, 50, 100, 999, 1000]:
               self.check_over_configs(num_train_timesteps=lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
          __A	:       Union[str, Any]      =							self.full_loop(scheduler=lowerCamelCase		)
          __A	:       Optional[Any]      =							torch.mean(torch.abs(lowerCamelCase		)		)

          assert abs(result_mean.item() - 0.2791		) < 1E-3

          __A	:       Dict      =							DEISMultistepScheduler.from_config(scheduler.config		)
          __A	:       Union[str, Any]      =							DPMSolverMultistepScheduler.from_config(scheduler.config		)
          __A	:       Dict      =							UniPCMultistepScheduler.from_config(scheduler.config		)
          __A	:       List[Any]      =							DPMSolverSinglestepScheduler.from_config(scheduler.config		)

          __A	:       Any      =							self.full_loop(scheduler=lowerCamelCase		)
          __A	:       str      =							torch.mean(torch.abs(lowerCamelCase		)		)

          assert abs(result_mean.item() - 0.2791		) < 1E-3


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.check_over_configs(thresholding=lowerCamelCase		)
          for order in [1, 2, 3]:
               for solver_type in ["midpoint", "heun"]:
                    for threshold in [0.5, 1.0, 2.0]:
                         for prediction_type in ["epsilon", "sample"]:
                              self.check_over_configs(
                                  thresholding=lowerCamelCase			,     prediction_type=lowerCamelCase			,     sample_max_value=lowerCamelCase			,     algorithm_type="dpmsolver++"			,     solver_order=lowerCamelCase			,     solver_type=lowerCamelCase			,     )


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          for algorithm_type in ["dpmsolver", "dpmsolver++"]:
               for solver_type in ["midpoint", "heun"]:
                    for order in [1, 2, 3]:
                         for prediction_type in ["epsilon", "sample"]:
                              self.check_over_configs(
                                  solver_order=lowerCamelCase			,     solver_type=lowerCamelCase			,     prediction_type=lowerCamelCase			,     algorithm_type=lowerCamelCase			,     )
                              __A	:       Optional[int]      =							self.full_loop(
                                  solver_order=lowerCamelCase			,     solver_type=lowerCamelCase			,     prediction_type=lowerCamelCase			,     algorithm_type=lowerCamelCase			,     )
                              assert not torch.isnan(lowerCamelCase		).any(), "Samples have nan numbers"


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.check_over_configs(lower_order_final=lowerCamelCase		)
          self.check_over_configs(lower_order_final=lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
          self.check_over_configs(lambda_min_clipped=-5.1		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.check_over_configs(variance_type=lowerCamelCase		)
          self.check_over_configs(variance_type="learned_range"		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
               self.check_over_forward(num_inference_steps=lowerCamelCase			,     time_step=0		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       int      =							self.full_loop()
          __A	:       Tuple      =							torch.mean(torch.abs(lowerCamelCase		)		)

          assert abs(result_mean.item() - 0.2791		) < 1E-3


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       str      =							self.full_loop(use_karras_sigmas=lowerCamelCase		)
          __A	:       Optional[Any]      =							torch.mean(torch.abs(lowerCamelCase		)		)

          assert abs(result_mean.item() - 0.2248		) < 1E-3


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       int      =							self.full_loop(prediction_type="v_prediction"		)
          __A	:       str      =							torch.mean(torch.abs(lowerCamelCase		)		)

          assert abs(result_mean.item() - 0.1453		) < 1E-3


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       List[Any]      =							self.full_loop(prediction_type="v_prediction"			,     use_karras_sigmas=lowerCamelCase		)
          __A	:       Dict      =							torch.mean(torch.abs(lowerCamelCase		)		)

          assert abs(result_mean.item() - 0.0649		) < 1E-3


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       List[Any]      =							self.scheduler_classes[0]
          __A	:       Tuple      =							self.get_scheduler_config(thresholding=lowerCamelCase			,     dynamic_thresholding_ratio=0		)
          __A	:       Union[str, Any]      =							scheduler_class(**lowerCamelCase		)

          __A	:       Any      =							10
          __A	:       int      =							self.dummy_model()
          __A	:       List[Any]      =							self.dummy_sample_deter.half()
          scheduler.set_timesteps(lowerCamelCase		)

          for i, t in enumerate(scheduler.timesteps		):
               __A	:       Optional[int]      =							model(lowerCamelCase			,     lowerCamelCase		)
               __A	:       List[Any]      =							scheduler.step(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		).prev_sample

          assert sample.dtype == torch.floataa





'''simple docstring'''


import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

_UpperCamelCase 							=  logging.get_logger(__name__)


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


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''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	:       Tuple      =							TOKENIZER_CLASSES
     else:
          __A	:       Dict      =							{tokenizer_name: getattr(SCREAMING_SNAKE_CASE       ,					tokenizer_name + "Fast"							)}

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

     for tokenizer_name in tokenizer_names:
          __A	:       int      =							TOKENIZER_CLASSES[tokenizer_name]

          __A	:       Union[str, Any]      =							True
          if checkpoint_name is None:
               __A	:       Dict      =							list(tokenizer_class.max_model_input_sizes.keys()							)
          else:
               __A	:       str      =							[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	:       List[str]      =							tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE       ,					force_download=SCREAMING_SNAKE_CASE							)

               # 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	:       Optional[Any]      =							checkpoint.split("/"							)
                    __A	:       str      =							os.path.join(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
               elif add_prefix:
                    __A	:       Optional[int]      =							checkpoint
                    __A	:       Any      =							dump_path
               else:
                    __A	:       int      =							None
                    __A	:       Optional[int]      =							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	:       Any      =							list(tokenizer.pretrained_vocab_files_map.values()							)[0][checkpoint]
                    __A	:       Optional[int]      =							file_path.split(SCREAMING_SNAKE_CASE							)[-1][0]
                    if next_char == "/":
                         __A	:       Union[str, Any]      =							os.path.join(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
                         __A	:       Union[str, Any]      =							None

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

               __A	:       Tuple      =							tokenizer.save_pretrained(
                   SCREAMING_SNAKE_CASE       ,					legacy_format=SCREAMING_SNAKE_CASE       ,					filename_prefix=SCREAMING_SNAKE_CASE							)
               logger.info(f"=> File names {file_names}"							)

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


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

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

