






"""simple docstring"""




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 	__UpperCAmelCase		(				_lowerCamelCase	):


	'''simple docstring'''





	lowercase		:	Any							     =				(DPMSolverSinglestepScheduler,)
	lowercase		:	List[str]							     =				(("num_inference_steps", 25),)


	def 				UpperCamelCase_  (     self      ,      **_A					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						={
		    "num_train_timesteps": 1_0_0_0,
		    "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(**_A					)
		return config


	def 				UpperCamelCase_  (     self      ,      _A=0      ,      **_A					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=dict(self.forward_default_kwargs					)
		_SCREAMING_SNAKE_CASE						=kwargs.pop('''num_inference_steps'''      ,      _A					)
		_SCREAMING_SNAKE_CASE						=self.dummy_sample
		_SCREAMING_SNAKE_CASE						=0.1 * sample
		_SCREAMING_SNAKE_CASE						=[residual + 0.2, residual + 0.15, residual + 0.10]

		for scheduler_class in self.scheduler_classes:
			_SCREAMING_SNAKE_CASE						=self.get_scheduler_config(**_A					)
			_SCREAMING_SNAKE_CASE						=scheduler_class(**_A					)
			scheduler.set_timesteps(_A					)
			# copy over dummy past residuals
			_SCREAMING_SNAKE_CASE						=dummy_past_residuals[: scheduler.config.solver_order]

			with tempfile.TemporaryDirectory() as tmpdirname:
				scheduler.save_config(_A					)
				_SCREAMING_SNAKE_CASE						=scheduler_class.from_pretrained(_A					)
				new_scheduler.set_timesteps(_A					)
				# copy over dummy past residuals
				_SCREAMING_SNAKE_CASE						=dummy_past_residuals[: new_scheduler.config.solver_order]

			_SCREAMING_SNAKE_CASE						=sample, sample
			for t in range(_A      ,      time_step + scheduler.config.solver_order + 1					):
				_SCREAMING_SNAKE_CASE						=scheduler.step(_A      ,      _A      ,      _A      ,      **_A					).prev_sample
				_SCREAMING_SNAKE_CASE						=new_scheduler.step(_A      ,      _A      ,      _A      ,      **_A					).prev_sample

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


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		pass


	def 				UpperCamelCase_  (     self      ,      _A=0      ,      **_A					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=dict(self.forward_default_kwargs					)
		_SCREAMING_SNAKE_CASE						=kwargs.pop('''num_inference_steps'''      ,      _A					)
		_SCREAMING_SNAKE_CASE						=self.dummy_sample
		_SCREAMING_SNAKE_CASE						=0.1 * sample
		_SCREAMING_SNAKE_CASE						=[residual + 0.2, residual + 0.15, residual + 0.10]

		for scheduler_class in self.scheduler_classes:
			_SCREAMING_SNAKE_CASE						=self.get_scheduler_config()
			_SCREAMING_SNAKE_CASE						=scheduler_class(**_A					)
			scheduler.set_timesteps(_A					)

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

			with tempfile.TemporaryDirectory() as tmpdirname:
				scheduler.save_config(_A					)
				_SCREAMING_SNAKE_CASE						=scheduler_class.from_pretrained(_A					)
				# copy over dummy past residuals
				new_scheduler.set_timesteps(_A					)

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

			_SCREAMING_SNAKE_CASE						=scheduler.step(_A      ,      _A      ,      _A      ,      **_A					).prev_sample
			_SCREAMING_SNAKE_CASE						=new_scheduler.step(_A      ,      _A      ,      _A      ,      **_A					).prev_sample

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


	def 				UpperCamelCase_  (     self      ,      _A=None      ,      **_A					):



		'''simple docstring'''

		if scheduler is None:
			_SCREAMING_SNAKE_CASE						=self.scheduler_classes[0]
			_SCREAMING_SNAKE_CASE						=self.get_scheduler_config(**_A					)
			_SCREAMING_SNAKE_CASE						=scheduler_class(**_A					)

		_SCREAMING_SNAKE_CASE						=self.scheduler_classes[0]
		_SCREAMING_SNAKE_CASE						=self.get_scheduler_config(**_A					)
		_SCREAMING_SNAKE_CASE						=scheduler_class(**_A					)

		_SCREAMING_SNAKE_CASE						=1_0
		_SCREAMING_SNAKE_CASE						=self.dummy_model()
		_SCREAMING_SNAKE_CASE						=self.dummy_sample_deter
		scheduler.set_timesteps(_A					)

		for i, t in enumerate(scheduler.timesteps					):
			_SCREAMING_SNAKE_CASE						=model(_A      ,      _A					)
			_SCREAMING_SNAKE_CASE						=scheduler.step(_A      ,      _A      ,      _A					).prev_sample

		return sample


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=DPMSolverSinglestepScheduler(**self.get_scheduler_config()					)
		_SCREAMING_SNAKE_CASE						=5_0
		_SCREAMING_SNAKE_CASE						=self.dummy_model()
		_SCREAMING_SNAKE_CASE						=self.dummy_sample_deter
		scheduler.set_timesteps(_A					)

		# make sure that the first t is uneven
		for i, t in enumerate(scheduler.timesteps[3:]					):
			_SCREAMING_SNAKE_CASE						=model(_A      ,      _A					)
			_SCREAMING_SNAKE_CASE						=scheduler.step(_A      ,      _A      ,      _A					).prev_sample

		_SCREAMING_SNAKE_CASE						=torch.mean(torch.abs(_A					)					)

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


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
			self.check_over_configs(num_train_timesteps=_A					)


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=DPMSolverSinglestepScheduler(**self.get_scheduler_config()					)
		_SCREAMING_SNAKE_CASE						=self.full_loop(scheduler=_A					)
		_SCREAMING_SNAKE_CASE						=torch.mean(torch.abs(_A					)					)

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

		_SCREAMING_SNAKE_CASE						=DEISMultistepScheduler.from_config(scheduler.config					)
		_SCREAMING_SNAKE_CASE						=DPMSolverMultistepScheduler.from_config(scheduler.config					)
		_SCREAMING_SNAKE_CASE						=UniPCMultistepScheduler.from_config(scheduler.config					)
		_SCREAMING_SNAKE_CASE						=DPMSolverSinglestepScheduler.from_config(scheduler.config					)

		_SCREAMING_SNAKE_CASE						=self.full_loop(scheduler=_A					)
		_SCREAMING_SNAKE_CASE						=torch.mean(torch.abs(_A					)					)

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


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		self.check_over_configs(thresholding=_A					)
		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=_A      ,      prediction_type=_A      ,      sample_max_value=_A      ,      algorithm_type='''dpmsolver++'''      ,      solver_order=_A      ,      solver_type=_A      ,      )


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

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


	def 				UpperCamelCase_  (     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=_A      ,      solver_type=_A      ,      prediction_type=_A      ,      algorithm_type=_A      ,      )
						_SCREAMING_SNAKE_CASE						=self.full_loop(
						    solver_order=_A      ,      solver_type=_A      ,      prediction_type=_A      ,      algorithm_type=_A      ,      )
						assert not torch.isnan(_A					).any(), "Samples have nan numbers"


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		self.check_over_configs(lower_order_final=_A					)
		self.check_over_configs(lower_order_final=_A					)


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

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


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		self.check_over_configs(variance_type=_A					)
		self.check_over_configs(variance_type='''learned_range'''					)


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
			self.check_over_forward(num_inference_steps=_A      ,      time_step=0					)


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=self.full_loop()
		_SCREAMING_SNAKE_CASE						=torch.mean(torch.abs(_A					)					)

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


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=self.full_loop(use_karras_sigmas=_A					)
		_SCREAMING_SNAKE_CASE						=torch.mean(torch.abs(_A					)					)

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


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=self.full_loop(prediction_type='''v_prediction'''					)
		_SCREAMING_SNAKE_CASE						=torch.mean(torch.abs(_A					)					)

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


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=self.full_loop(prediction_type='''v_prediction'''      ,      use_karras_sigmas=_A					)
		_SCREAMING_SNAKE_CASE						=torch.mean(torch.abs(_A					)					)

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







	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=self.scheduler_classes[0]
		_SCREAMING_SNAKE_CASE						=self.get_scheduler_config(thresholding=_A      ,      dynamic_thresholding_ratio=0					)
		_SCREAMING_SNAKE_CASE						=scheduler_class(**_A					)

		_SCREAMING_SNAKE_CASE						=1_0
		_SCREAMING_SNAKE_CASE						=self.dummy_model()
		_SCREAMING_SNAKE_CASE						=self.dummy_sample_deter.half()
		scheduler.set_timesteps(_A					)

		for i, t in enumerate(scheduler.timesteps					):
			_SCREAMING_SNAKE_CASE						=model(_A      ,      _A					)
			_SCREAMING_SNAKE_CASE						=scheduler.step(_A      ,      _A      ,      _A					).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)

