






"""simple docstring"""







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      (DEISMultistepScheduler,)
    SCREAMING_SNAKE_CASE		=      (('''num_inference_steps''', 25),)






    def     _UpperCamelCase							(			self     ,**A ):
          UpperCAmelCase          =	{
              """num_train_timesteps""": 1_000,
              """beta_start""": 0.0001,
              """beta_end""": 0.02,
              """beta_schedule""": """linear""",
              """solver_order""": 2,
          }

          config.update(**A )
          return config






    def     _UpperCamelCase							(			self     ,A=0     ,**A ):
          UpperCAmelCase          =	dict(self.forward_default_kwargs )
          UpperCAmelCase          =	kwargs.pop("""num_inference_steps"""     ,A )
          UpperCAmelCase          =	self.dummy_sample
          UpperCAmelCase          =	0.1 * sample
          UpperCAmelCase          =	[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

                UpperCAmelCase      , UpperCAmelCase          =	sample, sample
                for t in range(A     ,time_step + scheduler.config.solver_order + 1 ):
                      UpperCAmelCase          =	scheduler.step(A     ,A     ,A     ,**A ).prev_sample
                      UpperCAmelCase          =	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 ):
          pass






    def     _UpperCamelCase							(			self     ,A=0     ,**A ):
          UpperCAmelCase          =	dict(self.forward_default_kwargs )
          UpperCAmelCase          =	kwargs.pop("""num_inference_steps"""     ,A )
          UpperCAmelCase          =	self.dummy_sample
          UpperCAmelCase          =	0.1 * sample
          UpperCAmelCase          =	[residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase          =	self.get_scheduler_config()
                UpperCAmelCase          =	scheduler_class(**A )
                scheduler.set_timesteps(A )

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

                with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(A )
                      UpperCAmelCase          =	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)
                      UpperCAmelCase          =	dummy_past_residuals[: new_scheduler.config.solver_order]

                UpperCAmelCase          =	scheduler.step(A     ,A     ,A     ,**A ).prev_sample
                UpperCAmelCase          =	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 ):
          if scheduler is None:
                UpperCAmelCase          =	self.scheduler_classes[0]
                UpperCAmelCase          =	self.get_scheduler_config(**A )
                UpperCAmelCase          =	scheduler_class(**A )

          UpperCAmelCase          =	self.scheduler_classes[0]
          UpperCAmelCase          =	self.get_scheduler_config(**A )
          UpperCAmelCase          =	scheduler_class(**A )

          UpperCAmelCase          =	10
          UpperCAmelCase          =	self.dummy_model()
          UpperCAmelCase          =	self.dummy_sample_deter
          scheduler.set_timesteps(A )

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

          return sample






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	dict(self.forward_default_kwargs )

          UpperCAmelCase          =	kwargs.pop("""num_inference_steps"""     ,A )

          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase          =	self.get_scheduler_config()
                UpperCAmelCase          =	scheduler_class(**A )

                UpperCAmelCase          =	self.dummy_sample
                UpperCAmelCase          =	0.1 * sample

                if num_inference_steps is not None and hasattr(A     ,"""set_timesteps""" ):
                      scheduler.set_timesteps(A )
                elif num_inference_steps is not None and not hasattr(A     ,"""set_timesteps""" ):
                      UpperCAmelCase          =	num_inference_steps

                # copy over dummy past residuals (must be done after set_timesteps)
                UpperCAmelCase          =	[residual + 0.2, residual + 0.15, residual + 0.10]
                UpperCAmelCase          =	dummy_past_residuals[: scheduler.config.solver_order]

                UpperCAmelCase          =	scheduler.timesteps[5]
                UpperCAmelCase          =	scheduler.timesteps[6]

                UpperCAmelCase          =	scheduler.step(A     ,A     ,A     ,**A ).prev_sample
                UpperCAmelCase          =	scheduler.step(A     ,A     ,A     ,**A ).prev_sample

                self.assertEqual(output_a.shape     ,sample.shape )
                self.assertEqual(output_a.shape     ,output_a.shape )






    def     _UpperCamelCase							(			self ):
          # make sure that iterating over schedulers with same config names gives same results
          # for defaults
          UpperCAmelCase          =	DEISMultistepScheduler(**self.get_scheduler_config() )
          UpperCAmelCase          =	self.full_loop(scheduler=A )
          UpperCAmelCase          =	torch.mean(torch.abs(A ) )

          assert abs(result_mean.item() - 0.23916 ) < 1e-3

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

          UpperCAmelCase          =	self.full_loop(scheduler=A )
          UpperCAmelCase          =	torch.mean(torch.abs(A ) )

          assert abs(result_mean.item() - 0.23916 ) < 1e-3






    def     _UpperCamelCase							(			self ):
          for timesteps in [25, 50, 100, 999, 1_000]:
                self.check_over_configs(num_train_timesteps=A )






    def     _UpperCamelCase							(			self ):
          self.check_over_configs(thresholding=A )
          for order in [1, 2, 3]:
                for solver_type in ["logrho"]:
                      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="""deis"""     ,solver_order=A     ,solver_type=A     ,)






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






    def     _UpperCamelCase							(			self ):
          for algorithm_type in ["deis"]:
                for solver_type in ["logrho"]:
                      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     ,)
                                  UpperCAmelCase          =	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 ):
          self.check_over_configs(lower_order_final=A )
          self.check_over_configs(lower_order_final=A )






    def     _UpperCamelCase							(			self ):
          for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
                self.check_over_forward(num_inference_steps=A     ,time_step=0 )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.full_loop()
          UpperCAmelCase          =	torch.mean(torch.abs(A ) )

          assert abs(result_mean.item() - 0.23916 ) < 1e-3






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.full_loop(prediction_type="""v_prediction""" )
          UpperCAmelCase          =	torch.mean(torch.abs(A ) )

          assert abs(result_mean.item() - 0.091 ) < 1e-3






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.scheduler_classes[0]
          UpperCAmelCase          =	self.get_scheduler_config(thresholding=A     ,dynamic_thresholding_ratio=0 )
          UpperCAmelCase          =	scheduler_class(**A )

          UpperCAmelCase          =	10
          UpperCAmelCase          =	self.dummy_model()
          UpperCAmelCase          =	self.dummy_sample_deter.half()
          scheduler.set_timesteps(A )

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

          assert sample.dtype == torch.floataa











"""simple docstring"""







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_pegasus import PegasusTokenizer
else:
  _UpperCamelCase					      =  None


_UpperCamelCase					      =  logging.get_logger(__name__)


_UpperCamelCase					      =  """▁"""

_UpperCamelCase					      =  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCamelCase					      =  {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
    """tokenizer_file""": {
        """google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
    },
}

_UpperCamelCase					      =  {
    """google/pegasus-xsum""": 512,
}







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE		=      PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE		=      PegasusTokenizer
    SCREAMING_SNAKE_CASE		=      ['''input_ids''', '''attention_mask''']






    def __init__(			self     ,A=None     ,A=None     ,A="<pad>"     ,A="</s>"     ,A="<unk>"     ,A="<mask_2>"     ,A="<mask_1>"     ,A=None     ,A=103     ,**A     ,):
          UpperCAmelCase          =	offset

          if additional_special_tokens is not None:
                if not isinstance(A     ,A ):
                      raise TypeError(
                          F'''additional_special_tokens should be of type {type(A )}, but is'''
                          F''' {type(A )}''' )

                UpperCAmelCase          =	(
                    ([mask_token_sent] + additional_special_tokens)
                    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                    else additional_special_tokens
                )
                # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                additional_special_tokens_extended += [
                    F'''<unk_{i}>''' for i in range(len(A )     ,self.offset - 1 )
                ]

                if len(set(A ) ) != len(A ):
                      raise ValueError(
                          """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
                          F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
                UpperCAmelCase          =	additional_special_tokens_extended
          else:
                UpperCAmelCase          =	[mask_token_sent] if mask_token_sent is not None else []
                additional_special_tokens += [F'''<unk_{i}>''' for i in range(2     ,self.offset )]

          super().__init__(
              A     ,tokenizer_file=A     ,pad_token=A     ,eos_token=A     ,unk_token=A     ,mask_token=A     ,mask_token_sent=A     ,offset=A     ,additional_special_tokens=A     ,**A     ,)
          UpperCAmelCase          =	vocab_file
          UpperCAmelCase          =	False if not self.vocab_file else True






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	set(self.all_special_ids )  # call it once instead of inside list comp
          all_special_ids.remove(self.unk_token_id )  # <unk> is only sometimes special

          if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
                raise ValueError(
                    """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
                    F''' {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}''' )

          return [1 if x in all_special_ids else 0 for x in seq]






    def     _UpperCamelCase							(			self     ,A     ,A = None     ,A = False ):
          if already_has_special_tokens:
                return self._special_token_mask(A )
          elif token_ids_a is None:
                return self._special_token_mask(A ) + [1]
          else:
                return self._special_token_mask(token_ids_a + token_ids_a ) + [1]






    def     _UpperCamelCase							(			self     ,A     ,A=None ):
          if token_ids_a is None:
                return token_ids_a + [self.eos_token_id]
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return token_ids_a + token_ids_a + [self.eos_token_id]






    def     _UpperCamelCase							(			self     ,A     ,A = None ):
          if not self.can_save_slow_tokenizer:
                raise ValueError(
                    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                    """tokenizer.""" )

          if not os.path.isdir(A ):
                logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
                return
          UpperCAmelCase          =	os.path.join(
              A     ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

          if os.path.abspath(self.vocab_file ) != os.path.abspath(A ):
                copyfile(self.vocab_file     ,A )

          return (out_vocab_file,)



