


'''simple docstring'''
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class 			lowerCamelCase_   (	__a      ):
      lowerCAmelCase__		     =     (UniPCMultistepScheduler,)
      lowerCAmelCase__		     =     (('num_inference_steps', 2_5),)






      def lowercase_							(					self				:    Optional[int]						,						**_A				:    Dict ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				{
              '''num_train_timesteps''': 1_000,
              '''beta_start''': 0.0_0_0_1,
              '''beta_end''': 0.0_2,
              '''beta_schedule''': '''linear''',
              '''solver_order''': 2,
              '''solver_type''': '''bh2''',
          }

          config.update(**_A )
          return config






      def lowercase_							(					self				:    Optional[Any]						,						_A				:    str=0						,						**_A				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				dict(self.forward_default_kwargs )
          UpperCAmelCase__			:		str													=				kwargs.pop('''num_inference_steps'''						,						_A )
          UpperCAmelCase__			:		List[str]													=				self.dummy_sample
          UpperCAmelCase__			:		Dict													=				0.1 * sample
          UpperCAmelCase__			:		Union[str, Any]													=				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

              UpperCAmelCase__						,						UpperCAmelCase__			:		Dict													=				sample, sample
              for t in range(_A						,						time_step + scheduler.config.solver_order + 1 ):
                  UpperCAmelCase__			:		int													=				scheduler.step(_A						,						_A						,						_A						,						**_A ).prev_sample
                  UpperCAmelCase__			:		int													=				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 lowercase_							(					self				:    Any						,						_A				:    List[str]=0						,						**_A				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				dict(self.forward_default_kwargs )
          UpperCAmelCase__			:		str													=				kwargs.pop('''num_inference_steps'''						,						_A )
          UpperCAmelCase__			:		List[Any]													=				self.dummy_sample
          UpperCAmelCase__			:		List[Any]													=				0.1 * sample
          UpperCAmelCase__			:		str													=				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

          for scheduler_class in self.scheduler_classes:
              UpperCAmelCase__			:		List[Any]													=				self.get_scheduler_config()
              UpperCAmelCase__			:		int													=				scheduler_class(**_A )
              scheduler.set_timesteps(_A )

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

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(_A )
                  UpperCAmelCase__			:		Optional[int]													=				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__			:		Optional[int]													=				dummy_past_residuals[: new_scheduler.config.solver_order]

              UpperCAmelCase__			:		str													=				scheduler.step(_A						,						_A						,						_A						,						**_A ).prev_sample
              UpperCAmelCase__			:		Dict													=				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 lowercase_							(					self				:    Union[str, Any]						,						_A				:    Union[str, Any]=None						,						**_A				:    Optional[Any] ):


          '''simple docstring'''


          if scheduler is None:
              UpperCAmelCase__			:		Optional[Any]													=				self.scheduler_classes[0]
              UpperCAmelCase__			:		int													=				self.get_scheduler_config(**_A )
              UpperCAmelCase__			:		Any													=				scheduler_class(**_A )

          UpperCAmelCase__			:		Any													=				self.scheduler_classes[0]
          UpperCAmelCase__			:		Optional[int]													=				self.get_scheduler_config(**_A )
          UpperCAmelCase__			:		Tuple													=				scheduler_class(**_A )

          UpperCAmelCase__			:		Optional[int]													=				10
          UpperCAmelCase__			:		str													=				self.dummy_model()
          UpperCAmelCase__			:		Tuple													=				self.dummy_sample_deter
          scheduler.set_timesteps(_A )

          for i, t in enumerate(scheduler.timesteps ):
              UpperCAmelCase__			:		List[Any]													=				model(_A						,						_A )
              UpperCAmelCase__			:		List[str]													=				scheduler.step(_A						,						_A						,						_A ).prev_sample

          return sample






      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				dict(self.forward_default_kwargs )

          UpperCAmelCase__			:		Any													=				kwargs.pop('''num_inference_steps'''						,						_A )

          for scheduler_class in self.scheduler_classes:
              UpperCAmelCase__			:		Any													=				self.get_scheduler_config()
              UpperCAmelCase__			:		Any													=				scheduler_class(**_A )

              UpperCAmelCase__			:		Tuple													=				self.dummy_sample
              UpperCAmelCase__			:		Dict													=				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__			:		List[str]													=				num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              UpperCAmelCase__			:		Optional[Any]													=				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
              UpperCAmelCase__			:		List[str]													=				dummy_past_residuals[: scheduler.config.solver_order]

              UpperCAmelCase__			:		Dict													=				scheduler.timesteps[5]
              UpperCAmelCase__			:		List[Any]													=				scheduler.timesteps[6]

              UpperCAmelCase__			:		Optional[int]													=				scheduler.step(_A						,						_A						,						_A						,						**_A ).prev_sample
              UpperCAmelCase__			:		str													=				scheduler.step(_A						,						_A						,						_A						,						**_A ).prev_sample

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






      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				UniPCMultistepScheduler(**self.get_scheduler_config() )
          UpperCAmelCase__			:		Tuple													=				self.full_loop(scheduler=_A )
          UpperCAmelCase__			:		str													=				torch.mean(torch.abs(_A ) )

          assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3

          UpperCAmelCase__			:		Tuple													=				DPMSolverSinglestepScheduler.from_config(scheduler.config )
          UpperCAmelCase__			:		List[Any]													=				DEISMultistepScheduler.from_config(scheduler.config )
          UpperCAmelCase__			:		List[Any]													=				DPMSolverMultistepScheduler.from_config(scheduler.config )
          UpperCAmelCase__			:		int													=				UniPCMultistepScheduler.from_config(scheduler.config )

          UpperCAmelCase__			:		int													=				self.full_loop(scheduler=_A )
          UpperCAmelCase__			:		str													=				torch.mean(torch.abs(_A ) )

          assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


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






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          self.check_over_configs(thresholding=_A )
          for order in [1, 2, 3]:
              for solver_type in ["bh1", "bh2"]:
                  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						,						solver_order=_A						,						solver_type=_A						,						)






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


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






      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          for solver_type in ["bh1", "bh2"]:
              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						,						)
                      UpperCAmelCase__			:		str													=				self.full_loop(
                          solver_order=_A						,						solver_type=_A						,						prediction_type=_A						,						)
                      assert not torch.isnan(_A ).any(), "Samples have nan numbers"






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


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






      def lowercase_							(					self				:    Union[str, Any] ):


          '''simple docstring'''


          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 lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				self.full_loop()
          UpperCAmelCase__			:		str													=				torch.mean(torch.abs(_A ) )

          assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[Any]													=				self.full_loop(prediction_type='''v_prediction''' )
          UpperCAmelCase__			:		List[str]													=				torch.mean(torch.abs(_A ) )

          assert abs(result_mean.item() - 0.1_0_1_4 ) < 1e-3






      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				self.scheduler_classes[0]
          UpperCAmelCase__			:		Optional[int]													=				self.get_scheduler_config(thresholding=_A						,						dynamic_thresholding_ratio=0 )
          UpperCAmelCase__			:		int													=				scheduler_class(**_A )

          UpperCAmelCase__			:		str													=				10
          UpperCAmelCase__			:		Dict													=				self.dummy_model()
          UpperCAmelCase__			:		Dict													=				self.dummy_sample_deter.half()
          scheduler.set_timesteps(_A )

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

          assert sample.dtype == torch.floataa






      def lowercase_							(					self				:    Optional[int]						,						**_A				:    int ):


          '''simple docstring'''


          for scheduler_class in self.scheduler_classes:
              UpperCAmelCase__			:		Optional[int]													=				self.get_scheduler_config(**_A )
              UpperCAmelCase__			:		Any													=				scheduler_class(**_A )

              scheduler.set_timesteps(scheduler.config.num_train_timesteps )
              assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps




'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__    					=      logging.get_logger(__name__)

UpperCamelCase__    					=      '''▁'''

UpperCamelCase__    					=      {'''vocab_file''': '''sentencepiece.bpe.model'''}

UpperCamelCase__    					=      {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

UpperCamelCase__    					=      {
    '''facebook/xglm-564M''': 2_0_4_8,
}




class 			lowerCamelCase_   (	__a      ):
      lowerCAmelCase__		     =     VOCAB_FILES_NAMES
      lowerCAmelCase__		     =     PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__		     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__		     =     ['input_ids', 'attention_mask']






      def __init__(					self				:    Optional[int]						,						_A				:    Optional[Any]						,						_A				:    Optional[Any]="<s>"						,						_A				:    List[str]="</s>"						,						_A				:    Optional[Any]="</s>"						,						_A				:    List[str]="<s>"						,						_A				:    Tuple="<unk>"						,						_A				:    List[str]="<pad>"						,						_A				:    Optional[Dict[str, Any]] = None						,						**_A				:    Union[str, Any]						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				{} if sp_model_kwargs is None else sp_model_kwargs

          # Compatibility with the original tokenizer
          UpperCAmelCase__			:		Optional[int]													=				7
          UpperCAmelCase__			:		Optional[int]													=				[f"""<madeupword{i}>""" for i in range(self.num_madeup_words )]

          UpperCAmelCase__			:		Tuple													=				kwargs.get('''additional_special_tokens'''						,						[] )
          kwargs["additional_special_tokens"] += [
              word for word in madeup_words if word not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              bos_token=_A						,						eos_token=_A						,						unk_token=_A						,						sep_token=_A						,						cls_token=_A						,						pad_token=_A						,						sp_model_kwargs=self.sp_model_kwargs						,						**_A						,						)

          UpperCAmelCase__			:		Any													=				spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(str(_A ) )
          UpperCAmelCase__			:		List[str]													=				vocab_file

          # Original fairseq vocab and spm vocab must be "aligned":
          # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
          # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
          # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
          # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

          # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
          UpperCAmelCase__			:		Any													=				1

          # Mimic fairseq token-to-id alignment for the first 4 token
          UpperCAmelCase__			:		Any													=				{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

          UpperCAmelCase__			:		int													=				len(self.sp_model )
          UpperCAmelCase__			:		Optional[int]													=				{f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
          self.fairseq_tokens_to_ids.update(_A )

          UpperCAmelCase__			:		Tuple													=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}






      def __getstate__(					self				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				self.__dict__.copy()
          UpperCAmelCase__			:		Optional[int]													=				None
          UpperCAmelCase__			:		Optional[Any]													=				self.sp_model.serialized_model_proto()
          return state






      def __setstate__(					self				:    Union[str, Any]						,						_A				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[Any]													=				d

          # for backward compatibility
          if not hasattr(self						,						'''sp_model_kwargs''' ):
              UpperCAmelCase__			:		Dict													=				{}

          UpperCAmelCase__			:		str													=				spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto )






      def lowercase_							(					self				:    Any						,						_A				:    List[int]						,						_A				:    Optional[List[int]] = None ):


          '''simple docstring'''


          if token_ids_a is None:
              return [self.sep_token_id] + token_ids_a
          UpperCAmelCase__			:		Dict													=				[self.sep_token_id]
          return sep + token_ids_a + sep + sep + token_ids_a






      def lowercase_							(					self				:    str						,						_A				:    List[int]						,						_A				:    Optional[List[int]] = None						,						_A				:    bool = False ):


          '''simple docstring'''


          if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=_A						,						token_ids_a=_A						,						already_has_special_tokens=_A )

          if token_ids_a is None:
              return [1] + ([0] * len(_A ))
          return [1] + ([0] * len(_A )) + [1, 1] + ([0] * len(_A ))






      def lowercase_							(					self				:    Any						,						_A				:    List[int]						,						_A				:    Optional[List[int]] = None ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				[self.sep_token_id]

          if token_ids_a is None:
              return len(sep + token_ids_a ) * [0]
          return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]






      @property
      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words






      def lowercase_							(					self				:    Union[str, Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				{self.convert_ids_to_tokens(_A ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab






      def lowercase_							(					self				:    Optional[Any]						,						_A				:    str ):


          '''simple docstring'''


          return self.sp_model.encode(_A						,						out_type=_A )






      def lowercase_							(					self				:    List[str]						,						_A				:    List[Any] ):


          '''simple docstring'''


          if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
          UpperCAmelCase__			:		Union[str, Any]													=				self.sp_model.PieceToId(_A )

          # Need to return unknown token if the SP model returned 0
          return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






      def lowercase_							(					self				:    List[Any]						,						_A				:    str ):


          '''simple docstring'''


          if index in self.fairseq_ids_to_tokens:
              return self.fairseq_ids_to_tokens[index]
          return self.sp_model.IdToPiece(index - self.fairseq_offset )






      def lowercase_							(					self				:    int						,						_A				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				''''''.join(_A ).replace(_A						,						''' ''' ).strip()
          return out_string






      def lowercase_							(					self				:    Any						,						_A				:    str						,						_A				:    Optional[str] = None ):


          '''simple docstring'''


          if not os.path.isdir(_A ):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
              return
          UpperCAmelCase__			:		List[str]													=				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 ) and os.path.isfile(self.vocab_file ):
              copyfile(self.vocab_file						,						_A )
          elif not os.path.isfile(self.vocab_file ):
              with open(_A						,						'''wb''' ) as fi:
                  UpperCAmelCase__			:		List[Any]													=				self.sp_model.serialized_model_proto()
                  fi.write(_A )

          return (out_vocab_file,)
