



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class 							lowerCAmelCase_						(    __a				):
       UpperCAmelCase__							:   Optional[Any]  	=    (UniPCMultistepScheduler,)
       UpperCAmelCase__							:   List[Any]  	=    (("""num_inference_steps""", 25),)
       def    snake_case_			(     self,     **SCREAMING_SNAKE_CASE_			)	->						str:
          UpperCamelCase       :    Union[str, Any]		      =      {
              'num_train_timesteps': 1000,
              'beta_start': 0.00_01,
              'beta_end': 0.02,
              'beta_schedule': 'linear',
              'solver_order': 2,
              'solver_type': 'bh2',
          }

          config.update(**_lowerCamelCase			)
          return config
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_=0,     **SCREAMING_SNAKE_CASE_			)	->						Any:
          UpperCamelCase       :    Tuple		      =      dict(self.forward_default_kwargs			)
          UpperCamelCase       :    Any		      =      kwargs.pop('num_inference_steps',     _lowerCamelCase			)
          UpperCamelCase       :    Optional[Any]		      =      self.dummy_sample
          UpperCamelCase       :    int		      =      0.1 * sample
          UpperCamelCase       :    Union[str, Any]		      =      [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
             UpperCamelCase       :    List[str]		      =      self.get_scheduler_config(**_lowerCamelCase			)
             UpperCamelCase       :    int		      =      scheduler_class(**_lowerCamelCase			)
             scheduler.set_timesteps(_lowerCamelCase			)
             # copy over dummy past residuals
             UpperCamelCase       :    Optional[int]		      =      dummy_past_residuals[: scheduler.config.solver_order]

             with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(_lowerCamelCase			)
                UpperCamelCase       :    Tuple		      =      scheduler_class.from_pretrained(_lowerCamelCase			)
                new_scheduler.set_timesteps(_lowerCamelCase			)
                # copy over dummy past residuals
                UpperCamelCase       :    Optional[int]		      =      dummy_past_residuals[: new_scheduler.config.solver_order]

             UpperCamelCase      ,	UpperCamelCase       :    Union[str, Any]		      =      sample, sample
             for t in range(_lowerCamelCase,     time_step + scheduler.config.solver_order + 1			):
                UpperCamelCase       :    Tuple		      =      scheduler.step(_lowerCamelCase,     _lowerCamelCase,     _lowerCamelCase,     **_lowerCamelCase			).prev_sample
                UpperCamelCase       :    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    snake_case_			(     self,     SCREAMING_SNAKE_CASE_=0,     **SCREAMING_SNAKE_CASE_			)	->						Any:
          UpperCamelCase       :    Dict		      =      dict(self.forward_default_kwargs			)
          UpperCamelCase       :    Dict		      =      kwargs.pop('num_inference_steps',     _lowerCamelCase			)
          UpperCamelCase       :    Union[str, Any]		      =      self.dummy_sample
          UpperCamelCase       :    Tuple		      =      0.1 * sample
          UpperCamelCase       :    List[Any]		      =      [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
             UpperCamelCase       :    Optional[int]		      =      self.get_scheduler_config()
             UpperCamelCase       :    Optional[Any]		      =      scheduler_class(**_lowerCamelCase			)
             scheduler.set_timesteps(_lowerCamelCase			)

             # copy over dummy past residuals (must be after setting timesteps)
             UpperCamelCase       :    Union[str, Any]		      =      dummy_past_residuals[: scheduler.config.solver_order]

             with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(_lowerCamelCase			)
                UpperCamelCase       :    Any		      =      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)
                UpperCamelCase       :    List[str]		      =      dummy_past_residuals[: new_scheduler.config.solver_order]

             UpperCamelCase       :    Tuple		      =      scheduler.step(_lowerCamelCase,     _lowerCamelCase,     _lowerCamelCase,     **_lowerCamelCase			).prev_sample
             UpperCamelCase       :    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    snake_case_			(     self,     SCREAMING_SNAKE_CASE_=None,     **SCREAMING_SNAKE_CASE_			)	->						List[Any]:
          if scheduler is None:
             UpperCamelCase       :    Optional[int]		      =      self.scheduler_classes[0]
             UpperCamelCase       :    Any		      =      self.get_scheduler_config(**_lowerCamelCase			)
             UpperCamelCase       :    Optional[int]		      =      scheduler_class(**_lowerCamelCase			)

          UpperCamelCase       :    Union[str, Any]		      =      self.scheduler_classes[0]
          UpperCamelCase       :    Optional[int]		      =      self.get_scheduler_config(**_lowerCamelCase			)
          UpperCamelCase       :    Optional[int]		      =      scheduler_class(**_lowerCamelCase			)

          UpperCamelCase       :    str		      =      10
          UpperCamelCase       :    List[Any]		      =      self.dummy_model()
          UpperCamelCase       :    Tuple		      =      self.dummy_sample_deter
          scheduler.set_timesteps(_lowerCamelCase			)

          for i, t in enumerate(scheduler.timesteps			):
             UpperCamelCase       :    Dict		      =      model(_lowerCamelCase,     _lowerCamelCase			)
             UpperCamelCase       :    int		      =      scheduler.step(_lowerCamelCase,     _lowerCamelCase,     _lowerCamelCase			).prev_sample

          return sample
       def    snake_case_			(     self			)	->						Tuple:
          UpperCamelCase       :    Tuple		      =      dict(self.forward_default_kwargs			)

          UpperCamelCase       :    Optional[Any]		      =      kwargs.pop('num_inference_steps',     _lowerCamelCase			)

          for scheduler_class in self.scheduler_classes:
             UpperCamelCase       :    Tuple		      =      self.get_scheduler_config()
             UpperCamelCase       :    Union[str, Any]		      =      scheduler_class(**_lowerCamelCase			)

             UpperCamelCase       :    Tuple		      =      self.dummy_sample
             UpperCamelCase       :    Tuple		      =      0.1 * sample

             if num_inference_steps is not None and hasattr(_lowerCamelCase,     'set_timesteps'			):
                scheduler.set_timesteps(_lowerCamelCase			)
             elif num_inference_steps is not None and not hasattr(_lowerCamelCase,     'set_timesteps'			):
                UpperCamelCase       :    Dict		      =      num_inference_steps

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

             UpperCamelCase       :    Optional[int]		      =      scheduler.timesteps[5]
             UpperCamelCase       :    Any		      =      scheduler.timesteps[6]

             UpperCamelCase       :    List[str]		      =      scheduler.step(_lowerCamelCase,     _lowerCamelCase,     _lowerCamelCase,     **_lowerCamelCase			).prev_sample
             UpperCamelCase       :    str		      =      scheduler.step(_lowerCamelCase,     _lowerCamelCase,     _lowerCamelCase,     **_lowerCamelCase			).prev_sample

             self.assertEqual(output_a.shape,     sample.shape			)
             self.assertEqual(output_a.shape,     output_a.shape			)
       def    snake_case_			(     self			)	->						Union[str, Any]:
          UpperCamelCase       :    Tuple		      =      UniPCMultistepScheduler(**self.get_scheduler_config()			)
          UpperCamelCase       :    Any		      =      self.full_loop(scheduler=_lowerCamelCase			)
          UpperCamelCase       :    Union[str, Any]		      =      torch.mean(torch.abs(_lowerCamelCase			)			)

          assert abs(result_mean.item() - 0.24_64			) < 1e-3

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

          UpperCamelCase       :    Optional[int]		      =      self.full_loop(scheduler=_lowerCamelCase			)
          UpperCamelCase       :    List[str]		      =      torch.mean(torch.abs(_lowerCamelCase			)			)

          assert abs(result_mean.item() - 0.24_64			) < 1e-3
       def    snake_case_			(     self			)	->						int:
          for timesteps in [25, 50, 100, 999, 1000]:
             self.check_over_configs(num_train_timesteps=_lowerCamelCase			)
       def    snake_case_			(     self			)	->						int:
          self.check_over_configs(thresholding=_lowerCamelCase			)
          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=_lowerCamelCase,     prediction_type=_lowerCamelCase,     sample_max_value=_lowerCamelCase,     solver_order=_lowerCamelCase,     solver_type=_lowerCamelCase,     )
       def    snake_case_			(     self			)	->						Tuple:
          for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=_lowerCamelCase			)
       def    snake_case_			(     self			)	->						Optional[Any]:
          for solver_type in ["bh1", "bh2"]:
             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,     )
                   UpperCamelCase       :    Union[str, Any]		      =      self.full_loop(
                       solver_order=_lowerCamelCase,     solver_type=_lowerCamelCase,     prediction_type=_lowerCamelCase,     )
                   assert not torch.isnan(_lowerCamelCase			).any(), "Samples have nan numbers"
       def    snake_case_			(     self			)	->						Dict:
          self.check_over_configs(lower_order_final=_lowerCamelCase			)
          self.check_over_configs(lower_order_final=_lowerCamelCase			)
       def    snake_case_			(     self			)	->						Tuple:
          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    snake_case_			(     self			)	->						str:
          UpperCamelCase       :    str		      =      self.full_loop()
          UpperCamelCase       :    int		      =      torch.mean(torch.abs(_lowerCamelCase			)			)

          assert abs(result_mean.item() - 0.24_64			) < 1e-3
       def    snake_case_			(     self			)	->						Union[str, Any]:
          UpperCamelCase       :    Optional[int]		      =      self.full_loop(prediction_type='v_prediction'			)
          UpperCamelCase       :    List[str]		      =      torch.mean(torch.abs(_lowerCamelCase			)			)

          assert abs(result_mean.item() - 0.10_14			) < 1e-3
       def    snake_case_			(     self			)	->						Any:
          UpperCamelCase       :    Union[str, Any]		      =      self.scheduler_classes[0]
          UpperCamelCase       :    int		      =      self.get_scheduler_config(thresholding=_lowerCamelCase,     dynamic_thresholding_ratio=0			)
          UpperCamelCase       :    Union[str, Any]		      =      scheduler_class(**_lowerCamelCase			)

          UpperCamelCase       :    Tuple		      =      10
          UpperCamelCase       :    Tuple		      =      self.dummy_model()
          UpperCamelCase       :    Optional[int]		      =      self.dummy_sample_deter.half()
          scheduler.set_timesteps(_lowerCamelCase			)

          for i, t in enumerate(scheduler.timesteps			):
             UpperCamelCase       :    int		      =      model(_lowerCamelCase,     _lowerCamelCase			)
             UpperCamelCase       :    Any		      =      scheduler.step(_lowerCamelCase,     _lowerCamelCase,     _lowerCamelCase			).prev_sample

          assert sample.dtype == torch.floataa






       def    snake_case_			(     self,     **SCREAMING_SNAKE_CASE_			)	->						str:
          for scheduler_class in self.scheduler_classes:
             UpperCamelCase       :    Dict		      =      self.get_scheduler_config(**_lowerCamelCase			)
             UpperCamelCase       :    Tuple		      =      scheduler_class(**_lowerCamelCase			)

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





"""simple docstring"""






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

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


_lowercase							=				logging.get_logger(__name__)

_lowercase							=				{
    '''vocab_file''': '''vocab.txt''',
    '''merges_file''': '''bpe.codes''',
}

_lowercase							=				{
    '''vocab_file''': {
        '''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt''',
        '''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt''',
    },
    '''merges_file''': {
        '''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes''',
        '''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes''',
    },
}

_lowercase							=				{
    '''vinai/phobert-base''': 256,
    '''vinai/phobert-large''': 256,
}



def      lowerCAmelCase__			(		__magic_name__      )						->Optional[int]:
			__lowercase       =       set()
			__lowercase       =       word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)      )
						__lowercase       =       char

			__lowercase       =       set(__magic_name__      )
			return pairs







class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			int          =   VOCAB_FILES_NAMES
							_lowerCamelCase					:			List[str]          =   PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase					:			Optional[Any]          =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase="<s>"  ,    _lowerCamelCase="</s>"  ,    _lowerCamelCase="</s>"  ,    _lowerCamelCase="<s>"  ,    _lowerCamelCase="<unk>"  ,    _lowerCamelCase="<pad>"  ,    _lowerCamelCase="<mask>"  ,    **_lowerCamelCase  ,    )			->							Tuple:




										'''simple docstring'''
										super().__init__(
										    bos_token=_lowerCamelCase  ,    eos_token=_lowerCamelCase  ,    unk_token=_lowerCamelCase  ,    sep_token=_lowerCamelCase  ,    cls_token=_lowerCamelCase  ,    pad_token=_lowerCamelCase  ,    mask_token=_lowerCamelCase  ,    **_lowerCamelCase  ,    )

										__lowercase       =       vocab_file
										__lowercase       =       merges_file

										__lowercase       =       {}
										__lowercase       =       0
										__lowercase       =       1
										__lowercase       =       2
										__lowercase       =       3

										self.add_from_file(_lowerCamelCase       )

										__lowercase       =       {v: k for k, v in self.encoder.items()}

										with open(_lowerCamelCase  ,    encoding="utf-8"       ) as merges_handle:
													__lowercase       =       merges_handle.read().split("\n"       )[:-1]
										__lowercase       =       [tuple(merge.split()[:-1]       ) for merge in merges]
										__lowercase       =       dict(zip(_lowerCamelCase  ,    range(len(_lowerCamelCase       )       )       )       )
										__lowercase       =       {}
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							List[int]:




										'''simple docstring'''

										if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										__lowercase       =       [self.cls_token_id]
										__lowercase       =       [self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None  ,    _lowerCamelCase = False       )			->							List[int]:




										'''simple docstring'''

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

										if token_ids_a is None:
													return [1] + ([0] * len(_lowerCamelCase       )) + [1]
										return [1] + ([0] * len(_lowerCamelCase       )) + [1, 1] + ([0] * len(_lowerCamelCase       )) + [1]
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							List[int]:




										'''simple docstring'''
										__lowercase       =       [self.sep_token_id]
										__lowercase       =       [self.cls_token_id]

										if token_ids_a is None:
													return len(cls + token_ids_a + sep       ) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[Any]:




										'''simple docstring'''
										return len(self.encoder       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										return dict(self.encoder  ,    **self.added_tokens_encoder       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							List[Any]:




										'''simple docstring'''
										if token in self.cache:
													return self.cache[token]
										__lowercase       =       tuple(_lowerCamelCase       )
										__lowercase       =       tuple(list(word[:-1]       ) + [word[-1] + "</w>"]       )
										__lowercase       =       get_pairs(_lowerCamelCase       )

										if not pairs:
													return token

										while True:
													__lowercase       =       min(_lowerCamelCase  ,    key=lambda _lowerCamelCase       : self.bpe_ranks.get(_lowerCamelCase  ,    float("inf"       )       )       )
													if bigram not in self.bpe_ranks:
																break
													__lowercase   ,					__lowercase       =       bigram
													__lowercase       =       []
													__lowercase       =       0
													while i < len(_lowerCamelCase       ):
																try:
																			__lowercase       =       word.index(_lowerCamelCase  ,    _lowerCamelCase       )
																except ValueError:
																			new_word.extend(word[i:]       )
																			break
																else:
																			new_word.extend(word[i:j]       )
																			__lowercase       =       j

																if word[i] == first and i < len(_lowerCamelCase       ) - 1 and word[i + 1] == second:
																			new_word.append(first + second       )
																			i += 2
																else:
																			new_word.append(word[i]       )
																			i += 1
													__lowercase       =       tuple(_lowerCamelCase       )
													__lowercase       =       new_word
													if len(_lowerCamelCase       ) == 1:
																break
													else:
																__lowercase       =       get_pairs(_lowerCamelCase       )
										__lowercase       =       "@@ ".join(_lowerCamelCase       )
										__lowercase       =       word[:-4]
										__lowercase       =       word
										return word
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							str:




										'''simple docstring'''
										__lowercase       =       []

										__lowercase       =       re.findall(R"\S+\n?"  ,    _lowerCamelCase       )

										for token in words:
													split_tokens.extend(list(self.bpe(_lowerCamelCase       ).split(" "       )       )       )
										return split_tokens
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Optional[Any]:




										'''simple docstring'''
										return self.encoder.get(_lowerCamelCase  ,    self.encoder.get(self.unk_token       )       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Optional[int]:




										'''simple docstring'''
										return self.decoder.get(_lowerCamelCase  ,    self.unk_token       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Tuple:




										'''simple docstring'''
										__lowercase       =       " ".join(_lowerCamelCase       ).replace("@@ "  ,    ""       ).strip()
										return out_string
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							Tuple[str]:




										'''simple docstring'''
										if not os.path.isdir(_lowerCamelCase       ):
													logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
													return
										__lowercase       =       os.path.join(
										    _lowerCamelCase  ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )
										__lowercase       =       os.path.join(
										    _lowerCamelCase  ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]       )

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

										if os.path.abspath(self.merges_file       ) != os.path.abspath(_lowerCamelCase       ):
													copyfile(self.merges_file  ,    _lowerCamelCase       )

										return out_vocab_file, out_merge_file





							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Dict:




										'''simple docstring'''
										if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ):
													try:
																with open(_lowerCamelCase  ,    "r"  ,    encoding="utf-8"       ) as fd:
																			self.add_from_file(_lowerCamelCase       )
													except FileNotFoundError as fnfe:
																raise fnfe
													except UnicodeError:
																raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset'''       )
													return

										__lowercase       =       f.readlines()
										for lineTmp in lines:
													__lowercase       =       lineTmp.strip()
													__lowercase       =       line.rfind(" "       )
													if idx == -1:
																raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'"       )
													__lowercase       =       line[:idx]
													__lowercase       =       len(self.encoder       )





