






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







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


  def  lowerCamelCase       (      self		:			Optional[int]       ,				**lowerCamelCase		:			int							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    {
             """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		:			Optional[int]       ,				lowerCamelCase		:			List[str]=0       ,				**lowerCamelCase		:			Union[str, Any]							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    dict(self.forward_default_kwargs							)
         _UpperCAmelCase							=    kwargs.pop("""num_inference_steps"""       ,				lowerCamelCase							)
         _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(**lowerCamelCase							)
                _UpperCAmelCase							=    scheduler_class(**lowerCamelCase							)
                scheduler.set_timesteps(lowerCamelCase							)
                # copy over dummy past residuals
                _UpperCAmelCase							=    dummy_past_residuals[: scheduler.config.solver_order]

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

                _UpperCAmelCase	,			_UpperCAmelCase							=    sample, sample
                for t in range(lowerCamelCase       ,				time_step + scheduler.config.solver_order + 1							):
                       _UpperCAmelCase							=    scheduler.step(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				**lowerCamelCase							).prev_sample
                       _UpperCAmelCase							=    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		:			Tuple							)						->	Optional[Any]:


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			Optional[int]=0       ,				**lowerCamelCase		:			Tuple							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    dict(self.forward_default_kwargs							)
         _UpperCAmelCase							=    kwargs.pop("""num_inference_steps"""       ,				lowerCamelCase							)
         _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(**lowerCamelCase							)
                scheduler.set_timesteps(lowerCamelCase							)

                # 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(lowerCamelCase							)
                       _UpperCAmelCase							=    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							=    dummy_past_residuals[: new_scheduler.config.solver_order]

                _UpperCAmelCase							=    scheduler.step(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				**lowerCamelCase							).prev_sample
                _UpperCAmelCase							=    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		:			Any       ,				lowerCamelCase		:			Dict=None       ,				**lowerCamelCase		:			Optional[Any]							)						->	str:


         """simple docstring"""
         if scheduler is None:
                _UpperCAmelCase							=    self.scheduler_classes[0]
                _UpperCAmelCase							=    self.get_scheduler_config(**lowerCamelCase							)
                _UpperCAmelCase							=    scheduler_class(**lowerCamelCase							)

         _UpperCAmelCase							=    self.scheduler_classes[0]
         _UpperCAmelCase							=    self.get_scheduler_config(**lowerCamelCase							)
         _UpperCAmelCase							=    scheduler_class(**lowerCamelCase							)

         _UpperCAmelCase							=    10
         _UpperCAmelCase							=    self.dummy_model()
         _UpperCAmelCase							=    self.dummy_sample_deter
         scheduler.set_timesteps(lowerCamelCase							)

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

         return sample


  def  lowerCamelCase       (      self		:			Dict							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    DPMSolverSinglestepScheduler(**self.get_scheduler_config()							)
         _UpperCAmelCase							=    50
         _UpperCAmelCase							=    self.dummy_model()
         _UpperCAmelCase							=    self.dummy_sample_deter
         scheduler.set_timesteps(lowerCamelCase							)

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

         _UpperCAmelCase							=    torch.mean(torch.abs(lowerCamelCase							)							)

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


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[str]:


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


  def  lowerCamelCase       (      self		:			Any							)						->	str:


         """simple docstring"""
         # make sure that iterating over schedulers with same config names gives same results
         # for defaults
         _UpperCAmelCase							=    DPMSolverSinglestepScheduler(**self.get_scheduler_config()							)
         _UpperCAmelCase							=    self.full_loop(scheduler=lowerCamelCase							)
         _UpperCAmelCase							=    torch.mean(torch.abs(lowerCamelCase							)							)

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

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

         _UpperCAmelCase							=    self.full_loop(scheduler=lowerCamelCase							)
         _UpperCAmelCase							=    torch.mean(torch.abs(lowerCamelCase							)							)

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


  def  lowerCamelCase       (      self		:			List[str]							)						->	Optional[Any]:


         """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		:			Tuple							)						->	int:


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


  def  lowerCamelCase       (      self		:			List[str]							)						->	Dict:


         """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       ,				)
                                     _UpperCAmelCase							=    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		:			Optional[int]							)						->	Dict:


         """simple docstring"""
         self.check_over_configs(lower_order_final=lowerCamelCase							)
         self.check_over_configs(lower_order_final=lowerCamelCase							)


  def  lowerCamelCase       (      self		:			int							)						->	int:


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


  def  lowerCamelCase       (      self		:			str							)						->	Optional[Any]:


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


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Tuple:


         """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		:			int							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    self.full_loop()
         _UpperCAmelCase							=    torch.mean(torch.abs(lowerCamelCase							)							)

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


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    self.full_loop(use_karras_sigmas=lowerCamelCase							)
         _UpperCAmelCase							=    torch.mean(torch.abs(lowerCamelCase							)							)

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


  def  lowerCamelCase       (      self		:			List[str]							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    self.full_loop(prediction_type="""v_prediction"""							)
         _UpperCAmelCase							=    torch.mean(torch.abs(lowerCamelCase							)							)

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


  def  lowerCamelCase       (      self		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    self.full_loop(prediction_type="""v_prediction"""       ,				use_karras_sigmas=lowerCamelCase							)
         _UpperCAmelCase							=    torch.mean(torch.abs(lowerCamelCase							)							)

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





  def  lowerCamelCase       (      self		:			Dict							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    self.scheduler_classes[0]
         _UpperCAmelCase							=    self.get_scheduler_config(thresholding=lowerCamelCase       ,				dynamic_thresholding_ratio=0							)
         _UpperCAmelCase							=    scheduler_class(**lowerCamelCase							)

         _UpperCAmelCase							=    10
         _UpperCAmelCase							=    self.dummy_model()
         _UpperCAmelCase							=    self.dummy_sample_deter.half()
         scheduler.set_timesteps(lowerCamelCase							)

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

         assert sample.dtype == torch.floataa



import json
import os
from typing import Dict, List, Optional, Tuple

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


SCREAMING_SNAKE_CASE						:Union[str, Any]			=  logging.get_logger(__name__)


SCREAMING_SNAKE_CASE						:Dict			=  {
    '''vocab_file''': '''vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
    '''merges_file''': '''merges.txt''',
}

SCREAMING_SNAKE_CASE						:Optional[int]			=  {
    '''vocab_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'''
        ),
    },
    '''tokenizer_config_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'''
        ),
    },
    '''merges_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'''
        ),
    },
}

SCREAMING_SNAKE_CASE						:List[str]			=  '''</w>'''
SCREAMING_SNAKE_CASE						:int			=  '''@@ '''



def       _lowerCAmelCase    (				lowerCAmelCase_				:int				)->int:
   '''simple docstring'''






   snake_case_            =       set()
   snake_case_            =       word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)				)
      snake_case_            =       char
   return pairs


# Speech2Text2 has no max input length
SCREAMING_SNAKE_CASE						:Tuple			=  {'''facebook/s2t-wav2vec2-large-en-de''': 10_24}



class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							VOCAB_FILES_NAMES
  _SCREAMING_SNAKE_CASE		     =							PRETRAINED_VOCAB_FILES_MAP
  _SCREAMING_SNAKE_CASE		     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _SCREAMING_SNAKE_CASE		     =							['input_ids', 'attention_mask']



  def __init__(  self							:							List[Any]     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Union[str, Any]="<s>"     ,					_lowerCAmelCase							:							Tuple="<pad>"     ,					_lowerCAmelCase							:							Union[str, Any]="</s>"     ,					_lowerCAmelCase							:							str="<unk>"     ,					_lowerCAmelCase							:							Union[str, Any]=False     ,					_lowerCAmelCase							:							List[str]=None     ,					**_lowerCAmelCase							:							List[str]     ,					) ->			List[str]:




     """simple docstring"""
     super().__init__(
         unk_token=_lowerCAmelCase     ,					bos_token=_lowerCAmelCase     ,					eos_token=_lowerCAmelCase     ,					pad_token=_lowerCAmelCase     ,					do_lower_case=_lowerCAmelCase     ,					**_lowerCAmelCase     ,					)

     snake_case_            =       do_lower_case

     with open(_lowerCAmelCase     ,					encoding="utf-8"	) as vocab_handle:
        snake_case_            =       json.load(_lowerCAmelCase	)
     snake_case_            =       {v: k for k, v in self.encoder.items()}

     if merges_file is None:
        logger.info(F'''No merges files provided. {self.__class__.__name__} can only be used for decoding.'''	)

        snake_case_            =       None
        snake_case_            =       None
     else:
        with open(_lowerCAmelCase     ,					encoding="utf-8"	) as merges_handle:
           snake_case_            =       merges_handle.read().split("\n"	)[:-1]

        snake_case_            =       [tuple(merge.split()[:2]	) for merge in merges]
        snake_case_            =       dict(zip(_lowerCAmelCase     ,					range(len(_lowerCAmelCase	)	)	)	)
        snake_case_            =       {}



  @property
  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			int:




     """simple docstring"""
     return len(self.decoder	)



  def        lowerCAmelCase__	(  self							:							Any	) ->			Dict:




     """simple docstring"""
     return dict(self.encoder     ,					**self.added_tokens_encoder	)



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							Any	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       tuple(token[:-1]	) + (token[-1] + BPE_TOKEN_MERGES,)
     if token in self.cache:
        return self.cache[token]
     snake_case_            =       get_pairs(_lowerCAmelCase	)

     if not pairs:
        return token

     while True:
        snake_case_            =       min(_lowerCAmelCase     ,					key=lambda _lowerCAmelCase	: self.bpe_ranks.get(_lowerCAmelCase     ,					float("inf"	)	)	)
        if bigram not in self.bpe_ranks:
           break
        snake_case_     ,     snake_case_            =       bigram
        snake_case_            =       []
        snake_case_            =       0
        while i < len(_lowerCAmelCase	):
           try:
              snake_case_            =       word.index(_lowerCAmelCase     ,					_lowerCAmelCase	)
           except ValueError:
              new_word.extend(word[i:]	)
              break
           else:
              new_word.extend(word[i:j]	)
              snake_case_            =       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
        snake_case_            =       tuple(_lowerCAmelCase	)
        snake_case_            =       new_word
        if len(_lowerCAmelCase	) == 1:
           break
        else:
           snake_case_            =       get_pairs(_lowerCAmelCase	)
     snake_case_            =       " ".join(_lowerCAmelCase	)
     if word == "\n  " + BPE_TOKEN_MERGES:
        snake_case_            =       "\n" + BPE_TOKEN_MERGES

     if word.endswith(_lowerCAmelCase	):
        snake_case_            =       word.replace(_lowerCAmelCase     ,					""	)

     snake_case_            =       word.replace(" "     ,					_lowerCAmelCase	)
     snake_case_            =       word
     return word



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							List[str]	) ->			List[Any]:




     """simple docstring"""

     if self.bpe_ranks is None:
        raise ValueError(
            "This tokenizer was instantiated without a `merges.txt` file, so"
            " that it can only be used for decoding, not for encoding."
            "Make sure to provide `merges.txt` file at instantiation to enable "
            "encoding."	)

     if self.do_lower_case:
        snake_case_            =       text.lower()

     snake_case_            =       text.split()

     snake_case_            =       []
     for token in text:
        if token:
           split_tokens.extend(list(self.bpe(_lowerCAmelCase	).split(" "	)	)	)

     return split_tokens



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							str	) ->			int:




     """simple docstring"""
     return self.encoder.get(_lowerCAmelCase     ,					self.encoder.get(self.unk_token	)	)



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							int	) ->			str:




     """simple docstring"""
     snake_case_            =       self.decoder.get(_lowerCAmelCase     ,					self.unk_token	)
     return result



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[str]	) ->			str:




     """simple docstring"""
     snake_case_            =       " ".join(_lowerCAmelCase	)

     # make sure @@ tokens are concatenated
     snake_case_            =       "".join(string.split(_lowerCAmelCase	)	)

     return string



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Optional[str] = None	) ->			Tuple[str]:




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

     with open(_lowerCAmelCase     ,					"w"     ,					encoding="utf-8"	) as f:
        f.write(json.dumps(self.encoder     ,					indent=2     ,					sort_keys=_lowerCAmelCase     ,					ensure_ascii=_lowerCAmelCase	) + "\n"	)

     snake_case_            =       0
     if self.bpe_ranks is None:
        return (vocab_file,)

     with open(_lowerCAmelCase     ,					"w"     ,					encoding="utf-8"	) as writer:
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,					key=lambda _lowerCAmelCase	: kv[1]	):
           if index != token_index:
              logger.warning(
                  F'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
                  " Please check that the tokenizer is not corrupted!"	)
              snake_case_            =       token_index
           writer.write(" ".join(_lowerCAmelCase	) + "\n"	)
           index += 1

     return (vocab_file, merges_file)




