


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       (DDPMParallelScheduler,)




 def        SCREAMING_SNAKE_CASE__				(		self					,  **_lowerCamelCase    ):
       a						:List[Any] 							=       {
           '''num_train_timesteps''': 1000,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''variance_type''': '''fixed_small''',
           '''clip_sample''': True,
       }

       config.update(**_lowerCamelCase    )
       return config




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       for timesteps in [1, 5, 100, 1000]:
             self.check_over_configs(num_train_timesteps=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]					,  [0.002, 0.02, 0.2, 2]    ):
             self.check_over_configs(beta_start=_lowerCamelCase					,  beta_end=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       for variance in ["fixed_small", "fixed_large", "other"]:
             self.check_over_configs(variance_type=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       self.check_over_configs(thresholding=_lowerCamelCase    )
       for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "sample", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=_lowerCamelCase					,  prediction_type=_lowerCamelCase					,  sample_max_value=_lowerCamelCase					,  )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(prediction_type=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       for t in [0, 500, 999]:
             self.check_over_forward(time_step=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       self.scheduler_classes[0]
       a						:Optional[Any] 							=       self.get_scheduler_config()
       a						:Any 							=       scheduler_class(**_lowerCamelCase    )

       assert torch.sum(torch.abs(scheduler._get_variance(0    ) - 0.0    )    ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(487    ) - 0.0_0979    )    ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(999    ) - 0.02    )    ) < 1e-5




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Tuple 							=       self.scheduler_classes[0]
       a						:Any 							=       self.get_scheduler_config()
       a						:Union[str, Any] 							=       scheduler_class(**_lowerCamelCase    )

       a						:Dict 							=       len(_lowerCamelCase    )

       a						:Tuple 							=       self.dummy_model()
       a						:Optional[Any] 							=       self.dummy_sample_deter
       a						:str 							=       self.dummy_sample_deter + 0.1
       a						:Optional[Any] 							=       self.dummy_sample_deter - 0.1

       a						:int 							=       samplea.shape[0]
       a						:List[str] 							=       torch.stack([samplea, samplea, samplea]					,  dim=0    )
       a						:List[str] 							=       torch.arange(_lowerCamelCase    )[0:3, None].repeat(1					,  _lowerCamelCase    )

       a						:str 							=       model(samples.flatten(0					,  1    )					,  timesteps.flatten(0					,  1    )    )
       a						:List[Any] 							=       scheduler.batch_step_no_noise(_lowerCamelCase					,  timesteps.flatten(0					,  1    )					,  samples.flatten(0					,  1    )    )

       a						:str 							=       torch.sum(torch.abs(_lowerCamelCase    )    )
       a						:str 							=       torch.mean(torch.abs(_lowerCamelCase    )    )

       assert abs(result_sum.item() - 1153.1833    ) < 1e-2
       assert abs(result_mean.item() - 0.5005    ) < 1e-3




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       self.scheduler_classes[0]
       a						:Any 							=       self.get_scheduler_config()
       a						:Dict 							=       scheduler_class(**_lowerCamelCase    )

       a						:Tuple 							=       len(_lowerCamelCase    )

       a						:Optional[int] 							=       self.dummy_model()
       a						:Optional[Any] 							=       self.dummy_sample_deter
       a						:Optional[int] 							=       torch.manual_seed(0    )

       for t in reversed(range(_lowerCamelCase    )    ):
             # 1. predict noise residual
             a						:Dict 							=       model(_lowerCamelCase					,  _lowerCamelCase    )

             # 2. predict previous mean of sample x_t-1
             a						:int 							=       scheduler.step(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  generator=_lowerCamelCase    ).prev_sample

             a						:Any 							=       pred_prev_sample

       a						:int 							=       torch.sum(torch.abs(_lowerCamelCase    )    )
       a						:Dict 							=       torch.mean(torch.abs(_lowerCamelCase    )    )

       assert abs(result_sum.item() - 258.9606    ) < 1e-2
       assert abs(result_mean.item() - 0.3372    ) < 1e-3




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Union[str, Any] 							=       self.scheduler_classes[0]
       a						:List[Any] 							=       self.get_scheduler_config(prediction_type='''v_prediction'''    )
       a						:Any 							=       scheduler_class(**_lowerCamelCase    )

       a						:Tuple 							=       len(_lowerCamelCase    )

       a						:str 							=       self.dummy_model()
       a						:List[str] 							=       self.dummy_sample_deter
       a						:Any 							=       torch.manual_seed(0    )

       for t in reversed(range(_lowerCamelCase    )    ):
             # 1. predict noise residual
             a						:Union[str, Any] 							=       model(_lowerCamelCase					,  _lowerCamelCase    )

             # 2. predict previous mean of sample x_t-1
             a						:str 							=       scheduler.step(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  generator=_lowerCamelCase    ).prev_sample

             a						:List[str] 							=       pred_prev_sample

       a						:Optional[int] 							=       torch.sum(torch.abs(_lowerCamelCase    )    )
       a						:Any 							=       torch.mean(torch.abs(_lowerCamelCase    )    )

       assert abs(result_sum.item() - 202.0296    ) < 1e-2
       assert abs(result_mean.item() - 0.2631    ) < 1e-3




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Union[str, Any] 							=       self.scheduler_classes[0]
       a						:str 							=       self.get_scheduler_config()
       a						:Tuple 							=       scheduler_class(**_lowerCamelCase    )

       a						:int 							=       [100, 87, 50, 1, 0]

       scheduler.set_timesteps(timesteps=_lowerCamelCase    )

       a						:List[str] 							=       scheduler.timesteps

       for i, timestep in enumerate(_lowerCamelCase    ):
             if i == len(_lowerCamelCase    ) - 1:
                   a						:str 							=       -1
             else:
                   a						:List[str] 							=       timesteps[i + 1]

             a						:Union[str, Any] 							=       scheduler.previous_timestep(_lowerCamelCase    )
             a						:List[str] 							=       prev_t.item()

             self.assertEqual(_lowerCamelCase					,  _lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[int] 							=       self.scheduler_classes[0]
       a						:List[Any] 							=       self.get_scheduler_config()
       a						:Dict 							=       scheduler_class(**_lowerCamelCase    )

       a						:int 							=       [100, 87, 50, 51, 0]

       with self.assertRaises(_lowerCamelCase					,  msg='''`custom_timesteps` must be in descending order.'''    ):
             scheduler.set_timesteps(timesteps=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Tuple 							=       self.scheduler_classes[0]
       a						:Any 							=       self.get_scheduler_config()
       a						:str 							=       scheduler_class(**_lowerCamelCase    )

       a						:Tuple 							=       [100, 87, 50, 1, 0]
       a						:Optional[Any] 							=       len(_lowerCamelCase    )

       with self.assertRaises(_lowerCamelCase					,  msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''    ):
             scheduler.set_timesteps(num_inference_steps=_lowerCamelCase					,  timesteps=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[str] 							=       self.scheduler_classes[0]
       a						:Optional[Any] 							=       self.get_scheduler_config()
       a						:Dict 							=       scheduler_class(**_lowerCamelCase    )

       a						:int 							=       [scheduler.config.num_train_timesteps]

       with self.assertRaises(
           _lowerCamelCase					,  msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''					,  ):
             scheduler.set_timesteps(timesteps=_lowerCamelCase    )











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

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


lowerCamelCase__ :   List[Any]					=							logging.get_logger(__name__)


lowerCamelCase__ :   List[str]					=							{
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

lowerCamelCase__ :   Union[str, Any]					=							{
    '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'
        ),
    },
}

lowerCamelCase__ :   Optional[Any]					=							'</w>'
lowerCamelCase__ :   Union[str, Any]					=							'@@ '

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Optional[Any]							)					-> Dict:
      SCREAMING_SNAKE_CASE_							=			set()
      SCREAMING_SNAKE_CASE_							=			word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)							)
            SCREAMING_SNAKE_CASE_							=			char
      return pairs


# Speech2Text2 has no max input length
lowerCamelCase__ :   Any					=							{'facebook/s2t-wav2vec2-large-en-de': 1_024}





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 lowercase_				    =    VOCAB_FILES_NAMES
 lowercase_				    =    PRETRAINED_VOCAB_FILES_MAP
 lowercase_				    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_				    =    ["input_ids", "attention_mask"]



 def __init__(							self  :		List[str]				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		List[Any]="<s>"				,				_lowerCAmelCase  :		Any="<pad>"				,				_lowerCAmelCase  :		List[str]="</s>"				,				_lowerCAmelCase  :		int="<unk>"				,				_lowerCAmelCase  :		Optional[Any]=False				,				_lowerCAmelCase  :		Dict=None				,				**_lowerCAmelCase  :		Tuple				,				):
       super().__init__(
           unk_token=_lowerCAmelCase				,				bos_token=_lowerCAmelCase				,				eos_token=_lowerCAmelCase				,				pad_token=_lowerCAmelCase				,				do_lower_case=_lowerCAmelCase				,				**_lowerCAmelCase				,				)

       SCREAMING_SNAKE_CASE_							=			do_lower_case

       with open(_lowerCAmelCase				,				encoding='utf-8'						) as vocab_handle:
             SCREAMING_SNAKE_CASE_							=			json.load(_lowerCAmelCase						)
       SCREAMING_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."						)

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

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



 @property
 def       lowerCAmelCase_  (							self  :		List[str]						):
       return len(self.decoder						)



 def       lowerCAmelCase_  (							self  :		Tuple						):
       return dict(self.encoder				,				**self.added_tokens_encoder						)



 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		Tuple						):
       SCREAMING_SNAKE_CASE_							=			tuple(token[:-1]						) + (token[-1] + BPE_TOKEN_MERGES,)
       if token in self.cache:
             return self.cache[token]
       SCREAMING_SNAKE_CASE_							=			get_pairs(_lowerCAmelCase						)

       if not pairs:
             return token

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

       if word.endswith(_lowerCAmelCase						):
             SCREAMING_SNAKE_CASE_							=			word.replace(_lowerCAmelCase				,				''						)

       SCREAMING_SNAKE_CASE_							=			word.replace(' '				,				_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			word
       return word



 def       lowerCAmelCase_  (							self  :		Optional[int]				,				_lowerCAmelCase  :		Optional[int]						):

       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:
             SCREAMING_SNAKE_CASE_							=			text.lower()

       SCREAMING_SNAKE_CASE_							=			text.split()

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

       return split_tokens



 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		str						):
       return self.encoder.get(_lowerCAmelCase				,				self.encoder.get(self.unk_token						)						)



 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		int						):
       SCREAMING_SNAKE_CASE_							=			self.decoder.get(_lowerCAmelCase				,				self.unk_token						)
       return result



 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			' '.join(_lowerCAmelCase						)

       # make sure @@ tokens are concatenated
       SCREAMING_SNAKE_CASE_							=			''.join(string.split(_lowerCAmelCase						)						)

       return string




 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[str] = None						):
       if not os.path.isdir(_lowerCAmelCase						):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
             return
       SCREAMING_SNAKE_CASE_							=			os.path.join(
           _lowerCAmelCase				,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)
       SCREAMING_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'						)

       SCREAMING_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!'						)
                         SCREAMING_SNAKE_CASE_							=			token_index
                   writer.write(' '.join(_lowerCAmelCase						) + '\n'						)
                   index += 1

       return (vocab_file, merges_file)