






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class 			UpperCAmelCase_						(					_SCREAMING_SNAKE_CASE ):



      """simple docstring"""
      UpperCamelCase_				:					Tuple           =(UniPCMultistepScheduler,)
      UpperCamelCase_				:					List[str]           =(('num_inference_steps', 25),)
      def     UpperCAmelCase						(				self    ,				**SCREAMING_SNAKE_CASE_ )				->      List[Any]:
          UpperCamelCase       :int								=   {
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.0001,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''solver_order''': 2,
              '''solver_type''': '''bh2''',
          }

          config.update(**_lowerCAmelCase )
          return config
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_=0    ,				**SCREAMING_SNAKE_CASE_ )				->      List[str]:
          UpperCamelCase       :Optional[Any]								=   dict(self.forward_default_kwargs )
          UpperCamelCase       :Optional[Any]								=   kwargs.pop('''num_inference_steps'''    ,				_lowerCAmelCase )
          UpperCamelCase       :str								=   self.dummy_sample
          UpperCamelCase       :Any								=   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[Any]								=   self.get_scheduler_config(**_lowerCAmelCase )
              UpperCamelCase       :Tuple								=   scheduler_class(**_lowerCAmelCase )
              scheduler.set_timesteps(_lowerCAmelCase )
              # copy over dummy past residuals
              UpperCamelCase       :int								=   dummy_past_residuals[: scheduler.config.solver_order]

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(_lowerCAmelCase )
                  UpperCamelCase       :Union[str, Any]								=   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       :Tuple								=   sample, sample
              for t in range(_lowerCAmelCase    ,				time_step + scheduler.config.solver_order + 1 ):
                  UpperCamelCase       :List[Any]								=   scheduler.step(_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase    ,				**_lowerCAmelCase ).prev_sample
                  UpperCamelCase       :str								=   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     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_=0    ,				**SCREAMING_SNAKE_CASE_ )				->      Dict:
          UpperCamelCase       :Optional[int]								=   dict(self.forward_default_kwargs )
          UpperCamelCase       :str								=   kwargs.pop('''num_inference_steps'''    ,				_lowerCAmelCase )
          UpperCamelCase       :List[str]								=   self.dummy_sample
          UpperCamelCase       :Optional[int]								=   0.1 * sample
          UpperCamelCase       :List[str]								=   [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
              UpperCamelCase       :Dict								=   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       :int								=   dummy_past_residuals[: scheduler.config.solver_order]

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(_lowerCAmelCase )
                  UpperCamelCase       :Optional[int]								=   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       :Optional[Any]								=   dummy_past_residuals[: new_scheduler.config.solver_order]

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

          UpperCamelCase       :Dict								=   self.scheduler_classes[0]
          UpperCamelCase       :List[Any]								=   self.get_scheduler_config(**_lowerCAmelCase )
          UpperCamelCase       :Dict								=   scheduler_class(**_lowerCAmelCase )

          UpperCamelCase       :Dict								=   10
          UpperCamelCase       :Any								=   self.dummy_model()
          UpperCamelCase       :str								=   self.dummy_sample_deter
          scheduler.set_timesteps(_lowerCAmelCase )

          for i, t in enumerate(scheduler.timesteps ):
              UpperCamelCase       :Tuple								=   model(_lowerCAmelCase    ,				_lowerCAmelCase )
              UpperCamelCase       :Optional[Any]								=   scheduler.step(_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase ).prev_sample

          return sample
      def     UpperCAmelCase						(				self )				->      int:
          UpperCamelCase       :str								=   dict(self.forward_default_kwargs )

          UpperCamelCase       :Union[str, Any]								=   kwargs.pop('''num_inference_steps'''    ,				_lowerCAmelCase )

          for scheduler_class in self.scheduler_classes:
              UpperCamelCase       :Any								=   self.get_scheduler_config()
              UpperCamelCase       :Dict								=   scheduler_class(**_lowerCAmelCase )

              UpperCamelCase       :int								=   self.dummy_sample
              UpperCamelCase       :Any								=   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       :Optional[Any]								=   num_inference_steps

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

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

              UpperCamelCase       :Any								=   scheduler.step(_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase    ,				**_lowerCAmelCase ).prev_sample
              UpperCamelCase       :List[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     UpperCAmelCase						(				self )				->      Any:
          # make sure that iterating over schedulers with same config names gives same results
          # for defaults
          UpperCamelCase       :Tuple								=   UniPCMultistepScheduler(**self.get_scheduler_config() )
          UpperCamelCase       :List[str]								=   self.full_loop(scheduler=_lowerCAmelCase )
          UpperCamelCase       :Tuple								=   torch.mean(torch.abs(_lowerCAmelCase ) )

          assert abs(result_mean.item() - 0.2464 ) < 1e-3

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

          UpperCamelCase       :Optional[int]								=   self.full_loop(scheduler=_lowerCAmelCase )
          UpperCamelCase       :Any								=   torch.mean(torch.abs(_lowerCAmelCase ) )

          assert abs(result_mean.item() - 0.2464 ) < 1e-3
      def     UpperCAmelCase						(				self )				->      Tuple:
          for timesteps in [25, 50, 100, 999, 1000]:
              self.check_over_configs(num_train_timesteps=_lowerCAmelCase )
      def     UpperCAmelCase						(				self )				->      Dict:
          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     UpperCAmelCase						(				self )				->      List[str]:
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=_lowerCAmelCase )
      def     UpperCAmelCase						(				self )				->      Optional[int]:
          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       :str								=   self.full_loop(
                          solver_order=_lowerCAmelCase    ,				solver_type=_lowerCAmelCase    ,				prediction_type=_lowerCAmelCase    ,				)
                      assert not torch.isnan(_lowerCAmelCase ).any(), "Samples have nan numbers"
      def     UpperCAmelCase						(				self )				->      Optional[Any]:
          self.check_over_configs(lower_order_final=_lowerCAmelCase )
          self.check_over_configs(lower_order_final=_lowerCAmelCase )
      def     UpperCAmelCase						(				self )				->      Optional[int]:
          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     UpperCAmelCase						(				self )				->      str:
          UpperCamelCase       :Optional[int]								=   self.full_loop()
          UpperCamelCase       :Union[str, Any]								=   torch.mean(torch.abs(_lowerCAmelCase ) )

          assert abs(result_mean.item() - 0.2464 ) < 1e-3
      def     UpperCAmelCase						(				self )				->      Optional[int]:
          UpperCamelCase       :Dict								=   self.full_loop(prediction_type='''v_prediction''' )
          UpperCamelCase       :Tuple								=   torch.mean(torch.abs(_lowerCAmelCase ) )

          assert abs(result_mean.item() - 0.1014 ) < 1e-3
      def     UpperCAmelCase						(				self )				->      Any:
          UpperCamelCase       :Optional[Any]								=   self.scheduler_classes[0]
          UpperCamelCase       :Optional[Any]								=   self.get_scheduler_config(thresholding=_lowerCAmelCase    ,				dynamic_thresholding_ratio=0 )
          UpperCamelCase       :Any								=   scheduler_class(**_lowerCAmelCase )

          UpperCamelCase       :int								=   10
          UpperCamelCase       :str								=   self.dummy_model()
          UpperCamelCase       :Union[str, Any]								=   self.dummy_sample_deter.half()
          scheduler.set_timesteps(_lowerCAmelCase )

          for i, t in enumerate(scheduler.timesteps ):
              UpperCamelCase       :List[Any]								=   model(_lowerCAmelCase    ,				_lowerCAmelCase )
              UpperCamelCase       :Tuple								=   scheduler.step(_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase ).prev_sample

          assert sample.dtype == torch.floataa




      def     UpperCAmelCase						(				self    ,				**SCREAMING_SNAKE_CASE_ )				->      int:
          for scheduler_class in self.scheduler_classes:
              UpperCamelCase       :List[str]								=   self.get_scheduler_config(**_lowerCAmelCase )
              UpperCamelCase       :List[str]								=   scheduler_class(**_lowerCAmelCase )

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











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)