





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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_ta import TaTokenizer
else:
     __lowercase  :   List[Any]						      =None


__lowercase  :   Dict						      =logging.get_logger(__name__)

__lowercase  :   Tuple						      ={"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__lowercase  :   int						      ={
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__lowercase  :   Tuple						      ={
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}

class      A       (	lowercase__						):
      _snake_case							     =VOCAB_FILES_NAMES
      _snake_case							     =PRETRAINED_VOCAB_FILES_MAP
      _snake_case							     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _snake_case							     =['input_ids', 'attention_mask']
      _snake_case							     =TaTokenizer

      _snake_case							     =[]



      def __init__(	self:						List[Any]		,  _lowerCAmelCase:						Any=None		,  _lowerCAmelCase:						List[Any]=None		,  _lowerCAmelCase:						Optional[int]="</s>"		,  _lowerCAmelCase:						Optional[int]="<unk>"		,  _lowerCAmelCase:						Optional[int]="<pad>"		,  _lowerCAmelCase:						Tuple=100		,  _lowerCAmelCase:						List[Any]=None		,  **_lowerCAmelCase:						Tuple		,  )						->    int:
         '''simple docstring'''

         if extra_ids > 0 and additional_special_tokens is None:
            UpperCAmelCase_											=[F'<extra_id_{i}>' for i in range(_lowerCAmelCase       )]
         elif extra_ids > 0 and additional_special_tokens is not None:
            # Check that we have the right number of extra special tokens
            UpperCAmelCase_											=len(set(filter(lambda _lowerCAmelCase       : bool("extra_id_" in str(_lowerCAmelCase       )       )		,  _lowerCAmelCase       )       )       )
            if extra_tokens != extra_ids:
               raise ValueError(
                   F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                   " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
                   " tokens"       )

         super().__init__(
             _lowerCAmelCase		,  tokenizer_file=_lowerCAmelCase		,  eos_token=_lowerCAmelCase		,  unk_token=_lowerCAmelCase		,  pad_token=_lowerCAmelCase		,  extra_ids=_lowerCAmelCase		,  additional_special_tokens=_lowerCAmelCase		,  **_lowerCAmelCase		,  )

         UpperCAmelCase_											=vocab_file
         UpperCAmelCase_											=False if not self.vocab_file else True
         UpperCAmelCase_											=extra_ids



      @staticmethod
      def 	lowerCAmelCase__    (	_lowerCAmelCase:						Dict		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						str       )						->    Tuple:
         '''simple docstring'''

         if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
            UpperCAmelCase_											=TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
            if init_max_model_length is not None and init_max_model_length != max_model_length:
               return init_max_model_length
            elif init_max_model_length is None:
               warnings.warn(
                   "This tokenizer was incorrectly instantiated with a model max length of"
                   F' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
                   " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
                   " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
                   F' {pretrained_model_name_or_path} automatically truncating your input to'
                   F' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
                   F' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
                   " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
                   " instantiate this tokenizer with `model_max_length` set to your preferred value."		,  _lowerCAmelCase		,  )

         return max_model_length



      def 	lowerCAmelCase__    (	self:						List[Any]		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						Any = None       )						->    int:
         '''simple docstring'''

         if not self.can_save_slow_tokenizer:
            raise ValueError(
                "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                "tokenizer."       )

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

         if os.path.abspath(self.vocab_file       ) != os.path.abspath(_lowerCAmelCase       ):
            copyfile(self.vocab_file		,  _lowerCAmelCase       )
            logger.info(F'Copy vocab file to {out_vocab_file}'       )

         return (out_vocab_file,)



      def 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						Union[str, Any]		,  _lowerCAmelCase:						Optional[Any] = None       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
            return self.prefix_tokens + token_ids_a
         else:
            UpperCAmelCase_											=token_ids_a + [self.eos_token_id]
            return self.prefix_tokens + token_ids_a + token_ids_a



      def 	lowerCAmelCase__    (	self:						int		,  _lowerCAmelCase:						Optional[Any]		,  _lowerCAmelCase:						Dict = None       )						->    List[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=[self.eos_token_id]

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



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Tuple:
         '''simple docstring'''

         return list(
             set(filter(lambda _lowerCAmelCase       : bool(re.search(r"<extra_id_\d+>"		,  _lowerCAmelCase       )       ) is not None		,  self.additional_special_tokens       )       )       )






      def 	lowerCAmelCase__    (	self:						List[str]       )						->    Dict:
         '''simple docstring'''

         return [self.convert_tokens_to_ids(_lowerCAmelCase       ) for token in self.get_sentinel_tokens()]











import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class      A       (	__lowercase						):
      _snake_case							     =(DDIMParallelScheduler,)
      _snake_case							     =(('''eta''', 0.0), ('''num_inference_steps''', 50))



      def 	lowerCAmelCase__    (	self:						str		,  **_lowerCAmelCase:						str       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											={
             "num_train_timesteps": 1000,
             "beta_start": 0.00_01,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "clip_sample": True,
         }

         config.update(**_lowerCAmelCase       )
         return config



      def 	lowerCAmelCase__    (	self:						int		,  **_lowerCAmelCase:						Tuple       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config(**_lowerCAmelCase       )
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )

         UpperCAmelCase_						,       UpperCAmelCase_											=10, 0.0

         UpperCAmelCase_											=self.dummy_model()
         UpperCAmelCase_											=self.dummy_sample_deter

         scheduler.set_timesteps(_lowerCAmelCase       )

         for t in scheduler.timesteps:
            UpperCAmelCase_											=model(_lowerCAmelCase		,  _lowerCAmelCase       )
            UpperCAmelCase_											=scheduler.step(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       ).prev_sample

         return sample



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[int]:
         '''simple docstring'''

         for timesteps in [100, 500, 1000]:
            self.check_over_configs(num_train_timesteps=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						int       )						->    List[str]:
         '''simple docstring'''

         for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=_lowerCAmelCase       )

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config(steps_offset=1       )
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )
         scheduler.set_timesteps(5       )
         assert torch.equal(scheduler.timesteps		,  torch.LongTensor([801, 601, 401, 201, 1]       )       )



      def 	lowerCAmelCase__    (	self:						int       )						->    Dict:
         '''simple docstring'''

         for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]		,  [0.0_02, 0.02, 0.2, 2]       ):
            self.check_over_configs(beta_start=_lowerCAmelCase		,  beta_end=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						int       )						->    Any:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Optional[Any]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Tuple:
         '''simple docstring'''

         for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    List[Any]:
         '''simple docstring'''

         for timestep_spacing in ["trailing", "leading"]:
            self.check_over_configs(timestep_spacing=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Any:
         '''simple docstring'''

         for rescale_betas_zero_snr in [True, False]:
            self.check_over_configs(rescale_betas_zero_snr=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Optional[Any]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    List[str]:
         '''simple docstring'''

         for t in [1, 10, 49]:
            self.check_over_forward(time_step=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Any       )						->    List[str]:
         '''simple docstring'''

         for t, num_inference_steps in zip([1, 10, 50]		,  [10, 50, 500]       ):
            self.check_over_forward(time_step=_lowerCAmelCase		,  num_inference_steps=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						int       )						->    Tuple:
         '''simple docstring'''

         for t, eta in zip([1, 10, 49]		,  [0.0, 0.5, 1.0]       ):
            self.check_over_forward(time_step=_lowerCAmelCase		,  eta=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config()
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )

         assert torch.sum(torch.abs(scheduler._get_variance(0		,  0       ) - 0.0       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(420		,  400       ) - 0.1_47_71       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(980		,  960       ) - 0.3_24_60       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(0		,  0       ) - 0.0       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(487		,  486       ) - 0.0_09_79       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(999		,  998       ) - 0.02       )       ) < 1e-5



      def 	lowerCAmelCase__    (	self:						int       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config()
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )

         UpperCAmelCase_						,       UpperCAmelCase_											=10, 0.0
         scheduler.set_timesteps(_lowerCAmelCase       )

         UpperCAmelCase_											=self.dummy_model()
         UpperCAmelCase_											=self.dummy_sample_deter
         UpperCAmelCase_											=self.dummy_sample_deter + 0.1
         UpperCAmelCase_											=self.dummy_sample_deter - 0.1

         UpperCAmelCase_											=samplea.shape[0]
         UpperCAmelCase_											=torch.stack([samplea, samplea, samplea]		,  dim=0       )
         UpperCAmelCase_											=torch.arange(_lowerCAmelCase       )[0:3, None].repeat(1		,  _lowerCAmelCase       )

         UpperCAmelCase_											=model(samples.flatten(0		,  1       )		,  timesteps.flatten(0		,  1       )       )
         UpperCAmelCase_											=scheduler.batch_step_no_noise(_lowerCAmelCase		,  timesteps.flatten(0		,  1       )		,  samples.flatten(0		,  1       )		,  _lowerCAmelCase       )

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         assert abs(result_sum.item() - 11_47.79_04       ) < 1e-2
         assert abs(result_mean.item() - 0.49_82       ) < 1e-3



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop()

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         assert abs(result_sum.item() - 1_72.00_67       ) < 1e-2
         assert abs(result_mean.item() - 0.22_39_67       ) < 1e-3



      def 	lowerCAmelCase__    (	self:						int       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop(prediction_type="v_prediction"       )

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         assert abs(result_sum.item() - 52.53_02       ) < 1e-2
         assert abs(result_mean.item() - 0.06_84       ) < 1e-3



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop(set_alpha_to_one=_lowerCAmelCase		,  beta_start=0.01       )
         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         assert abs(result_sum.item() - 1_49.82_95       ) < 1e-2
         assert abs(result_mean.item() - 0.19_51       ) < 1e-3






      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop(set_alpha_to_one=_lowerCAmelCase		,  beta_start=0.01       )
         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         assert abs(result_sum.item() - 1_49.07_84       ) < 1e-2
         assert abs(result_mean.item() - 0.19_41       ) < 1e-3




