
import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


_A							=       logging.get_logger(__name__)

_A							=       {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_A							=       {
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''allenai/longformer-base-4096''': (
            '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'''
        ),
    },
}

_A							=       {
    '''allenai/longformer-base-4096''': 4_096,
    '''allenai/longformer-large-4096''': 4_096,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 4_096,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 4_096,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 4_096,
}

class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):
 A__    :					int    =     VOCAB_FILES_NAMES
 A__    :					List[str]    =     PRETRAINED_VOCAB_FILES_MAP
 A__    :					Optional[Any]    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A__    :					List[str]    =     ["""input_ids""", """attention_mask"""]
 A__    :					Tuple    =     LongformerTokenizer

 def __init__(   self  ,						__UpperCamelCase=None  ,						__UpperCamelCase=None  ,						__UpperCamelCase=None  ,						__UpperCamelCase="replace"  ,						__UpperCamelCase="<s>"  ,						__UpperCamelCase="</s>"  ,						__UpperCamelCase="</s>"  ,						__UpperCamelCase="<s>"  ,						__UpperCamelCase="<unk>"  ,						__UpperCamelCase="<pad>"  ,						__UpperCamelCase="<mask>"  ,						__UpperCamelCase=False  ,						__UpperCamelCase=True  ,						**__UpperCamelCase  ,						):




        """simple docstring"""


        super().__init__(
            __UpperCamelCase  ,						__UpperCamelCase  ,						tokenizer_file=__UpperCamelCase  ,						errors=__UpperCamelCase  ,						bos_token=__UpperCamelCase  ,						eos_token=__UpperCamelCase  ,						sep_token=__UpperCamelCase  ,						cls_token=__UpperCamelCase  ,						unk_token=__UpperCamelCase  ,						pad_token=__UpperCamelCase  ,						mask_token=__UpperCamelCase  ,						add_prefix_space=__UpperCamelCase  ,						trim_offsets=__UpperCamelCase  ,						**__UpperCamelCase  ,						)

        UpperCamelCase_											=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
        if pre_tok_state.get("""add_prefix_space"""  ,						__UpperCamelCase  ) != add_prefix_space:
               UpperCamelCase_											=							getattr(__UpperCamelCase  ,						pre_tok_state.pop("""type"""  )  )
               UpperCamelCase_											=							add_prefix_space
               UpperCamelCase_											=							pre_tok_class(**__UpperCamelCase  )

        UpperCamelCase_											=							add_prefix_space

        UpperCamelCase_											=							"""post_processor"""
        UpperCamelCase_											=							getattr(self.backend_tokenizer  ,						__UpperCamelCase  ,						__UpperCamelCase  )
        if tokenizer_component_instance:
               UpperCamelCase_											=							json.loads(tokenizer_component_instance.__getstate__()  )

               # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
               if "sep" in state:
                      UpperCamelCase_											=							tuple(state["""sep"""]  )
               if "cls" in state:
                      UpperCamelCase_											=							tuple(state["""cls"""]  )

               UpperCamelCase_											=							False

               if state.get("""add_prefix_space"""  ,						__UpperCamelCase  ) != add_prefix_space:
                      UpperCamelCase_											=							add_prefix_space
                      UpperCamelCase_											=							True

               if state.get("""trim_offsets"""  ,						__UpperCamelCase  ) != trim_offsets:
                      UpperCamelCase_											=							trim_offsets
                      UpperCamelCase_											=							True

               if changes_to_apply:
                      UpperCamelCase_											=							getattr(__UpperCamelCase  ,						state.pop("""type"""  )  )
                      UpperCamelCase_											=							component_class(**__UpperCamelCase  )
                      setattr(self.backend_tokenizer  ,						__UpperCamelCase  ,						__UpperCamelCase  )

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        if self._mask_token is None:
               if self.verbose:
                      logger.error("""Using mask_token, but it is not set yet."""  )
               return None
        return str(self._mask_token  )

 @mask_token.setter
 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							AddedToken(__UpperCamelCase  ,						lstrip=__UpperCamelCase  ,						rstrip=__UpperCamelCase  ) if isinstance(__UpperCamelCase  ,						__UpperCamelCase  ) else value
        UpperCamelCase_											=							value

 def lowerCamelCase_					(   self  ,						*__UpperCamelCase  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							kwargs.get("""is_split_into_words"""  ,						__UpperCamelCase  )
        assert self.add_prefix_space or not is_split_into_words, (
            f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."
        )

        return super()._batch_encode_plus(*__UpperCamelCase  ,						**__UpperCamelCase  )

 def lowerCamelCase_					(   self  ,						*__UpperCamelCase  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							kwargs.get("""is_split_into_words"""  ,						__UpperCamelCase  )

        assert self.add_prefix_space or not is_split_into_words, (
            f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."
        )

        return super()._encode_plus(*__UpperCamelCase  ,						**__UpperCamelCase  )

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ):




        """simple docstring"""


        UpperCamelCase_											=							self._tokenizer.model.save(__UpperCamelCase  ,						name=__UpperCamelCase  )
        return tuple(__UpperCamelCase  )

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase=None  ):




        """simple docstring"""


        UpperCamelCase_											=							[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
               return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ):




        """simple docstring"""


        UpperCamelCase_											=							[self.sep_token_id]
        UpperCamelCase_											=							[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]





import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 		lowercase_							(							unittest.TestCase					):
 # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
 A__    :					int    =     (ScoreSdeVeScheduler,)
 A__    :					Union[str, Any]    =     ()

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							4
        UpperCamelCase_											=							3
        UpperCamelCase_											=							8
        UpperCamelCase_											=							8

        UpperCamelCase_											=							torch.rand((batch_size, num_channels, height, width)  )

        return sample

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							4
        UpperCamelCase_											=							3
        UpperCamelCase_											=							8
        UpperCamelCase_											=							8

        UpperCamelCase_											=							batch_size * num_channels * height * width
        UpperCamelCase_											=							torch.arange(__UpperCamelCase  )
        UpperCamelCase_											=							sample.reshape(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  )
        UpperCamelCase_											=							sample / num_elems
        UpperCamelCase_											=							sample.permute(3  ,						0  ,						1  ,						2  )

        return sample

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        def model(__UpperCamelCase  ,						__UpperCamelCase  ,						*__UpperCamelCase  ):
               return sample * t / (t + 1)

        return model

 def lowerCamelCase_					(   self  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							{
            """num_train_timesteps""": 2_0_0_0,
            """snr""": 0.15,
            """sigma_min""": 0.01,
            """sigma_max""": 1_3_4_8,
            """sampling_eps""": 1e-5,
        }

        config.update(**__UpperCamelCase  )
        return config

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=0  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               UpperCamelCase_											=							self.get_scheduler_config(**__UpperCamelCase  )
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler_class.from_pretrained(__UpperCamelCase  )

               UpperCamelCase_											=							scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

               assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

               UpperCamelCase_											=							scheduler.step_correct(__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_correct(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

               assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=0  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )
        kwargs.update(__UpperCamelCase  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               UpperCamelCase_											=							self.get_scheduler_config()
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler_class.from_pretrained(__UpperCamelCase  )

               UpperCamelCase_											=							scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

               assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

               UpperCamelCase_											=							scheduler.step_correct(__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_correct(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

               assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for timesteps in [1_0, 1_0_0, 1_0_0_0]:
               self.check_over_configs(num_train_timesteps=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]  ,						[1, 1_0_0, 1_0_0_0]  ):
               self.check_over_configs(sigma_min=__UpperCamelCase  ,						sigma_max=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for t in [0.1, 0.5, 0.75]:
               self.check_over_forward(time_step=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        UpperCamelCase_											=							self.scheduler_classes[0]
        UpperCamelCase_											=							self.get_scheduler_config()
        UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

        UpperCamelCase_											=							3

        UpperCamelCase_											=							self.dummy_model()
        UpperCamelCase_											=							self.dummy_sample_deter

        scheduler.set_sigmas(__UpperCamelCase  )
        scheduler.set_timesteps(__UpperCamelCase  )
        UpperCamelCase_											=							torch.manual_seed(0  )

        for i, t in enumerate(scheduler.timesteps  ):
               UpperCamelCase_											=							scheduler.sigmas[i]

               for _ in range(scheduler.config.correct_steps  ):
                      with torch.no_grad():
                             UpperCamelCase_											=							model(__UpperCamelCase  ,						__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler.step_correct(__UpperCamelCase  ,						__UpperCamelCase  ,						generator=__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample

               with torch.no_grad():
                      UpperCamelCase_											=							model(__UpperCamelCase  ,						__UpperCamelCase  )

               UpperCamelCase_											=							scheduler.step_pred(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=__UpperCamelCase  ,						**__UpperCamelCase  )
               UpperCamelCase_							,					UpperCamelCase_											=							output.prev_sample, output.prev_sample_mean

        UpperCamelCase_											=							torch.sum(torch.abs(__UpperCamelCase  )  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert np.isclose(result_sum.item()  ,						14_372_758_528.0  )
        assert np.isclose(result_mean.item()  ,						18_714_530.0  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        UpperCamelCase_											=							kwargs.pop("""num_inference_steps"""  ,						__UpperCamelCase  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.get_scheduler_config()
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               if num_inference_steps is not None and hasattr(__UpperCamelCase  ,						"""set_timesteps"""  ):
                      scheduler.set_timesteps(__UpperCamelCase  )
               elif num_inference_steps is not None and not hasattr(__UpperCamelCase  ,						"""set_timesteps"""  ):
                      UpperCamelCase_											=							num_inference_steps

               UpperCamelCase_											=							scheduler.step_pred(__UpperCamelCase  ,						0  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							scheduler.step_pred(__UpperCamelCase  ,						1  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

               self.assertEqual(output_a.shape  ,						sample.shape  )
               self.assertEqual(output_a.shape  ,						output_a.shape  )



