


'''simple docstring'''







import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
       import torch


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


lowerCAmelCase    :     int					=						logging.get_logger(__name__)
lowerCAmelCase    :     Union[str, Any]					=						{"""vocab_file""": """spiece.model"""}

lowerCAmelCase    :     Optional[int]					=						{
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

lowerCAmelCase    :     Union[str, Any]					=						{
    """AI-Sweden/gpt-sw3-126m""": 20_48,
    """AI-Sweden/gpt-sw3-350m""": 20_48,
    """AI-Sweden/gpt-sw3-1.6b""": 20_48,
    """AI-Sweden/gpt-sw3-6.7b""": 20_48,
    """AI-Sweden/gpt-sw3-20b""": 20_48,
}
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






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







     def __init__( self      ,	snake_case__      ,	snake_case__=False      ,	snake_case__=False      ,	snake_case__=False      ,	snake_case__=None      ,	snake_case__=None      ,	snake_case__=None      ,	snake_case__=None      ,	snake_case__ = None      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       {} if sp_model_kwargs is None else sp_model_kwargs

            _lowerCAmelCase    :							List[Any]   			=       kwargs.get('name_or_path'   )
            if name_or_path is None:
                   logger.warning(
                       'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
                       ' you are testing the model, this can safely be ignored'   )
                   _lowerCAmelCase    :							Any   			=       'None'

            # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
            _lowerCAmelCase    :							str   			=       '<|endoftext|>' if eos_token is None else eos_token
            _lowerCAmelCase    :							Tuple   			=       '<unk>' if unk_token is None else unk_token
            if "gpt-sw3-7b" in name_or_path:
                   _lowerCAmelCase    :							List[str]   			=       unk_token if pad_token is None else pad_token
                   _lowerCAmelCase    :							Optional[int]   			=       eos_token if bos_token is None else bos_token
            else:
                   _lowerCAmelCase    :							Tuple   			=       '<pad>' if pad_token is None else pad_token
                   _lowerCAmelCase    :							Union[str, Any]   			=       '<s>' if bos_token is None else bos_token

            super().__init__(
                do_lower_case=snake_case__      ,	remove_space=snake_case__      ,	keep_accents=snake_case__      ,	bos_token=snake_case__      ,	eos_token=snake_case__      ,	unk_token=snake_case__      ,	pad_token=snake_case__      ,	sp_model_kwargs=self.sp_model_kwargs      ,	**snake_case__      ,	)

            _lowerCAmelCase    :							Union[str, Any]   			=       do_lower_case
            _lowerCAmelCase    :							Optional[int]   			=       remove_space
            _lowerCAmelCase    :							Any   			=       keep_accents
            _lowerCAmelCase    :							Optional[int]   			=       vocab_file

            _lowerCAmelCase    :							Dict   			=       spm.SentencePieceProcessor(**self.sp_model_kwargs   )
            self.sp_model.Load(snake_case__   )

            # Used for whitespace normalization in input texts
            # fmt : off
            _lowerCAmelCase    :							Optional[Any]   			=       {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
            # fmt : on

            # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
            _lowerCAmelCase    :							Optional[Any]   			=       re.compile(
                F'[{"".join(map(snake_case__      ,	list(range(0      ,	9   )   ) + list(range(11      ,	32   )   ) + list(range(127      ,	160   )   ) + [160, 173, 8203]   )   )}]'   )







     def __getstate__( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       self.__dict__.copy()
            _lowerCAmelCase    :							int   			=       None
            return state







     def __setstate__( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       d

            # for backward compatibility
            if not hasattr(self      ,	'sp_model_kwargs'   ):
                   _lowerCAmelCase    :							int   			=       {}

            _lowerCAmelCase    :							List[str]   			=       spm.SentencePieceProcessor(**self.sp_model_kwargs   )
            self.sp_model.Load(self.vocab_file   )







     @property
     # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
     def a	( self   ):

            '''simple docstring'''

            return len(self.sp_model   )







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.non_printing_characters_re.sub(''      ,	snake_case__   )

            # Normalize whitespaces
            _lowerCAmelCase    :							Tuple   			=       ''.join([char if char not in self.whitespaces else ' ' for char in text]   )

            # NFC Unicode normalization
            _lowerCAmelCase    :							Union[str, Any]   			=       unicodedata.normalize('NFC'      ,	snake_case__   )
            return text







     def a	( self      ,	snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.preprocess_text(snake_case__   )
            return self.sp_model.encode(snake_case__      ,	out_type=snake_case__   )







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            return self.sp_model.PieceToId(snake_case__   )







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            return self.sp_model.IdToPiece(snake_case__   )







     @staticmethod
     def a	( snake_case__   ):

            '''simple docstring'''

            return out_string







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       []
            _lowerCAmelCase    :							Optional[Any]   			=       ''
            _lowerCAmelCase    :							Tuple   			=       False
            for token in tokens:
                   # make sure that special tokens are not decoded using sentencepiece model
                   if token in self.all_special_tokens:
                          # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                          if not prev_is_special:
                                 out_string += " "

                          out_string += self.sp_model.decode(snake_case__   ) + token
                          _lowerCAmelCase    :							Union[str, Any]   			=       True
                          _lowerCAmelCase    :							List[Any]   			=       []
                   else:
                          current_sub_tokens.append(snake_case__   )
                          _lowerCAmelCase    :							List[Any]   			=       False
            out_string += self.sp_model.decode(snake_case__   )

            return out_string







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       {self.convert_ids_to_tokens(snake_case__   ): i for i in range(self.vocab_size   )}
            vocab.update(self.added_tokens_encoder   )
            return vocab







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

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

            if os.path.abspath(self.vocab_file   ) != os.path.abspath(snake_case__   ) and os.path.isfile(self.vocab_file   ):
                   copyfile(self.vocab_file      ,	snake_case__   )
            elif not os.path.isfile(self.vocab_file   ):
                   with open(snake_case__      ,	'wb'   ) as fi:
                          _lowerCAmelCase    :							Any   			=       self.sp_model.serialized_model_proto()
                          fi.write(snake_case__   )

            return (out_vocab_file,)







     def a	( self      ,	snake_case__      ,	snake_case__ = False   ):

            '''simple docstring'''

            if isinstance(snake_case__      ,	snake_case__   ):
                   _lowerCAmelCase    :							Optional[Any]   			=       self.preprocess_text(snake_case__   )
                   _lowerCAmelCase    :							List[str]   			=       self.sp_model.encode(snake_case__   )
            else:
                   _lowerCAmelCase    :							Tuple   			=       [self.preprocess_text(snake_case__   ) for t in text]
                   _lowerCAmelCase    :							List[str]   			=       self.sp_model.encode(snake_case__   )

            if return_tensors is True or return_tensors == "pt":
                   _lowerCAmelCase    :							int   			=       torch.tensor(snake_case__   )

            return token_ids







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            return self.sp_model.decode(snake_case__   )







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       [F'User: {text}' if is_user else F'Bot: {text}' for is_user, text in conversation.iter_texts()]
            _lowerCAmelCase    :							str   			=       (
                F'{self.eos_token}{self.bos_token}' + F'{self.bos_token}'.join(snake_case__   ) + F'{self.bos_token}Bot:'
            )
            return self.encode(text=snake_case__   )





'''simple docstring'''







import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    (DDPMScheduler,)







     def a	( self      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, 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(**snake_case__   )
            return config







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

            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=snake_case__      ,	beta_end=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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

            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 a	( self   ):

            '''simple docstring'''

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

            _lowerCAmelCase    :							Optional[int]   			=       len(snake_case__   )

            _lowerCAmelCase    :							str   			=       self.dummy_model()
            _lowerCAmelCase    :							str   			=       self.dummy_sample_deter
            _lowerCAmelCase    :							Union[str, Any]   			=       torch.manual_seed(0   )

            for t in reversed(range(snake_case__   )   ):
                   # 1. predict noise residual
                   _lowerCAmelCase    :							List[Any]   			=       model(snake_case__      ,	snake_case__   )

                   # 2. predict previous mean of sample x_t-1
                   _lowerCAmelCase    :							Any   			=       scheduler.step(snake_case__      ,	snake_case__      ,	snake_case__      ,	generator=snake_case__   ).prev_sample

                   # if t > 0:
                   #     noise = self.dummy_sample_deter
                   #     variance = scheduler.get_variance(t) ** (0.5) * noise
                   #
                   # sample = pred_prev_sample + variance
                   _lowerCAmelCase    :							Dict   			=       pred_prev_sample

            _lowerCAmelCase    :							Dict   			=       torch.sum(torch.abs(snake_case__   )   )
            _lowerCAmelCase    :							List[str]   			=       torch.mean(torch.abs(snake_case__   )   )

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Any   			=       self.get_scheduler_config(prediction_type='v_prediction'   )
            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       len(snake_case__   )

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

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

                   # 2. predict previous mean of sample x_t-1
                   _lowerCAmelCase    :							Dict   			=       scheduler.step(snake_case__      ,	snake_case__      ,	snake_case__      ,	generator=snake_case__   ).prev_sample

                   # if t > 0:
                   #     noise = self.dummy_sample_deter
                   #     variance = scheduler.get_variance(t) ** (0.5) * noise
                   #
                   # sample = pred_prev_sample + variance
                   _lowerCAmelCase    :							Tuple   			=       pred_prev_sample

            _lowerCAmelCase    :							Any   			=       torch.sum(torch.abs(snake_case__   )   )
            _lowerCAmelCase    :							Dict   			=       torch.mean(torch.abs(snake_case__   )   )

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Optional[int]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Union[str, Any]   			=       [100, 87, 50, 1, 0]

            scheduler.set_timesteps(timesteps=snake_case__   )

            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler.timesteps

            for i, timestep in enumerate(snake_case__   ):
                   if i == len(snake_case__   ) - 1:
                          _lowerCAmelCase    :							str   			=       -1
                   else:
                          _lowerCAmelCase    :							Optional[Any]   			=       timesteps[i + 1]

                   _lowerCAmelCase    :							int   			=       scheduler.previous_timestep(snake_case__   )
                   _lowerCAmelCase    :							int   			=       prev_t.item()

                   self.assertEqual(snake_case__      ,	snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Tuple   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							List[Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       [100, 87, 50, 51, 0]

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							List[str]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       [100, 87, 50, 1, 0]
            _lowerCAmelCase    :							int   			=       len(snake_case__   )

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							int   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							Any   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Any   			=       [scheduler.config.num_train_timesteps]

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

