

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


a						=logging.get_logger(__name__)
a						={"""vocab_file""": """spiece.model"""}

a						={
    """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""",
    }
}

a						={
    """AI-Sweden/gpt-sw3-126m""": 2048,
    """AI-Sweden/gpt-sw3-350m""": 2048,
    """AI-Sweden/gpt-sw3-1.6b""": 2048,
    """AI-Sweden/gpt-sw3-6.7b""": 2048,
    """AI-Sweden/gpt-sw3-20b""": 2048,
}






class 			A_						(			SCREAMING_SNAKE_CASE							):
							_UpperCAmelCase       :    int		 = VOCAB_FILES_NAMES
							_UpperCAmelCase       :    Union[str, Any]		 = PRETRAINED_VOCAB_FILES_MAP
							_UpperCAmelCase       :    Any		 = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_UpperCAmelCase       :    Tuple		 = ['''input_ids''', '''attention_mask''']
							def __init__(						self						:     str  ,SCREAMING_SNAKE_CASE__						:     int  ,SCREAMING_SNAKE_CASE__						:     List[Any]=False  ,SCREAMING_SNAKE_CASE__						:     Dict=False  ,SCREAMING_SNAKE_CASE__						:     Dict=False  ,SCREAMING_SNAKE_CASE__						:     Any=None  ,SCREAMING_SNAKE_CASE__						:     int=None  ,SCREAMING_SNAKE_CASE__						:     List[str]=None  ,SCREAMING_SNAKE_CASE__						:     Tuple=None  ,SCREAMING_SNAKE_CASE__						:     Tuple = None  ,**SCREAMING_SNAKE_CASE__						:     Any  ,):
								__lowerCamelCase							:							List[Any]   			=					{} if sp_model_kwargs is None else sp_model_kwargs

								__lowerCamelCase							:							Optional[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							:							Dict   			=					'None'

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

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

								__lowerCamelCase							:							Tuple   			=					do_lower_case
								__lowerCamelCase							:							Optional[Any]   			=					remove_space
								__lowerCamelCase							:							List[Any]   			=					keep_accents
								__lowerCamelCase							:							Tuple   			=					vocab_file

								__lowerCamelCase							:							Dict   			=					spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(SCREAMING_SNAKE_CASE__)

								# Used for whitespace normalization in input texts
								# fmt : off
								__lowerCamelCase							:							Any   			=					{' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
								# fmt : on

								# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
								__lowerCamelCase							:							str   			=					re.compile(
								    F"[{''.join(map(SCREAMING_SNAKE_CASE__  ,list(range(0  ,9)) + list(range(1_1  ,3_2)) + list(range(1_2_7  ,1_6_0)) + [1_6_0, 1_7_3, 8_2_0_3]))}]")
							def __getstate__(						self						:     str):
								__lowerCamelCase							:							str   			=					self.__dict__.copy()
								__lowerCamelCase							:							Optional[int]   			=					None
								return state
							def __setstate__(						self						:     List[str]  ,SCREAMING_SNAKE_CASE__						:     Any):
								__lowerCamelCase							:							Dict   			=					d

								# for backward compatibility
								if not hasattr(self  ,'sp_model_kwargs'):
									__lowerCamelCase							:							Optional[Any]   			=					{}

								__lowerCamelCase							:							List[Any]   			=					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        lowerCAmelCase	(						self						:     Any):
								return len(self.sp_model)
							def        lowerCAmelCase	(						self						:     List[str]  ,SCREAMING_SNAKE_CASE__						:     Optional[int]):
								__lowerCamelCase							:							List[str]   			=					self.non_printing_characters_re.sub(''  ,SCREAMING_SNAKE_CASE__)

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

								# NFC Unicode normalization
								__lowerCamelCase							:							List[str]   			=					unicodedata.normalize('NFC'  ,SCREAMING_SNAKE_CASE__)
								return text
							def        lowerCAmelCase	(						self						:     int  ,SCREAMING_SNAKE_CASE__						:     Optional[int]  ,**SCREAMING_SNAKE_CASE__						:     List[Any]):
								__lowerCamelCase							:							List[str]   			=					self.preprocess_text(SCREAMING_SNAKE_CASE__)
								return self.sp_model.encode(SCREAMING_SNAKE_CASE__  ,out_type=SCREAMING_SNAKE_CASE__)
							def        lowerCAmelCase	(						self						:     Union[str, Any]  ,SCREAMING_SNAKE_CASE__						:     Optional[Any]):
								return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__)
							def        lowerCAmelCase	(						self						:     Dict  ,SCREAMING_SNAKE_CASE__						:     Dict):
								return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__)
							@staticmethod
							def        lowerCAmelCase	(						SCREAMING_SNAKE_CASE__						:     str):
								return out_string
							def        lowerCAmelCase	(						self						:     str  ,SCREAMING_SNAKE_CASE__						:     Dict):
								__lowerCamelCase							:							Tuple   			=					[]
								__lowerCamelCase							:							str   			=					''
								__lowerCamelCase							:							Optional[int]   			=					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(SCREAMING_SNAKE_CASE__) + token
										__lowerCamelCase							:							str   			=					True
										__lowerCamelCase							:							List[str]   			=					[]
									else:
										current_sub_tokens.append(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase							:							int   			=					False
								out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__)

								return out_string
							def        lowerCAmelCase	(						self						:     Union[str, Any]):
								__lowerCamelCase							:							Optional[int]   			=					{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__): i for i in range(self.vocab_size)}
								vocab.update(self.added_tokens_encoder)
								return vocab
							def        lowerCAmelCase	(						self						:     Union[str, Any]  ,SCREAMING_SNAKE_CASE__						:     Dict  ,SCREAMING_SNAKE_CASE__						:     List[str] = None):







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

								if os.path.abspath(self.vocab_file) != os.path.abspath(SCREAMING_SNAKE_CASE__) and os.path.isfile(self.vocab_file):
									copyfile(self.vocab_file  ,SCREAMING_SNAKE_CASE__)
								elif not os.path.isfile(self.vocab_file):
									with open(SCREAMING_SNAKE_CASE__  ,'wb') as fi:
										__lowerCamelCase							:							Union[str, Any]   			=					self.sp_model.serialized_model_proto()
										fi.write(SCREAMING_SNAKE_CASE__)

								return (out_vocab_file,)
							def        lowerCAmelCase	(						self						:     Optional[int]  ,SCREAMING_SNAKE_CASE__						:     List[Any]  ,SCREAMING_SNAKE_CASE__						:     Any = False):







								if isinstance(SCREAMING_SNAKE_CASE__  ,SCREAMING_SNAKE_CASE__):
									__lowerCamelCase							:							Optional[int]   			=					self.preprocess_text(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase							:							Tuple   			=					self.sp_model.encode(SCREAMING_SNAKE_CASE__)
								else:
									__lowerCamelCase							:							List[Any]   			=					[self.preprocess_text(SCREAMING_SNAKE_CASE__) for t in text]
									__lowerCamelCase							:							Optional[Any]   			=					self.sp_model.encode(SCREAMING_SNAKE_CASE__)

								if return_tensors is True or return_tensors == "pt":
									__lowerCamelCase							:							Union[str, Any]   			=					torch.tensor(SCREAMING_SNAKE_CASE__)

								return token_ids
							def        lowerCAmelCase	(						self						:     Dict  ,SCREAMING_SNAKE_CASE__						:     str):
								return self.sp_model.decode(SCREAMING_SNAKE_CASE__)


							def        lowerCAmelCase	(						self						:     Any  ,SCREAMING_SNAKE_CASE__						:     Union[str, Any]):
								__lowerCamelCase							:							str   			=					[F"User: {text}" if is_user else F"Bot: {text}" for is_user, text in conversation.iter_texts()]
								__lowerCamelCase							:							Any   			=					(
								    F"{self.eos_token}{self.bos_token}" + F"{self.bos_token}".join(SCREAMING_SNAKE_CASE__) + F"{self.bos_token}Bot:"
								)
								return self.encode(text=SCREAMING_SNAKE_CASE__)










'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_    	=				{
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				["RoFormerTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				[
          "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RoFormerForCausalLM",
          "RoFormerForMaskedLM",
          "RoFormerForMultipleChoice",
          "RoFormerForQuestionAnswering",
          "RoFormerForSequenceClassification",
          "RoFormerForTokenClassification",
          "RoFormerLayer",
          "RoFormerModel",
          "RoFormerPreTrainedModel",
          "load_tf_weights_in_roformer",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				[
          "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRoFormerForCausalLM",
          "TFRoFormerForMaskedLM",
          "TFRoFormerForMultipleChoice",
          "TFRoFormerForQuestionAnswering",
          "TFRoFormerForSequenceClassification",
          "TFRoFormerForTokenClassification",
          "TFRoFormerLayer",
          "TFRoFormerModel",
          "TFRoFormerPreTrainedModel",
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				[
          "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "FlaxRoFormerForMaskedLM",
          "FlaxRoFormerForMultipleChoice",
          "FlaxRoFormerForQuestionAnswering",
          "FlaxRoFormerForSequenceClassification",
          "FlaxRoFormerForTokenClassification",
          "FlaxRoFormerModel",
          "FlaxRoFormerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

      UpperCamelCase_    	=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


