



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


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

lowercase_						=       {
    """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""",
    }
}

lowercase_						=       {
    """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 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   str       						=VOCAB_FILES_NAMES
				UpperCamelCase__			:   Optional[int]       						=PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase__			:   Optional[Any]       						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase__			:   Tuple       						=["""input_ids""", """attention_mask"""]




				def __init__(  self   :  int,   __lowercase   :  Any,   __lowercase   :  Optional[Any]=False,   __lowercase   :  Optional[Any]=False,   __lowercase   :  List[str]=False,   __lowercase   :  Optional[Any]=None,   __lowercase   :  Dict=None,   __lowercase   :  Optional[Any]=None,   __lowercase   :  Union[str, Any]=None,   __lowercase   :  Optional[Dict[str, Any]] = None,   **__lowercase   :  Dict,   ):
							lowercase__   					=			{} if sp_model_kwargs is None else sp_model_kwargs

							lowercase__   					=			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"  )
										lowercase__   					=			"None"

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

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

							lowercase__   					=			do_lower_case
							lowercase__   					=			remove_space
							lowercase__   					=			keep_accents
							lowercase__   					=			vocab_file

							lowercase__   					=			spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.Load(__lowercase  )

							# Used for whitespace normalization in input texts
							# fmt : off
							lowercase__   					=			{" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
							# fmt : on

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




				def __getstate__(  self   :  List[str]  ):
							lowercase__   					=			self.__dict__.copy()
							lowercase__   					=			None
							return state




				def __setstate__(  self   :  str,   __lowercase   :  int  ):
							lowercase__   					=			d

							# for backward compatibility
							if not hasattr(self,   "sp_model_kwargs"  ):
										lowercase__   					=			{}

							lowercase__   					=			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   :  Union[str, Any]  ):
							return len(self.sp_model  )




				def 							A__ (  self   :  str,   __lowercase   :  str  ):
							lowercase__   					=			self.non_printing_characters_re.sub("",   __lowercase  )

							# Normalize whitespaces
							lowercase__   					=			"".join([char if char not in self.whitespaces else " " for char in text]  )

							# NFC Unicode normalization
							lowercase__   					=			unicodedata.normalize("NFC",   __lowercase  )
							return text




				def 							A__ (  self   :  Optional[int],   __lowercase   :  str,   **__lowercase   :  Any  ):
							lowercase__   					=			self.preprocess_text(__lowercase  )
							return self.sp_model.encode(__lowercase,   out_type=__lowercase  )




				def 							A__ (  self   :  str,   __lowercase   :  str  ):
							return self.sp_model.PieceToId(__lowercase  )




				def 							A__ (  self   :  str,   __lowercase   :  int  ):
							return self.sp_model.IdToPiece(__lowercase  )




				@staticmethod
				def 							A__ (  __lowercase   :  str  ):
							return out_string




				def 							A__ (  self   :  str,   __lowercase   :  List[str]  ):
							lowercase__   					=			[]
							lowercase__   					=			""
							lowercase__   					=			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(__lowercase  ) + token
													lowercase__   					=			True
													lowercase__   					=			[]
										else:
													current_sub_tokens.append(__lowercase  )
													lowercase__   					=			False
							out_string += self.sp_model.decode(__lowercase  )

							return out_string




				def 							A__ (  self   :  Any  ):
							lowercase__   					=			{self.convert_ids_to_tokens(__lowercase  ): i for i in range(self.vocab_size  )}
							vocab.update(self.added_tokens_encoder  )
							return vocab




				def 							A__ (  self   :  List[Any],   __lowercase   :  str,   __lowercase   :  Optional[str] = None  ):
							if not os.path.isdir(__lowercase  ):
										logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
										return
							lowercase__   					=			os.path.join(
							    __lowercase,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

							if os.path.abspath(self.vocab_file  ) != os.path.abspath(__lowercase  ) and os.path.isfile(self.vocab_file  ):
										copyfile(self.vocab_file,   __lowercase  )
							elif not os.path.isfile(self.vocab_file  ):
										with open(__lowercase,   "wb"  ) as fi:
													lowercase__   					=			self.sp_model.serialized_model_proto()
													fi.write(__lowercase  )

							return (out_vocab_file,)




				def 							A__ (  self   :  Dict,   __lowercase   :  Union[str, List[str]],   __lowercase   :  Union[str, bool] = False  ):

							if isinstance(__lowercase,   __lowercase  ):
										lowercase__   					=			self.preprocess_text(__lowercase  )
										lowercase__   					=			self.sp_model.encode(__lowercase  )
							else:
										lowercase__   					=			[self.preprocess_text(__lowercase  ) for t in text]
										lowercase__   					=			self.sp_model.encode(__lowercase  )

							if return_tensors is True or return_tensors == "pt":
										lowercase__   					=			torch.tensor(__lowercase  )

							return token_ids




				def 							A__ (  self   :  Optional[Any],   __lowercase   :  Union[int, List[int]]  ):
							return self.sp_model.decode(__lowercase  )




				def 							A__ (  self   :  str,   __lowercase   :  "Conversation"  ):
							lowercase__   					=			[F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
							lowercase__   					=			(
							    F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(__lowercase  ) + F'''{self.bos_token}Bot:'''
							)
							return self.encode(text=__lowercase  )









from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging


if TYPE_CHECKING:
							from ...feature_extraction_utils import FeatureExtractionMixin
							from ...tokenization_utils_base import PreTrainedTokenizerBase
							from ...utils import TensorType

lowercase_						=       logging.get_logger(__name__)

lowercase_						=       {
    """openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}

# fmt: off
lowercase_						=       [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
    705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
    1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
    4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 1_0563, 1_0786,
    1_1420, 1_1709, 1_1907, 1_3163, 1_3697, 1_3700, 1_4808, 1_5306, 1_6410, 1_6791,
    1_7992, 1_9203, 1_9510, 2_0724, 2_2305, 2_2935, 2_7007, 3_0109, 3_0420, 3_3409,
    3_4949, 4_0283, 4_0493, 4_0549, 4_7282, 4_9146, 5_0257, 5_0359, 5_0360, 5_0361
]
lowercase_						=       [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
    893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
    3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
    7273, 9061, 9383, 1_0428, 1_0929, 1_1938, 1_2033, 1_2331, 1_2562, 1_3793,
    1_4157, 1_4635, 1_5265, 1_5618, 1_6553, 1_6604, 1_8362, 1_8956, 2_0075, 2_1675,
    2_2520, 2_6130, 2_6161, 2_6435, 2_8279, 2_9464, 3_1650, 3_2302, 3_2470, 3_6865,
    4_2863, 4_7425, 4_9870, 5_0254, 5_0258, 5_0360, 5_0361, 5_0362
]
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   Optional[int]       						="""whisper"""
				UpperCamelCase__			:   Optional[int]       						=["""past_key_values"""]
				UpperCamelCase__			:   Optional[Any]       						={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




				def __init__(  self   :  List[Any],   __lowercase   :  List[str]=5_1865,   __lowercase   :  Dict=80,   __lowercase   :  List[Any]=6,   __lowercase   :  Union[str, Any]=4,   __lowercase   :  Tuple=6,   __lowercase   :  Dict=4,   __lowercase   :  List[Any]=1536,   __lowercase   :  Tuple=1536,   __lowercase   :  Any=0.0,   __lowercase   :  List[str]=0.0,   __lowercase   :  List[Any]=5_0257,   __lowercase   :  List[str]=True,   __lowercase   :  str=True,   __lowercase   :  int="gelu",   __lowercase   :  Tuple=256,   __lowercase   :  Tuple=0.0,   __lowercase   :  List[Any]=0.0,   __lowercase   :  Optional[int]=0.0,   __lowercase   :  List[Any]=0.02,   __lowercase   :  Union[str, Any]=False,   __lowercase   :  str=1500,   __lowercase   :  Optional[int]=448,   __lowercase   :  Optional[Any]=5_0256,   __lowercase   :  Tuple=5_0256,   __lowercase   :  Any=5_0256,   __lowercase   :  Union[str, Any]=None,   __lowercase   :  Any=[220, 5_0256],   __lowercase   :  List[Any]=False,   __lowercase   :  int=256,   __lowercase   :  int=False,   __lowercase   :  Tuple=0.05,   __lowercase   :  int=10,   __lowercase   :  Dict=2,   __lowercase   :  List[Any]=0.0,   __lowercase   :  Optional[int]=10,   __lowercase   :  Union[str, Any]=0,   __lowercase   :  Tuple=7,   **__lowercase   :  Union[str, Any],   ):
							lowercase__   					=			vocab_size
							lowercase__   					=			num_mel_bins
							lowercase__   					=			d_model
							lowercase__   					=			encoder_layers
							lowercase__   					=			encoder_attention_heads
							lowercase__   					=			decoder_layers
							lowercase__   					=			decoder_attention_heads
							lowercase__   					=			decoder_ffn_dim
							lowercase__   					=			encoder_ffn_dim
							lowercase__   					=			dropout
							lowercase__   					=			attention_dropout
							lowercase__   					=			activation_dropout
							lowercase__   					=			activation_function
							lowercase__   					=			init_std
							lowercase__   					=			encoder_layerdrop
							lowercase__   					=			decoder_layerdrop
							lowercase__   					=			use_cache
							lowercase__   					=			encoder_layers
							lowercase__   					=			scale_embedding  # scale factor will be sqrt(d_model) if True
							lowercase__   					=			max_source_positions
							lowercase__   					=			max_target_positions

							# Audio Classification-specific parameters. Feel free to ignore for other classes.
							lowercase__   					=			classifier_proj_size
							lowercase__   					=			use_weighted_layer_sum

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							lowercase__   					=			apply_spec_augment
							lowercase__   					=			mask_time_prob
							lowercase__   					=			mask_time_length
							lowercase__   					=			mask_time_min_masks
							lowercase__   					=			mask_feature_prob
							lowercase__   					=			mask_feature_length
							lowercase__   					=			mask_feature_min_masks

							lowercase__   					=			median_filter_width

							super().__init__(
							    pad_token_id=__lowercase,   bos_token_id=__lowercase,   eos_token_id=__lowercase,   is_encoder_decoder=__lowercase,   decoder_start_token_id=__lowercase,   suppress_tokens=__lowercase,   begin_suppress_tokens=__lowercase,   **__lowercase,   )
class 			_snake_case     (   lowercase__):




				@property
				def 							A__ (  self   :  str  ):
							lowercase__   					=			OrderedDict(
							    [
							        ("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
							    ]  )
							if self.use_past:
										lowercase__   					=			{0: "batch"}
							else:
										lowercase__   					=			{0: "batch", 1: "decoder_sequence"}

							if self.use_past:
										self.fill_with_past_key_values_(__lowercase,   direction="inputs"  )

							return common_inputs




				def 							A__ (  self   :  int,   __lowercase   :  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],   __lowercase   :  int = -1,   __lowercase   :  int = -1,   __lowercase   :  bool = False,   __lowercase   :  Optional["TensorType"] = None,   __lowercase   :  int = 2_2050,   __lowercase   :  float = 5.0,   __lowercase   :  int = 220,   ):
							lowercase__   					=			OrderedDict()
							lowercase__   					=			OnnxConfig.generate_dummy_inputs(
							    self,   preprocessor=preprocessor.feature_extractor,   batch_size=__lowercase,   framework=__lowercase,   sampling_rate=__lowercase,   time_duration=__lowercase,   frequency=__lowercase,   )
							lowercase__   					=			encoder_inputs["input_features"].shape[2]
							lowercase__   					=			encoder_sequence_length // 2 if self.use_past else seq_length

							lowercase__   					=			super().generate_dummy_inputs(
							    preprocessor.tokenizer,   __lowercase,   __lowercase,   __lowercase,   __lowercase  )

							lowercase__   					=			encoder_inputs.pop("input_features"  )
							lowercase__   					=			decoder_inputs.pop("decoder_input_ids"  )

							if "past_key_values" in decoder_inputs:
										lowercase__   					=			decoder_inputs.pop("past_key_values"  )

							return dummy_inputs




				@property
				def 							A__ (  self   :  int  ):
							return 1e-3




