'''simple docstring'''





import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_:	Optional[int]    =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:	Union[str, Any]    ='▁'

SCREAMING_SNAKE_CASE_:	str    ={
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
}

SCREAMING_SNAKE_CASE_:	Any    ={
    'vocab_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'
        ),
    },
    'spm_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'
        )
    },
}

SCREAMING_SNAKE_CASE_:	Tuple    ={
    'facebook/s2t-small-librispeech-asr': 10_24,
}

SCREAMING_SNAKE_CASE_:	Any    =['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']

SCREAMING_SNAKE_CASE_:	Dict    ={'mustc': MUSTC_LANGS}




class 			__A (					UpperCamelCase__      ):
    a__			:		List[str]			  =    VOCAB_FILES_NAMES
    a__			:		Tuple			  =    PRETRAINED_VOCAB_FILES_MAP
    a__			:		List[Any]			  =    MAX_MODEL_INPUT_SIZES
    a__			:		int			  =    ["""input_ids""", """attention_mask"""]

    a__			:		List[int]			  =    []



    def __init__(self       :       Optional[Any]  ,     __a       :       Optional[int]  ,     __a       :       str  ,     __a       :       Union[str, Any]="<s>"  ,     __a       :       List[str]="</s>"  ,     __a       :       Tuple="<pad>"  ,     __a       :       Optional[Any]="<unk>"  ,     __a       :       Optional[int]=False  ,     __a       :       str=False  ,     __a       :       Tuple=None  ,     __a       :       List[str]=None  ,     __a       :       Optional[Dict[str, Any]] = None  ,     **__a       :       Any  ,     ):
         UpperCAmelCase_			       =  {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=__a  ,     eos_token=__a  ,     unk_token=__a  ,     pad_token=__a  ,     do_upper_case=__a  ,     do_lower_case=__a  ,     tgt_lang=__a  ,     lang_codes=__a  ,     sp_model_kwargs=self.sp_model_kwargs  ,     **__a  ,     )
         UpperCAmelCase_			       =  do_upper_case
         UpperCAmelCase_			       =  do_lower_case

         UpperCAmelCase_			       =  load_json(__a					)
         UpperCAmelCase_			       =  {v: k for k, v in self.encoder.items()}
         UpperCAmelCase_			       =  spm_file
         UpperCAmelCase_			       =  load_spm(__a  ,     self.sp_model_kwargs					)

         if lang_codes is not None:
              UpperCAmelCase_			       =  lang_codes
              UpperCAmelCase_			       =  LANGUAGES[lang_codes]
              UpperCAmelCase_			       =  [f"""<lang:{lang}>""" for lang in self.langs]
              UpperCAmelCase_			       =  {lang: self.sp_model.PieceToId(f"""<lang:{lang}>"""					) for lang in self.langs}

              UpperCAmelCase_			       =  self.lang_tokens
              UpperCAmelCase_			       =  tgt_lang if tgt_lang is not None else self.langs[0]

              self.set_tgt_lang_special_tokens(self._tgt_lang					)
         else:
              UpperCAmelCase_			       =  {}



    @property
    def    _lowercase		(self       :       List[str]					):
         return len(self.encoder					)



    @property
    def    _lowercase		(self       :       int					):
         return self._tgt_lang



    @tgt_lang.setter
    def    _lowercase		(self       :       Any  ,     __a       :       int					):
         UpperCAmelCase_			       =  new_tgt_lang
         self.set_tgt_lang_special_tokens(__a					)



    def    _lowercase		(self       :       Any  ,     __a       :       str					):
         UpperCAmelCase_			       =  self.lang_code_to_id[tgt_lang]
         UpperCAmelCase_			       =  [lang_code_id]



    def    _lowercase		(self       :       Optional[int]  ,     __a       :       str					):
         return self.sp_model.encode(__a  ,     out_type=__a					)



    def    _lowercase		(self       :       str  ,     __a       :       str					):
         return self.encoder.get(__a  ,     self.encoder[self.unk_token]					)



    def    _lowercase		(self       :       Dict  ,     __a       :       int					):
         return self.decoder.get(__a  ,     self.unk_token					)



    def    _lowercase		(self       :       Optional[int]  ,     __a       :       List[str]					):
         UpperCAmelCase_			       =  []
         UpperCAmelCase_			       =  ""
         for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                   UpperCAmelCase_			       =  self.sp_model.decode(__a					)
                   out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                   UpperCAmelCase_			       =  []
              else:
                   current_sub_tokens.append(__a					)
         UpperCAmelCase_			       =  self.sp_model.decode(__a					)
         out_string += decoded.upper() if self.do_upper_case else decoded
         return out_string.strip()



    def    _lowercase		(self       :       Union[str, Any]  ,     __a       :       List[Any]  ,     __a       :       Dict=None					):
         if token_ids_a is None:
              return self.prefix_tokens + token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]



    def    _lowercase		(self       :       Any  ,     __a       :       List[int]  ,     __a       :       Optional[List[int]] = None  ,     __a       :       bool = False					):

         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=__a  ,     token_ids_a=__a  ,     already_has_special_tokens=__a					)

         UpperCAmelCase_			       =  [1] * len(self.prefix_tokens					)
         UpperCAmelCase_			       =  [1]
         if token_ids_a is None:
              return prefix_ones + ([0] * len(__a					)) + suffix_ones
         return prefix_ones + ([0] * len(__a					)) + ([0] * len(__a					)) + suffix_ones



    def    _lowercase		(self       :       Optional[int]					):
         UpperCAmelCase_			       =  self.encoder.copy()
         vocab.update(self.added_tokens_encoder					)
         return vocab



    def __getstate__(self       :       Dict					):
         UpperCAmelCase_			       =  self.__dict__.copy()
         UpperCAmelCase_			       =  None
         return state



    def __setstate__(self       :       Tuple  ,     __a       :       Dict					):
         UpperCAmelCase_			       =  d

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

         UpperCAmelCase_			       =  load_spm(self.spm_file  ,     self.sp_model_kwargs					)






    def    _lowercase		(self       :       Union[str, Any]  ,     __a       :       str  ,     __a       :       Optional[str] = None					):
         UpperCAmelCase_			       =  Path(__a					)
         assert save_dir.is_dir(), f"""{save_directory} should be a directory"""
         UpperCAmelCase_			       =  save_dir / (
             (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
         )
         UpperCAmelCase_			       =  save_dir / (
             (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
         )

         save_json(self.encoder  ,     __a					)

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

         return (str(__a					), str(__a					))





def       lowerCAmelCase_							(   snake_case_			:					str   ,       snake_case_			:					Dict[str, Any]     )       -> sentencepiece.SentencePieceProcessor:


     '''simple docstring'''




     UpperCAmelCase_			       =  sentencepiece.SentencePieceProcessor(**snake_case_     )
     spm.Load(str(snake_case_     )     )
     return spm





def       lowerCAmelCase_							(   snake_case_			:					str     )       -> Union[Dict, List]:


     '''simple docstring'''




     with open(snake_case_   ,       "r"     ) as f:
          return json.load(snake_case_     )






def       lowerCAmelCase_							(   snake_case_			:					Any   ,       snake_case_			:					str     )       -> None:


     '''simple docstring'''




     with open(snake_case_   ,       "w"     ) as f:
          json.dump(snake_case_   ,       snake_case_   ,       indent=2     )







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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


A_			:					List[Any]    			=     logging.get_logger(__name__)

A_			:					Any    			=     {
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}



class        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  List[str]    				=			'''gpt_neo'''
					UpperCAmelCase__:  str    				=			['''past_key_values''']
					UpperCAmelCase__:  Dict    				=			{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}






					def __init__(					self       ,			A__=5_0257       ,			A__=2048       ,			A__=2048       ,			A__=24       ,			A__=[[["global", "local"], 12]]       ,			A__=16       ,			A__=None       ,			A__=256       ,			A__="gelu_new"       ,			A__=0.0       ,			A__=0.0       ,			A__=0.0       ,			A__=0.1       ,			A__=1e-5       ,			A__=0.0_2       ,			A__=True       ,			A__=5_0256       ,			A__=5_0256       ,			**A__       ,			):
						A__				:       Optional[Any]	  =						vocab_size
						A__				:       Optional[int]	  =						max_position_embeddings
						A__				:       Optional[int]	  =						hidden_size
						A__				:       Dict	  =						num_layers
						A__				:       List[str]	  =						num_heads
						A__				:       Optional[int]	  =						intermediate_size
						A__				:       Union[str, Any]	  =						window_size
						A__				:       Optional[int]	  =						activation_function
						A__				:       Optional[Any]	  =						resid_dropout
						A__				:       Optional[int]	  =						embed_dropout
						A__				:       Any	  =						attention_dropout
						A__				:       Optional[Any]	  =						classifier_dropout
						A__				:       int	  =						layer_norm_epsilon
						A__				:       Tuple	  =						initializer_range
						A__				:       str	  =						use_cache

						A__				:       List[str]	  =						bos_token_id
						A__				:       List[Any]	  =						eos_token_id

						A__				:       Any	  =						attention_types
						A__				:       List[str]	  =						self.expand_attention_types_params(A__     )

						if len(self.attention_layers     ) != self.num_layers:
							raise ValueError(
							    """Configuration for convolutional module is incorrect. """
							    """It is required that `len(config.attention_layers)` == `config.num_layers` """
							    F"""but is `len(config.attention_layers) = {len(self.attention_layers     )}`, """
							    F"""`config.num_layers = {self.num_layers}`. """
							    """`config.attention_layers` is prepared using `config.attention_types`. """
							    """Please verify the value of `config.attention_types` argument."""     )

						super().__init__(bos_token_id=A__       ,			eos_token_id=A__       ,			**A__     )






					@staticmethod
					def     __A     (					A__     ):
						A__				:       int	  =						[]
						for item in attention_types:
							for _ in range(item[1]     ):
								attentions.extend(item[0]     )
						return attentions



def 		UpperCamelCase   (lowercase_:					Dict       , lowercase_:					Any       , lowercase_:					Union[str, Any]       , lowercase_:					Optional[Any]   )	-> Dict:
	import torch

	A__				:       List[Any]	  =						input.size()
	A__				:       List[str]	  =						len(lowercase_   )
	A__				:       Union[str, Any]	  =						shape[dimension]

	A__				:       Optional[int]	  =						torch.arange(0       , lowercase_       , lowercase_   )
	A__				:       str	  =						torch.div(sizedim - size       , lowercase_       , rounding_mode="""floor"""   ) + 1
	A__				:       Any	  =						torch.arange(lowercase_   ) + low_indices[:min_length][:, None]

	A__				:       Union[str, Any]	  =						[slice(lowercase_   )] * rank
	A__				:       Any	  =						indices
	A__				:       List[str]	  =						input[s]

	A__				:       Dict	  =						list(range(0       , rank + 1   )   )
	perm.append(perm.pop(dimension + 1   )   )

	return sliced.permute(lowercase_   )

def 		UpperCamelCase   (lowercase_:					str       , lowercase_:					int   )	-> int:
	import torch

	A__				:       Union[str, Any]	  =						torch.arange(1       , lowercase_   )
	A__				:       str	  =						torch.remainder(lowercase_       , lowercase_   )
	A__				:       Optional[Any]	  =						remainders == 0
	A__				:       Any	  =						candidates[divisor_indices]
	A__				:       Optional[Any]	  =						torch.max(lowercase_   )
	return largest_divisor, torch.div(lowercase_       , lowercase_       , rounding_mode="""floor"""   )



class        _a		(__magic_name__							):


					'''simple docstring'''






					@property
					def     __A     (					self     ):
						A__				:       Any	  =						OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}     )
						if self.use_past:
							self.fill_with_past_key_values_(A__       ,			direction="""inputs"""     )
							A__				:       Optional[Any]	  =						{0: """batch""", 1: """past_sequence + sequence"""}
						else:
							A__				:       List[str]	  =						{0: """batch""", 1: """sequence"""}

						return common_inputs






					@property
					def     __A     (					self     ):
						return self._config.num_heads






					def     __A     (					self       ,			A__       ,			A__ = -1       ,			A__ = -1       ,			A__ = False       ,			A__ = None       ,			):
						A__				:       int	  =						super(A__       ,			self     ).generate_dummy_inputs(
						    A__       ,			batch_size=A__       ,			seq_length=A__       ,			is_pair=A__       ,			framework=A__     )

						# We need to order the input in the way they appears in the forward()
						A__				:       Optional[int]	  =						OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}     )

						# Need to add the past_keys
						if self.use_past:
							if not is_torch_available():
								raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""     )
							else:
								import torch

								A__  ,  A__				:       Dict	  =						common_inputs["""input_ids"""].shape
								# Not using the same length for past_key_values
								A__				:       int	  =						seqlen + 2
								A__				:       int	  =						(
								    batch,
								    self.num_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // self.num_attention_heads,
								)
								A__				:       Dict	  =						[
								    (torch.zeros(A__     ), torch.zeros(A__     )) for _ in range(self.num_layers     )
								]

						A__				:       List[Any]	  =						common_inputs["""attention_mask"""]
						if self.use_past:
							A__				:       int	  =						ordered_inputs["""attention_mask"""].dtype
							A__				:       str	  =						torch.cat(
							    [ordered_inputs["""attention_mask"""], torch.ones(A__       ,			A__       ,			dtype=A__     )]       ,			dim=1     )

						return ordered_inputs






					@property
					def     __A     (					self     ):
						return 13



