




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


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

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

lowerCamelCase__					:  Any      							=				{
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
}

lowerCamelCase__					:  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'
        )
    },
}

lowerCamelCase__					:  Union[str, Any]      							=				{
    'facebook/s2t-small-librispeech-asr': 1_024,
}

lowerCamelCase__					:  Dict      							=				['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']

lowerCamelCase__					:  List[Any]      							=				{'mustc': MUSTC_LANGS}





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       VOCAB_FILES_NAMES
 lowercase_							       =       PRETRAINED_VOCAB_FILES_MAP
 lowercase_							       =       MAX_MODEL_INPUT_SIZES
 lowercase_							       =       ["input_ids", "attention_mask"]

 lowercase_							       =       []



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

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

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

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

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

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



 @property
 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       return len(self.encoder		)



 @property
 def  lowerCAmelCase_    (				self    :						Dict		):
       return self._tgt_lang



 @tgt_lang.setter
 def  lowerCAmelCase_    (				self    :						str							,							_lowerCAmelCase    :						List[Any]		):
       SCREAMING_SNAKE_CASE_      		=     new_tgt_lang
       self.set_tgt_lang_special_tokens(_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						str							,							_lowerCAmelCase    :						str		):
       SCREAMING_SNAKE_CASE_      		=     self.lang_code_to_id[tgt_lang]
       SCREAMING_SNAKE_CASE_      		=     [lang_code_id]



 def  lowerCAmelCase_    (				self    :						List[str]							,							_lowerCAmelCase    :						str		):
       return self.sp_model.encode(_lowerCAmelCase							,							out_type=_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						int							,							_lowerCAmelCase    :						Optional[Any]		):
       return self.encoder.get(_lowerCAmelCase							,							self.encoder[self.unk_token]		)



 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						int		):
       return self.decoder.get(_lowerCAmelCase							,							self.unk_token		)



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



 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						Any							,							_lowerCAmelCase    :						str=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  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						List[int]							,							_lowerCAmelCase    :						Optional[List[int]] = None							,							_lowerCAmelCase    :						bool = False		):

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

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



 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.encoder.copy()
       vocab.update(self.added_tokens_encoder		)
       return vocab



 def __getstate__(				self    :						Optional[Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.__dict__.copy()
       SCREAMING_SNAKE_CASE_      		=     None
       return state



 def __setstate__(				self    :						int							,							_lowerCAmelCase    :						Dict		):
       SCREAMING_SNAKE_CASE_      		=     d

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

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




 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						Optional[str] = None		):
       SCREAMING_SNAKE_CASE_      		=     Path(_lowerCAmelCase		)
       assert save_dir.is_dir(), F"{save_directory} should be a directory"
       SCREAMING_SNAKE_CASE_      		=     save_dir / (
           (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
       )
       SCREAMING_SNAKE_CASE_      		=     save_dir / (
           (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
       )

       save_json(self.encoder							,							_lowerCAmelCase		)

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

       return (str(_lowerCAmelCase		), str(_lowerCAmelCase		))


def    UpperCAmelCase_				( __UpperCAmelCase		:      str     ,     __UpperCAmelCase		:      Dict[str, Any]				)   ->					sentencepiece.SentencePieceProcessor:
      SCREAMING_SNAKE_CASE_      		=     sentencepiece.SentencePieceProcessor(**__UpperCAmelCase				)
      spm.Load(str(__UpperCAmelCase				)				)
      return spm

def    UpperCAmelCase_				( __UpperCAmelCase		:      str				)   ->					Union[Dict, List]:
      with open(__UpperCAmelCase     ,     'r'				) as f:
            return json.load(__UpperCAmelCase				)





def    UpperCAmelCase_				( __UpperCAmelCase		:      Optional[Any]     ,     __UpperCAmelCase		:      str				)   ->					None:
      with open(__UpperCAmelCase     ,     'w'				) as f:
            json.dump(__UpperCAmelCase     ,     __UpperCAmelCase     ,     indent=2				)





import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						Any							,							_lowerCAmelCase    :						Optional[int]=2							,							_lowerCAmelCase    :						Any=3							,							_lowerCAmelCase    :						Tuple=64							,							_lowerCAmelCase    :						List[str]=None		):
       SCREAMING_SNAKE_CASE_      		=     np.random.default_rng(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     length
       SCREAMING_SNAKE_CASE_      		=     rng.normal(size=(length,)		).astype(np.floataa		)
       SCREAMING_SNAKE_CASE_      		=     a * self.x + b + rng.normal(scale=0.1							,							size=(length,)		).astype(np.floataa		)



 def __len__(				self    :						Optional[int]		):
       return self.length




 def __getitem__(				self    :						str							,							_lowerCAmelCase    :						Union[str, Any]		):
       return {"x": self.x[i], "y": self.y[i]}





class 			lowerCamelCase_				( torch.nn.Module   ):


 '''simple docstring'''







 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						Dict=0							,							_lowerCAmelCase    :						List[str]=0							,							_lowerCAmelCase    :						str=False		):
       super().__init__()
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
       SCREAMING_SNAKE_CASE_      		=     True




 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						Union[str, Any]=None		):
       if self.first_batch:
             print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"		)
             SCREAMING_SNAKE_CASE_      		=     False
       return x * self.a[0] + self.b[0]





class 			lowerCamelCase_				( torch.nn.Module   ):


 '''simple docstring'''







 def __init__(				self    :						Optional[int]							,							_lowerCAmelCase    :						Any=0							,							_lowerCAmelCase    :						Any=0							,							_lowerCAmelCase    :						Optional[Any]=False		):
       super().__init__()
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor(_lowerCAmelCase		).float()		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor(_lowerCAmelCase		).float()		)
       SCREAMING_SNAKE_CASE_      		=     True




 def  lowerCAmelCase_    (				self    :						Optional[Any]							,							_lowerCAmelCase    :						Optional[int]=None		):
       if self.first_batch:
             print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"		)
             SCREAMING_SNAKE_CASE_      		=     False
       return x * self.a + self.b


def    UpperCAmelCase_				( __UpperCAmelCase		:      Dict     ,     __UpperCAmelCase		:      int = 16				)   ->					Union[str, Any]:
      from datasets import load_dataset
      from transformers import AutoTokenizer

      SCREAMING_SNAKE_CASE_      		=     AutoTokenizer.from_pretrained('bert-base-cased'				)
      SCREAMING_SNAKE_CASE_      		=     {'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
      SCREAMING_SNAKE_CASE_      		=     load_dataset('csv'     ,     data_files=__UpperCAmelCase				)
      SCREAMING_SNAKE_CASE_      		=     datasets['train'].unique('label'				)

      SCREAMING_SNAKE_CASE_      		=     {v: i for i, v in enumerate(__UpperCAmelCase				)}

      def tokenize_function(__UpperCAmelCase		:      Optional[int]				):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE_      		=     tokenizer(
                examples['sentence1']     ,     examples['sentence2']     ,     truncation=__UpperCAmelCase     ,     max_length=__UpperCAmelCase     ,     padding='max_length'				)
            if "label" in examples:
                  SCREAMING_SNAKE_CASE_      		=     [label_to_id[l] for l in examples['label']]
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      SCREAMING_SNAKE_CASE_      		=     datasets.map(
          __UpperCAmelCase     ,     batched=__UpperCAmelCase     ,     remove_columns=['sentence1', 'sentence2', 'label']     ,     )

      def collate_fn(__UpperCAmelCase		:      Dict				):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(__UpperCAmelCase     ,     padding='max_length'     ,     max_length=1_28     ,     return_tensors='pt'				)
            return tokenizer.pad(__UpperCAmelCase     ,     padding='longest'     ,     return_tensors='pt'				)

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE_      		=     DataLoader(tokenized_datasets['train']     ,     shuffle=__UpperCAmelCase     ,     collate_fn=__UpperCAmelCase     ,     batch_size=2				)
      SCREAMING_SNAKE_CASE_      		=     DataLoader(tokenized_datasets['validation']     ,     shuffle=__UpperCAmelCase     ,     collate_fn=__UpperCAmelCase     ,     batch_size=1				)

      return train_dataloader, eval_dataloader