
"""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


_a								=						logging.get_logger(__name__)

_a								=						"""▁"""

_a								=						{
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
}

_a								=						{
    """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"""
        )
    },
}

_a								=						{
    """facebook/s2t-small-librispeech-asr""": 1024,
}

_a								=						["""pt""", """fr""", """ru""", """nl""", """ro""", """it""", """es""", """de"""]

_a								=						{"""mustc""": MUSTC_LANGS}






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = VOCAB_FILES_NAMES
		lowercase__         = PRETRAINED_VOCAB_FILES_MAP
		lowercase__         = MAX_MODEL_INPUT_SIZES
		lowercase__         = ['input_ids', 'attention_mask']

		lowercase__         = []



		def __init__(   self							,						__a							,						__a							,						__a="<s>"							,						__a="</s>"							,						__a="<pad>"							,						__a="<unk>"							,						__a=False							,						__a=False							,						__a=None							,						__a=None							,						__a = None							,						**__a							,						)			->			None:






							'''simple docstring'''
							_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 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							return len(self.encoder)



		@property
		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							return self._tgt_lang



		@tgt_lang.setter
		def 		UpperCAmelCase				(   self							,						__a)			->			None:






							'''simple docstring'''
							_UpperCamelCase									=    new_tgt_lang
							self.set_tgt_lang_special_tokens(__a)



		def 		UpperCAmelCase				(   self							,						__a)			->			None:






							'''simple docstring'''
							_UpperCamelCase									=    self.lang_code_to_id[tgt_lang]
							_UpperCamelCase									=    [lang_code_id]



		def 		UpperCAmelCase				(   self							,						__a)			->			List[str]:






							'''simple docstring'''
							return self.sp_model.encode(__a							,						out_type=__a)



		def 		UpperCAmelCase				(   self							,						__a)			->			Any:






							'''simple docstring'''
							return self.encoder.get(__a							,						self.encoder[self.unk_token])



		def 		UpperCAmelCase				(   self							,						__a)			->			str:






							'''simple docstring'''
							return self.decoder.get(__a							,						self.unk_token)



		def 		UpperCAmelCase				(   self							,						__a)			->			str:






							'''simple docstring'''
							_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 		UpperCAmelCase				(   self							,						__a							,						__a=None)			->			List[int]:






							'''simple docstring'''
							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 		UpperCAmelCase				(   self							,						__a							,						__a = None							,						__a = False)			->			List[int]:






							'''simple docstring'''

							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 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.encoder.copy()
							vocab.update(self.added_tokens_encoder)
							return vocab



		def __getstate__(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.__dict__.copy()
							_UpperCamelCase									=    None
							return state



		def __setstate__(   self							,						__a)			->			None:






							'''simple docstring'''
							_UpperCamelCase									=    d

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

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




		def 		UpperCAmelCase				(   self							,						__a							,						__a = None)			->			Tuple[str]:






							'''simple docstring'''
							_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,	__snake_case      )				->						sentencepiece.SentencePieceProcessor:


					"""simple docstring"""





					_UpperCamelCase									=    sentencepiece.SentencePieceProcessor(**__snake_case      )
					spm.Load(str(__snake_case      )      )
					return spm



def  lowerCamelCase__						(     __snake_case      )				->						Union[Dict, List]:


					"""simple docstring"""





					with open(__snake_case,	'''r'''      ) as f:
										return json.load(__snake_case      )



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						None:


					"""simple docstring"""





					with open(__snake_case,	'''w'''      ) as f:
										json.dump(__snake_case,	__snake_case,	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