
import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


UpperCAmelCase__					:	Tuple							       =					'''true'''



def    _lowercase       (   __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE=82					,     __SCREAMING_SNAKE_CASE=16			)						->  List[str]:
			set_seed(42			)
			UpperCamelCase__     :		Dict	 =					RegressionModel()
			UpperCamelCase__     :		List[str]	 =					deepcopy(__A			)
			UpperCamelCase__     :		Optional[Any]	 =					RegressionDataset(length=__A			)
			UpperCamelCase__     :		List[Any]	 =					DataLoader(__A					,     batch_size=__A			)
			model.to(accelerator.device			)
			UpperCamelCase__ ,      UpperCamelCase__     :		int	 =					accelerator.prepare(__A					,     __A			)
			return model, ddp_model, dataloader



def    _lowercase       (   __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE=False			)						->  int:
			UpperCamelCase__     :		Optional[int]	 =					AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'			)
			UpperCamelCase__     :		Optional[Any]	 =					load_dataset('glue'					,     'mrpc'					,     split='validation'			)

			def tokenize_function(__SCREAMING_SNAKE_CASE			):
						UpperCamelCase__     :		List[str]	 =					tokenizer(examples['sentence1']					,     examples['sentence2']					,     truncation=__A					,     max_length=__A			)
						return outputs

			with accelerator.main_process_first():
						UpperCamelCase__     :		Dict	 =					dataset.map(
						    __A					,     batched=__A					,     remove_columns=['idx', 'sentence1', 'sentence2']					,     )

			UpperCamelCase__     :		Dict	 =					tokenized_datasets.rename_column('label'					,     'labels'			)

			def collate_fn(__SCREAMING_SNAKE_CASE			):
						if use_longest:
									return tokenizer.pad(__A					,     padding='longest'					,     return_tensors='pt'			)
						return tokenizer.pad(__A					,     padding='max_length'					,     max_length=128					,     return_tensors='pt'			)

			return DataLoader(__A					,     shuffle=__A					,     collate_fn=__A					,     batch_size=16			)



def    _lowercase       (   __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE			)						->  int:
			UpperCamelCase__     :		Optional[int]	 =					Accelerator(dispatch_batches=__A					,     split_batches=__A			)
			UpperCamelCase__     :		List[Any]	 =					get_dataloader(__A					,     not dispatch_batches			)
			UpperCamelCase__     :		Optional[int]	 =					AutoModelForSequenceClassification.from_pretrained(
			    'hf-internal-testing/mrpc-bert-base-cased'					,     return_dict=__A			)
			UpperCamelCase__ ,      UpperCamelCase__     :		int	 =					accelerator.prepare(__A					,     __A			)
			return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator



def    _lowercase       (   __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE			)						->  List[Any]:
			UpperCamelCase__     :		int	 =					[]
			for batch in dataloader:
						UpperCamelCase__ ,      UpperCamelCase__     :		Union[str, Any]	 =					batch.values()
						with torch.no_grad():
									UpperCamelCase__     :		Tuple	 =					model(__A			)
									UpperCamelCase__ ,      UpperCamelCase__     :		List[Any]	 =					accelerator.gather_for_metrics((logit, target)			)
									logits_and_targets.append((logit, target)			)
			UpperCamelCase__ ,      UpperCamelCase__     :		Dict	 =					[], []
			for logit, targ in logits_and_targets:
						logits.append(__A			)
						targs.append(__A			)
			UpperCamelCase__ ,      UpperCamelCase__     :		Tuple	 =					torch.cat(__A			), torch.cat(__A			)
			return logits, targs



def    _lowercase       (   __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE=82					,     __SCREAMING_SNAKE_CASE=False					,     __SCREAMING_SNAKE_CASE=False					,     __SCREAMING_SNAKE_CASE=16			)						->  str:
			UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__     :		Dict	 =					get_basic_setup(__A					,     __A					,     __A			)
			UpperCamelCase__ ,      UpperCamelCase__     :		int	 =					generate_predictions(__A					,     __A					,     __A			)
			assert (
			    len(__A			) == num_samples
			), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__A			)}"""



def    _lowercase       (   __SCREAMING_SNAKE_CASE = False					,     __SCREAMING_SNAKE_CASE = False			)						->  Tuple:
			UpperCamelCase__     :		int	 =					evaluate.load('glue'					,     'mrpc'			)
			UpperCamelCase__ ,      UpperCamelCase__     :		Union[str, Any]	 =					get_mrpc_setup(__A					,     __A			)
			# First do baseline
			UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__     :		Dict	 =					setup['no']
			model.to(__A			)
			model.eval()
			for batch in dataloader:
						batch.to(__A			)
						with torch.inference_mode():
									UpperCamelCase__     :		Optional[Any]	 =					model(**__A			)
						UpperCamelCase__     :		str	 =					outputs.logits.argmax(dim=-1			)
						metric.add_batch(predictions=__A					,     references=batch['labels']			)
			UpperCamelCase__     :		Tuple	 =					metric.compute()

			# Then do distributed
			UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__     :		int	 =					setup['ddp']
			model.eval()
			for batch in dataloader:
						with torch.inference_mode():
									UpperCamelCase__     :		Tuple	 =					model(**__A			)
						UpperCamelCase__     :		Dict	 =					outputs.logits.argmax(dim=-1			)
						UpperCamelCase__     :		Dict	 =					batch['labels']
						UpperCamelCase__ ,      UpperCamelCase__     :		List[Any]	 =					accelerator.gather_for_metrics((preds, references)			)
						metric.add_batch(predictions=__A					,     references=__A			)
			UpperCamelCase__     :		Optional[Any]	 =					metric.compute()

			for key in "accuracy f1".split():
						assert math.isclose(
						    baseline[key]					,     distributed[key]			), F"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""



def    _lowercase       (   )						->  Optional[int]:
			UpperCamelCase__     :		int	 =					Accelerator(split_batches=__A					,     dispatch_batches=__A			)
			if accelerator.is_local_main_process:
						datasets.utils.logging.set_verbosity_warning()
						transformers.utils.logging.set_verbosity_warning()
			else:
						datasets.utils.logging.set_verbosity_error()
						transformers.utils.logging.set_verbosity_error()
			# These are a bit slower so they should only be ran on the GPU or TPU
			if torch.cuda.is_available() or is_tpu_available():
						if accelerator.is_local_main_process:
									print('**Testing gather_for_metrics**'			)
						for split_batches in [True, False]:
									for dispatch_batches in [True, False]:
												if accelerator.is_local_main_process:
															print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"""			)
												test_mrpc(__A					,     __A			)
												accelerator.state._reset_state()
			if accelerator.is_local_main_process:
						print('**Test torch metrics**'			)
			for split_batches in [True, False]:
						for dispatch_batches in [True, False]:
									UpperCamelCase__     :		str	 =					Accelerator(split_batches=__A					,     dispatch_batches=__A			)
									if accelerator.is_local_main_process:
												print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""			)
									test_torch_metrics(__A					,     99			)
									accelerator.state._reset_state()
			if accelerator.is_local_main_process:
						print('**Test last batch is not dropped when perfectly divisible**'			)
			UpperCamelCase__     :		Any	 =					Accelerator()
			test_torch_metrics(__A					,     512			)
			accelerator.state._reset_state()



def    _lowercase       (   __SCREAMING_SNAKE_CASE			)						->  Tuple:
			main()


if __name__ == "__main__":
							main()






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


UpperCamelCase__		 =     logging.get_logger(__name__)

UpperCamelCase__		 =     '''▁'''

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

UpperCamelCase__		 =     {
    '''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'''
        )
    },
}

UpperCamelCase__		 =     {
    '''facebook/s2t-small-librispeech-asr''': 1024,
}

UpperCamelCase__		 =     ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

UpperCamelCase__		 =     {'''mustc''': MUSTC_LANGS}

class     __snake_case    (				snake_case__  ):
       """simple docstring"""







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

       __SCREAMING_SNAKE_CASE				    =							[]


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






              """simple docstring"""


              __snake_case       	=							{} if sp_model_kwargs is None else sp_model_kwargs

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

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

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

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

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


       @property
       def 			a		(		self   )      ->							int:






              """simple docstring"""


              return len(self.encoder   )


       @property
       def 			a		(		self   )      ->							str:






              """simple docstring"""


              return self._tgt_lang


       @tgt_lang.setter
       def 			a		(		self      , _UpperCamelCase   )      ->							None:






              """simple docstring"""


              __snake_case       	=							new_tgt_lang
              self.set_tgt_lang_special_tokens(_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase   )      ->							None:






              """simple docstring"""


              __snake_case       	=							self.lang_code_to_id[tgt_lang]
              __snake_case       	=							[lang_code_id]


       def 			a		(		self      , _UpperCamelCase   )      ->							List[str]:






              """simple docstring"""


              return self.sp_model.encode(_UpperCamelCase      , out_type=_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase   )      ->							Optional[Any]:






              """simple docstring"""


              return self.encoder.get(_UpperCamelCase      , self.encoder[self.unk_token]   )


       def 			a		(		self      , _UpperCamelCase   )      ->							str:






              """simple docstring"""


              return self.decoder.get(_UpperCamelCase      , self.unk_token   )


       def 			a		(		self      , _UpperCamelCase   )      ->							str:






              """simple docstring"""


              __snake_case       	=							[]
              __snake_case       	=							""""""
              for token in tokens:
                     # make sure that special tokens are not decoded using sentencepiece model
                     if token in self.all_special_tokens:
                            __snake_case       	=							self.sp_model.decode(_UpperCamelCase   )
                            out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                            __snake_case       	=							[]
                     else:
                            current_sub_tokens.append(_UpperCamelCase   )
              __snake_case       	=							self.sp_model.decode(_UpperCamelCase   )
              out_string += decoded.upper() if self.do_upper_case else decoded
              return out_string.strip()


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase=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 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None      , _UpperCamelCase = False   )      ->							List[int]:






              """simple docstring"""


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

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


       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							self.encoder.copy()
              vocab.update(self.added_tokens_encoder   )
              return vocab


       def __getstate__(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							self.__dict__.copy()
              __snake_case       	=							None
              return state


       def __setstate__(		self      , _UpperCamelCase   )      ->							None:






              """simple docstring"""


              __snake_case       	=							d

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

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






       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None   )      ->							Tuple[str]:






              """simple docstring"""


              __snake_case       	=							Path(_UpperCamelCase   )
              assert save_dir.is_dir(), F'{save_directory} should be a directory'
              __snake_case       	=							save_dir / (
                  (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
              )
              __snake_case       	=							save_dir / (
                  (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
              )

              save_json(self.encoder      , _UpperCamelCase   )

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

              return (str(_UpperCamelCase   ), str(_UpperCamelCase   ))







def 						lowerCamelCase__      ( __A    :str	,__A    :Dict[str, Any]		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :str		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :Optional[Any]	,__A    :str		):


       """simple docstring"""




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

