



from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
					import datasets
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		@slow
		@require_torch
		def       A(  self		:       Any		)    ->   Any:


						'''simple docstring'''
						A	 =      EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'						,'prajjwal1/bert-tiny'		)
						A	 =      BertTokenizer.from_pretrained('bert-base-uncased'		)

						A	 =      bertabert.config.encoder.vocab_size
						A	 =      tokenizer.sep_token_id
						A	 =      tokenizer.cls_token_id
						A	 =      1_2_8

						A	 =      datasets.load_dataset('cnn_dailymail'						,'3.0.0'						,split='train[:1%]'		)
						A	 =      datasets.load_dataset('cnn_dailymail'						,'3.0.0'						,split='validation[:1%]'		)

						A	 =      train_dataset.select(range(3_2		)		)
						A	 =      val_dataset.select(range(1_6		)		)

						A	 =      4

						def _map_to_encoder_decoder_inputs(_SCREAMING_SNAKE_CASE		:       Dict		):
										# Tokenizer will automatically set [BOS] <text> [EOS]
										A	 =      tokenizer(batch['article']						,padding='max_length'						,truncation=UpperCamelCase_						,max_length=5_1_2		)
										A	 =      tokenizer(batch['highlights']						,padding='max_length'						,truncation=UpperCamelCase_						,max_length=1_2_8		)
										A	 =      inputs.input_ids
										A	 =      inputs.attention_mask

										A	 =      outputs.input_ids
										A	 =      outputs.input_ids.copy()
										A	 =      [
										    [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
										]
										A	 =      outputs.attention_mask

										assert all(len(UpperCamelCase_		) == 5_1_2 for x in inputs.input_ids		)
										assert all(len(UpperCamelCase_		) == 1_2_8 for x in outputs.input_ids		)

										return batch

						def _compute_metrics(_SCREAMING_SNAKE_CASE		:       Union[str, Any]		):
										A	 =      pred.label_ids
										A	 =      pred.predictions

										# all unnecessary tokens are removed
										A	 =      tokenizer.batch_decode(UpperCamelCase_						,skip_special_tokens=UpperCamelCase_		)
										A	 =      tokenizer.batch_decode(UpperCamelCase_						,skip_special_tokens=UpperCamelCase_		)

										A	 =      sum([int(pred_str[i] == label_str[i]		) for i in range(len(UpperCamelCase_		)		)]		) / len(UpperCamelCase_		)

										return {"accuracy": accuracy}

						# map train dataset
						A	 =      train_dataset.map(
						    _map_to_encoder_decoder_inputs						,batched=UpperCamelCase_						,batch_size=UpperCamelCase_						,remove_columns=['article', 'highlights']						,)
						train_dataset.set_format(
						    type='torch'						,columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']						,)

						# same for validation dataset
						A	 =      val_dataset.map(
						    _map_to_encoder_decoder_inputs						,batched=UpperCamelCase_						,batch_size=UpperCamelCase_						,remove_columns=['article', 'highlights']						,)
						val_dataset.set_format(
						    type='torch'						,columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']						,)

						A	 =      self.get_auto_remove_tmp_dir()

						A	 =      SeqaSeqTrainingArguments(
						    output_dir=UpperCamelCase_						,per_device_train_batch_size=UpperCamelCase_						,per_device_eval_batch_size=UpperCamelCase_						,predict_with_generate=UpperCamelCase_						,evaluation_strategy='steps'						,do_train=UpperCamelCase_						,do_eval=UpperCamelCase_						,warmup_steps=0						,eval_steps=2						,logging_steps=2						,)

						# instantiate trainer
						A	 =      SeqaSeqTrainer(
						    model=UpperCamelCase_						,args=UpperCamelCase_						,compute_metrics=_compute_metrics						,train_dataset=UpperCamelCase_						,eval_dataset=UpperCamelCase_						,tokenizer=UpperCamelCase_						,)

						# start training
						trainer.train()













import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


lowerCAmelCase_           = logging.get_logger(__name__)

lowerCAmelCase_           = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase_           = {
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase_           = {
    'gpt-neox-20b': 2_048,
}
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   VOCAB_FILES_NAMES
		snake_case    =   PRETRAINED_VOCAB_FILES_MAP
		snake_case    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		snake_case    =   ["input_ids", "attention_mask"]
		def __init__(  self		:       str						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=None						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=None						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=None						,_SCREAMING_SNAKE_CASE		:       Dict="<|endoftext|>"						,_SCREAMING_SNAKE_CASE		:       int="<|endoftext|>"						,_SCREAMING_SNAKE_CASE		:       Optional[int]="<|endoftext|>"						,_SCREAMING_SNAKE_CASE		:       List[Any]=False						,**_SCREAMING_SNAKE_CASE		:       Union[str, Any]						,)    ->   int:


						'''simple docstring'''
						super().__init__(
						    _SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,tokenizer_file=_SCREAMING_SNAKE_CASE						,unk_token=_SCREAMING_SNAKE_CASE						,bos_token=_SCREAMING_SNAKE_CASE						,eos_token=_SCREAMING_SNAKE_CASE						,add_prefix_space=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE						,)

						A	 =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
						if pre_tok_state.get('add_prefix_space'						,_SCREAMING_SNAKE_CASE		) != add_prefix_space:
										A	 =      getattr(_SCREAMING_SNAKE_CASE						,pre_tok_state.pop('type'		)		)
										A	 =      add_prefix_space
										A	 =      pre_tok_class(**_SCREAMING_SNAKE_CASE		)

						A	 =      add_prefix_space
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       Optional[str] = None		)    ->   Tuple[str]:


						'''simple docstring'''
						A	 =      self._tokenizer.model.save(_SCREAMING_SNAKE_CASE						,name=_SCREAMING_SNAKE_CASE		)
						return tuple(_SCREAMING_SNAKE_CASE		)







		def       A(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       "Conversation"		)    ->   List[int]:


						'''simple docstring'''
						A	 =      []
						for is_user, text in conversation.iter_texts():
										input_ids.extend(self.encode(_SCREAMING_SNAKE_CASE						,add_special_tokens=_SCREAMING_SNAKE_CASE		) + [self.eos_token_id]		)

						if len(_SCREAMING_SNAKE_CASE		) > self.model_max_length:
										A	 =      input_ids[-self.model_max_length :]
						return input_ids





