






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

from accelerate.utils.dataclasses import DistributedType

class     __SCREAMING_SNAKE_CASE					:






						def __init__(	self     ,						_UpperCamelCase=2     ,						_UpperCamelCase=3     ,						_UpperCamelCase=64     ,						_UpperCamelCase=None   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						np.random.default_rng(_UpperCamelCase   )
												lowerCAmelCase__    						=						length
												lowerCAmelCase__    						=						rng.normal(size=(length,)   ).astype(np.floataa   )
												lowerCAmelCase__    						=						a * self.x + b + rng.normal(scale=0.1     ,						size=(length,)   ).astype(np.floataa   )






						def __len__(	self   ):






												"""simple docstring"""
												return self.length







						def __getitem__(	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												return {"x": self.x[i], "y": self.y[i]}




class     __SCREAMING_SNAKE_CASE					(   torch.nn.Module):






						def __init__(	self     ,						_UpperCamelCase=0     ,						_UpperCamelCase=0     ,						_UpperCamelCase=False   ):






												"""simple docstring"""
												super().__init__()
												lowerCAmelCase__    						=						torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
												lowerCAmelCase__    						=						torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
												lowerCAmelCase__    						=						True







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase=None   ):






												"""simple docstring"""
												if self.first_batch:
																		print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"   )
																		lowerCAmelCase__    						=						False
												return x * self.a[0] + self.b[0]




class     __SCREAMING_SNAKE_CASE					(   torch.nn.Module):






						def __init__(	self     ,						_UpperCamelCase=0     ,						_UpperCamelCase=0     ,						_UpperCamelCase=False   ):






												"""simple docstring"""
												super().__init__()
												lowerCAmelCase__    						=						torch.nn.Parameter(torch.tensor(_UpperCamelCase   ).float()   )
												lowerCAmelCase__    						=						torch.nn.Parameter(torch.tensor(_UpperCamelCase   ).float()   )
												lowerCAmelCase__    						=						True







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase=None   ):






												"""simple docstring"""
												if self.first_batch:
																		print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"   )
																		lowerCAmelCase__    						=						False
												return x * self.a + self.b







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					int = 16		)  ->   Optional[Any]:

						"""simple docstring"""



						from datasets import load_dataset
						from transformers import AutoTokenizer

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

						lowerCAmelCase__    						=						{v: i for i, v in enumerate(UpperCamelCase_		)}

						def tokenize_function(UpperCamelCase_       :					List[Any]		):
												# max_length=None => use the model max length (it's actually the default)
												lowerCAmelCase__    						=						tokenizer(
												    examples['sentence1']    ,       examples['sentence2']    ,       truncation=UpperCamelCase_    ,       max_length=UpperCamelCase_    ,       padding='max_length'		)
												if "label" in examples:
																		lowerCAmelCase__    						=						[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
						lowerCAmelCase__    						=						datasets.map(
						    UpperCamelCase_    ,       batched=UpperCamelCase_    ,       remove_columns=['sentence1', 'sentence2', 'label']    ,       )

						def collate_fn(UpperCamelCase_       :					List[str]		):
												# 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=128    ,       return_tensors='pt'		)
												return tokenizer.pad(UpperCamelCase_    ,       padding='longest'    ,       return_tensors='pt'		)

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

						return train_dataloader, eval_dataloader













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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


__snake_case    :    Any         =     logging.get_logger(__name__)

__snake_case    :    Tuple         =     {"""tokenizer_file""": """tokenizer.json"""}

__snake_case    :    str         =     {
    """tokenizer_file""": {
        """bigscience/tokenizer""": """https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json""",
        """bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json""",
        """bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json""",
        """bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json""",
        """bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json""",
        """bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json""",
        """bigscience/bloom""": """https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json""",
    },
}


class     __SCREAMING_SNAKE_CASE					(   __lowercase):
						_SCREAMING_SNAKE_CASE	:       Dict   =					VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE	:       Union[str, Any]   =					PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE	:       Union[str, Any]   =					['''input_ids''', '''attention_mask''']
						_SCREAMING_SNAKE_CASE	:       Union[str, Any]   =					None






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






												"""simple docstring"""
												super().__init__(
												    _UpperCamelCase     ,						_UpperCamelCase     ,						tokenizer_file=_UpperCamelCase     ,						unk_token=_UpperCamelCase     ,						bos_token=_UpperCamelCase     ,						eos_token=_UpperCamelCase     ,						pad_token=_UpperCamelCase     ,						add_prefix_space=_UpperCamelCase     ,						clean_up_tokenization_spaces=_UpperCamelCase     ,						**_UpperCamelCase     ,						)
												lowerCAmelCase__    						=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
												if pre_tok_state.get('add_prefix_space'     ,						_UpperCamelCase   ) != add_prefix_space:
																		lowerCAmelCase__    						=						getattr(_UpperCamelCase     ,						pre_tok_state.pop('type'   )   )
																		lowerCAmelCase__    						=						add_prefix_space
																		lowerCAmelCase__    						=						pre_tok_class(**_UpperCamelCase   )

												lowerCAmelCase__    						=						add_prefix_space






						def       UpperCamelCase__      (	self     ,						*_UpperCamelCase     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						kwargs.get('is_split_into_words'     ,						_UpperCamelCase   )
												if not (self.add_prefix_space or not is_split_into_words):
																		raise Exception(
																		    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
																		    ' pretokenized inputs.'   )

												return super()._batch_encode_plus(*_UpperCamelCase     ,						**_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						*_UpperCamelCase     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						kwargs.get('is_split_into_words'     ,						_UpperCamelCase   )

												if not (self.add_prefix_space or not is_split_into_words):
																		raise Exception(
																		    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
																		    ' pretokenized inputs.'   )

												return super()._encode_plus(*_UpperCamelCase     ,						**_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase = None   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self._tokenizer.model.save(_UpperCamelCase     ,						name=_UpperCamelCase   )
												return tuple(_UpperCamelCase   )







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						[]
												for is_user, text in conversation.iter_texts():
																		input_ids.extend(self.encode(_UpperCamelCase     ,						add_special_tokens=_UpperCamelCase   ) + [self.eos_token_id]   )

												if len(_UpperCamelCase   ) > self.model_max_length:
																		lowerCAmelCase__    						=						input_ids[-self.model_max_length :]
												return input_ids





