





import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


a : List[Any]		=				logging.get_logger(__name__)
class 	lowerCamelCase_       :



					'''simple docstring'''







					def __init__(				self ,       snake_case_ ,       snake_case_						)   ->			str:







								'''simple docstring'''

								__lowercase 					=   question_encoder
								__lowercase 					=   generator
								__lowercase 					=   self.question_encoder






					def A      (				self ,       snake_case_						)   ->			Optional[int]:







								'''simple docstring'''

								if os.path.isfile(UpperCamelCase__						):
											raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'						)
								os.makedirs(UpperCamelCase__ ,       exist_ok=UpperCamelCase__						)
								__lowercase 					=   os.path.join(UpperCamelCase__ ,       '''question_encoder_tokenizer'''						)
								__lowercase 					=   os.path.join(UpperCamelCase__ ,       '''generator_tokenizer'''						)
								self.question_encoder.save_pretrained(UpperCamelCase__						)
								self.generator.save_pretrained(UpperCamelCase__						)






					@classmethod
					def A      (				cls ,       snake_case_ ,       **snake_case_						)   ->			Any:







								'''simple docstring'''

								from ..auto.tokenization_auto import AutoTokenizer

								__lowercase 					=   kwargs.pop('''config''' ,       UpperCamelCase__						)

								if config is None:
											__lowercase 					=   RagConfig.from_pretrained(UpperCamelCase__						)

								__lowercase 					=   AutoTokenizer.from_pretrained(
								    UpperCamelCase__ ,       config=config.question_encoder ,       subfolder='''question_encoder_tokenizer'''						)
								__lowercase 					=   AutoTokenizer.from_pretrained(
								    UpperCamelCase__ ,       config=config.generator ,       subfolder='''generator_tokenizer'''						)
								return cls(question_encoder=UpperCamelCase__ ,       generator=UpperCamelCase__						)






					def __call__(				self ,       *snake_case_ ,       **snake_case_						)   ->			Any:







								'''simple docstring'''

								return self.current_tokenizer(*UpperCamelCase__ ,       **UpperCamelCase__						)






					def A      (				self ,       *snake_case_ ,       **snake_case_						)   ->			Tuple:







								'''simple docstring'''

								return self.generator.batch_decode(*UpperCamelCase__ ,       **UpperCamelCase__						)






					def A      (				self ,       *snake_case_ ,       **snake_case_						)   ->			Tuple:







								'''simple docstring'''

								return self.generator.decode(*UpperCamelCase__ ,       **UpperCamelCase__						)






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   self.question_encoder






					def A      (				self						)   ->			Optional[int]:







								'''simple docstring'''

								__lowercase 					=   self.generator






					def A      (				self ,       snake_case_ ,       snake_case_ = None ,       snake_case_ = None ,       snake_case_ = None ,       snake_case_ = "longest" ,       snake_case_ = None ,       snake_case_ = True ,       **snake_case_ ,       )   ->			Optional[Any]:







								'''simple docstring'''

								warnings.warn(
								    '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
								    '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
								    '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
								    '''details''' ,       UpperCamelCase__ ,       )
								if max_length is None:
											__lowercase 					=   self.current_tokenizer.model_max_length
								__lowercase 					=   self(
								    UpperCamelCase__ ,       add_special_tokens=UpperCamelCase__ ,       return_tensors=UpperCamelCase__ ,       max_length=UpperCamelCase__ ,       padding=UpperCamelCase__ ,       truncation=UpperCamelCase__ ,       **UpperCamelCase__ ,       )
								if tgt_texts is None:
											return model_inputs
								# Process tgt_texts
								if max_target_length is None:
											__lowercase 					=   self.current_tokenizer.model_max_length
								__lowercase 					=   self(
								    text_target=UpperCamelCase__ ,       add_special_tokens=UpperCamelCase__ ,       return_tensors=UpperCamelCase__ ,       padding=UpperCamelCase__ ,       max_length=UpperCamelCase__ ,       truncation=UpperCamelCase__ ,       **UpperCamelCase__ ,       )
								__lowercase 					=   labels['''input_ids''']
								return model_inputs






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 ,       snake_case_=2 ,       snake_case_=3 ,       snake_case_=6_4 ,       snake_case_=None						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   np.random.default_rng(snake_case_						)
								__lowercase 					=   length
								__lowercase 					=   rng.normal(size=(length,)						).astype(np.floataa						)
								__lowercase 					=   a * self.x + b + rng.normal(scale=0.1 ,       size=(length,)						).astype(np.floataa						)






					def __len__(				self						)   ->			Dict:







								'''simple docstring'''

								return self.length






					def __getitem__(				self ,       snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

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




class 	lowerCamelCase_       (				torch.nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_=0 ,       snake_case_=0 ,       snake_case_=False						)   ->			Optional[Any]:







								'''simple docstring'''

								super().__init__()
								__lowercase 					=   torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
								__lowercase 					=   torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
								__lowercase 					=   True






					def A      (				self ,       snake_case_=None						)   ->			List[Any]:







								'''simple docstring'''

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




class 	lowerCamelCase_       (				torch.nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_=0 ,       snake_case_=0 ,       snake_case_=False						)   ->			List[str]:







								'''simple docstring'''

								super().__init__()
								__lowercase 					=   torch.nn.Parameter(torch.tensor(snake_case_						).float()						)
								__lowercase 					=   torch.nn.Parameter(torch.tensor(snake_case_						).float()						)
								__lowercase 					=   True






					def A      (				self ,       snake_case_=None						)   ->			str:







								'''simple docstring'''

								if self.first_batch:
											print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'						)
											__lowercase 					=   False
								return x * self.a + self.b




def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase = 16  ):


			'''simple docstring'''







			from datasets import load_dataset
			from transformers import AutoTokenizer

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

			__lowercase 					=   {v: i for i, v in enumerate(_UpperCamelCase  )}

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

			def collate_fn(_UpperCamelCase  ):
						# 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.
			__lowercase 					=   DataLoader(tokenized_datasets['''train''']       ,			shuffle=_UpperCamelCase       ,			collate_fn=_UpperCamelCase       ,			batch_size=2  )
			__lowercase 					=   DataLoader(tokenized_datasets['''validation''']       ,			shuffle=_UpperCamelCase       ,			collate_fn=_UpperCamelCase       ,			batch_size=1  )

			return train_dataloader, eval_dataloader


