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

from accelerate.utils.dataclasses import DistributedType




class   UpperCAmelCase_					:






						'''simple docstring'''






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

												"""simple docstring"""



												_lowerCAmelCase  						=			np.random.default_rng(_lowercase				)
												_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   ,       _lowercase				):

												"""simple docstring"""



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






class   UpperCAmelCase_					(		torch.nn.Module			):






						'''simple docstring'''






						def __init__( self   ,       _lowercase=0   ,       _lowercase=0   ,       _lowercase=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  _lowercase     ( self   ,       _lowercase=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   UpperCAmelCase_					(		torch.nn.Module			):






						'''simple docstring'''






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

												"""simple docstring"""



												super().__init__()
												_lowerCAmelCase  						=			torch.nn.Parameter(torch.tensor(_lowercase				).float()				)
												_lowerCAmelCase  						=			torch.nn.Parameter(torch.tensor(_lowercase				).float()				)
												_lowerCAmelCase  						=			True




						def  _lowercase     ( self   ,       _lowercase=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 A				(__lowerCamelCase				:Tuple			,     __lowerCamelCase				:int = 16		):
						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=__lowerCamelCase		)
						_lowerCAmelCase  						=			datasets["""train"""].unique("""label"""		)

						_lowerCAmelCase  						=			{v: i for i, v in enumerate(__lowerCamelCase		)}

						def tokenize_function(__lowerCamelCase				:List[Any]		):
												# max_length=None => use the model max length (it's actually the default)
												_lowerCAmelCase  						=			tokenizer(
												    examples["""sentence1"""]			,     examples["""sentence2"""]			,     truncation=__lowerCamelCase			,     max_length=__lowerCamelCase			,     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(
						    __lowerCamelCase			,     batched=__lowerCamelCase			,     remove_columns=["""sentence1""", """sentence2""", """label"""]			,     )

						def collate_fn(__lowerCamelCase				:Optional[Any]		):
												# 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(__lowerCamelCase			,     padding="""max_length"""			,     max_length=128			,     return_tensors="""pt"""		)
												return tokenizer.pad(__lowerCamelCase			,     padding="""longest"""			,     return_tensors="""pt"""		)

						# Instantiate dataloaders.
						_lowerCAmelCase  						=			DataLoader(tokenized_datasets["""train"""]			,     shuffle=__lowerCamelCase			,     collate_fn=__lowerCamelCase			,     batch_size=2		)
						_lowerCAmelCase  						=			DataLoader(tokenized_datasets["""validation"""]			,     shuffle=__lowerCamelCase			,     collate_fn=__lowerCamelCase			,     batch_size=1		)

						return train_dataloader, eval_dataloader


'''simple docstring'''



from ...configuration_utils import PretrainedConfig




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				Tuple									=       '''bert-generation'''




						def __init__( self   ,       _lowercase=50_358   ,       _lowercase=1_024   ,       _lowercase=24   ,       _lowercase=16   ,       _lowercase=4_096   ,       _lowercase="gelu"   ,       _lowercase=0.1   ,       _lowercase=0.1   ,       _lowercase=512   ,       _lowercase=0.02   ,       _lowercase=1e-12   ,       _lowercase=0   ,       _lowercase=2   ,       _lowercase=1   ,       _lowercase="absolute"   ,       _lowercase=True   ,       **_lowercase   ,       ):

												"""simple docstring"""



												super().__init__(pad_token_id=_lowercase   ,       bos_token_id=_lowercase   ,       eos_token_id=_lowercase   ,       **_lowercase				)

												_lowerCAmelCase  						=			vocab_size
												_lowerCAmelCase  						=			hidden_size
												_lowerCAmelCase  						=			num_hidden_layers
												_lowerCAmelCase  						=			num_attention_heads
												_lowerCAmelCase  						=			hidden_act
												_lowerCAmelCase  						=			intermediate_size
												_lowerCAmelCase  						=			hidden_dropout_prob
												_lowerCAmelCase  						=			attention_probs_dropout_prob
												_lowerCAmelCase  						=			max_position_embeddings
												_lowerCAmelCase  						=			initializer_range
												_lowerCAmelCase  						=			layer_norm_eps
												_lowerCAmelCase  						=			position_embedding_type
												_lowerCAmelCase  						=			use_cache
