



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

from accelerate.utils.dataclasses import DistributedType






class 					UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__=2			,	UpperCAmelCase__=3			,	UpperCAmelCase__=64			,	UpperCAmelCase__=None		):
							A__							=       np.random.default_rng(_a		)
							A__							=       length
							A__							=       rng.normal(size=(length,)		).astype(np.floataa		)
							A__							=       a * self.x + b + rng.normal(scale=0.1			,	size=(length,)		).astype(np.floataa		)



	def __len__(       self		):
							return self.length



	def __getitem__(       self			,	UpperCAmelCase__		):
							return {"x": self.x[i], "y": self.y[i]}






class 					UpperCamelCase    (						torch.nn.Module     ):



	def __init__(       self			,	UpperCAmelCase__=0			,	UpperCAmelCase__=0			,	UpperCAmelCase__=False		):
							super().__init__()
							A__							=       torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
							A__							=       torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
							A__							=       True



	def 			__A							(       self			,	UpperCAmelCase__=None		):
							if self.first_batch:
													print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""		)
													A__							=       False
							return x * self.a[0] + self.b[0]






class 					UpperCamelCase    (						torch.nn.Module     ):



	def __init__(       self			,	UpperCAmelCase__=0			,	UpperCAmelCase__=0			,	UpperCAmelCase__=False		):
							super().__init__()
							A__							=       torch.nn.Parameter(torch.tensor(_a		).float()		)
							A__							=       torch.nn.Parameter(torch.tensor(_a		).float()		)
							A__							=       True



	def 			__A							(       self			,	UpperCAmelCase__=None		):
							if self.first_batch:
													print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""		)
													A__							=       False
							return x * self.a + self.b





def  UpperCamelCase						(       _A			:				Optional[Any]			,							_A			:				int = 16    )->       List[str]:





						"""simple docstring"""





						from datasets import load_dataset
						from transformers import AutoTokenizer

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

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

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

						def collate_fn(_A			:				List[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.
						A__							=       DataLoader(tokenized_datasets["train"]			,							shuffle=lowerCAmelCase__			,							collate_fn=lowerCAmelCase__			,							batch_size=2    )
						A__							=       DataLoader(tokenized_datasets["validation"]			,							shuffle=lowerCAmelCase__			,							collate_fn=lowerCAmelCase__			,							batch_size=1    )

						return train_dataloader, eval_dataloader










from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_							:      Dict      =  logging.get_logger(__name__)

UpperCAmelCase_							:      List[Any]      =  {
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   int				=	"""gpt_neox"""



	def __init__(       self			,	UpperCAmelCase__=50_432			,	UpperCAmelCase__=6_144			,	UpperCAmelCase__=44			,	UpperCAmelCase__=64			,	UpperCAmelCase__=24_576			,	UpperCAmelCase__="gelu"			,	UpperCAmelCase__=0.25			,	UpperCAmelCase__=10_000			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=2_048			,	UpperCAmelCase__=0.02			,	UpperCAmelCase__=1e-5			,	UpperCAmelCase__=True			,	UpperCAmelCase__=0			,	UpperCAmelCase__=2			,	UpperCAmelCase__=False			,	UpperCAmelCase__=True			,	UpperCAmelCase__=None			,	**UpperCAmelCase__			,	):
							super().__init__(bos_token_id=UpperCAmelCase__			,	eos_token_id=UpperCAmelCase__			,	**UpperCAmelCase__		)
							A__							=       vocab_size
							A__							=       max_position_embeddings
							A__							=       hidden_size
							A__							=       num_hidden_layers
							A__							=       num_attention_heads
							A__							=       intermediate_size
							A__							=       hidden_act
							A__							=       rotary_pct
							A__							=       rotary_emb_base
							A__							=       attention_dropout
							A__							=       hidden_dropout
							A__							=       classifier_dropout
							A__							=       initializer_range
							A__							=       layer_norm_eps
							A__							=       use_cache
							A__							=       tie_word_embeddings
							A__							=       use_parallel_residual
							A__							=       rope_scaling
							self._rope_scaling_validation()

							if self.hidden_size % self.num_attention_heads != 0:
													raise ValueError(
													    "The hidden size is not divisble by the number of attention heads! Make sure to update them!"		)



	def 			__A							(       self		):
							if self.rope_scaling is None:
													return

							if not isinstance(self.rope_scaling			,	UpperCAmelCase__		) or len(self.rope_scaling		) != 2:
													raise ValueError(
													    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
													    F"""got {self.rope_scaling}"""		)
							A__							=       self.rope_scaling.get("type"			,	UpperCAmelCase__		)
							A__							=       self.rope_scaling.get("factor"			,	UpperCAmelCase__		)
							if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
													raise ValueError(
													    F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""		)
							if rope_scaling_factor is None or not isinstance(UpperCAmelCase__			,	UpperCAmelCase__		) or rope_scaling_factor <= 1.0:
													raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""		)





