




'''simple docstring'''






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

from accelerate.utils.dataclasses import DistributedType



class 					A		:




				'''simple docstring'''





				def __init__(self ,  _UpperCAmelCase=2 ,  _UpperCAmelCase=3 ,  _UpperCAmelCase=6_4 ,  _UpperCAmelCase=None			)	->       Optional[int]:
							__UpperCamelCase      :			Union[str, Any]									=   np.random.default_rng(__a			)
							__UpperCamelCase      :			Union[str, Any]									=   length
							__UpperCamelCase      :			Tuple									=   rng.normal(size=(length,)			).astype(np.floataa			)
							__UpperCamelCase      :			List[Any]									=   a * self.x + b + rng.normal(scale=0.1 ,  size=(length,)			).astype(np.floataa			)
				def __len__(self			)	->       Dict:
							return self.length





				def __getitem__(self ,  _UpperCAmelCase			)	->       Tuple:
							return {"x": self.x[i], "y": self.y[i]}







class 					A		( torch.nn.Module		):




				'''simple docstring'''





				def __init__(self ,  _UpperCAmelCase=0 ,  _UpperCAmelCase=0 ,  _UpperCAmelCase=False			)	->       int:
							super().__init__()
							__UpperCamelCase      :			Optional[int]									=   torch.nn.Parameter(torch.tensor([2, 3]			).float()			)
							__UpperCamelCase      :			int									=   torch.nn.Parameter(torch.tensor([2, 3]			).float()			)
							__UpperCamelCase      :			Optional[int]									=   True





				def        a_       (self ,  _UpperCAmelCase=None			)	->       Optional[Any]:
							if self.first_batch:
										print(f"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"			)
										__UpperCamelCase      :			Dict									=   False
							return x * self.a[0] + self.b[0]







class 					A		( torch.nn.Module		):




				'''simple docstring'''





				def __init__(self ,  _UpperCAmelCase=0 ,  _UpperCAmelCase=0 ,  _UpperCAmelCase=False			)	->       Union[str, Any]:
							super().__init__()
							__UpperCamelCase      :			Optional[int]									=   torch.nn.Parameter(torch.tensor(__a			).float()			)
							__UpperCamelCase      :			Optional[int]									=   torch.nn.Parameter(torch.tensor(__a			).float()			)
							__UpperCamelCase      :			str									=   True





				def        a_       (self ,  _UpperCAmelCase=None			)	->       Any:
							if self.first_batch:
										print(f"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"			)
										__UpperCamelCase      :			int									=   False
							return x * self.a + self.b







def 			__lowerCAmelCase					(    snake_case__					,      snake_case__ = 16						):
			from datasets import load_dataset
			from transformers import AutoTokenizer

			__UpperCamelCase      :			int									=   AutoTokenizer.from_pretrained("bert-base-cased"						)
			__UpperCamelCase      :			Union[str, Any]									=   {"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
			__UpperCamelCase      :			List[str]									=   load_dataset("csv"					,      data_files=snake_case__						)
			__UpperCamelCase      :			List[Any]									=   datasets["train"].unique("label"						)

			__UpperCamelCase      :			List[Any]									=   {v: i for i, v in enumerate(snake_case__						)}

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

			def collate_fn(snake_case__						):
						# 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(snake_case__					,      padding="max_length"					,      max_length=128					,      return_tensors="pt"						)
						return tokenizer.pad(snake_case__					,      padding="longest"					,      return_tensors="pt"						)

			# Instantiate dataloaders.
			__UpperCamelCase      :			Tuple									=   DataLoader(tokenized_datasets["train"]					,      shuffle=snake_case__					,      collate_fn=snake_case__					,      batch_size=2						)
			__UpperCamelCase      :			Any									=   DataLoader(tokenized_datasets["validation"]					,      shuffle=snake_case__					,      collate_fn=snake_case__					,      batch_size=1						)

			return train_dataloader, eval_dataloader










'''simple docstring'''







from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase_					:    int      	=			{'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_					:    Optional[int]      	=			['GPTNeoXTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_					:    List[str]      	=			[
					    'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GPTNeoXForCausalLM',
					    'GPTNeoXForQuestionAnswering',
					    'GPTNeoXForSequenceClassification',
					    'GPTNeoXForTokenClassification',
					    'GPTNeoXLayer',
					    'GPTNeoXModel',
					    'GPTNeoXPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gpt_neox import (
										    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTNeoXForCausalLM,
										    GPTNeoXForQuestionAnswering,
										    GPTNeoXForSequenceClassification,
										    GPTNeoXForTokenClassification,
										    GPTNeoXLayer,
										    GPTNeoXModel,
										    GPTNeoXPreTrainedModel,
										)


else:
					import sys

					lowerCAmelCase_					:    List[str]      	=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
