




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 :     List[str]				,							lowerCamelCase__ :     Union[str, Any]=2				,							lowerCamelCase__ :     Optional[Any]=3				,							lowerCamelCase__ :     str=64				,							lowerCamelCase__ :     Dict=None				)    ->						int:
													"""simple docstring"""


													__lowercase      =    np.random.default_rng(lowerCamelCase__				)
													__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 :     Tuple				)    ->						Tuple:
													"""simple docstring"""


													return self.length






						def __getitem__( self :     List[Any]				,							lowerCamelCase__ :     Tuple				)    ->						str:
													"""simple docstring"""


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







class a (     torch.nn.Module  ):
						"""simple docstring"""

						def __init__( self :     Optional[Any]				,							lowerCamelCase__ :     Optional[Any]=0				,							lowerCamelCase__ :     Union[str, Any]=0				,							lowerCamelCase__ :     Dict=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     UpperCAmelCase_      ( self :     Tuple				,							lowerCamelCase__ :     Optional[int]=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 a (     torch.nn.Module  ):
						"""simple docstring"""

						def __init__( self :     Dict				,							lowerCamelCase__ :     List[Any]=0				,							lowerCamelCase__ :     int=0				,							lowerCamelCase__ :     int=False				)    ->						Union[str, Any]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Any				,							lowerCamelCase__ :     Dict=None				)    ->						Union[str, 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 + self.b







def 		_A(       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			int = 16				)    -> Tuple:
							'''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__							:			Union[str, Any]				):
														# 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__							:			Tuple				):
														# 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.
							__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











from typing import TYPE_CHECKING

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


UpperCAmelCase__				     = {
    "configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
    "tokenization_m2m_100": ["M2M100Tokenizer"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__				     = [
			    "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "M2M100ForConditionalGeneration",
			    "M2M100Model",
			    "M2M100PreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
			from .tokenization_mam_aaa import MaMaaaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mam_aaa import (
						    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MaMaaaForConditionalGeneration,
						    MaMaaaModel,
						    MaMaaaPreTrainedModel,
						)


else:
			import sys

			UpperCAmelCase__				     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






