


"""simple docstring"""


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

from accelerate.utils.dataclasses import DistributedType



class 			lowerCamelCase__						:







						def __init__(							self   :    Optional[Any]			,			_lowercase   :    List[str]=2			,			_lowercase   :    Optional[int]=3			,			_lowercase   :    int=64			,			_lowercase   :    Any=None		):
								A					       = np.random.default_rng(_UpperCamelCase		)
								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   :    Dict		):
								return self.length







						def __getitem__(							self   :    List[Any]			,			_lowercase   :    str		):
								return {"x": self.x[i], "y": self.y[i]}



class 			lowerCamelCase__						(   torch.nn.Module			):







						def __init__(							self   :    Optional[int]			,			_lowercase   :    Tuple=0			,			_lowercase   :    str=0			,			_lowercase   :    Optional[Any]=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   :    Optional[int]			,			_lowercase   :    Union[str, Any]=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 			lowerCamelCase__						(   torch.nn.Module			):







						def __init__(							self   :    Tuple			,			_lowercase   :    Optional[int]=0			,			_lowercase   :    Any=0			,			_lowercase   :    Optional[Any]=False		):
								super().__init__()
								A					       = torch.nn.Parameter(torch.tensor(_UpperCamelCase		).float()		)
								A					       = torch.nn.Parameter(torch.tensor(_UpperCamelCase		).float()		)
								A					       = True







						def __a						(							self   :    List[str]			,			_lowercase   :    int=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  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ = 16	)      ->		Any:







		"""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=__A	)
		A					       = datasets["""train"""].unique('label'	)

		A					       = {v: i for i, v in enumerate(__A	)}

		def tokenize_function(UpperCamelCase__	):
				# max_length=None => use the model max length (it's actually the default)
				A					       = tokenizer(
				    examples['sentence1'] ,			examples['sentence2'] ,			truncation=__A ,			max_length=__A ,			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(
		    __A ,			batched=__A ,			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(__A ,			padding='max_length' ,			max_length=128 ,			return_tensors='pt'	)
				return tokenizer.pad(__A ,			padding='longest' ,			return_tensors='pt'	)

		# Instantiate dataloaders.
		A					       = DataLoader(tokenized_datasets['train'] ,			shuffle=__A ,			collate_fn=__A ,			batch_size=2	)
		A					       = DataLoader(tokenized_datasets['validation'] ,			shuffle=__A ,			collate_fn=__A ,			batch_size=1	)

		return train_dataloader, eval_dataloader









"""simple docstring"""


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
	import tensorflow as tf


UpperCamelCase						:				Optional[Any]             =      logging.get_logger(__name__)



@dataclass
class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     [
						    """no_inference""",
						    """no_cuda""",
						    """no_tpu""",
						    """no_speed""",
						    """no_memory""",
						    """no_env_print""",
						    """no_multi_process""",
						]







						def __init__(							self   :    str			,			**_lowercase   :    Union[str, Any]		):
								for deprecated_arg in self.deprecated_args:
										if deprecated_arg in kwargs:
												A					       = deprecated_arg[3:]
												A					       = not kwargs.pop(_lowercase		)
												logger.warning(
												    f'{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'
												    f' {positive_arg}={kwargs[positive_arg]}'		)
								A					       = kwargs.pop('tpu_name'			,			self.tpu_name		)
								A					       = kwargs.pop('device_idx'			,			self.device_idx		)
								A					       = kwargs.pop('eager_mode'			,			self.eager_mode		)
								A					       = kwargs.pop('use_xla'			,			self.use_xla		)
								super().__init__(**_lowercase		)

						lowerCAmelCase       =     field(
						    default=UpperCAmelCase_							,    metadata={"""help""": """Name of TPU"""}							,    )
						lowerCAmelCase       =     field(
						    default=0							,    metadata={"""help""": """CPU / GPU device index. Defaults to 0."""}							,    )
						lowerCAmelCase       =     field(default=UpperCAmelCase_							,    metadata={"""help""": """Benchmark models in eager model."""}			)
						lowerCAmelCase       =     field(
						    default=UpperCAmelCase_							,    metadata={
						        """help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
						    }							,    )







						@cached_property
						def __a						(							self   :    Optional[Any]		):
								requires_backends(self			,			['tf']		)
								A					       = None
								if self.tpu:
										try:
												if self.tpu_name:
														A					       = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name		)
												else:
														A					       = tf.distribute.cluster_resolver.TPUClusterResolver()
										except ValueError:
												A					       = None
								return tpu







						@cached_property
						def __a						(							self   :    Dict		):
								requires_backends(self			,			['tf']		)
								if self.is_tpu:
										tf.config.experimental_connect_to_cluster(self._setup_tpu		)
										tf.tpu.experimental.initialize_tpu_system(self._setup_tpu		)

										A					       = tf.distribute.TPUStrategy(self._setup_tpu		)
								else:
										# currently no multi gpu is allowed
										if self.is_gpu:
												# TODO: Currently only single GPU is supported
												tf.config.set_visible_devices(self.gpu_list[self.device_idx]			,			'GPU'		)
												A					       = tf.distribute.OneDeviceStrategy(device=f'/gpu:{self.device_idx}'		)
										else:
												tf.config.set_visible_devices([]			,			'GPU'		)  # disable GPU
												A					       = tf.distribute.OneDeviceStrategy(device=f'/cpu:{self.device_idx}'		)

								return strategy







						@property
						def __a						(							self   :    List[Any]		):
								requires_backends(self			,			['tf']		)
								return self._setup_tpu is not None







						@property
						def __a						(							self   :    Optional[Any]		):
								requires_backends(self			,			['tf']		)
								return self._setup_strategy







						@property
						def __a						(							self   :    str		):
								requires_backends(self			,			['tf']		)
								return tf.config.list_physical_devices('GPU'		)







						@property
						def __a						(							self   :    Any		):
								requires_backends(self			,			['tf']		)
								if self.cuda:
										return len(self.gpu_list		)
								return 0







						@property
						def __a						(							self   :    Dict		):
								return self.n_gpu > 0





