






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

from accelerate.utils.dataclasses import DistributedType




class     _lowerCAmelCase:





       """simple docstring"""







       def __init__( self  ,      _lowerCamelCase=2  ,      _lowerCamelCase=3  ,      _lowerCamelCase=6_4  ,      _lowerCamelCase=None							):
         UpperCamelCase_: Tuple   				=    np.random.default_rng(_lowerCamelCase							)
         UpperCamelCase_: Optional[int]   				=    length
         UpperCamelCase_: Optional[int]   				=    rng.normal(size=(length,)							).astype(np.floataa							)
         UpperCamelCase_: List[str]   				=    a * self.x + b + rng.normal(scale=0.1  ,      size=(length,)							).astype(np.floataa							)





       def __len__( self							):
         return self.length





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







class     _lowerCAmelCase(	torch.nn.Module ):





       """simple docstring"""







       def __init__( self  ,      _lowerCamelCase=0  ,      _lowerCamelCase=0  ,      _lowerCamelCase=False							):
         super().__init__()
         UpperCamelCase_: str   				=    torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
         UpperCamelCase_: Union[str, Any]   				=    torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
         UpperCamelCase_: List[Any]   				=    True





       def       _a   ( self  ,      _lowerCamelCase=None							):
         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     _lowerCAmelCase(	torch.nn.Module ):





       """simple docstring"""







       def __init__( self  ,      _lowerCamelCase=0  ,      _lowerCamelCase=0  ,      _lowerCamelCase=False							):
         super().__init__()
         UpperCamelCase_: Any   				=    torch.nn.Parameter(torch.tensor(_lowerCamelCase							).float()							)
         UpperCamelCase_: Union[str, Any]   				=    torch.nn.Parameter(torch.tensor(_lowerCamelCase							).float()							)
         UpperCamelCase_: Any   				=    True





       def       _a   ( self  ,      _lowerCamelCase=None							):
         if self.first_batch:
           print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''							)
           UpperCamelCase_: List[Any]   				=    False
         return x * self.a + self.b







def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__ = 1_6  )	->    Optional[Any]:
  from datasets import load_dataset
  from transformers import AutoTokenizer

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

  UpperCamelCase_: Any   				=    {v: i for i, v in enumerate(UpperCAmelCase__  )}

  def tokenize_function(UpperCAmelCase__  ):
    # max_length=None => use the model max length (it's actually the default)
    UpperCamelCase_: Dict   				=    tokenizer(
        examples['sentence1']     ,   examples['sentence2']     ,   truncation=UpperCAmelCase__     ,   max_length=UpperCAmelCase__     ,   padding='max_length'  )
    if "label" in examples:
      UpperCamelCase_: Any   				=    [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_: int   				=    datasets.map(
      UpperCAmelCase__     ,   batched=UpperCAmelCase__     ,   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(UpperCAmelCase__     ,   padding='max_length'     ,   max_length=1_2_8     ,   return_tensors='pt'  )
    return tokenizer.pad(UpperCAmelCase__     ,   padding='longest'     ,   return_tensors='pt'  )

  # Instantiate dataloaders.
  UpperCamelCase_: int   				=    DataLoader(tokenized_datasets['train']     ,   shuffle=UpperCAmelCase__     ,   collate_fn=UpperCAmelCase__     ,   batch_size=2  )
  UpperCamelCase_: Optional[Any]   				=    DataLoader(tokenized_datasets['validation']     ,   shuffle=UpperCAmelCase__     ,   collate_fn=UpperCAmelCase__     ,   batch_size=1  )

  return train_dataloader, eval_dataloader







# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


A_		:		Any		     =			'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'

def   snake_case ()	->    Union[str, Any]:
  UpperCamelCase_: Tuple   				=    _ask_options(
      'In which compute environment are you running?'     ,   ['This machine', 'AWS (Amazon SageMaker)']     ,   _convert_compute_environment     ,   )
  if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
    UpperCamelCase_: List[str]   				=    get_sagemaker_input()
  else:
    UpperCamelCase_: List[str]   				=    get_cluster_input()
  return config

def   snake_case (UpperCAmelCase__=None  )	->    Union[str, Any]:
  if subparsers is not None:
    UpperCamelCase_: List[Any]   				=    subparsers.add_parser('config'     ,   description=UpperCAmelCase__  )
  else:
    UpperCamelCase_: List[Any]   				=    argparse.ArgumentParser('Accelerate config command'     ,   description=UpperCAmelCase__  )

  parser.add_argument(
      '--config_file'     ,   default=UpperCAmelCase__     ,   help=(
          'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
          'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
          'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
          'with \'huggingface\'.'
      )     ,   )

  if subparsers is not None:
    parser.set_defaults(func=UpperCAmelCase__  )
  return parser

def   snake_case (UpperCAmelCase__  )	->    List[Any]:
  UpperCamelCase_: Union[str, Any]   				=    get_user_input()
  if args.config_file is not None:
    UpperCamelCase_: Tuple   				=    args.config_file
  else:
    if not os.path.isdir(UpperCAmelCase__  ):
      os.makedirs(UpperCAmelCase__  )
    UpperCamelCase_: Dict   				=    default_yaml_config_file

  if config_file.endswith('.json'  ):
    config.to_json_file(UpperCAmelCase__  )
  else:
    config.to_yaml_file(UpperCAmelCase__  )
  print(F'''accelerate configuration saved at {config_file}'''  )

def   snake_case ()	->    str:
  UpperCamelCase_: Tuple   				=    config_command_parser()
  UpperCamelCase_: int   				=    parser.parse_args()
  config_command(UpperCAmelCase__  )


if __name__ == "__main__":
   main()