






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
   import torch

   from transformers import XLMRobertaModel




@require_sentencepiece
@require_tokenizers
@require_torch
class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       @slow
       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    XLMRobertaModel.from_pretrained('xlm-roberta-base'							)
         UpperCamelCase_: int   				=    torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]							)
         # The dog is cute and lives in the garden house

         UpperCamelCase_: Union[str, Any]   				=    torch.Size((1, 1_2, 7_6_8)							)  # batch_size, sequence_length, embedding_vector_dim
         UpperCamelCase_: int   				=    torch.tensor(
             [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]							)
         #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
         #  xlmr.eval()
         #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
         with torch.no_grad():
           UpperCamelCase_: Tuple   				=    model(_lowerCamelCase							)['last_hidden_state'].detach()
         self.assertEqual(output.shape  ,      _lowerCamelCase							)
         # compare the actual values for a slice of last dim
         self.assertTrue(torch.allclose(output[:, :, -1]  ,      _lowerCamelCase  ,      atol=1e-3							)							)





       @slow
       def       _a   ( self							):
         UpperCamelCase_: List[str]   				=    XLMRobertaModel.from_pretrained('xlm-roberta-large'							)
         UpperCamelCase_: Any   				=    torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]							)
         # The dog is cute and lives in the garden house

         UpperCamelCase_: Optional[int]   				=    torch.Size((1, 1_2, 1_0_2_4)							)  # batch_size, sequence_length, embedding_vector_dim
         UpperCamelCase_: Tuple   				=    torch.tensor(
             [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]							)
         #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
         #  xlmr.eval()
         #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
         with torch.no_grad():
           UpperCamelCase_: Optional[int]   				=    model(_lowerCamelCase							)['last_hidden_state'].detach()
         self.assertEqual(output.shape  ,      _lowerCamelCase							)
         # compare the actual values for a slice of last dim
         self.assertTrue(torch.allclose(output[:, :, -1]  ,      _lowerCamelCase  ,      atol=1e-3							)							)







# 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()