






"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
   from transformers import (
       AutoConfig,
       BertConfig,
       GPTaConfig,
       TaConfig,
       TFAutoModel,
       TFAutoModelForCausalLM,
       TFAutoModelForMaskedLM,
       TFAutoModelForPreTraining,
       TFAutoModelForQuestionAnswering,
       TFAutoModelForSeqaSeqLM,
       TFAutoModelForSequenceClassification,
       TFAutoModelWithLMHead,
       TFBertForMaskedLM,
       TFBertForPreTraining,
       TFBertForQuestionAnswering,
       TFBertForSequenceClassification,
       TFBertModel,
       TFGPTaLMHeadModel,
       TFRobertaForMaskedLM,
       TFTaForConditionalGeneration,
   )
   from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
   from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
   from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
   from transformers import (
       AutoModel,
       AutoModelForCausalLM,
       AutoModelForMaskedLM,
       AutoModelForPreTraining,
       AutoModelForQuestionAnswering,
       AutoModelForSeqaSeqLM,
       AutoModelForSequenceClassification,
       AutoModelWithLMHead,
       BertForMaskedLM,
       BertForPreTraining,
       BertForQuestionAnswering,
       BertForSequenceClassification,
       BertModel,
       GPTaLMHeadModel,
       RobertaForMaskedLM,
       TaForConditionalGeneration,
   )




@is_pt_tf_cross_test
class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       @slow
       def       _a   ( self							):
         # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         for model_name in ["bert-base-uncased"]:
           UpperCamelCase_: Optional[int]   				=    AutoConfig.from_pretrained(a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)

           UpperCamelCase_: Dict   				=    TFAutoModel.from_pretrained(a__  ,      from_pt=a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)

           UpperCamelCase_: List[Any]   				=    AutoModel.from_pretrained(a__  ,      from_tf=a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)





       @slow
       def       _a   ( self							):
         # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         for model_name in ["bert-base-uncased"]:
           UpperCamelCase_: str   				=    AutoConfig.from_pretrained(a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)

           UpperCamelCase_: Dict   				=    TFAutoModelForPreTraining.from_pretrained(a__  ,      from_pt=a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)

           UpperCamelCase_: int   				=    AutoModelForPreTraining.from_pretrained(a__  ,      from_tf=a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)





       @slow
       def       _a   ( self							):
         for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase_: str   				=    AutoConfig.from_pretrained(a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)

           UpperCamelCase_: Any   				=    TFAutoModelForCausalLM.from_pretrained(a__  ,      from_pt=a__							)
           UpperCamelCase_				,UpperCamelCase_: List[Any]   				=    TFAutoModelForCausalLM.from_pretrained(
               a__  ,      output_loading_info=a__  ,      from_pt=a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)

           UpperCamelCase_: Union[str, Any]   				=    AutoModelForCausalLM.from_pretrained(a__  ,      from_tf=a__							)
           UpperCamelCase_				,UpperCamelCase_: Union[str, Any]   				=    AutoModelForCausalLM.from_pretrained(
               a__  ,      output_loading_info=a__  ,      from_tf=a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)





       @slow
       def       _a   ( self							):
         for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase_: str   				=    AutoConfig.from_pretrained(a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)

           UpperCamelCase_: int   				=    TFAutoModelWithLMHead.from_pretrained(a__  ,      from_pt=a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)

           UpperCamelCase_: Optional[int]   				=    AutoModelWithLMHead.from_pretrained(a__  ,      from_tf=a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)





       @slow
       def       _a   ( self							):
         for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase_: List[Any]   				=    AutoConfig.from_pretrained(a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)

           UpperCamelCase_: Optional[Any]   				=    TFAutoModelForMaskedLM.from_pretrained(a__  ,      from_pt=a__							)
           UpperCamelCase_				,UpperCamelCase_: Dict   				=    TFAutoModelForMaskedLM.from_pretrained(
               a__  ,      output_loading_info=a__  ,      from_pt=a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)

           UpperCamelCase_: Any   				=    AutoModelForMaskedLM.from_pretrained(a__  ,      from_tf=a__							)
           UpperCamelCase_				,UpperCamelCase_: str   				=    AutoModelForMaskedLM.from_pretrained(
               a__  ,      output_loading_info=a__  ,      from_tf=a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)





       @slow
       def       _a   ( self							):
         for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase_: List[Any]   				=    AutoConfig.from_pretrained(a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)

           UpperCamelCase_: str   				=    TFAutoModelForSeqaSeqLM.from_pretrained(a__  ,      from_pt=a__							)
           UpperCamelCase_				,UpperCamelCase_: List[str]   				=    TFAutoModelForSeqaSeqLM.from_pretrained(
               a__  ,      output_loading_info=a__  ,      from_pt=a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)

           UpperCamelCase_: Tuple   				=    AutoModelForSeqaSeqLM.from_pretrained(a__  ,      from_tf=a__							)
           UpperCamelCase_				,UpperCamelCase_: Optional[Any]   				=    AutoModelForSeqaSeqLM.from_pretrained(
               a__  ,      output_loading_info=a__  ,      from_tf=a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)





       @slow
       def       _a   ( self							):
         # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         for model_name in ["bert-base-uncased"]:
           UpperCamelCase_: str   				=    AutoConfig.from_pretrained(a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)

           UpperCamelCase_: int   				=    TFAutoModelForSequenceClassification.from_pretrained(a__  ,      from_pt=a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)

           UpperCamelCase_: Optional[int]   				=    AutoModelForSequenceClassification.from_pretrained(a__  ,      from_tf=a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)





       @slow
       def       _a   ( self							):
         # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         for model_name in ["bert-base-uncased"]:
           UpperCamelCase_: List[Any]   				=    AutoConfig.from_pretrained(a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)

           UpperCamelCase_: List[Any]   				=    TFAutoModelForQuestionAnswering.from_pretrained(a__  ,      from_pt=a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)

           UpperCamelCase_: str   				=    AutoModelForQuestionAnswering.from_pretrained(a__  ,      from_tf=a__							)
           self.assertIsNotNone(a__							)
           self.assertIsInstance(a__  ,      a__							)





       def       _a   ( self							):
         UpperCamelCase_: Dict   				=    TFAutoModelWithLMHead.from_pretrained(a__  ,      from_pt=a__							)
         self.assertIsInstance(a__  ,      a__							)
         self.assertEqual(model.num_parameters()  ,      1_4_4_1_0							)
         self.assertEqual(model.num_parameters(only_trainable=a__							)  ,      1_4_4_1_0							)

         UpperCamelCase_: Optional[Any]   				=    AutoModelWithLMHead.from_pretrained(a__  ,      from_tf=a__							)
         self.assertIsInstance(a__  ,      a__							)
         self.assertEqual(model.num_parameters()  ,      1_4_4_1_0							)
         self.assertEqual(model.num_parameters(only_trainable=a__							)  ,      1_4_4_1_0							)





       def       _a   ( self							):
         UpperCamelCase_: str   				=    TFAutoModelWithLMHead.from_pretrained(a__  ,      from_pt=a__							)
         self.assertIsInstance(a__  ,      a__							)
         self.assertEqual(model.num_parameters()  ,      1_4_4_1_0							)
         self.assertEqual(model.num_parameters(only_trainable=a__							)  ,      1_4_4_1_0							)

         UpperCamelCase_: Any   				=    AutoModelWithLMHead.from_pretrained(a__  ,      from_tf=a__							)
         self.assertIsInstance(a__  ,      a__							)
         self.assertEqual(model.num_parameters()  ,      1_4_4_1_0							)
         self.assertEqual(model.num_parameters(only_trainable=a__							)  ,      1_4_4_1_0							)







# 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()