


'''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 					lowercase_     (    unittest.TestCase   ):


    @slow
    def    __a							(     self    ):
          # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          for model_name in ["bert-base-uncased"]:
                UpperCamelCase__										=				AutoConfig.from_pretrained(a    )
                self.assertIsNotNone(a    )
                self.assertIsInstance(a  ,					a    )

                UpperCamelCase__										=				TFAutoModel.from_pretrained(a  ,					from_pt=a    )
                self.assertIsNotNone(a    )
                self.assertIsInstance(a  ,					a    )

                UpperCamelCase__										=				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__										=				AutoConfig.from_pretrained(a    )
                self.assertIsNotNone(a    )
                self.assertIsInstance(a  ,					a    )

                UpperCamelCase__										=				TFAutoModelForPreTraining.from_pretrained(a  ,					from_pt=a    )
                self.assertIsNotNone(a    )
                self.assertIsInstance(a  ,					a    )

                UpperCamelCase__										=				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__										=				AutoConfig.from_pretrained(a    )
                self.assertIsNotNone(a    )
                self.assertIsInstance(a  ,					a    )

                UpperCamelCase__										=				TFAutoModelForCausalLM.from_pretrained(a  ,					from_pt=a    )
                UpperCamelCase__					,	UpperCamelCase__										=				TFAutoModelForCausalLM.from_pretrained(
                    a  ,					output_loading_info=a  ,					from_pt=a    )
                self.assertIsNotNone(a    )
                self.assertIsInstance(a  ,					a    )

                UpperCamelCase__										=				AutoModelForCausalLM.from_pretrained(a  ,					from_tf=a    )
                UpperCamelCase__					,	UpperCamelCase__										=				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__										=				AutoConfig.from_pretrained(a    )
                self.assertIsNotNone(a    )
                self.assertIsInstance(a  ,					a    )

                UpperCamelCase__										=				TFAutoModelWithLMHead.from_pretrained(a  ,					from_pt=a    )
                self.assertIsNotNone(a    )
                self.assertIsInstance(a  ,					a    )

                UpperCamelCase__										=				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__										=				AutoConfig.from_pretrained(a    )
                self.assertIsNotNone(a    )
                self.assertIsInstance(a  ,					a    )

                UpperCamelCase__										=				TFAutoModelForMaskedLM.from_pretrained(a  ,					from_pt=a    )
                UpperCamelCase__					,	UpperCamelCase__										=				TFAutoModelForMaskedLM.from_pretrained(
                    a  ,					output_loading_info=a  ,					from_pt=a    )
                self.assertIsNotNone(a    )
                self.assertIsInstance(a  ,					a    )

                UpperCamelCase__										=				AutoModelForMaskedLM.from_pretrained(a  ,					from_tf=a    )
                UpperCamelCase__					,	UpperCamelCase__										=				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__										=				AutoConfig.from_pretrained(a    )
                self.assertIsNotNone(a    )
                self.assertIsInstance(a  ,					a    )

                UpperCamelCase__										=				TFAutoModelForSeqaSeqLM.from_pretrained(a  ,					from_pt=a    )
                UpperCamelCase__					,	UpperCamelCase__										=				TFAutoModelForSeqaSeqLM.from_pretrained(
                    a  ,					output_loading_info=a  ,					from_pt=a    )
                self.assertIsNotNone(a    )
                self.assertIsInstance(a  ,					a    )

                UpperCamelCase__										=				AutoModelForSeqaSeqLM.from_pretrained(a  ,					from_tf=a    )
                UpperCamelCase__					,	UpperCamelCase__										=				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__										=				AutoConfig.from_pretrained(a    )
                self.assertIsNotNone(a    )
                self.assertIsInstance(a  ,					a    )

                UpperCamelCase__										=				TFAutoModelForSequenceClassification.from_pretrained(a  ,					from_pt=a    )
                self.assertIsNotNone(a    )
                self.assertIsInstance(a  ,					a    )

                UpperCamelCase__										=				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__										=				AutoConfig.from_pretrained(a    )
                self.assertIsNotNone(a    )
                self.assertIsInstance(a  ,					a    )

                UpperCamelCase__										=				TFAutoModelForQuestionAnswering.from_pretrained(a  ,					from_pt=a    )
                self.assertIsNotNone(a    )
                self.assertIsInstance(a  ,					a    )

                UpperCamelCase__										=				AutoModelForQuestionAnswering.from_pretrained(a  ,					from_tf=a    )
                self.assertIsNotNone(a    )
                self.assertIsInstance(a  ,					a    )


    def    __a							(     self    ):
          UpperCamelCase__										=				TFAutoModelWithLMHead.from_pretrained(a  ,					from_pt=a    )
          self.assertIsInstance(a  ,					a    )
          self.assertEqual(model.num_parameters()  ,					1_44_10    )
          self.assertEqual(model.num_parameters(only_trainable=a    )  ,					1_44_10    )

          UpperCamelCase__										=				AutoModelWithLMHead.from_pretrained(a  ,					from_tf=a    )
          self.assertIsInstance(a  ,					a    )
          self.assertEqual(model.num_parameters()  ,					1_44_10    )
          self.assertEqual(model.num_parameters(only_trainable=a    )  ,					1_44_10    )


    def    __a							(     self    ):
          UpperCamelCase__										=				TFAutoModelWithLMHead.from_pretrained(a  ,					from_pt=a    )
          self.assertIsInstance(a  ,					a    )
          self.assertEqual(model.num_parameters()  ,					1_44_10    )
          self.assertEqual(model.num_parameters(only_trainable=a    )  ,					1_44_10    )

          UpperCamelCase__										=				AutoModelWithLMHead.from_pretrained(a  ,					from_tf=a    )
          self.assertIsInstance(a  ,					a    )
          self.assertEqual(model.num_parameters()  ,					1_44_10    )
          self.assertEqual(model.num_parameters(only_trainable=a    )  ,					1_44_10    )








"""simple docstring"""





# 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.test_utils import execute_subprocess_async

def   __SCREAMING_SNAKE_CASE  (      A_=None ):
	if subparsers is not None:
		lowerCAmelCase__	:						Optional[Any]  				=	subparsers.add_parser('''test''' )
	else:
		lowerCAmelCase__	:						List[str]  				=	argparse.ArgumentParser('''Accelerate test command''' )

	parser.add_argument(
	    '''--config_file'''						,     default=A_						,     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=A_ )
	return parser

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						Optional[int]  				=	os.path.sep.join(__file__.split(os.path.sep )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py'''] )

	if args.config_file is None:
		lowerCAmelCase__	:						Optional[Any]  				=	script_name
	else:
		lowerCAmelCase__	:						Any  				=	f'--config_file={args.config_file} {script_name}'

	lowerCAmelCase__	:						List[Any]  				=	['''accelerate-launch'''] + test_args.split()
	lowerCAmelCase__	:						int  				=	execute_subprocess_async(A_						,     env=os.environ.copy() )
	if result.returncode == 0:
		print('''Test is a success! You are ready for your distributed training!''' )

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						Any  				=	test_command_parser()
	lowerCAmelCase__	:						List[Any]  				=	parser.parse_args()
	test_command(A_ )


if __name__ == "__main__":
							main()




