'''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    a (       unittest.TestCase				):



       """simple docstring"""







       @slow
       def    lowercase_  (							self					):

         '''simple docstring'''







         for model_name in ["bert-base-uncased"]:
           __UpperCAmelCase:					Optional[int]		= AutoConfig.from_pretrained(snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)

           __UpperCAmelCase:					Optional[Any]		= TFAutoModel.from_pretrained(snake_case_   ,    from_pt=snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)

           __UpperCAmelCase:					Tuple		= AutoModel.from_pretrained(snake_case_   ,    from_tf=snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)







       @slow
       def    lowercase_  (							self					):

         '''simple docstring'''







         for model_name in ["bert-base-uncased"]:
           __UpperCAmelCase:					Tuple		= AutoConfig.from_pretrained(snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)

           __UpperCAmelCase:					Optional[Any]		= TFAutoModelForPreTraining.from_pretrained(snake_case_   ,    from_pt=snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)

           __UpperCAmelCase:					Dict		= AutoModelForPreTraining.from_pretrained(snake_case_   ,    from_tf=snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)







       @slow
       def    lowercase_  (							self					):

         '''simple docstring'''







         for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __UpperCAmelCase:					Dict		= AutoConfig.from_pretrained(snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)

           __UpperCAmelCase:					Tuple		= TFAutoModelForCausalLM.from_pretrained(snake_case_   ,    from_pt=snake_case_					)
           __UpperCAmelCase:					int		= TFAutoModelForCausalLM.from_pretrained(
               snake_case_   ,    output_loading_info=snake_case_   ,    from_pt=snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)

           __UpperCAmelCase:					Union[str, Any]		= AutoModelForCausalLM.from_pretrained(snake_case_   ,    from_tf=snake_case_					)
           __UpperCAmelCase:					List[Any]		= AutoModelForCausalLM.from_pretrained(
               snake_case_   ,    output_loading_info=snake_case_   ,    from_tf=snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)







       @slow
       def    lowercase_  (							self					):

         '''simple docstring'''







         for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __UpperCAmelCase:					Dict		= AutoConfig.from_pretrained(snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)

           __UpperCAmelCase:					Optional[Any]		= TFAutoModelWithLMHead.from_pretrained(snake_case_   ,    from_pt=snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)

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







       @slow
       def    lowercase_  (							self					):

         '''simple docstring'''







         for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __UpperCAmelCase:					str		= AutoConfig.from_pretrained(snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)

           __UpperCAmelCase:					int		= TFAutoModelForMaskedLM.from_pretrained(snake_case_   ,    from_pt=snake_case_					)
           __UpperCAmelCase:					List[Any]		= TFAutoModelForMaskedLM.from_pretrained(
               snake_case_   ,    output_loading_info=snake_case_   ,    from_pt=snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)

           __UpperCAmelCase:					List[Any]		= AutoModelForMaskedLM.from_pretrained(snake_case_   ,    from_tf=snake_case_					)
           __UpperCAmelCase:					Optional[Any]		= AutoModelForMaskedLM.from_pretrained(
               snake_case_   ,    output_loading_info=snake_case_   ,    from_tf=snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)







       @slow
       def    lowercase_  (							self					):

         '''simple docstring'''







         for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __UpperCAmelCase:					Tuple		= AutoConfig.from_pretrained(snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)

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

           __UpperCAmelCase:					int		= AutoModelForSeqaSeqLM.from_pretrained(snake_case_   ,    from_tf=snake_case_					)
           __UpperCAmelCase:					Dict		= AutoModelForSeqaSeqLM.from_pretrained(
               snake_case_   ,    output_loading_info=snake_case_   ,    from_tf=snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)







       @slow
       def    lowercase_  (							self					):

         '''simple docstring'''







         for model_name in ["bert-base-uncased"]:
           __UpperCAmelCase:					List[Any]		= AutoConfig.from_pretrained(snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)

           __UpperCAmelCase:					Tuple		= TFAutoModelForSequenceClassification.from_pretrained(snake_case_   ,    from_pt=snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)

           __UpperCAmelCase:					Tuple		= AutoModelForSequenceClassification.from_pretrained(snake_case_   ,    from_tf=snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)







       @slow
       def    lowercase_  (							self					):

         '''simple docstring'''







         for model_name in ["bert-base-uncased"]:
           __UpperCAmelCase:					Optional[int]		= AutoConfig.from_pretrained(snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)

           __UpperCAmelCase:					Tuple		= TFAutoModelForQuestionAnswering.from_pretrained(snake_case_   ,    from_pt=snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)

           __UpperCAmelCase:					Any		= AutoModelForQuestionAnswering.from_pretrained(snake_case_   ,    from_tf=snake_case_					)
           self.assertIsNotNone(snake_case_					)
           self.assertIsInstance(snake_case_   ,    snake_case_					)







       def    lowercase_  (							self					):

         '''simple docstring'''







         __UpperCAmelCase:					str		= TFAutoModelWithLMHead.from_pretrained(snake_case_   ,    from_pt=snake_case_					)
         self.assertIsInstance(snake_case_   ,    snake_case_					)
         self.assertEqual(model.num_parameters()   ,    1_4410					)
         self.assertEqual(model.num_parameters(only_trainable=snake_case_					)   ,    1_4410					)

         __UpperCAmelCase:					Tuple		= AutoModelWithLMHead.from_pretrained(snake_case_   ,    from_tf=snake_case_					)
         self.assertIsInstance(snake_case_   ,    snake_case_					)
         self.assertEqual(model.num_parameters()   ,    1_4410					)
         self.assertEqual(model.num_parameters(only_trainable=snake_case_					)   ,    1_4410					)







       def    lowercase_  (							self					):

         '''simple docstring'''







         __UpperCAmelCase:					str		= TFAutoModelWithLMHead.from_pretrained(snake_case_   ,    from_pt=snake_case_					)
         self.assertIsInstance(snake_case_   ,    snake_case_					)
         self.assertEqual(model.num_parameters()   ,    1_4410					)
         self.assertEqual(model.num_parameters(only_trainable=snake_case_					)   ,    1_4410					)

         __UpperCAmelCase:					Dict		= AutoModelWithLMHead.from_pretrained(snake_case_   ,    from_tf=snake_case_					)
         self.assertIsInstance(snake_case_   ,    snake_case_					)
         self.assertEqual(model.num_parameters()   ,    1_4410					)
         self.assertEqual(model.num_parameters(only_trainable=snake_case_					)   ,    1_4410					)
'''simple docstring'''






def  UpperCamelCase__					(					_lowercase    :				list[int]   ,    _lowercase    :				list[int]     )							->	None:
  __UpperCAmelCase:					Dict		= len(_lowercase     )
  print("""The following activities are selected:"""     )

  # The first activity is always selected
  __UpperCAmelCase:					Dict		= 0
  print(_lowercase   ,    end=""","""     )

  # Consider rest of the activities
  for j in range(_lowercase     ):
    # If this activity has start time greater than
    # or equal to the finish time of previously
    # selected activity, then select it
    if start[j] >= finish[i]:
      print(_lowercase   ,    end=""","""     )
      __UpperCAmelCase:					Optional[int]		= j


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    SCREAMING_SNAKE_CASE_	   =			[1, 3, 0, 5, 8, 5]
    SCREAMING_SNAKE_CASE_	   =			[2, 4, 6, 7, 9, 9]
    print_max_activities(start, finish)