

"""simple docstring"""



from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
 from transformers import (
     TFAutoModel,
     TFAutoModelForCausalLM,
     TFAutoModelForMaskedLM,
     TFAutoModelForPreTraining,
     TFAutoModelForQuestionAnswering,
     TFAutoModelForSeqaSeqLM,
     TFAutoModelForSequenceClassification,
     TFAutoModelForTableQuestionAnswering,
     TFAutoModelForTokenClassification,
     TFAutoModelWithLMHead,
     TFBertForMaskedLM,
     TFBertForPreTraining,
     TFBertForQuestionAnswering,
     TFBertForSequenceClassification,
     TFBertModel,
     TFFunnelBaseModel,
     TFFunnelModel,
     TFGPTaLMHeadModel,
     TFRobertaForMaskedLM,
     TFTaForConditionalGeneration,
     TFTapasForQuestionAnswering,
 )
 from transformers.models.auto.modeling_tf_auto import (
     TF_MODEL_FOR_CAUSAL_LM_MAPPING,
     TF_MODEL_FOR_MASKED_LM_MAPPING,
     TF_MODEL_FOR_PRETRAINING_MAPPING,
     TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
     TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
     TF_MODEL_MAPPING,
 )
 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
 from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST



class 					a						(       a__				):



    """simple docstring"""



    UpperCAmelCase						 =					"new-model"


if is_tf_available():



 class 					a						(       a__				):



     """simple docstring"""



     UpperCAmelCase						 =					NewModelConfig



@require_tf
class 					a						(       unittest.TestCase				):



    """simple docstring"""



    @slow
    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         A__										=  'bert-base-cased'
         A__										=  AutoConfig.from_pretrained(_A       )
         self.assertIsNotNone(_A       )
         self.assertIsInstance(_A    ,						_A       )

         A__										=  TFAutoModel.from_pretrained(_A       )
         self.assertIsNotNone(_A       )
         self.assertIsInstance(_A    ,						_A       )

    @slow
    def 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


         A__										=  'bert-base-cased'
         A__										=  AutoConfig.from_pretrained(_A       )
         self.assertIsNotNone(_A       )
         self.assertIsInstance(_A    ,						_A       )

         A__										=  TFAutoModelForPreTraining.from_pretrained(_A       )
         self.assertIsNotNone(_A       )
         self.assertIsInstance(_A    ,						_A       )

    @slow
    def 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


         for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              A__										=  AutoConfig.from_pretrained(_A       )
              self.assertIsNotNone(_A       )
              self.assertIsInstance(_A    ,						_A       )

              A__										=  TFAutoModelForCausalLM.from_pretrained(_A       )
              A__										=  TFAutoModelForCausalLM.from_pretrained(_A    ,						output_loading_info=_A       )
              self.assertIsNotNone(_A       )
              self.assertIsInstance(_A    ,						_A       )

    @slow
    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              A__										=  AutoConfig.from_pretrained(_A       )
              self.assertIsNotNone(_A       )
              self.assertIsInstance(_A    ,						_A       )

              A__										=  TFAutoModelWithLMHead.from_pretrained(_A       )
              self.assertIsNotNone(_A       )
              self.assertIsInstance(_A    ,						_A       )

    @slow
    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              A__										=  AutoConfig.from_pretrained(_A       )
              self.assertIsNotNone(_A       )
              self.assertIsInstance(_A    ,						_A       )

              A__										=  TFAutoModelForMaskedLM.from_pretrained(_A       )
              A__										=  TFAutoModelForMaskedLM.from_pretrained(_A    ,						output_loading_info=_A       )
              self.assertIsNotNone(_A       )
              self.assertIsInstance(_A    ,						_A       )

    @slow
    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              A__										=  AutoConfig.from_pretrained(_A       )
              self.assertIsNotNone(_A       )
              self.assertIsInstance(_A    ,						_A       )

              A__										=  TFAutoModelForSeqaSeqLM.from_pretrained(_A       )
              A__										=  TFAutoModelForSeqaSeqLM.from_pretrained(_A    ,						output_loading_info=_A       )
              self.assertIsNotNone(_A       )
              self.assertIsInstance(_A    ,						_A       )

    @slow
    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


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

              A__										=  TFAutoModelForSequenceClassification.from_pretrained(_A       )
              self.assertIsNotNone(_A       )
              self.assertIsInstance(_A    ,						_A       )

    @slow
    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


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

              A__										=  TFAutoModelForQuestionAnswering.from_pretrained(_A       )
              self.assertIsNotNone(_A       )
              self.assertIsInstance(_A    ,						_A       )

    @slow
    @require_tensorflow_probability
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
              A__										=  AutoConfig.from_pretrained(_A       )
              self.assertIsNotNone(_A       )
              self.assertIsInstance(_A    ,						_A       )

              A__										=  TFAutoModelForTableQuestionAnswering.from_pretrained(_A       )
              A__										=  TFAutoModelForTableQuestionAnswering.from_pretrained(
                  _A    ,						output_loading_info=_A       )
              self.assertIsNotNone(_A       )
              self.assertIsInstance(_A    ,						_A       )

    def 				UpperCamelCase	(     self: Tuple       ):






         """simple docstring"""


         A__										=  TFAutoModelWithLMHead.from_pretrained(_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 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


         A__										=  TFAutoModelWithLMHead.from_pretrained(_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 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         A__										=  TFAutoModel.from_pretrained("""sgugger/funnel-random-tiny"""       )
         self.assertIsInstance(_A    ,						_A       )

         A__										=  copy.deepcopy(model.config       )
         A__										=  ['FunnelBaseModel']
         A__										=  TFAutoModel.from_config(_A       )
         self.assertIsInstance(_A    ,						_A       )

         with tempfile.TemporaryDirectory() as tmp_dir:
              model.save_pretrained(_A       )
              A__										=  TFAutoModel.from_pretrained(_A       )
              self.assertIsInstance(_A    ,						_A       )

    def 				UpperCamelCase	(     self: Tuple       ):






         """simple docstring"""


         try:
              AutoConfig.register("""new-model"""    ,						_A       )

              A__										=  [
                  TFAutoModel,
                  TFAutoModelForCausalLM,
                  TFAutoModelForMaskedLM,
                  TFAutoModelForPreTraining,
                  TFAutoModelForQuestionAnswering,
                  TFAutoModelForSequenceClassification,
                  TFAutoModelForTokenClassification,
              ]

              for auto_class in auto_classes:
                   with self.subTest(auto_class.__name__       ):
                        # Wrong config class will raise an error
                        with self.assertRaises(_A       ):
                             auto_class.register(_A    ,						_A       )
                        auto_class.register(_A    ,						_A       )
                        # Trying to register something existing in the Transformers library will raise an error
                        with self.assertRaises(_A       ):
                             auto_class.register(_A    ,						_A       )

                        # Now that the config is registered, it can be used as any other config with the auto-API
                        A__										=  BertModelTester(self       ).get_config()
                        A__										=  NewModelConfig(**tiny_config.to_dict()       )
                        A__										=  auto_class.from_config(_A       )
                        self.assertIsInstance(_A    ,						_A       )

                        with tempfile.TemporaryDirectory() as tmp_dir:
                             model.save_pretrained(_A       )
                             A__										=  auto_class.from_pretrained(_A       )
                             self.assertIsInstance(_A    ,						_A       )

         finally:
              if "new-model" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["new-model"]
              for mapping in (
                  TF_MODEL_MAPPING,
                  TF_MODEL_FOR_PRETRAINING_MAPPING,
                  TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
                  TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
                  TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
                  TF_MODEL_FOR_CAUSAL_LM_MAPPING,
                  TF_MODEL_FOR_MASKED_LM_MAPPING,
              ):
                   if NewModelConfig in mapping._extra_content:
                        del mapping._extra_content[NewModelConfig]

    def 				UpperCamelCase	(     self: Tuple       ):






         """simple docstring"""


         with self.assertRaisesRegex(
             _A    ,						"""bert-base is not a local folder and is not a valid model identifier"""       ):
              A__										=  TFAutoModel.from_pretrained("""bert-base"""       )

    def 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


         with self.assertRaisesRegex(
             _A    ,						r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""       ):
              A__										=  TFAutoModel.from_pretrained(_A    ,						revision="""aaaaaa"""       )

    def 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


         with self.assertRaisesRegex(
             _A    ,						"""hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin"""    ,						):
              A__										=  TFAutoModel.from_pretrained("""hf-internal-testing/config-no-model"""       )

    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         with self.assertRaisesRegex(_A    ,						"""Use `from_pt=True` to load this model"""       ):
              A__										=  TFAutoModel.from_pretrained("""hf-internal-testing/tiny-bert-pt-only"""       )





    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         A__										=  TFAutoModel.from_pretrained("""hf-internal-testing/tiny-random-bert"""       )
         with RequestCounter() as counter:
              A__										=  TFAutoModel.from_pretrained("""hf-internal-testing/tiny-random-bert"""       )
              self.assertEqual(counter.get_request_count    ,						0       )
              self.assertEqual(counter.head_request_count    ,						1       )
              self.assertEqual(counter.other_request_count    ,						0       )

         # With a sharded checkpoint
         A__										=  TFAutoModel.from_pretrained("""ArthurZ/tiny-random-bert-sharded"""       )
         with RequestCounter() as counter:
              A__										=  TFAutoModel.from_pretrained("""ArthurZ/tiny-random-bert-sharded"""       )
              self.assertEqual(counter.get_request_count    ,						0       )
              self.assertEqual(counter.head_request_count    ,						1       )
              self.assertEqual(counter.other_request_count    ,						0       )








"""simple docstring"""



def  _snake_case     (    UpperCAmelCase_  :					list			):
     if len(UpperCAmelCase_			) <= 1:
          return lst

     A__										=  1

     while i < len(UpperCAmelCase_			):
          if lst[i - 1] <= lst[i]:
               i += 1
          else:
               A__			,				A__										=  lst[i], lst[i - 1]
               i -= 1
               if i == 0:
                    A__										=  1

     return lst


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						input('Enter numbers separated by a comma:\n').strip()
 SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						[int(item) for item in user_input.split(',')]
 print(gnome_sort(unsorted))






