





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 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					str						=			"""new-model"""


if is_tf_available():







     class 				__lowerCAmelCase       (    UpperCamelCase__):
           _lowercase				:					Optional[int]						=			NewModelConfig







@require_tf
class 				__lowerCAmelCase       (    unittest.TestCase):







      @slow
      def    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					str										="bert-base-cased"
        a__	:					Any										=AutoConfig.from_pretrained(lowerCAmelCase__							)
        self.assertIsNotNone(lowerCAmelCase__							)
        self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)

        a__	:					Optional[Any]										=TFAutoModel.from_pretrained(lowerCAmelCase__							)
        self.assertIsNotNone(lowerCAmelCase__							)
        self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)







      @slow
      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        a__	:					List[Any]										="bert-base-cased"
        a__	:					Dict										=AutoConfig.from_pretrained(lowerCAmelCase__							)
        self.assertIsNotNone(lowerCAmelCase__							)
        self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)

        a__	:					Tuple										=TFAutoModelForPreTraining.from_pretrained(lowerCAmelCase__							)
        self.assertIsNotNone(lowerCAmelCase__							)
        self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)







      @slow
      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__	:					Any										=AutoConfig.from_pretrained(lowerCAmelCase__							)
          self.assertIsNotNone(lowerCAmelCase__							)
          self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)

          a__	:					Optional[int]										=TFAutoModelForCausalLM.from_pretrained(lowerCAmelCase__							)
          a__					,							a__	:					Union[str, Any]										=TFAutoModelForCausalLM.from_pretrained(lowerCAmelCase__		,      output_loading_info=lowerCAmelCase__							)
          self.assertIsNotNone(lowerCAmelCase__							)
          self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)







      @slow
      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




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

          a__	:					List[str]										=TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase__							)
          self.assertIsNotNone(lowerCAmelCase__							)
          self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)







      @slow
      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__	:					Optional[int]										=AutoConfig.from_pretrained(lowerCAmelCase__							)
          self.assertIsNotNone(lowerCAmelCase__							)
          self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)

          a__	:					Optional[int]										=TFAutoModelForMaskedLM.from_pretrained(lowerCAmelCase__							)
          a__					,							a__	:					Dict										=TFAutoModelForMaskedLM.from_pretrained(lowerCAmelCase__		,      output_loading_info=lowerCAmelCase__							)
          self.assertIsNotNone(lowerCAmelCase__							)
          self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)







      @slow
      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__	:					int										=AutoConfig.from_pretrained(lowerCAmelCase__							)
          self.assertIsNotNone(lowerCAmelCase__							)
          self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)

          a__	:					Any										=TFAutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__							)
          a__					,							a__	:					List[Any]										=TFAutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__		,      output_loading_info=lowerCAmelCase__							)
          self.assertIsNotNone(lowerCAmelCase__							)
          self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)







      @slow
      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




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

          a__	:					List[Any]										=TFAutoModelForSequenceClassification.from_pretrained(lowerCAmelCase__							)
          self.assertIsNotNone(lowerCAmelCase__							)
          self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)







      @slow
      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




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

          a__	:					Dict										=TFAutoModelForQuestionAnswering.from_pretrained(lowerCAmelCase__							)
          self.assertIsNotNone(lowerCAmelCase__							)
          self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)







      @slow
      @require_tensorflow_probability
      def    _lowercase  (    self							)   ->						Union[str, Any]:



        '''simple docstring'''




        for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
          a__	:					Any										=AutoConfig.from_pretrained(lowerCAmelCase__							)
          self.assertIsNotNone(lowerCAmelCase__							)
          self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)

          a__	:					Union[str, Any]										=TFAutoModelForTableQuestionAnswering.from_pretrained(lowerCAmelCase__							)
          a__					,							a__	:					List[str]										=TFAutoModelForTableQuestionAnswering.from_pretrained(
              lowerCAmelCase__		,      output_loading_info=lowerCAmelCase__							)
          self.assertIsNotNone(lowerCAmelCase__							)
          self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					Optional[int]										=TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase__							)
        self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)
        self.assertEqual(model.num_parameters()		,      1_4_4_1_0							)
        self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase__							)		,      1_4_4_1_0							)







      def    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					int										=TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase__							)
        self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)
        self.assertEqual(model.num_parameters()		,      1_4_4_1_0							)
        self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase__							)		,      1_4_4_1_0							)







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					str										=TFAutoModel.from_pretrained("sgugger/funnel-random-tiny"							)
        self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)

        a__	:					Union[str, Any]										=copy.deepcopy(model.config							)
        a__	:					Union[str, Any]										=["FunnelBaseModel"]
        a__	:					Dict										=TFAutoModel.from_config(lowerCAmelCase__							)
        self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)

        with tempfile.TemporaryDirectory() as tmp_dir:
          model.save_pretrained(lowerCAmelCase__							)
          a__	:					List[str]										=TFAutoModel.from_pretrained(lowerCAmelCase__							)
          self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




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

          a__	:					Tuple										=[
              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(lowerCAmelCase__							):
                auto_class.register(lowerCAmelCase__		,      lowerCAmelCase__							)
              auto_class.register(lowerCAmelCase__		,      lowerCAmelCase__							)
              # Trying to register something existing in the Transformers library will raise an error
              with self.assertRaises(lowerCAmelCase__							):
                auto_class.register(lowerCAmelCase__		,      lowerCAmelCase__							)

              # Now that the config is registered, it can be used as any other config with the auto-API
              a__	:					List[str]										=BertModelTester(self							).get_config()
              a__	:					Dict										=NewModelConfig(**tiny_config.to_dict()							)
              a__	:					Optional[int]										=auto_class.from_config(lowerCAmelCase__							)
              self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)

              with tempfile.TemporaryDirectory() as tmp_dir:
                model.save_pretrained(lowerCAmelCase__							)
                a__	:					int										=auto_class.from_pretrained(lowerCAmelCase__							)
                self.assertIsInstance(lowerCAmelCase__		,      lowerCAmelCase__							)

        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    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




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







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




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







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        with self.assertRaisesRegex(
            lowerCAmelCase__		,      "hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin"		,      ):
          a__	:					Optional[int]										=TFAutoModel.from_pretrained("hf-internal-testing/config-no-model"							)







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        with self.assertRaisesRegex(lowerCAmelCase__		,      "Use `from_pt=True` to load this model"							):
          a__	:					Optional[Any]										=TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only"							)







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Any										=TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert"							)
        with RequestCounter() as counter:
          a__	:					List[str]										=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__	:					Any										=TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded"							)
        with RequestCounter() as counter:
          a__	:					Dict										=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							)








def 						UpperCAmelCase			(	a_						)		->	list:







				"""simple docstring"""



				if len(a_						) <= 1:
								return lst

				__A   			= 1

				while i < len(a_						):
								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							:List[Any]							 =				input('Enter numbers separated by a comma:\n').strip()
					SCREAMING_SNAKE_CASE							:List[Any]							 =				[int(item) for item in user_input.split(',')]
					print(gnome_sort(unsorted))
