





"""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 			__UpperCamelCase							(		UpperCAmelCase_		):
							lowerCamelCase  :   Optional[Any]    ='''new-model'''


if is_tf_available():


		class 			__UpperCamelCase							(		UpperCAmelCase_		):
									lowerCamelCase  :   Dict    =NewModelConfig


@require_tf
class 			__UpperCamelCase							(		unittest.TestCase		):






							@slow
							def        __a  (  self )      ->					int:
								a :						List[str]      =							"""bert-base-cased"""
								a :						Dict      =							AutoConfig.from_pretrained(lowerCamelCase__ )
								self.assertIsNotNone(lowerCamelCase__ )
								self.assertIsInstance(lowerCamelCase__ ,   lowerCamelCase__ )

								a :						str      =							TFAutoModel.from_pretrained(lowerCamelCase__ )
								self.assertIsNotNone(lowerCamelCase__ )
								self.assertIsInstance(lowerCamelCase__ ,   lowerCamelCase__ )






							@slow
							def        __a  (  self )      ->					Union[str, Any]:
								a :						Optional[Any]      =							"""bert-base-cased"""
								a :						Dict      =							AutoConfig.from_pretrained(lowerCamelCase__ )
								self.assertIsNotNone(lowerCamelCase__ )
								self.assertIsInstance(lowerCamelCase__ ,   lowerCamelCase__ )

								a :						str      =							TFAutoModelForPreTraining.from_pretrained(lowerCamelCase__ )
								self.assertIsNotNone(lowerCamelCase__ )
								self.assertIsInstance(lowerCamelCase__ ,   lowerCamelCase__ )






							@slow
							def        __a  (  self )      ->					Dict:

								for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a :						List[Any]      =							AutoConfig.from_pretrained(lowerCamelCase__ )
									self.assertIsNotNone(lowerCamelCase__ )
									self.assertIsInstance(lowerCamelCase__ ,   lowerCamelCase__ )

									a :						Optional[Any]      =							TFAutoModelForCausalLM.from_pretrained(lowerCamelCase__ )
									a :						List[Any]      =							TFAutoModelForCausalLM.from_pretrained(lowerCamelCase__ ,   output_loading_info=lowerCamelCase__ )
									self.assertIsNotNone(lowerCamelCase__ )
									self.assertIsInstance(lowerCamelCase__ ,   lowerCamelCase__ )






							@slow
							def        __a  (  self )      ->					str:

								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 :						int      =							TFAutoModelWithLMHead.from_pretrained(lowerCamelCase__ )
									self.assertIsNotNone(lowerCamelCase__ )
									self.assertIsInstance(lowerCamelCase__ ,   lowerCamelCase__ )






							@slow
							def        __a  (  self )      ->					Any:

								for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a :						List[Any]      =							AutoConfig.from_pretrained(lowerCamelCase__ )
									self.assertIsNotNone(lowerCamelCase__ )
									self.assertIsInstance(lowerCamelCase__ ,   lowerCamelCase__ )

									a :						Any      =							TFAutoModelForMaskedLM.from_pretrained(lowerCamelCase__ )
									a :						List[str]      =							TFAutoModelForMaskedLM.from_pretrained(lowerCamelCase__ ,   output_loading_info=lowerCamelCase__ )
									self.assertIsNotNone(lowerCamelCase__ )
									self.assertIsInstance(lowerCamelCase__ ,   lowerCamelCase__ )






							@slow
							def        __a  (  self )      ->					str:

								for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a :						Union[str, Any]      =							AutoConfig.from_pretrained(lowerCamelCase__ )
									self.assertIsNotNone(lowerCamelCase__ )
									self.assertIsInstance(lowerCamelCase__ ,   lowerCamelCase__ )

									a :						List[Any]      =							TFAutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__ )
									a :						Optional[Any]      =							TFAutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__ ,   output_loading_info=lowerCamelCase__ )
									self.assertIsNotNone(lowerCamelCase__ )
									self.assertIsInstance(lowerCamelCase__ ,   lowerCamelCase__ )






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

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

									a :						Dict      =							TFAutoModelForSequenceClassification.from_pretrained(lowerCamelCase__ )
									self.assertIsNotNone(lowerCamelCase__ )
									self.assertIsInstance(lowerCamelCase__ ,   lowerCamelCase__ )






							@slow
							def        __a  (  self )      ->					int:

								for model_name in ["bert-base-uncased"]:
									a :						Union[str, Any]      =							AutoConfig.from_pretrained(lowerCamelCase__ )
									self.assertIsNotNone(lowerCamelCase__ )
									self.assertIsInstance(lowerCamelCase__ ,   lowerCamelCase__ )

									a :						List[Any]      =							TFAutoModelForQuestionAnswering.from_pretrained(lowerCamelCase__ )
									self.assertIsNotNone(lowerCamelCase__ )
									self.assertIsInstance(lowerCamelCase__ ,   lowerCamelCase__ )






							@slow
							@require_tensorflow_probability
							def        __a  (  self )      ->					Tuple:

								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 :						Tuple      =							TFAutoModelForTableQuestionAnswering.from_pretrained(lowerCamelCase__ )
									a :						Optional[int]      =							TFAutoModelForTableQuestionAnswering.from_pretrained(
									    lowerCamelCase__ ,   output_loading_info=lowerCamelCase__ )
									self.assertIsNotNone(lowerCamelCase__ )
									self.assertIsInstance(lowerCamelCase__ ,   lowerCamelCase__ )






							def        __a  (  self )      ->					str:
								a :						Any      =							TFAutoModelWithLMHead.from_pretrained(lowerCamelCase__ )
								self.assertIsInstance(lowerCamelCase__ ,   lowerCamelCase__ )
								self.assertEqual(model.num_parameters() ,   1_4410 )
								self.assertEqual(model.num_parameters(only_trainable=lowerCamelCase__ ) ,   1_4410 )






							def        __a  (  self )      ->					List[Any]:
								a :						Dict      =							TFAutoModelWithLMHead.from_pretrained(lowerCamelCase__ )
								self.assertIsInstance(lowerCamelCase__ ,   lowerCamelCase__ )
								self.assertEqual(model.num_parameters() ,   1_4410 )
								self.assertEqual(model.num_parameters(only_trainable=lowerCamelCase__ ) ,   1_4410 )






							def        __a  (  self )      ->					List[Any]:
								a :						str      =							TFAutoModel.from_pretrained("sgugger/funnel-random-tiny" )
								self.assertIsInstance(lowerCamelCase__ ,   lowerCamelCase__ )

								a :						Union[str, Any]      =							copy.deepcopy(model.config )
								a :						List[Any]      =							["""FunnelBaseModel"""]
								a :						Optional[int]      =							TFAutoModel.from_config(lowerCamelCase__ )
								self.assertIsInstance(lowerCamelCase__ ,   lowerCamelCase__ )

								with tempfile.TemporaryDirectory() as tmp_dir:
									model.save_pretrained(lowerCamelCase__ )
									a :						str      =							TFAutoModel.from_pretrained(lowerCamelCase__ )
									self.assertIsInstance(lowerCamelCase__ ,   lowerCamelCase__ )






							def        __a  (  self )      ->					Optional[int]:

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

									a :						List[str]      =							[
									    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 :						Dict      =							BertModelTester(self ).get_config()
											a :						Optional[Any]      =							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 :						Union[str, Any]      =							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        __a  (  self )      ->					Union[str, Any]:

								with self.assertRaisesRegex(
								    lowerCamelCase__ ,   "bert-base is not a local folder and is not a valid model identifier" ):
									a :						Optional[int]      =							TFAutoModel.from_pretrained("bert-base" )






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

								with self.assertRaisesRegex(
								    lowerCamelCase__ ,   R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ):
									a :						Optional[Any]      =							TFAutoModel.from_pretrained(lowerCamelCase__ ,   revision="aaaaaa" )






							def        __a  (  self )      ->					Tuple:

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






							def        __a  (  self )      ->					Union[str, Any]:

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






							def        __a  (  self )      ->					int:
								a :						str      =							TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert" )
								with RequestCounter() as counter:
									a :						Optional[int]      =							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 :						Union[str, Any]      =							TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded" )
								with RequestCounter() as counter:
									a :						List[Any]      =							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 )










from __future__ import annotations







class 		_A						:

  def __init__(    self						:	List[str]   ,				lowerCamelCase__						:	Any=None			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					data
     __UpperCamelCase	:			Union[str, Any]					=					None

  def __repr__(    self						:	int			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					[]
     __UpperCamelCase	:			Optional[int]					=					self
     while temp:
        string_rep.append(f'{temp.data}'			)
        __UpperCamelCase	:			Dict					=					temp.next
     return "->".join(lowerCamelCase__			)







def 				__lowerCamelCase     (							__lowerCAmelCase							:					list							)     ->  Tuple:
   if not elements_list:
      raise Exception("""The Elements List is empty"""							)

   __UpperCamelCase	:			Dict					=					Node(elements_list[0]							)
   for i in range(1					,							len(__lowerCAmelCase							)							):
      __UpperCamelCase	:			List[str]					=					Node(elements_list[i]							)
      __UpperCamelCase	:			Optional[int]					=					current.next
   return head


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Node							)     ->  None:
   if head_node is not None and isinstance(__lowerCAmelCase					,							__lowerCAmelCase							):
      print_reverse(head_node.next							)
      print(head_node.data							)


def 				__lowerCamelCase     (							)     ->  Union[str, Any]:
   from doctest import testmod

   testmod()

   __UpperCamelCase	:			Any					=					make_linked_list([14, 52, 14, 12, 43]							)
   print("""Linked List:"""							)
   print(__lowerCAmelCase							)
   print("""Elements in Reverse:"""							)
   print_reverse(__lowerCAmelCase							)


if __name__ == "__main__":
       main()



