

'''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 					_snake_case		(__SCREAMING_SNAKE_CASE):
			__A  :	List[Any]        ="new-model"


if is_tf_available():
					class 					_snake_case		(__SCREAMING_SNAKE_CASE):
								__A  :	Tuple        =NewModelConfig
@require_tf
class 					_snake_case		(unittest.TestCase):






			@slow
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Any						=  "bert-base-cased"
				UpperCAmelCase_     :      Optional[Any]						=  AutoConfig.from_pretrained(_snake_case     )
				self.assertIsNotNone(_snake_case     )
				self.assertIsInstance(_snake_case			,_snake_case     )

				UpperCAmelCase_     :      Tuple						=  TFAutoModel.from_pretrained(_snake_case     )
				self.assertIsNotNone(_snake_case     )
				self.assertIsInstance(_snake_case			,_snake_case     )






			@slow
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[str]						=  "bert-base-cased"
				UpperCAmelCase_     :      Dict						=  AutoConfig.from_pretrained(_snake_case     )
				self.assertIsNotNone(_snake_case     )
				self.assertIsInstance(_snake_case			,_snake_case     )

				UpperCAmelCase_     :      Union[str, Any]						=  TFAutoModelForPreTraining.from_pretrained(_snake_case     )
				self.assertIsNotNone(_snake_case     )
				self.assertIsInstance(_snake_case			,_snake_case     )






			@slow
			def 		UpperCamelCase__							(  self     ):
				for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					UpperCAmelCase_     :      List[str]						=  AutoConfig.from_pretrained(_snake_case     )
					self.assertIsNotNone(_snake_case     )
					self.assertIsInstance(_snake_case			,_snake_case     )

					UpperCAmelCase_     :      Optional[int]						=  TFAutoModelForCausalLM.from_pretrained(_snake_case     )
					UpperCAmelCase_					,					UpperCAmelCase_     :      str						=  TFAutoModelForCausalLM.from_pretrained(_snake_case			,output_loading_info=_snake_case     )
					self.assertIsNotNone(_snake_case     )
					self.assertIsInstance(_snake_case			,_snake_case     )






			@slow
			def 		UpperCamelCase__							(  self     ):
				for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					UpperCAmelCase_     :      Union[str, Any]						=  AutoConfig.from_pretrained(_snake_case     )
					self.assertIsNotNone(_snake_case     )
					self.assertIsInstance(_snake_case			,_snake_case     )

					UpperCAmelCase_     :      List[str]						=  TFAutoModelWithLMHead.from_pretrained(_snake_case     )
					self.assertIsNotNone(_snake_case     )
					self.assertIsInstance(_snake_case			,_snake_case     )






			@slow
			def 		UpperCamelCase__							(  self     ):
				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_     :      Dict						=  TFAutoModelForMaskedLM.from_pretrained(_snake_case     )
					UpperCAmelCase_					,					UpperCAmelCase_     :      Union[str, Any]						=  TFAutoModelForMaskedLM.from_pretrained(_snake_case			,output_loading_info=_snake_case     )
					self.assertIsNotNone(_snake_case     )
					self.assertIsInstance(_snake_case			,_snake_case     )






			@slow
			def 		UpperCamelCase__							(  self     ):
				for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					UpperCAmelCase_     :      str						=  AutoConfig.from_pretrained(_snake_case     )
					self.assertIsNotNone(_snake_case     )
					self.assertIsInstance(_snake_case			,_snake_case     )

					UpperCAmelCase_     :      int						=  TFAutoModelForSeqaSeqLM.from_pretrained(_snake_case     )
					UpperCAmelCase_					,					UpperCAmelCase_     :      Union[str, Any]						=  TFAutoModelForSeqaSeqLM.from_pretrained(_snake_case			,output_loading_info=_snake_case     )
					self.assertIsNotNone(_snake_case     )
					self.assertIsInstance(_snake_case			,_snake_case     )






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

					UpperCAmelCase_     :      Optional[int]						=  TFAutoModelForSequenceClassification.from_pretrained(_snake_case     )
					self.assertIsNotNone(_snake_case     )
					self.assertIsInstance(_snake_case			,_snake_case     )






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

					UpperCAmelCase_     :      Any						=  TFAutoModelForQuestionAnswering.from_pretrained(_snake_case     )
					self.assertIsNotNone(_snake_case     )
					self.assertIsInstance(_snake_case			,_snake_case     )






			@slow
			@require_tensorflow_probability
			def 		UpperCamelCase__							(  self     ):
				for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
					UpperCAmelCase_     :      Any						=  AutoConfig.from_pretrained(_snake_case     )
					self.assertIsNotNone(_snake_case     )
					self.assertIsInstance(_snake_case			,_snake_case     )

					UpperCAmelCase_     :      str						=  TFAutoModelForTableQuestionAnswering.from_pretrained(_snake_case     )
					UpperCAmelCase_					,					UpperCAmelCase_     :      Tuple						=  TFAutoModelForTableQuestionAnswering.from_pretrained(
					    _snake_case			,output_loading_info=_snake_case     )
					self.assertIsNotNone(_snake_case     )
					self.assertIsInstance(_snake_case			,_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      str						=  TFAutoModelWithLMHead.from_pretrained(_snake_case     )
				self.assertIsInstance(_snake_case			,_snake_case     )
				self.assertEqual(model.num_parameters()			,1_44_10     )
				self.assertEqual(model.num_parameters(only_trainable=_snake_case     )			,1_44_10     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Tuple						=  TFAutoModelWithLMHead.from_pretrained(_snake_case     )
				self.assertIsInstance(_snake_case			,_snake_case     )
				self.assertEqual(model.num_parameters()			,1_44_10     )
				self.assertEqual(model.num_parameters(only_trainable=_snake_case     )			,1_44_10     )






			def 		UpperCamelCase__							(  self     ):
				# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
				UpperCAmelCase_     :      Optional[int]						=  TFAutoModel.from_pretrained("sgugger/funnel-random-tiny"     )
				self.assertIsInstance(_snake_case			,_snake_case     )

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

				with tempfile.TemporaryDirectory() as tmp_dir:
					model.save_pretrained(_snake_case     )
					UpperCAmelCase_     :      Optional[Any]						=  TFAutoModel.from_pretrained(_snake_case     )
					self.assertIsInstance(_snake_case			,_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				try:
					AutoConfig.register("new-model"			,_snake_case     )

					UpperCAmelCase_     :      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(_snake_case     ):
								auto_class.register(_snake_case			,_snake_case     )
							auto_class.register(_snake_case			,_snake_case     )
							# Trying to register something existing in the Transformers library will raise an error
							with self.assertRaises(_snake_case     ):
								auto_class.register(_snake_case			,_snake_case     )

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

							with tempfile.TemporaryDirectory() as tmp_dir:
								model.save_pretrained(_snake_case     )
								UpperCAmelCase_     :      Union[str, Any]						=  auto_class.from_pretrained(_snake_case     )
								self.assertIsInstance(_snake_case			,_snake_case     )

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






			def 		UpperCamelCase__							(  self     ):
				with self.assertRaisesRegex(
				    _snake_case			,R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"     ):
					UpperCAmelCase_     :      str						=  TFAutoModel.from_pretrained(_snake_case			,revision="aaaaaa"     )






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






			def 		UpperCamelCase__							(  self     ):
				with self.assertRaisesRegex(_snake_case			,"Use `from_pt=True` to load this model"     ):
					UpperCAmelCase_     :      List[str]						=  TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only"     )






			def 		UpperCamelCase__							(  self     ):
				# Make sure we have cached the model.
				UpperCAmelCase_     :      Optional[Any]						=  TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert"     )
				with RequestCounter() as counter:
					UpperCAmelCase_     :      Optional[Any]						=  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
				UpperCAmelCase_     :      List[Any]						=  TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded"     )
				with RequestCounter() as counter:
					UpperCAmelCase_     :      int						=  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"""






import functools






def      lowercase__ (					lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			str			)    ->			int:



			'''simple docstring'''
			a__  :	Any		 =			len(lowerCAmelCase__			)
			a__  :	Optional[int]		 =			len(lowerCAmelCase__			)

			@functools.cache
			def min_distance(lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int			) -> int:
						# if first word index is overflow - delete all from the second word
						if indexa >= len_worda:
									return len_worda - indexa
						# if second word index is overflow - delete all from the first word
						if indexa >= len_worda:
									return len_worda - indexa
						a__  :	List[Any]		 =			int(worda[indexa] != worda[indexa]			)  # current letters not identical
						return min(
						    1 + min_distance(indexa + 1							,			lowerCAmelCase__			)							,			1 + min_distance(lowerCAmelCase__							,			indexa + 1			)							,			diff + min_distance(indexa + 1							,			indexa + 1			)							,			)

			return min_distance(0							,			0			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()