

"""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 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					@slow
					def        __lowerCAmelCase (       self			:				str		):
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
							lowerCAmelCase__	:						int  				=	AutoConfig.from_pretrained(lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)

							lowerCAmelCase__	:						Dict  				=	TFAutoModel.from_pretrained(lowercase_   ,from_pt=lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)

							lowerCAmelCase__	:						Tuple  				=	AutoModel.from_pretrained(lowercase_   ,from_tf=lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)







					@slow
					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
							lowerCAmelCase__	:						List[str]  				=	AutoConfig.from_pretrained(lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)

							lowerCAmelCase__	:						Union[str, Any]  				=	TFAutoModelForPreTraining.from_pretrained(lowercase_   ,from_pt=lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)

							lowerCAmelCase__	:						Optional[int]  				=	AutoModelForPreTraining.from_pretrained(lowercase_   ,from_tf=lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)







					@slow
					def        __lowerCAmelCase (       self			:				int		):
						for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCAmelCase__	:						int  				=	AutoConfig.from_pretrained(lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)

							lowerCAmelCase__	:						str  				=	TFAutoModelForCausalLM.from_pretrained(lowercase_   ,from_pt=lowercase_		)
							lowerCAmelCase__				,lowerCAmelCase__	:						Dict  				=	TFAutoModelForCausalLM.from_pretrained(
							    lowercase_   ,output_loading_info=lowercase_   ,from_pt=lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)

							lowerCAmelCase__	:						Dict  				=	AutoModelForCausalLM.from_pretrained(lowercase_   ,from_tf=lowercase_		)
							lowerCAmelCase__				,lowerCAmelCase__	:						Union[str, Any]  				=	AutoModelForCausalLM.from_pretrained(
							    lowercase_   ,output_loading_info=lowercase_   ,from_tf=lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)







					@slow
					def        __lowerCAmelCase (       self			:				Tuple		):
						for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCAmelCase__	:						str  				=	AutoConfig.from_pretrained(lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)

							lowerCAmelCase__	:						List[Any]  				=	TFAutoModelWithLMHead.from_pretrained(lowercase_   ,from_pt=lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)

							lowerCAmelCase__	:						List[Any]  				=	AutoModelWithLMHead.from_pretrained(lowercase_   ,from_tf=lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)







					@slow
					def        __lowerCAmelCase (       self			:				Optional[int]		):
						for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCAmelCase__	:						Tuple  				=	AutoConfig.from_pretrained(lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)

							lowerCAmelCase__	:						Tuple  				=	TFAutoModelForMaskedLM.from_pretrained(lowercase_   ,from_pt=lowercase_		)
							lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	TFAutoModelForMaskedLM.from_pretrained(
							    lowercase_   ,output_loading_info=lowercase_   ,from_pt=lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)

							lowerCAmelCase__	:						Optional[int]  				=	AutoModelForMaskedLM.from_pretrained(lowercase_   ,from_tf=lowercase_		)
							lowerCAmelCase__				,lowerCAmelCase__	:						str  				=	AutoModelForMaskedLM.from_pretrained(
							    lowercase_   ,output_loading_info=lowercase_   ,from_tf=lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)







					@slow
					def        __lowerCAmelCase (       self			:				Dict		):
						for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCAmelCase__	:						Dict  				=	AutoConfig.from_pretrained(lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)

							lowerCAmelCase__	:						Dict  				=	TFAutoModelForSeqaSeqLM.from_pretrained(lowercase_   ,from_pt=lowercase_		)
							lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	TFAutoModelForSeqaSeqLM.from_pretrained(
							    lowercase_   ,output_loading_info=lowercase_   ,from_pt=lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)

							lowerCAmelCase__	:						Optional[Any]  				=	AutoModelForSeqaSeqLM.from_pretrained(lowercase_   ,from_tf=lowercase_		)
							lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	AutoModelForSeqaSeqLM.from_pretrained(
							    lowercase_   ,output_loading_info=lowercase_   ,from_tf=lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)







					@slow
					def        __lowerCAmelCase (       self			:				Any		):
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
							lowerCAmelCase__	:						List[str]  				=	AutoConfig.from_pretrained(lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)

							lowerCAmelCase__	:						Dict  				=	TFAutoModelForSequenceClassification.from_pretrained(lowercase_   ,from_pt=lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)

							lowerCAmelCase__	:						Any  				=	AutoModelForSequenceClassification.from_pretrained(lowercase_   ,from_tf=lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)







					@slow
					def        __lowerCAmelCase (       self			:				List[Any]		):
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
							lowerCAmelCase__	:						str  				=	AutoConfig.from_pretrained(lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)

							lowerCAmelCase__	:						int  				=	TFAutoModelForQuestionAnswering.from_pretrained(lowercase_   ,from_pt=lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)

							lowerCAmelCase__	:						str  				=	AutoModelForQuestionAnswering.from_pretrained(lowercase_   ,from_tf=lowercase_		)
							self.assertIsNotNone(lowercase_		)
							self.assertIsInstance(lowercase_   ,lowercase_		)







					def        __lowerCAmelCase (       self			:				Tuple		):
						lowerCAmelCase__	:						List[Any]  				=	TFAutoModelWithLMHead.from_pretrained(lowercase_   ,from_pt=lowercase_		)
						self.assertIsInstance(lowercase_   ,lowercase_		)
						self.assertEqual(model.num_parameters()   ,1_4_4_1_0		)
						self.assertEqual(model.num_parameters(only_trainable=lowercase_		)   ,1_4_4_1_0		)

						lowerCAmelCase__	:						Union[str, Any]  				=	AutoModelWithLMHead.from_pretrained(lowercase_   ,from_tf=lowercase_		)
						self.assertIsInstance(lowercase_   ,lowercase_		)
						self.assertEqual(model.num_parameters()   ,1_4_4_1_0		)
						self.assertEqual(model.num_parameters(only_trainable=lowercase_		)   ,1_4_4_1_0		)







					def        __lowerCAmelCase (       self			:				str		):
						lowerCAmelCase__	:						int  				=	TFAutoModelWithLMHead.from_pretrained(lowercase_   ,from_pt=lowercase_		)
						self.assertIsInstance(lowercase_   ,lowercase_		)
						self.assertEqual(model.num_parameters()   ,1_4_4_1_0		)
						self.assertEqual(model.num_parameters(only_trainable=lowercase_		)   ,1_4_4_1_0		)

						lowerCAmelCase__	:						Union[str, Any]  				=	AutoModelWithLMHead.from_pretrained(lowercase_   ,from_tf=lowercase_		)
						self.assertIsInstance(lowercase_   ,lowercase_		)
						self.assertEqual(model.num_parameters()   ,1_4_4_1_0		)
						self.assertEqual(model.num_parameters(only_trainable=lowercase_		)   ,1_4_4_1_0		)






import os






def 							_lowercase							(     )		->		List[str]:
					'''simple docstring'''


					with open(os.path.dirname(UpperCamelCase_		) + '/p022_names.txt'		) as file:
										SCREAMING_SNAKE_CASE__													= str(file.readlines()[0]		)
										SCREAMING_SNAKE_CASE__													= names.replace('"'	,	''		).split(','		)

					names.sort()

					SCREAMING_SNAKE_CASE__													= 0
					SCREAMING_SNAKE_CASE__													= 0

					for i, name in enumerate(UpperCamelCase_		):
										for letter in name:
															name_score += ord(UpperCamelCase_		) - 64

										total_score += (i + 1) * name_score
										SCREAMING_SNAKE_CASE__													= 0
					return total_score


if __name__ == "__main__":
				print(solution())





