



"""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  _UpperCAmelCase (    unittest.TestCase):

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

         snake_case_				:  str       =			TFAutoModel.from_pretrained(lowercase_						,  from_pt=lowercase_       )
         self.assertIsNotNone(lowercase_       )
         self.assertIsInstance(lowercase_						,  lowercase_       )

         snake_case_				:  List[str]       =			AutoModel.from_pretrained(lowercase_						,  from_tf=lowercase_       )
         self.assertIsNotNone(lowercase_       )
         self.assertIsInstance(lowercase_						,  lowercase_       )

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

         snake_case_				:  Any       =			TFAutoModelForPreTraining.from_pretrained(lowercase_						,  from_pt=lowercase_       )
         self.assertIsNotNone(lowercase_       )
         self.assertIsInstance(lowercase_						,  lowercase_       )

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

 @slow
 def 		_snake_case      (  self	: Any       ):
     for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case_				:  Union[str, Any]       =			AutoConfig.from_pretrained(lowercase_       )
         self.assertIsNotNone(lowercase_       )
         self.assertIsInstance(lowercase_						,  lowercase_       )

         snake_case_				:  int       =			TFAutoModelForCausalLM.from_pretrained(lowercase_						,  from_pt=lowercase_       )
         snake_case_,			snake_case_				:  Tuple       =			TFAutoModelForCausalLM.from_pretrained(
             lowercase_						,  output_loading_info=lowercase_						,  from_pt=lowercase_       )
         self.assertIsNotNone(lowercase_       )
         self.assertIsInstance(lowercase_						,  lowercase_       )

         snake_case_				:  int       =			AutoModelForCausalLM.from_pretrained(lowercase_						,  from_tf=lowercase_       )
         snake_case_,			snake_case_				:  List[str]       =			AutoModelForCausalLM.from_pretrained(
             lowercase_						,  output_loading_info=lowercase_						,  from_tf=lowercase_       )
         self.assertIsNotNone(lowercase_       )
         self.assertIsInstance(lowercase_						,  lowercase_       )

 @slow
 def 		_snake_case      (  self	: Tuple       ):
     for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case_				:  Dict       =			AutoConfig.from_pretrained(lowercase_       )
         self.assertIsNotNone(lowercase_       )
         self.assertIsInstance(lowercase_						,  lowercase_       )

         snake_case_				:  Dict       =			TFAutoModelWithLMHead.from_pretrained(lowercase_						,  from_pt=lowercase_       )
         self.assertIsNotNone(lowercase_       )
         self.assertIsInstance(lowercase_						,  lowercase_       )

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

 @slow
 def 		_snake_case      (  self	: Dict       ):
     for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case_				:  List[Any]       =			AutoConfig.from_pretrained(lowercase_       )
         self.assertIsNotNone(lowercase_       )
         self.assertIsInstance(lowercase_						,  lowercase_       )

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

         snake_case_				:  Tuple       =			AutoModelForMaskedLM.from_pretrained(lowercase_						,  from_tf=lowercase_       )
         snake_case_,			snake_case_				:  Union[str, Any]       =			AutoModelForMaskedLM.from_pretrained(
             lowercase_						,  output_loading_info=lowercase_						,  from_tf=lowercase_       )
         self.assertIsNotNone(lowercase_       )
         self.assertIsInstance(lowercase_						,  lowercase_       )

 @slow
 def 		_snake_case      (  self	: List[str]       ):
     for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case_				:  int       =			AutoConfig.from_pretrained(lowercase_       )
         self.assertIsNotNone(lowercase_       )
         self.assertIsInstance(lowercase_						,  lowercase_       )

         snake_case_				:  Dict       =			TFAutoModelForSeqaSeqLM.from_pretrained(lowercase_						,  from_pt=lowercase_       )
         snake_case_,			snake_case_				:  Union[str, Any]       =			TFAutoModelForSeqaSeqLM.from_pretrained(
             lowercase_						,  output_loading_info=lowercase_						,  from_pt=lowercase_       )
         self.assertIsNotNone(lowercase_       )
         self.assertIsInstance(lowercase_						,  lowercase_       )

         snake_case_				:  Any       =			AutoModelForSeqaSeqLM.from_pretrained(lowercase_						,  from_tf=lowercase_       )
         snake_case_,			snake_case_				:  str       =			AutoModelForSeqaSeqLM.from_pretrained(
             lowercase_						,  output_loading_info=lowercase_						,  from_tf=lowercase_       )
         self.assertIsNotNone(lowercase_       )
         self.assertIsInstance(lowercase_						,  lowercase_       )

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

         snake_case_				:  Any       =			TFAutoModelForSequenceClassification.from_pretrained(lowercase_						,  from_pt=lowercase_       )
         self.assertIsNotNone(lowercase_       )
         self.assertIsInstance(lowercase_						,  lowercase_       )

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

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

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

         snake_case_				:  int       =			AutoModelForQuestionAnswering.from_pretrained(lowercase_						,  from_tf=lowercase_       )
         self.assertIsNotNone(lowercase_       )
         self.assertIsInstance(lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: Tuple       ):
     snake_case_				:  Optional[int]       =			TFAutoModelWithLMHead.from_pretrained(lowercase_						,  from_pt=lowercase_       )
     self.assertIsInstance(lowercase_						,  lowercase_       )
     self.assertEqual(model.num_parameters()						,  14410       )
     self.assertEqual(model.num_parameters(only_trainable=lowercase_       )						,  14410       )

     snake_case_				:  Any       =			AutoModelWithLMHead.from_pretrained(lowercase_						,  from_tf=lowercase_       )
     self.assertIsInstance(lowercase_						,  lowercase_       )
     self.assertEqual(model.num_parameters()						,  14410       )
     self.assertEqual(model.num_parameters(only_trainable=lowercase_       )						,  14410       )






 def 		_snake_case      (  self	: str       ):
     snake_case_				:  Optional[Any]       =			TFAutoModelWithLMHead.from_pretrained(lowercase_						,  from_pt=lowercase_       )
     self.assertIsInstance(lowercase_						,  lowercase_       )
     self.assertEqual(model.num_parameters()						,  14410       )
     self.assertEqual(model.num_parameters(only_trainable=lowercase_       )						,  14410       )

     snake_case_				:  Dict       =			AutoModelWithLMHead.from_pretrained(lowercase_						,  from_tf=lowercase_       )
     self.assertIsInstance(lowercase_						,  lowercase_       )
     self.assertEqual(model.num_parameters()						,  14410       )
     self.assertEqual(model.num_parameters(only_trainable=lowercase_       )						,  14410       )










"""simple docstring"""

from statistics import mean, stdev




def __lowercase    (							_a	,    _a = 3					):
    snake_case_				:  Optional[int]       =			min(_a					)
    snake_case_				:  str       =			max(_a					)
    # normalize data
    return [round((x - x_min) / (x_max - x_min)	,    _a					) for x in data]






def __lowercase    (							_a	,    _a = 3					):
    snake_case_				:  Any       =			mean(_a					)
    snake_case_				:  str       =			stdev(_a					)
    # standardize data
    return [round((x - mu) / (sigma)	,    _a					) for x in data]





