
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        lowercase	(      unittest.TestCase	):




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

																	lowercase					=		TFAutoModel.from_pretrained(A__     ,from_pt=A__)
																	self.assertIsNotNone(A__)
																	self.assertIsInstance(A__     ,A__)

																	lowercase					=		AutoModel.from_pretrained(A__     ,from_tf=A__)
																	self.assertIsNotNone(A__)
																	self.assertIsInstance(A__     ,A__)




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

																	lowercase					=		TFAutoModelForPreTraining.from_pretrained(A__     ,from_pt=A__)
																	self.assertIsNotNone(A__)
																	self.assertIsInstance(A__     ,A__)

																	lowercase					=		AutoModelForPreTraining.from_pretrained(A__     ,from_tf=A__)
																	self.assertIsNotNone(A__)
																	self.assertIsInstance(A__     ,A__)




							@slow
							def 				A__   (       self):
												for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase					=		AutoConfig.from_pretrained(A__)
																	self.assertIsNotNone(A__)
																	self.assertIsInstance(A__     ,A__)

																	lowercase					=		TFAutoModelForCausalLM.from_pretrained(A__     ,from_pt=A__)
																	lowercase			,		lowercase					=		TFAutoModelForCausalLM.from_pretrained(
																	    A__     ,output_loading_info=A__     ,from_pt=A__)
																	self.assertIsNotNone(A__)
																	self.assertIsInstance(A__     ,A__)

																	lowercase					=		AutoModelForCausalLM.from_pretrained(A__     ,from_tf=A__)
																	lowercase			,		lowercase					=		AutoModelForCausalLM.from_pretrained(
																	    A__     ,output_loading_info=A__     ,from_tf=A__)
																	self.assertIsNotNone(A__)
																	self.assertIsInstance(A__     ,A__)




							@slow
							def 				A__   (       self):
												for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase					=		AutoConfig.from_pretrained(A__)
																	self.assertIsNotNone(A__)
																	self.assertIsInstance(A__     ,A__)

																	lowercase					=		TFAutoModelWithLMHead.from_pretrained(A__     ,from_pt=A__)
																	self.assertIsNotNone(A__)
																	self.assertIsInstance(A__     ,A__)

																	lowercase					=		AutoModelWithLMHead.from_pretrained(A__     ,from_tf=A__)
																	self.assertIsNotNone(A__)
																	self.assertIsInstance(A__     ,A__)




							@slow
							def 				A__   (       self):
												for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase					=		AutoConfig.from_pretrained(A__)
																	self.assertIsNotNone(A__)
																	self.assertIsInstance(A__     ,A__)

																	lowercase					=		TFAutoModelForMaskedLM.from_pretrained(A__     ,from_pt=A__)
																	lowercase			,		lowercase					=		TFAutoModelForMaskedLM.from_pretrained(
																	    A__     ,output_loading_info=A__     ,from_pt=A__)
																	self.assertIsNotNone(A__)
																	self.assertIsInstance(A__     ,A__)

																	lowercase					=		AutoModelForMaskedLM.from_pretrained(A__     ,from_tf=A__)
																	lowercase			,		lowercase					=		AutoModelForMaskedLM.from_pretrained(
																	    A__     ,output_loading_info=A__     ,from_tf=A__)
																	self.assertIsNotNone(A__)
																	self.assertIsInstance(A__     ,A__)




							@slow
							def 				A__   (       self):
												for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase					=		AutoConfig.from_pretrained(A__)
																	self.assertIsNotNone(A__)
																	self.assertIsInstance(A__     ,A__)

																	lowercase					=		TFAutoModelForSeqaSeqLM.from_pretrained(A__     ,from_pt=A__)
																	lowercase			,		lowercase					=		TFAutoModelForSeqaSeqLM.from_pretrained(
																	    A__     ,output_loading_info=A__     ,from_pt=A__)
																	self.assertIsNotNone(A__)
																	self.assertIsInstance(A__     ,A__)

																	lowercase					=		AutoModelForSeqaSeqLM.from_pretrained(A__     ,from_tf=A__)
																	lowercase			,		lowercase					=		AutoModelForSeqaSeqLM.from_pretrained(
																	    A__     ,output_loading_info=A__     ,from_tf=A__)
																	self.assertIsNotNone(A__)
																	self.assertIsInstance(A__     ,A__)




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

																	lowercase					=		TFAutoModelForSequenceClassification.from_pretrained(A__     ,from_pt=A__)
																	self.assertIsNotNone(A__)
																	self.assertIsInstance(A__     ,A__)

																	lowercase					=		AutoModelForSequenceClassification.from_pretrained(A__     ,from_tf=A__)
																	self.assertIsNotNone(A__)
																	self.assertIsInstance(A__     ,A__)




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

																	lowercase					=		TFAutoModelForQuestionAnswering.from_pretrained(A__     ,from_pt=A__)
																	self.assertIsNotNone(A__)
																	self.assertIsInstance(A__     ,A__)

																	lowercase					=		AutoModelForQuestionAnswering.from_pretrained(A__     ,from_tf=A__)
																	self.assertIsNotNone(A__)
																	self.assertIsInstance(A__     ,A__)




							def 				A__   (       self):
												lowercase					=		TFAutoModelWithLMHead.from_pretrained(A__     ,from_pt=A__)
												self.assertIsInstance(A__     ,A__)
												self.assertEqual(model.num_parameters()     ,1_4_4_1_0)
												self.assertEqual(model.num_parameters(only_trainable=A__)     ,1_4_4_1_0)

												lowercase					=		AutoModelWithLMHead.from_pretrained(A__     ,from_tf=A__)
												self.assertIsInstance(A__     ,A__)
												self.assertEqual(model.num_parameters()     ,1_4_4_1_0)
												self.assertEqual(model.num_parameters(only_trainable=A__)     ,1_4_4_1_0)





							def 				A__   (       self):
												lowercase					=		TFAutoModelWithLMHead.from_pretrained(A__     ,from_pt=A__)
												self.assertIsInstance(A__     ,A__)
												self.assertEqual(model.num_parameters()     ,1_4_4_1_0)
												self.assertEqual(model.num_parameters(only_trainable=A__)     ,1_4_4_1_0)

												lowercase					=		AutoModelWithLMHead.from_pretrained(A__     ,from_tf=A__)
												self.assertIsInstance(A__     ,A__)
												self.assertEqual(model.num_parameters()     ,1_4_4_1_0)
												self.assertEqual(model.num_parameters(only_trainable=A__)     ,1_4_4_1_0)






from numpy import exp, pi, sqrt



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__ = 0.0				,    lowerCAmelCase__ = 1.0	):
					'''simple docstring'''
					return 1 / sqrt(2 * pi * sigma**2	) * exp(-((x - mu) ** 2) / (2 * sigma**2)	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




