






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   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       "new-model"


if is_tf_available():


							class   __SCREAMING_SNAKE_CASE(	a_	):
														_UpperCAmelCase     =       NewModelConfig


@require_tf
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							@slow
							def lowerCAmelCase_ (     self:      List[Any]						)		->		Union[str, Any]:
											snake_case__									=     'bert-base-cased'
											snake_case__									=     AutoConfig.from_pretrained(UpperCamelCase						)
											self.assertIsNotNone(UpperCamelCase						)
											self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)

											snake_case__									=     TFAutoModel.from_pretrained(UpperCamelCase						)
											self.assertIsNotNone(UpperCamelCase						)
											self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)
							@slow
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Dict:
											snake_case__									=     'bert-base-cased'
											snake_case__									=     AutoConfig.from_pretrained(UpperCamelCase						)
											self.assertIsNotNone(UpperCamelCase						)
											self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)

											snake_case__									=     TFAutoModelForPreTraining.from_pretrained(UpperCamelCase						)
											self.assertIsNotNone(UpperCamelCase						)
											self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)
							@slow
							def lowerCAmelCase_ (     self:      Any						)		->		Optional[Any]:
											for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case__									=     AutoConfig.from_pretrained(UpperCamelCase						)
															self.assertIsNotNone(UpperCamelCase						)
															self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)

															snake_case__									=     TFAutoModelForCausalLM.from_pretrained(UpperCamelCase						)
															snake_case__							,						snake_case__									=     TFAutoModelForCausalLM.from_pretrained(UpperCamelCase       ,	output_loading_info=UpperCamelCase						)
															self.assertIsNotNone(UpperCamelCase						)
															self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)
							@slow
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		str:
											for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case__									=     AutoConfig.from_pretrained(UpperCamelCase						)
															self.assertIsNotNone(UpperCamelCase						)
															self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)

															snake_case__									=     TFAutoModelWithLMHead.from_pretrained(UpperCamelCase						)
															self.assertIsNotNone(UpperCamelCase						)
															self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)
							@slow
							def lowerCAmelCase_ (     self:      Any						)		->		Tuple:
											for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case__									=     AutoConfig.from_pretrained(UpperCamelCase						)
															self.assertIsNotNone(UpperCamelCase						)
															self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)

															snake_case__									=     TFAutoModelForMaskedLM.from_pretrained(UpperCamelCase						)
															snake_case__							,						snake_case__									=     TFAutoModelForMaskedLM.from_pretrained(UpperCamelCase       ,	output_loading_info=UpperCamelCase						)
															self.assertIsNotNone(UpperCamelCase						)
															self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)
							@slow
							def lowerCAmelCase_ (     self:      List[Any]						)		->		List[Any]:
											for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case__									=     AutoConfig.from_pretrained(UpperCamelCase						)
															self.assertIsNotNone(UpperCamelCase						)
															self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)

															snake_case__									=     TFAutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase						)
															snake_case__							,						snake_case__									=     TFAutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase       ,	output_loading_info=UpperCamelCase						)
															self.assertIsNotNone(UpperCamelCase						)
															self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)
							@slow
							def lowerCAmelCase_ (     self:      str						)		->		List[str]:
											# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											for model_name in ["bert-base-uncased"]:
															snake_case__									=     AutoConfig.from_pretrained(UpperCamelCase						)
															self.assertIsNotNone(UpperCamelCase						)
															self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)

															snake_case__									=     TFAutoModelForSequenceClassification.from_pretrained(UpperCamelCase						)
															self.assertIsNotNone(UpperCamelCase						)
															self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)
							@slow
							def lowerCAmelCase_ (     self:      str						)		->		List[str]:
											# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											for model_name in ["bert-base-uncased"]:
															snake_case__									=     AutoConfig.from_pretrained(UpperCamelCase						)
															self.assertIsNotNone(UpperCamelCase						)
															self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)

															snake_case__									=     TFAutoModelForQuestionAnswering.from_pretrained(UpperCamelCase						)
															self.assertIsNotNone(UpperCamelCase						)
															self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)
							@slow
							@require_tensorflow_probability
							def lowerCAmelCase_ (     self:      List[str]						)		->		str:
											for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
															snake_case__									=     AutoConfig.from_pretrained(UpperCamelCase						)
															self.assertIsNotNone(UpperCamelCase						)
															self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)

															snake_case__									=     TFAutoModelForTableQuestionAnswering.from_pretrained(UpperCamelCase						)
															snake_case__							,						snake_case__									=     TFAutoModelForTableQuestionAnswering.from_pretrained(
															    UpperCamelCase       ,	output_loading_info=UpperCamelCase						)
															self.assertIsNotNone(UpperCamelCase						)
															self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      List[str]						)		->		Union[str, Any]:
											snake_case__									=     TFAutoModelWithLMHead.from_pretrained(UpperCamelCase						)
											self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)
											self.assertEqual(model.num_parameters()       ,	1_44_10						)
											self.assertEqual(model.num_parameters(only_trainable=UpperCamelCase						)       ,	1_44_10						)
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		Dict:
											snake_case__									=     TFAutoModelWithLMHead.from_pretrained(UpperCamelCase						)
											self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)
											self.assertEqual(model.num_parameters()       ,	1_44_10						)
											self.assertEqual(model.num_parameters(only_trainable=UpperCamelCase						)       ,	1_44_10						)
							def lowerCAmelCase_ (     self:      Dict						)		->		int:
											# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
											snake_case__									=     TFAutoModel.from_pretrained('sgugger/funnel-random-tiny'						)
											self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)

											snake_case__									=     copy.deepcopy(model.config						)
											snake_case__									=     ['FunnelBaseModel']
											snake_case__									=     TFAutoModel.from_config(UpperCamelCase						)
											self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)

											with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(UpperCamelCase						)
															snake_case__									=     TFAutoModel.from_pretrained(UpperCamelCase						)
															self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Dict						)		->		Dict:
											try:
															AutoConfig.register('new-model'       ,	UpperCamelCase						)

															snake_case__									=     [
															    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(UpperCamelCase						):
																											auto_class.register(UpperCamelCase       ,	UpperCamelCase						)
																							auto_class.register(UpperCamelCase       ,	UpperCamelCase						)
																							# Trying to register something existing in the Transformers library will raise an error
																							with self.assertRaises(UpperCamelCase						):
																											auto_class.register(UpperCamelCase       ,	UpperCamelCase						)

																							# Now that the config is registered, it can be used as any other config with the auto-API
																							snake_case__									=     BertModelTester(self						).get_config()
																							snake_case__									=     NewModelConfig(**tiny_config.to_dict()						)
																							snake_case__									=     auto_class.from_config(UpperCamelCase						)
																							self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)

																							with tempfile.TemporaryDirectory() as tmp_dir:
																											model.save_pretrained(UpperCamelCase						)
																											snake_case__									=     auto_class.from_pretrained(UpperCamelCase						)
																											self.assertIsInstance(UpperCamelCase       ,	UpperCamelCase						)

											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 lowerCAmelCase_ (     self:      List[Any]						)		->		Union[str, Any]:
											with self.assertRaisesRegex(
											    UpperCamelCase       ,	'bert-base is not a local folder and is not a valid model identifier'						):
															snake_case__									=     TFAutoModel.from_pretrained('bert-base'						)
							def lowerCAmelCase_ (     self:      int						)		->		int:
											with self.assertRaisesRegex(
											    UpperCamelCase       ,	R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'						):
															snake_case__									=     TFAutoModel.from_pretrained(UpperCamelCase       ,	revision='aaaaaa'						)
							def lowerCAmelCase_ (     self:      str						)		->		Optional[Any]:
											with self.assertRaisesRegex(
											    UpperCamelCase       ,	'hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'       ,	):
															snake_case__									=     TFAutoModel.from_pretrained('hf-internal-testing/config-no-model'						)
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Tuple:
											with self.assertRaisesRegex(UpperCamelCase       ,	'Use `from_pt=True` to load this model'						):
															snake_case__									=     TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'						)


							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		List[str]:
											# Make sure we have cached the model.
											snake_case__									=     TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert'						)
											with RequestCounter() as counter:
															snake_case__									=     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
											snake_case__									=     TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded'						)
											with RequestCounter() as counter:
															snake_case__									=     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						)












def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				if digit_amount > 0:
								return round(number - int(_A  )      ,   _A  )
				return number - int(_A  )


if __name__ == "__main__":
							print(decimal_isolate(1.5_3, 0))
							print(decimal_isolate(3_5.3_4_5, 1))
							print(decimal_isolate(3_5.3_4_5, 2))
							print(decimal_isolate(3_5.3_4_5, 3))
							print(decimal_isolate(-1_4.7_8_9, 3))
							print(decimal_isolate(0, 2))
							print(decimal_isolate(-1_4.1_2_3, 1))
							print(decimal_isolate(-1_4.1_2_3, 2))
							print(decimal_isolate(-1_4.1_2_3, 3))




