

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 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''new-model'''


if is_tf_available():




		class 			UpperCAmelCase_      (   A       ):




									'''simple docstring'''



									a__						  =     NewModelConfig




@require_tf
class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							@slow
							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					"""bert-base-cased"""
										SCREAMING_SNAKE_CASE           =					AutoConfig.from_pretrained(a			)
										self.assertIsNotNone(a			)
										self.assertIsInstance(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					TFAutoModel.from_pretrained(a			)
										self.assertIsNotNone(a			)
										self.assertIsInstance(a       ,     a			)





							@slow
							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					"""bert-base-cased"""
										SCREAMING_SNAKE_CASE           =					AutoConfig.from_pretrained(a			)
										self.assertIsNotNone(a			)
										self.assertIsInstance(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					TFAutoModelForPreTraining.from_pretrained(a			)
										self.assertIsNotNone(a			)
										self.assertIsInstance(a       ,     a			)





							@slow
							def    _UpperCAmelCase     (					self       :	List[str]			)			->					Any:
										for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE           =					AutoConfig.from_pretrained(a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					TFAutoModelForCausalLM.from_pretrained(a			)
													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					TFAutoModelForCausalLM.from_pretrained(a       ,     output_loading_info=a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)





							@slow
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					int:
										for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE           =					AutoConfig.from_pretrained(a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					TFAutoModelWithLMHead.from_pretrained(a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)





							@slow
							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Optional[Any]:
										for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE           =					AutoConfig.from_pretrained(a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					TFAutoModelForMaskedLM.from_pretrained(a			)
													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					TFAutoModelForMaskedLM.from_pretrained(a       ,     output_loading_info=a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)





							@slow
							def    _UpperCAmelCase     (					self       :	str			)			->					Union[str, Any]:
										for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE           =					AutoConfig.from_pretrained(a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					TFAutoModelForSeqaSeqLM.from_pretrained(a			)
													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					TFAutoModelForSeqaSeqLM.from_pretrained(a       ,     output_loading_info=a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)





							@slow
							def    _UpperCAmelCase     (					self       :	Dict			)			->					Any:
										# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										for model_name in ["bert-base-uncased"]:
													SCREAMING_SNAKE_CASE           =					AutoConfig.from_pretrained(a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					TFAutoModelForSequenceClassification.from_pretrained(a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)





							@slow
							def    _UpperCAmelCase     (					self       :	Any			)			->					int:
										# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										for model_name in ["bert-base-uncased"]:
													SCREAMING_SNAKE_CASE           =					AutoConfig.from_pretrained(a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					TFAutoModelForQuestionAnswering.from_pretrained(a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)





							@slow
							@require_tensorflow_probability
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					str:
										for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
													SCREAMING_SNAKE_CASE           =					AutoConfig.from_pretrained(a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					TFAutoModelForTableQuestionAnswering.from_pretrained(a			)
													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					TFAutoModelForTableQuestionAnswering.from_pretrained(
													    a       ,     output_loading_info=a			)
													self.assertIsNotNone(a			)
													self.assertIsInstance(a       ,     a			)





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					TFAutoModelWithLMHead.from_pretrained(a			)
										self.assertIsInstance(a       ,     a			)
										self.assertEqual(model.num_parameters()       ,     14_410			)
										self.assertEqual(model.num_parameters(only_trainable=a			)       ,     14_410			)





							def    _UpperCAmelCase     (					self       :	Any			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					TFAutoModelWithLMHead.from_pretrained(a			)
										self.assertIsInstance(a       ,     a			)
										self.assertEqual(model.num_parameters()       ,     14_410			)
										self.assertEqual(model.num_parameters(only_trainable=a			)       ,     14_410			)





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

										SCREAMING_SNAKE_CASE           =					copy.deepcopy(model.config			)
										SCREAMING_SNAKE_CASE           =					["""FunnelBaseModel"""]
										SCREAMING_SNAKE_CASE           =					TFAutoModel.from_config(a			)
										self.assertIsInstance(a       ,     a			)

										with tempfile.TemporaryDirectory() as tmp_dir:
													model.save_pretrained(a			)
													SCREAMING_SNAKE_CASE           =					TFAutoModel.from_pretrained(a			)
													self.assertIsInstance(a       ,     a			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Tuple:
										try:
													AutoConfig.register("""new-model"""       ,     a			)

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

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

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

										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       :	Optional[int]			)			->					Union[str, Any]:
										with self.assertRaisesRegex(
										    a       ,     """bert-base is not a local folder and is not a valid model identifier"""			):
													SCREAMING_SNAKE_CASE           =					TFAutoModel.from_pretrained("""bert-base"""			)





							def    _UpperCAmelCase     (					self       :	Dict			)			->					Union[str, Any]:
										with self.assertRaisesRegex(
										    a       ,     R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""			):
													SCREAMING_SNAKE_CASE           =					TFAutoModel.from_pretrained(a       ,     revision="""aaaaaa"""			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					List[Any]:
										with self.assertRaisesRegex(
										    a       ,     """hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin"""       ,     ):
													SCREAMING_SNAKE_CASE           =					TFAutoModel.from_pretrained("""hf-internal-testing/config-no-model"""			)





							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					Any:
										with self.assertRaisesRegex(a       ,     """Use `from_pt=True` to load this model"""			):
													SCREAMING_SNAKE_CASE           =					TFAutoModel.from_pretrained("""hf-internal-testing/tiny-bert-pt-only"""			)







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












from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A						:							Any							=  logging.get_logger(__name__)

__A						:							int							=  {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''megatron-bert'''





							def __init__(					self       :	str       ,     a       :	Union[str, Any]=29_056       ,     a       :	Optional[int]=1_024       ,     a       :	str=24       ,     a       :	str=16       ,     a       :	str=4_096       ,     a       :	Any="gelu"       ,     a       :	Any=0.1       ,     a       :	Optional[int]=0.1       ,     a       :	Optional[int]=512       ,     a       :	Optional[Any]=2       ,     a       :	List[str]=0.02       ,     a       :	List[Any]=1E-1_2       ,     a       :	Any=0       ,     a       :	List[Any]="absolute"       ,     a       :	List[str]=True       ,     **a       :	Optional[Any]       ,     )			->					List[str]:
										super().__init__(pad_token_id=a       ,     **a			)

										SCREAMING_SNAKE_CASE           =					vocab_size
										SCREAMING_SNAKE_CASE           =					hidden_size
										SCREAMING_SNAKE_CASE           =					num_hidden_layers
										SCREAMING_SNAKE_CASE           =					num_attention_heads
										SCREAMING_SNAKE_CASE           =					hidden_act
										SCREAMING_SNAKE_CASE           =					intermediate_size
										SCREAMING_SNAKE_CASE           =					hidden_dropout_prob
										SCREAMING_SNAKE_CASE           =					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE           =					max_position_embeddings
										SCREAMING_SNAKE_CASE           =					type_vocab_size
										SCREAMING_SNAKE_CASE           =					initializer_range
										SCREAMING_SNAKE_CASE           =					layer_norm_eps
										SCREAMING_SNAKE_CASE           =					position_embedding_type
										SCREAMING_SNAKE_CASE           =					use_cache






