


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  _a							(    _lowercase):
	_a  :  Optional[int]				   =							'''new-model'''


if is_tf_available():





		class  _a							(    _lowercase):
			_a  :  List[Any]				   =							NewModelConfig





@require_tf
class  _a							(    unittest.TestCase):




	@slow
	def 							UpperCAmelCase__( self						:		List[str]		)->	List[str]:
			lowerCAmelCase__       :      List[str]   					= '''bert-base-cased'''
			lowerCAmelCase__       :      List[Any]   					= AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
			self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
			self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      Any   					= TFAutoModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
			self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
			self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)




	@slow
	def 							UpperCAmelCase__( self						:		int		)->	Optional[Any]:
			lowerCAmelCase__       :      Tuple   					= '''bert-base-cased'''
			lowerCAmelCase__       :      List[str]   					= AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
			self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
			self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      List[Any]   					= TFAutoModelForPreTraining.from_pretrained(_SCREAMING_SNAKE_CASE		)
			self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
			self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)




	@slow
	def 							UpperCAmelCase__( self						:		Optional[int]		)->	str:
			for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCAmelCase__       :      Dict   					= AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
					self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
					self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

					lowerCAmelCase__       :      Optional[Any]   					= TFAutoModelForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE		)
					lowerCAmelCase__					,     lowerCAmelCase__       :      Any   					= TFAutoModelForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE		, output_loading_info=_SCREAMING_SNAKE_CASE		)
					self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
					self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)




	@slow
	def 							UpperCAmelCase__( self						:		Optional[int]		)->	List[Any]:
			for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCAmelCase__       :      Any   					= AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
					self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
					self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

					lowerCAmelCase__       :      Union[str, Any]   					= TFAutoModelWithLMHead.from_pretrained(_SCREAMING_SNAKE_CASE		)
					self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
					self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)




	@slow
	def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	str:
			for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCAmelCase__       :      int   					= AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
					self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
					self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

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




	@slow
	def 							UpperCAmelCase__( self						:		int		)->	Optional[Any]:
			for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCAmelCase__       :      Union[str, Any]   					= AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
					self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
					self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

					lowerCAmelCase__       :      Union[str, Any]   					= TFAutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE		)
					lowerCAmelCase__					,     lowerCAmelCase__       :      int   					= TFAutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE		, output_loading_info=_SCREAMING_SNAKE_CASE		)
					self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
					self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)




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

					lowerCAmelCase__       :      List[Any]   					= TFAutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE		)
					self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
					self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)




	@slow
	def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	Tuple:
			# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			for model_name in ["bert-base-uncased"]:
					lowerCAmelCase__       :      Union[str, Any]   					= AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
					self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
					self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

					lowerCAmelCase__       :      Optional[Any]   					= TFAutoModelForQuestionAnswering.from_pretrained(_SCREAMING_SNAKE_CASE		)
					self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
					self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)




	@slow
	@require_tensorflow_probability
	def 							UpperCAmelCase__( self						:		Dict		)->	Any:
			for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
					lowerCAmelCase__       :      Optional[Any]   					= AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
					self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
					self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

					lowerCAmelCase__       :      List[Any]   					= TFAutoModelForTableQuestionAnswering.from_pretrained(_SCREAMING_SNAKE_CASE		)
					lowerCAmelCase__					,     lowerCAmelCase__       :      Union[str, Any]   					= TFAutoModelForTableQuestionAnswering.from_pretrained(
					    _SCREAMING_SNAKE_CASE		, output_loading_info=_SCREAMING_SNAKE_CASE		)
					self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
					self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		int		)->	str:
			lowerCAmelCase__       :      Optional[Any]   					= TFAutoModelWithLMHead.from_pretrained(_SCREAMING_SNAKE_CASE		)
			self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
			self.assertEqual(model.num_parameters()		, 1_4410		)
			self.assertEqual(model.num_parameters(only_trainable=_SCREAMING_SNAKE_CASE		)		, 1_4410		)




	def 							UpperCAmelCase__( self						:		Any		)->	Tuple:
			lowerCAmelCase__       :      List[str]   					= TFAutoModelWithLMHead.from_pretrained(_SCREAMING_SNAKE_CASE		)
			self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
			self.assertEqual(model.num_parameters()		, 1_4410		)
			self.assertEqual(model.num_parameters(only_trainable=_SCREAMING_SNAKE_CASE		)		, 1_4410		)




	def 							UpperCAmelCase__( self						:		Dict		)->	Optional[int]:
			# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
			lowerCAmelCase__       :      int   					= TFAutoModel.from_pretrained('''sgugger/funnel-random-tiny'''		)
			self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      str   					= copy.deepcopy(model.config		)
			lowerCAmelCase__       :      Union[str, Any]   					= ['''FunnelBaseModel''']
			lowerCAmelCase__       :      Dict   					= TFAutoModel.from_config(_SCREAMING_SNAKE_CASE		)
			self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

			with tempfile.TemporaryDirectory() as tmp_dir:
					model.save_pretrained(_SCREAMING_SNAKE_CASE		)
					lowerCAmelCase__       :      int   					= TFAutoModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
					self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		int		)->	Tuple:
			try:
					AutoConfig.register('''new-model'''		, _SCREAMING_SNAKE_CASE		)

					lowerCAmelCase__       :      Optional[int]   					= [
					    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(_SCREAMING_SNAKE_CASE		):
											auto_class.register(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
									auto_class.register(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
									# Trying to register something existing in the Transformers library will raise an error
									with self.assertRaises(_SCREAMING_SNAKE_CASE		):
											auto_class.register(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

									# Now that the config is registered, it can be used as any other config with the auto-API
									lowerCAmelCase__       :      Tuple   					= BertModelTester(self		).get_config()
									lowerCAmelCase__       :      Optional[int]   					= NewModelConfig(**tiny_config.to_dict()		)
									lowerCAmelCase__       :      List[Any]   					= auto_class.from_config(_SCREAMING_SNAKE_CASE		)
									self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

									with tempfile.TemporaryDirectory() as tmp_dir:
											model.save_pretrained(_SCREAMING_SNAKE_CASE		)
											lowerCAmelCase__       :      Any   					= auto_class.from_pretrained(_SCREAMING_SNAKE_CASE		)
											self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

			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						:		int		)->	List[str]:
			with self.assertRaisesRegex(
			    _SCREAMING_SNAKE_CASE		, '''bert-base is not a local folder and is not a valid model identifier'''		):
					lowerCAmelCase__       :      List[Any]   					= TFAutoModel.from_pretrained('''bert-base'''		)




	def 							UpperCAmelCase__( self						:		Optional[int]		)->	Union[str, Any]:
			with self.assertRaisesRegex(
			    _SCREAMING_SNAKE_CASE		, r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''		):
					lowerCAmelCase__       :      List[str]   					= TFAutoModel.from_pretrained(_SCREAMING_SNAKE_CASE		, revision='''aaaaaa'''		)




	def 							UpperCAmelCase__( self						:		Dict		)->	Tuple:
			with self.assertRaisesRegex(
			    _SCREAMING_SNAKE_CASE		, '''hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'''		, ):
					lowerCAmelCase__       :      Optional[int]   					= TFAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''		)




	def 							UpperCAmelCase__( self						:		List[Any]		)->	Dict:
			with self.assertRaisesRegex(_SCREAMING_SNAKE_CASE		, '''Use `from_pt=True` to load this model'''		):
					lowerCAmelCase__       :      str   					= TFAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''		)




	def 							UpperCAmelCase__( self						:		int		)->	Any:
			# Make sure we have cached the model.
			lowerCAmelCase__       :      int   					= TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''		)
			with RequestCounter() as counter:
					lowerCAmelCase__       :      int   					= 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
			lowerCAmelCase__       :      Optional[Any]   					= TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''		)
			with RequestCounter() as counter:
					lowerCAmelCase__       :      Any   					= 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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}





class  _a							(    _lowercase):
	_a  :  Optional[int]				   =							'''xlm-roberta-xl'''




	def __init__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		str=25_0880		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=2560		, _SCREAMING_SNAKE_CASE						:		int=36		, _SCREAMING_SNAKE_CASE						:		Optional[int]=32		, _SCREAMING_SNAKE_CASE						:		Any=1_0240		, _SCREAMING_SNAKE_CASE						:		List[str]="gelu"		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=0.1		, _SCREAMING_SNAKE_CASE						:		Dict=0.1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=514		, _SCREAMING_SNAKE_CASE						:		Optional[int]=1		, _SCREAMING_SNAKE_CASE						:		Tuple=0.02		, _SCREAMING_SNAKE_CASE						:		Dict=1E-05		, _SCREAMING_SNAKE_CASE						:		Tuple=1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0		, _SCREAMING_SNAKE_CASE						:		Tuple=2		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]="absolute"		, _SCREAMING_SNAKE_CASE						:		Any=True		, _SCREAMING_SNAKE_CASE						:		Any=None		, **_SCREAMING_SNAKE_CASE						:		Tuple		, )->	str:
			super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE		, bos_token_id=_SCREAMING_SNAKE_CASE		, eos_token_id=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Any   					= vocab_size
			lowerCAmelCase__       :      List[Any]   					= hidden_size
			lowerCAmelCase__       :      int   					= num_hidden_layers
			lowerCAmelCase__       :      Dict   					= num_attention_heads
			lowerCAmelCase__       :      str   					= hidden_act
			lowerCAmelCase__       :      List[Any]   					= intermediate_size
			lowerCAmelCase__       :      Tuple   					= hidden_dropout_prob
			lowerCAmelCase__       :      str   					= attention_probs_dropout_prob
			lowerCAmelCase__       :      List[str]   					= max_position_embeddings
			lowerCAmelCase__       :      Any   					= type_vocab_size
			lowerCAmelCase__       :      Optional[Any]   					= initializer_range
			lowerCAmelCase__       :      Optional[int]   					= layer_norm_eps
			lowerCAmelCase__       :      Optional[int]   					= position_embedding_type
			lowerCAmelCase__       :      Any   					= use_cache
			lowerCAmelCase__       :      List[Any]   					= classifier_dropout





class  _a							(    _lowercase):




	@property
	def 							UpperCAmelCase__( self						:		Any		)->	Mapping[str, Mapping[int, str]]:
			if self.task == "multiple-choice":
					lowerCAmelCase__       :      int   					= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
			else:
					lowerCAmelCase__       :      List[Any]   					= {0: '''batch''', 1: '''sequence'''}
			return OrderedDict(
			    [
			        ('''input_ids''', dynamic_axis),
			        ('''attention_mask''', dynamic_axis),
			    ]		)




