


"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import (
			    TFDebertaVaForMaskedLM,
			    TFDebertaVaForQuestionAnswering,
			    TFDebertaVaForSequenceClassification,
			    TFDebertaVaForTokenClassification,
			    TFDebertaVaModel,
			)



class 			_UpperCAmelCase :






		'''simple docstring'''

		def __init__(self				,       a_				,       a_=13				,       a_=7				,       a_=True				,       a_=True				,       a_=True				,       a_=True				,       a_=99				,       a_=32				,       a_=2				,       a_=4				,       a_=37				,       a_="gelu"				,       a_=0.1				,       a_=0.1				,       a_=5_12				,       a_=16				,       a_=2				,       a_=0.02				,       a_=False				,       a_=True				,       a_="None"				,       a_=3				,       a_=4				,       a_=None				,       ):





			'''simple docstring'''

			__snake_case	:						str						=				parent
			__snake_case	:						List[str]						=				batch_size
			__snake_case	:						Optional[int]						=				seq_length
			__snake_case	:						List[Any]						=				is_training
			__snake_case	:						Optional[Any]						=				use_input_mask
			__snake_case	:						Optional[int]						=				use_token_type_ids
			__snake_case	:						Optional[int]						=				use_labels
			__snake_case	:						Dict						=				vocab_size
			__snake_case	:						List[Any]						=				hidden_size
			__snake_case	:						str						=				num_hidden_layers
			__snake_case	:						Any						=				num_attention_heads
			__snake_case	:						Tuple						=				intermediate_size
			__snake_case	:						int						=				hidden_act
			__snake_case	:						int						=				hidden_dropout_prob
			__snake_case	:						List[Any]						=				attention_probs_dropout_prob
			__snake_case	:						Dict						=				max_position_embeddings
			__snake_case	:						str						=				type_vocab_size
			__snake_case	:						Any						=				type_sequence_label_size
			__snake_case	:						Optional[int]						=				initializer_range
			__snake_case	:						Union[str, Any]						=				num_labels
			__snake_case	:						Tuple						=				num_choices
			__snake_case	:						int						=				relative_attention
			__snake_case	:						List[Any]						=				position_biased_input
			__snake_case	:						Optional[Any]						=				pos_att_type
			__snake_case	:						str						=				scope



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size					)

			__snake_case	:						List[str]						=				None
			if self.use_input_mask:
				__snake_case	:						Optional[int]						=				random_attention_mask([self.batch_size, self.seq_length]					)

			__snake_case	:						List[str]						=				None
			if self.use_token_type_ids:
				__snake_case	:						Union[str, Any]						=				ids_tensor([self.batch_size, self.seq_length]				,       self.type_vocab_size					)

			__snake_case	:						Optional[int]						=				None
			__snake_case	:						Union[str, Any]						=				None
			__snake_case	:						Union[str, Any]						=				None
			if self.use_labels:
				__snake_case	:						List[str]						=				ids_tensor([self.batch_size]				,       self.type_sequence_label_size					)
				__snake_case	:						Dict						=				ids_tensor([self.batch_size, self.seq_length]				,       self.num_labels					)

			__snake_case	:						Dict						=				DebertaVaConfig(
			    vocab_size=self.vocab_size				,       hidden_size=self.hidden_size				,       num_hidden_layers=self.num_hidden_layers				,       num_attention_heads=self.num_attention_heads				,       intermediate_size=self.intermediate_size				,       hidden_act=self.hidden_act				,       hidden_dropout_prob=self.hidden_dropout_prob				,       attention_probs_dropout_prob=self.attention_probs_dropout_prob				,       max_position_embeddings=self.max_position_embeddings				,       type_vocab_size=self.type_vocab_size				,       relative_attention=self.relative_attention				,       position_biased_input=self.position_biased_input				,       initializer_range=self.initializer_range				,       return_dict=a_				,       )

			return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						int						=				TFDebertaVaModel(config=a_					)
			__snake_case	:						Dict						=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

			__snake_case	:						int						=				[input_ids, input_mask]
			__snake_case	:						List[str]						=				model(a_					)

			__snake_case	:						List[Any]						=				model(a_					)

			self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				TFDebertaVaForMaskedLM(config=a_					)
			__snake_case	:						str						=				{
			    '''input_ids''': input_ids,
			    '''attention_mask''': input_mask,
			    '''token_type_ids''': token_type_ids,
			}
			__snake_case	:						Optional[Any]						=				model(a_					)
			self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				self.num_labels
			__snake_case	:						List[Any]						=				TFDebertaVaForSequenceClassification(config=a_					)
			__snake_case	:						Optional[Any]						=				{
			    '''input_ids''': input_ids,
			    '''attention_mask''': input_mask,
			    '''token_type_ids''': token_type_ids,
			}

			__snake_case	:						int						=				model(a_					)
			self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				self.num_labels
			__snake_case	:						Any						=				TFDebertaVaForTokenClassification(config=a_					)
			__snake_case	:						str						=				{
			    '''input_ids''': input_ids,
			    '''attention_mask''': input_mask,
			    '''token_type_ids''': token_type_ids,
			}
			__snake_case	:						Dict						=				model(a_					)
			self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.num_labels)					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						int						=				TFDebertaVaForQuestionAnswering(config=a_					)
			__snake_case	:						Optional[Any]						=				{
			    '''input_ids''': input_ids,
			    '''attention_mask''': input_mask,
			    '''token_type_ids''': token_type_ids,
			}

			__snake_case	:						int						=				model(a_					)
			self.parent.assertEqual(result.start_logits.shape				,       (self.batch_size, self.seq_length)					)
			self.parent.assertEqual(result.end_logits.shape				,       (self.batch_size, self.seq_length)					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				self.prepare_config_and_inputs()
			(
			    (
			    __snake_case
			)	,							(
			    __snake_case
			)	,							(
			    __snake_case
			)	,							(
			    __snake_case
			)	,							(
			    __snake_case
			)	,							(
			    __snake_case
			)	,							(
			    __snake_case
			)	,							
			)	:						List[str]						=				config_and_inputs
			__snake_case	:						Dict						=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
			return config, inputs_dict



@require_tf
class 			_UpperCAmelCase (							__snake_case,				__snake_case,				unittest.TestCase				):






		'''simple docstring'''

		lowerCamelCase__				=(
		    (
		        TFDebertaVaModel,
		        TFDebertaVaForMaskedLM,
		        TFDebertaVaForQuestionAnswering,
		        TFDebertaVaForSequenceClassification,
		        TFDebertaVaForTokenClassification,
		    )
		    if is_tf_available()
		    else ()
		)
		lowerCamelCase__				=(
		    {
		        'feature-extraction': TFDebertaVaModel,
		        'fill-mask': TFDebertaVaForMaskedLM,
		        'question-answering': TFDebertaVaForQuestionAnswering,
		        'text-classification': TFDebertaVaForSequenceClassification,
		        'token-classification': TFDebertaVaForTokenClassification,
		        'zero-shot': TFDebertaVaForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)

		lowerCamelCase__				=False
		lowerCamelCase__				=False



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				TFDebertaVaModelTester(self					)
			__snake_case	:						Optional[Any]						=				ConfigTester(self				,       config_class=a_				,       hidden_size=37					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self.config_tester.run_common_tests()



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_lm(*a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_question_answering(*a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_sequence_classification(*a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_token_classification(*a_					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''					)
			self.assertIsNotNone(a_					)



@require_tf
class 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

		@unittest.skip(reason='''Model not available yet'''					)
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			pass



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''					)
			__snake_case	:						Tuple						=				tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]					)
			__snake_case	:						Tuple						=				tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
			__snake_case	:						str						=				model(a_				,       attention_mask=a_					)[0]

			__snake_case	:						List[str]						=				tf.constant(
			    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]					)
			tf.debugging.assert_near(output[:, 1:4, 1:4]				,       a_				,       atol=1E-4					)









"""simple docstring"""
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 (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				='new-model'


if is_tf_available():



			class 			_UpperCAmelCase (							__snake_case				):






					'''simple docstring'''

					lowerCamelCase__				=NewModelConfig



@require_tf
class 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				'''bert-base-cased'''
			__snake_case	:						Dict						=				AutoConfig.from_pretrained(a_					)
			self.assertIsNotNone(a_					)
			self.assertIsInstance(a_				,       a_					)

			__snake_case	:						int						=				TFAutoModel.from_pretrained(a_					)
			self.assertIsNotNone(a_					)
			self.assertIsInstance(a_				,       a_					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				'''bert-base-cased'''
			__snake_case	:						Optional[Any]						=				AutoConfig.from_pretrained(a_					)
			self.assertIsNotNone(a_					)
			self.assertIsInstance(a_				,       a_					)

			__snake_case	:						List[Any]						=				TFAutoModelForPreTraining.from_pretrained(a_					)
			self.assertIsNotNone(a_					)
			self.assertIsInstance(a_				,       a_					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__snake_case	:						int						=				AutoConfig.from_pretrained(a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)

				__snake_case	:						Dict						=				TFAutoModelForCausalLM.from_pretrained(a_					)
				__snake_case	,							__snake_case	:						int						=				TFAutoModelForCausalLM.from_pretrained(a_				,       output_loading_info=a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__snake_case	:						Tuple						=				AutoConfig.from_pretrained(a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)

				__snake_case	:						Tuple						=				TFAutoModelWithLMHead.from_pretrained(a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__snake_case	:						List[str]						=				AutoConfig.from_pretrained(a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)

				__snake_case	:						Optional[Any]						=				TFAutoModelForMaskedLM.from_pretrained(a_					)
				__snake_case	,							__snake_case	:						int						=				TFAutoModelForMaskedLM.from_pretrained(a_				,       output_loading_info=a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__snake_case	:						int						=				AutoConfig.from_pretrained(a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)

				__snake_case	:						List[Any]						=				TFAutoModelForSeqaSeqLM.from_pretrained(a_					)
				__snake_case	,							__snake_case	:						Union[str, Any]						=				TFAutoModelForSeqaSeqLM.from_pretrained(a_				,       output_loading_info=a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			for model_name in ["bert-base-uncased"]:
				__snake_case	:						Any						=				AutoConfig.from_pretrained(a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)

				__snake_case	:						Dict						=				TFAutoModelForSequenceClassification.from_pretrained(a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			for model_name in ["bert-base-uncased"]:
				__snake_case	:						Optional[int]						=				AutoConfig.from_pretrained(a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)

				__snake_case	:						Optional[Any]						=				TFAutoModelForQuestionAnswering.from_pretrained(a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)



		@slow
		@require_tensorflow_probability
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
				__snake_case	:						Dict						=				AutoConfig.from_pretrained(a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)

				__snake_case	:						List[str]						=				TFAutoModelForTableQuestionAnswering.from_pretrained(a_					)
				__snake_case	,							__snake_case	:						Dict						=				TFAutoModelForTableQuestionAnswering.from_pretrained(
				    a_				,       output_loading_info=a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				TFAutoModelWithLMHead.from_pretrained(a_					)
			self.assertIsInstance(a_				,       a_					)
			self.assertEqual(model.num_parameters()				,       1_44_10					)
			self.assertEqual(model.num_parameters(only_trainable=a_					)				,       1_44_10					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				TFAutoModelWithLMHead.from_pretrained(a_					)
			self.assertIsInstance(a_				,       a_					)
			self.assertEqual(model.num_parameters()				,       1_44_10					)
			self.assertEqual(model.num_parameters(only_trainable=a_					)				,       1_44_10					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				TFAutoModel.from_pretrained('''sgugger/funnel-random-tiny'''					)
			self.assertIsInstance(a_				,       a_					)

			__snake_case	:						Optional[Any]						=				copy.deepcopy(model.config					)
			__snake_case	:						int						=				['''FunnelBaseModel''']
			__snake_case	:						Any						=				TFAutoModel.from_config(a_					)
			self.assertIsInstance(a_				,       a_					)

			with tempfile.TemporaryDirectory() as tmp_dir:
				model.save_pretrained(a_					)
				__snake_case	:						Dict						=				TFAutoModel.from_pretrained(a_					)
				self.assertIsInstance(a_				,       a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			try:
				AutoConfig.register('''new-model'''				,       a_					)

				__snake_case	:						List[str]						=				[
				    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
						__snake_case	:						Union[str, Any]						=				BertModelTester(self					).get_config()
						__snake_case	:						str						=				NewModelConfig(**tiny_config.to_dict()					)
						__snake_case	:						Optional[int]						=				auto_class.from_config(a_					)
						self.assertIsInstance(a_				,       a_					)

						with tempfile.TemporaryDirectory() as tmp_dir:
							model.save_pretrained(a_					)
							__snake_case	:						Optional[int]						=				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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			with self.assertRaisesRegex(
			    a_				,       '''bert-base is not a local folder and is not a valid model identifier'''					):
				__snake_case	:						Any						=				TFAutoModel.from_pretrained('''bert-base'''					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			with self.assertRaisesRegex(
			    a_				,       R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''					):
				__snake_case	:						Dict						=				TFAutoModel.from_pretrained(a_				,       revision='''aaaaaa'''					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			with self.assertRaisesRegex(
			    a_				,       '''hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'''				,       ):
				__snake_case	:						Any						=				TFAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			with self.assertRaisesRegex(a_				,       '''Use `from_pt=True` to load this model'''					):
				__snake_case	:						Dict						=				TFAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''					)
			with RequestCounter() as counter:
				__snake_case	:						List[Any]						=				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	:						Dict						=				TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''					)
			with RequestCounter() as counter:
				__snake_case	:						Dict						=				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					)





