
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     lowercase (		_SCREAMING_SNAKE_CASE       ):
		__lowercase	:	Optional[int]     					=    "new-model"


if is_tf_available():


					class     lowercase (		_SCREAMING_SNAKE_CASE       ):
							__lowercase	:	List[Any]     					=    NewModelConfig


@require_tf
class     lowercase (		unittest.TestCase       ):



		@slow
		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					'bert-base-cased'
				UpperCamelCase          =					AutoConfig.from_pretrained(A_	)
				self.assertIsNotNone(A_	)
				self.assertIsInstance(A_    ,				A_	)

				UpperCamelCase          =					TFAutoModel.from_pretrained(A_	)
				self.assertIsNotNone(A_	)
				self.assertIsInstance(A_    ,				A_	)



		@slow
		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					'bert-base-cased'
				UpperCamelCase          =					AutoConfig.from_pretrained(A_	)
				self.assertIsNotNone(A_	)
				self.assertIsInstance(A_    ,				A_	)

				UpperCamelCase          =					TFAutoModelForPreTraining.from_pretrained(A_	)
				self.assertIsNotNone(A_	)
				self.assertIsInstance(A_    ,				A_	)



		@slow
		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase          =					AutoConfig.from_pretrained(A_	)
						self.assertIsNotNone(A_	)
						self.assertIsInstance(A_    ,				A_	)

						UpperCamelCase          =					TFAutoModelForCausalLM.from_pretrained(A_	)
						UpperCamelCase     ,  UpperCamelCase          =					TFAutoModelForCausalLM.from_pretrained(A_    ,				output_loading_info=A_	)
						self.assertIsNotNone(A_	)
						self.assertIsInstance(A_    ,				A_	)



		@slow
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase          =					AutoConfig.from_pretrained(A_	)
						self.assertIsNotNone(A_	)
						self.assertIsInstance(A_    ,				A_	)

						UpperCamelCase          =					TFAutoModelWithLMHead.from_pretrained(A_	)
						self.assertIsNotNone(A_	)
						self.assertIsInstance(A_    ,				A_	)



		@slow
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase          =					AutoConfig.from_pretrained(A_	)
						self.assertIsNotNone(A_	)
						self.assertIsInstance(A_    ,				A_	)

						UpperCamelCase          =					TFAutoModelForMaskedLM.from_pretrained(A_	)
						UpperCamelCase     ,  UpperCamelCase          =					TFAutoModelForMaskedLM.from_pretrained(A_    ,				output_loading_info=A_	)
						self.assertIsNotNone(A_	)
						self.assertIsInstance(A_    ,				A_	)



		@slow
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase          =					AutoConfig.from_pretrained(A_	)
						self.assertIsNotNone(A_	)
						self.assertIsInstance(A_    ,				A_	)

						UpperCamelCase          =					TFAutoModelForSeqaSeqLM.from_pretrained(A_	)
						UpperCamelCase     ,  UpperCamelCase          =					TFAutoModelForSeqaSeqLM.from_pretrained(A_    ,				output_loading_info=A_	)
						self.assertIsNotNone(A_	)
						self.assertIsInstance(A_    ,				A_	)



		@slow
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				for model_name in ["bert-base-uncased"]:
						UpperCamelCase          =					AutoConfig.from_pretrained(A_	)
						self.assertIsNotNone(A_	)
						self.assertIsInstance(A_    ,				A_	)

						UpperCamelCase          =					TFAutoModelForSequenceClassification.from_pretrained(A_	)
						self.assertIsNotNone(A_	)
						self.assertIsInstance(A_    ,				A_	)



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

						UpperCamelCase          =					TFAutoModelForQuestionAnswering.from_pretrained(A_	)
						self.assertIsNotNone(A_	)
						self.assertIsInstance(A_    ,				A_	)



		@slow
		@require_tensorflow_probability
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
						UpperCamelCase          =					AutoConfig.from_pretrained(A_	)
						self.assertIsNotNone(A_	)
						self.assertIsInstance(A_    ,				A_	)

						UpperCamelCase          =					TFAutoModelForTableQuestionAnswering.from_pretrained(A_	)
						UpperCamelCase     ,  UpperCamelCase          =					TFAutoModelForTableQuestionAnswering.from_pretrained(
						    A_    ,				output_loading_info=A_	)
						self.assertIsNotNone(A_	)
						self.assertIsInstance(A_    ,				A_	)



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					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]:
				"""simple docstring"""
				UpperCamelCase          =					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	)  -> int:
				"""simple docstring"""
				# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
				UpperCamelCase          =					TFAutoModel.from_pretrained('sgugger/funnel-random-tiny'	)
				self.assertIsInstance(A_    ,				A_	)

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

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



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				try:
						AutoConfig.register('new-model'    ,				A_	)

						UpperCamelCase          =					[
						    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
										UpperCamelCase          =					BertModelTester(self	).get_config()
										UpperCamelCase          =					NewModelConfig(**tiny_config.to_dict()	)
										UpperCamelCase          =					auto_class.from_config(A_	)
										self.assertIsInstance(A_    ,				A_	)

										with tempfile.TemporaryDirectory() as tmp_dir:
												model.save_pretrained(A_	)
												UpperCamelCase          =					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	)  -> Any:
				"""simple docstring"""
				with self.assertRaisesRegex(
				    A_    ,				'bert-base is not a local folder and is not a valid model identifier'	):
						UpperCamelCase          =					TFAutoModel.from_pretrained('bert-base'	)



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				with self.assertRaisesRegex(
				    A_    ,				r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'	):
						UpperCamelCase          =					TFAutoModel.from_pretrained(A_    ,				revision='aaaaaa'	)



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



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				with self.assertRaisesRegex(A_    ,				'Use `from_pt=True` to load this model'	):
						UpperCamelCase          =					TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'	)



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				# Make sure we have cached the model.
				UpperCamelCase          =					TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert'	)
				with RequestCounter() as counter:
						UpperCamelCase          =					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
				UpperCamelCase          =					TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded'	)
				with RequestCounter() as counter:
						UpperCamelCase          =					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	)


_UpperCAmelCase :			str              =	[
    999,
    800,
    799,
    600,
    599,
    500,
    400,
    399,
    377,
    355,
    333,
    311,
    288,
    266,
    244,
    222,
    200,
    199,
    177,
    155,
    133,
    111,
    88,
    66,
    44,
    22,
    0,
]

_UpperCAmelCase :			Any              =	[
    999,
    976,
    952,
    928,
    905,
    882,
    858,
    857,
    810,
    762,
    715,
    714,
    572,
    429,
    428,
    286,
    285,
    238,
    190,
    143,
    142,
    118,
    95,
    71,
    47,
    24,
    0,
]

_UpperCAmelCase :			List[Any]              =	[
    999,
    988,
    977,
    966,
    955,
    944,
    933,
    922,
    911,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    350,
    300,
    299,
    266,
    233,
    200,
    199,
    179,
    159,
    140,
    120,
    100,
    99,
    88,
    77,
    66,
    55,
    44,
    33,
    22,
    11,
    0,
]

_UpperCAmelCase :			Tuple              =	[
    999,
    995,
    992,
    989,
    985,
    981,
    978,
    975,
    971,
    967,
    964,
    961,
    957,
    956,
    951,
    947,
    942,
    937,
    933,
    928,
    923,
    919,
    914,
    913,
    908,
    903,
    897,
    892,
    887,
    881,
    876,
    871,
    870,
    864,
    858,
    852,
    846,
    840,
    834,
    828,
    827,
    820,
    813,
    806,
    799,
    792,
    785,
    784,
    777,
    770,
    763,
    756,
    749,
    742,
    741,
    733,
    724,
    716,
    707,
    699,
    698,
    688,
    677,
    666,
    656,
    655,
    645,
    634,
    623,
    613,
    612,
    598,
    584,
    570,
    569,
    555,
    541,
    527,
    526,
    505,
    484,
    483,
    462,
    440,
    439,
    396,
    395,
    352,
    351,
    308,
    307,
    264,
    263,
    220,
    219,
    176,
    132,
    88,
    44,
    0,
]

_UpperCAmelCase :			Union[str, Any]              =	[
    999,
    997,
    995,
    992,
    990,
    988,
    986,
    984,
    981,
    979,
    977,
    975,
    972,
    970,
    968,
    966,
    964,
    961,
    959,
    957,
    956,
    954,
    951,
    949,
    946,
    944,
    941,
    939,
    936,
    934,
    931,
    929,
    926,
    924,
    921,
    919,
    916,
    914,
    913,
    910,
    907,
    905,
    902,
    899,
    896,
    893,
    891,
    888,
    885,
    882,
    879,
    877,
    874,
    871,
    870,
    867,
    864,
    861,
    858,
    855,
    852,
    849,
    846,
    843,
    840,
    837,
    834,
    831,
    828,
    827,
    824,
    821,
    817,
    814,
    811,
    808,
    804,
    801,
    798,
    795,
    791,
    788,
    785,
    784,
    780,
    777,
    774,
    770,
    766,
    763,
    760,
    756,
    752,
    749,
    746,
    742,
    741,
    737,
    733,
    730,
    726,
    722,
    718,
    714,
    710,
    707,
    703,
    699,
    698,
    694,
    690,
    685,
    681,
    677,
    673,
    669,
    664,
    660,
    656,
    655,
    650,
    646,
    641,
    636,
    632,
    627,
    622,
    618,
    613,
    612,
    607,
    602,
    596,
    591,
    586,
    580,
    575,
    570,
    569,
    563,
    557,
    551,
    545,
    539,
    533,
    527,
    526,
    519,
    512,
    505,
    498,
    491,
    484,
    483,
    474,
    466,
    457,
    449,
    440,
    439,
    428,
    418,
    407,
    396,
    395,
    381,
    366,
    352,
    351,
    330,
    308,
    307,
    286,
    264,
    263,
    242,
    220,
    219,
    176,
    175,
    132,
    131,
    88,
    44,
    0,
]

_UpperCAmelCase :			List[str]              =	[
    999,
    991,
    982,
    974,
    966,
    958,
    950,
    941,
    933,
    925,
    916,
    908,
    900,
    899,
    874,
    850,
    825,
    800,
    799,
    700,
    600,
    500,
    400,
    300,
    200,
    100,
    0,
]

_UpperCAmelCase :			Tuple              =	[
    999,
    992,
    985,
    978,
    971,
    964,
    957,
    949,
    942,
    935,
    928,
    921,
    914,
    907,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    300,
    299,
    200,
    199,
    100,
    99,
    0,
]

_UpperCAmelCase :			Dict              =	[
    999,
    996,
    992,
    989,
    985,
    982,
    979,
    975,
    972,
    968,
    965,
    961,
    958,
    955,
    951,
    948,
    944,
    941,
    938,
    934,
    931,
    927,
    924,
    920,
    917,
    914,
    910,
    907,
    903,
    900,
    899,
    891,
    884,
    876,
    869,
    861,
    853,
    846,
    838,
    830,
    823,
    815,
    808,
    800,
    799,
    788,
    777,
    766,
    755,
    744,
    733,
    722,
    711,
    700,
    699,
    688,
    677,
    666,
    655,
    644,
    633,
    622,
    611,
    600,
    599,
    585,
    571,
    557,
    542,
    528,
    514,
    500,
    499,
    485,
    471,
    457,
    442,
    428,
    414,
    400,
    399,
    379,
    359,
    340,
    320,
    300,
    299,
    279,
    259,
    240,
    220,
    200,
    199,
    166,
    133,
    100,
    99,
    66,
    33,
    0,
]
