






import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
					from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   Tuple=None      ,    UpperCamelCase_    :   str=None      ,    UpperCamelCase_    :   int=None      ,    UpperCamelCase_    :   Optional[int]=None      ,    UpperCamelCase_    :   Dict=None      ,    )					->				Tuple:

						"""simple docstring"""



						if attention_mask is None:
												lowerCAmelCase__        =  input_ids.ne(config.pad_token_id						)
						if decoder_attention_mask is None:
												lowerCAmelCase__        =  decoder_input_ids.ne(config.pad_token_id						)
						if head_mask is None:
												lowerCAmelCase__        =  torch.ones(config.encoder_layers      ,    config.encoder_attention_heads      ,    device=UpperCamelCase_						)
						if decoder_head_mask is None:
												lowerCAmelCase__        =  torch.ones(config.decoder_layers      ,    config.decoder_attention_heads      ,    device=UpperCamelCase_						)
						if cross_attn_head_mask is None:
												lowerCAmelCase__        =  torch.ones(config.decoder_layers      ,    config.decoder_attention_heads      ,    device=UpperCamelCase_						)
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": attention_mask,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						    "cross_attn_head_mask": cross_attn_head_mask,
						}


class       __SCREAMING_SNAKE_CASE   :






						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase=13       ,  _UpperCamelCase=7       ,  _UpperCamelCase=True       ,  _UpperCamelCase=False       ,  _UpperCamelCase=99       ,  _UpperCamelCase=16       ,  _UpperCamelCase=2       ,  _UpperCamelCase=4       ,  _UpperCamelCase=4       ,  _UpperCamelCase="relu"       ,  _UpperCamelCase=0.1       ,  _UpperCamelCase=0.1       ,  _UpperCamelCase=0.0       ,  _UpperCamelCase=0.0       ,  _UpperCamelCase=20       ,  _UpperCamelCase=2       ,  _UpperCamelCase=1       ,  _UpperCamelCase=0       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  parent
												lowerCAmelCase__        =  batch_size
												lowerCAmelCase__        =  seq_length
												lowerCAmelCase__        =  is_training
												lowerCAmelCase__        =  use_labels
												lowerCAmelCase__        =  vocab_size
												lowerCAmelCase__        =  hidden_size
												lowerCAmelCase__        =  num_hidden_layers
												lowerCAmelCase__        =  num_attention_heads
												lowerCAmelCase__        =  intermediate_size
												lowerCAmelCase__        =  hidden_act
												lowerCAmelCase__        =  hidden_dropout_prob
												lowerCAmelCase__        =  attention_probs_dropout_prob
												lowerCAmelCase__        =  encoder_layerdrop
												lowerCAmelCase__        =  decoder_layerdrop
												lowerCAmelCase__        =  max_position_embeddings
												lowerCAmelCase__        =  eos_token_id
												lowerCAmelCase__        =  pad_token_id
												lowerCAmelCase__        =  bos_token_id






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size				)
												lowerCAmelCase__        =  self.eos_token_id  # Eos Token
												lowerCAmelCase__        =  ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size				)

												# we need to clamp the input ids here to avoid having pad token in between
												# this is because for M2M100 the position_ids are prepared such that
												# all pad tokens have pos id = 2 and rest are between 2..seq_length
												# and the seq_length here is seq_length - num_pad_tokens
												# but when using past, there is no way of knowing if the past input ids had
												# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
												# position_ids being off by num_pad_tokens in past input
												lowerCAmelCase__        =  input_ids.clamp(self.pad_token_id + 1				)
												lowerCAmelCase__        =  decoder_input_ids.clamp(self.pad_token_id + 1				)

												lowerCAmelCase__        =  self.get_config()
												lowerCAmelCase__        =  prepare_mam_aaa_inputs_dict(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				)
												return config, inputs_dict






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return MaMaaaConfig(
												    vocab_size=self.vocab_size       ,  d_model=self.hidden_size       ,  encoder_layers=self.num_hidden_layers       ,  decoder_layers=self.num_hidden_layers       ,  encoder_attention_heads=self.num_attention_heads       ,  decoder_attention_heads=self.num_attention_heads       ,  encoder_ffn_dim=self.intermediate_size       ,  decoder_ffn_dim=self.intermediate_size       ,  dropout=self.hidden_dropout_prob       ,  attention_dropout=self.attention_probs_dropout_prob       ,  encoder_layerdrop=self.encoder_layerdrop       ,  decoder_layerdrop=self.decoder_layerdrop       ,  max_position_embeddings=self.max_position_embeddings       ,  eos_token_id=self.eos_token_id       ,  bos_token_id=self.bos_token_id       ,  pad_token_id=self.pad_token_id       ,  )






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__  ,    lowerCAmelCase__        =  self.prepare_config_and_inputs()
												return config, inputs_dict






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  MaMaaaModel(config=_UpperCamelCase				).get_decoder().to(_UpperCamelCase				).eval()
												lowerCAmelCase__        =  inputs_dict['input_ids']
												lowerCAmelCase__        =  inputs_dict['attention_mask']
												lowerCAmelCase__        =  inputs_dict['head_mask']

												# first forward pass
												lowerCAmelCase__        =  model(_UpperCamelCase       ,  attention_mask=_UpperCamelCase       ,  head_mask=_UpperCamelCase       ,  use_cache=_UpperCamelCase				)

												lowerCAmelCase__  ,    lowerCAmelCase__        =  outputs.to_tuple()

												# create hypothetical multiple next token and extent to next_input_ids
												lowerCAmelCase__        =  ids_tensor((self.batch_size, 3)       ,  config.vocab_size				)
												lowerCAmelCase__        =  ids_tensor((self.batch_size, 3)       ,  2				)

												# append to next input_ids and
												lowerCAmelCase__        =  torch.cat([input_ids, next_tokens]       ,  dim=-1				)
												lowerCAmelCase__        =  torch.cat([attention_mask, next_attn_mask]       ,  dim=-1				)

												lowerCAmelCase__        =  model(_UpperCamelCase       ,  attention_mask=_UpperCamelCase				)['last_hidden_state']
												lowerCAmelCase__        =  model(_UpperCamelCase       ,  attention_mask=_UpperCamelCase       ,  past_key_values=_UpperCamelCase				)[
												    'last_hidden_state'
												]

												# select random slice
												lowerCAmelCase__        =  ids_tensor((1,)       ,  output_from_past.shape[-1]				).item()
												lowerCAmelCase__        =  output_from_no_past[:, -3:, random_slice_idx].detach()
												lowerCAmelCase__        =  output_from_past[:, :, random_slice_idx].detach()

												self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]				)

												# test that outputs are equal for slice
												self.parent.assertTrue(torch.allclose(_UpperCamelCase       ,  _UpperCamelCase       ,  atol=1E-2				)				)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  MaMaaaModel(config=_UpperCamelCase				).to(_UpperCamelCase				).eval()
												lowerCAmelCase__        =  model(**_UpperCamelCase				)

												lowerCAmelCase__        =  outputs.encoder_last_hidden_state
												lowerCAmelCase__        =  outputs.last_hidden_state

												with tempfile.TemporaryDirectory() as tmpdirname:
																		lowerCAmelCase__        =  model.get_encoder()
																		encoder.save_pretrained(_UpperCamelCase				)
																		lowerCAmelCase__        =  MaMaaaEncoder.from_pretrained(_UpperCamelCase				).to(_UpperCamelCase				)

												lowerCAmelCase__        =  encoder(inputs_dict['input_ids']       ,  attention_mask=inputs_dict['attention_mask']				)[
												    0
												]

												self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3				)

												with tempfile.TemporaryDirectory() as tmpdirname:
																		lowerCAmelCase__        =  model.get_decoder()
																		decoder.save_pretrained(_UpperCamelCase				)
																		lowerCAmelCase__        =  MaMaaaDecoder.from_pretrained(_UpperCamelCase				).to(_UpperCamelCase				)

												lowerCAmelCase__        =  decoder(
												    input_ids=inputs_dict['decoder_input_ids']       ,  attention_mask=inputs_dict['decoder_attention_mask']       ,  encoder_hidden_states=_UpperCamelCase       ,  encoder_attention_mask=inputs_dict['attention_mask']       ,  )[0]

												self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3				)




@require_torch
class       __SCREAMING_SNAKE_CASE   (				__lowercase						,   __lowercase						,   __lowercase						,   unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    Optional[Any]						=   (
						    (
						        MaMaaaModel,
						        MaMaaaForConditionalGeneration,
						    )
						    if is_torch_available()
						    else ()
						)
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   (
						    {
						        '''conversational''': MaMaaaForConditionalGeneration,
						        '''feature-extraction''': MaMaaaModel,
						        '''summarization''': MaMaaaForConditionalGeneration,
						        '''text2text-generation''': MaMaaaForConditionalGeneration,
						        '''translation''': MaMaaaForConditionalGeneration,
						    }
						    if is_torch_available()
						    else {}
						)
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   True
						_SCREAMING_SNAKE_CASE		:    Optional[Any]						=   True
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   False
						_SCREAMING_SNAKE_CASE		:    Tuple						=   False






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												if pipeline_test_casse_name == "TranslationPipelineTests":
																		# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
																		# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
																		return True

												return False






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  MaMaaaModelTester(self				)
												lowerCAmelCase__        =  ConfigTester(self       ,  config_class=_UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												self.config_tester.run_common_tests()






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__  ,    lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																		lowerCAmelCase__        =  model_class(_UpperCamelCase				)

																		with tempfile.TemporaryDirectory() as tmpdirname:
																								model.save_pretrained(_UpperCamelCase				)
																								lowerCAmelCase__  ,    lowerCAmelCase__        =  model_class.from_pretrained(_UpperCamelCase       ,  output_loading_info=_UpperCamelCase				)
																		self.assertEqual(info['missing_keys']       ,  []				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_decoder_model_past_large_inputs(*_UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_encoder_decoder_model_standalone(*_UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__  ,    lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
																		lowerCAmelCase__        =  model_class(_UpperCamelCase				)
																		model.to(_UpperCamelCase				)
																		model.eval()

																		lowerCAmelCase__        =  copy.deepcopy(self._prepare_for_class(_UpperCamelCase       ,  _UpperCamelCase				)				)

																		if not self.is_encoder_decoder:
																								lowerCAmelCase__        =  inputs['input_ids']
																								del inputs["input_ids"]
																		else:
																								lowerCAmelCase__        =  inputs['input_ids']
																								lowerCAmelCase__        =  inputs.get('decoder_input_ids'       ,  _UpperCamelCase				)
																								del inputs["input_ids"]
																								inputs.pop('decoder_input_ids'       ,  _UpperCamelCase				)

																		lowerCAmelCase__        =  model.get_input_embeddings()
																		if not self.is_encoder_decoder:
																								lowerCAmelCase__        =  wte(_UpperCamelCase				)
																		else:
																								lowerCAmelCase__        =  wte(_UpperCamelCase				)
																								lowerCAmelCase__        =  wte(_UpperCamelCase				)

																		with torch.no_grad():
																								model(**_UpperCamelCase				)[0]







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__  ,    lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs()
												lowerCAmelCase__        =  input_dict['input_ids']
												lowerCAmelCase__        =  input_ids.ne(1				).to(_UpperCamelCase				)
												lowerCAmelCase__        =  MaMaaaForConditionalGeneration(_UpperCamelCase				).eval().to(_UpperCamelCase				)
												if torch_device == "cuda":
																		model.half()
												model.generate(_UpperCamelCase       ,  attention_mask=_UpperCamelCase				)
												model.generate(num_beams=4       ,  do_sample=_UpperCamelCase       ,  early_stopping=_UpperCamelCase       ,  num_return_sequences=3				)







def 		_UpperCamelCase				(      UpperCamelCase_    :   int						)					->				Dict:

						"""simple docstring"""



						return torch.tensor(UpperCamelCase_      ,    dtype=torch.long      ,    device=UpperCamelCase_						)


__snake_case      :      List[str]             =       1e-4


@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class       __SCREAMING_SNAKE_CASE   (				unittest.TestCase):






						@cached_property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  MaMaaaModel.from_pretrained('facebook/m2m100_418M'				).to(_UpperCamelCase				)
												lowerCAmelCase__        =  _long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]				)
												lowerCAmelCase__        =  _long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]				)
												lowerCAmelCase__        =  prepare_mam_aaa_inputs_dict(model.config       ,  _UpperCamelCase       ,  _UpperCamelCase				)
												with torch.no_grad():
																		lowerCAmelCase__        =  model(**_UpperCamelCase				)[0]
												lowerCAmelCase__        =  torch.Size((1, 11, 10_24)				)
												self.assertEqual(output.shape       ,  _UpperCamelCase				)
												# change to expected output here
												lowerCAmelCase__        =  torch.tensor(
												    [[-0.77_80, -0.16_76, 0.10_38], [-6.75_56, -1.39_92, 0.05_67], [-7.53_83, -0.59_20, -0.27_79]]       ,  device=_UpperCamelCase				)
												self.assertTrue(torch.allclose(output[:, :3, :3]       ,  _UpperCamelCase       ,  atol=_UpperCamelCase				)				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'				).to(_UpperCamelCase				)

												# change to intended input
												lowerCAmelCase__        =  _long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]				)
												lowerCAmelCase__        =  _long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]				)
												lowerCAmelCase__        =  prepare_mam_aaa_inputs_dict(model.config       ,  _UpperCamelCase       ,  _UpperCamelCase				)
												with torch.no_grad():
																		lowerCAmelCase__        =  model(**_UpperCamelCase				)[0]
												lowerCAmelCase__        =  torch.Size((1, 11, model.config.vocab_size)				)
												self.assertEqual(output.shape       ,  _UpperCamelCase				)
												# change to expected output here
												lowerCAmelCase__        =  torch.tensor(
												    [[-1.04_48, -1.04_11, 3.79_92], [-3.21_91, -3.23_86, -1.34_51], [-3.62_10, -3.59_93, 0.49_25]]       ,  device=_UpperCamelCase				)
												self.assertTrue(torch.allclose(output[:, :3, :3]       ,  _UpperCamelCase       ,  atol=_UpperCamelCase				)				)







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'				).to(_UpperCamelCase				)
												lowerCAmelCase__        =  MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'       ,  src_lang='fr'       ,  tgt_lang='en'				)

												lowerCAmelCase__        =  [
												    'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
												    'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
												    'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
												    ' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
												    ' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
												]

												# The below article tests that we don't add any hypotheses outside of the top n_beams
												lowerCAmelCase__        =  tokenizer(_UpperCamelCase       ,  padding=_UpperCamelCase       ,  return_tensors='pt'				)

												lowerCAmelCase__        =  model.generate(
												    input_ids=dct['input_ids'].to(_UpperCamelCase				)       ,  attention_mask=dct['attention_mask'].to(_UpperCamelCase				)       ,  num_beams=5       ,  forced_bos_token_id=tokenizer.get_lang_id('en'				)       ,  )

												lowerCAmelCase__        =  [
												    'The NSA case highlights the total absence of intelligence debate',
												    'I think there are two levels of response from the French government.',
												    'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
												    ' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
												    ' communications in France.',
												]

												lowerCAmelCase__        =  tokenizer.batch_decode(
												    hypotheses_batch.tolist()       ,  clean_up_tokenization_spaces=_UpperCamelCase       ,  skip_special_tokens=_UpperCamelCase				)
												assert generated == expected_en













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case      :      Union[str, Any]             =       {
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      List[str]             =       ["""ConvBertTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      Optional[Any]             =       [
					    """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ConvBertForMaskedLM""",
					    """ConvBertForMultipleChoice""",
					    """ConvBertForQuestionAnswering""",
					    """ConvBertForSequenceClassification""",
					    """ConvBertForTokenClassification""",
					    """ConvBertLayer""",
					    """ConvBertModel""",
					    """ConvBertPreTrainedModel""",
					    """load_tf_weights_in_convbert""",
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      Union[str, Any]             =       [
					    """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFConvBertForMaskedLM""",
					    """TFConvBertForMultipleChoice""",
					    """TFConvBertForQuestionAnswering""",
					    """TFConvBertForSequenceClassification""",
					    """TFConvBertForTokenClassification""",
					    """TFConvBertLayer""",
					    """TFConvBertModel""",
					    """TFConvBertPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
					from .tokenization_convbert import ConvBertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_convbert_fast import ConvBertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_convbert import (
										    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ConvBertForMaskedLM,
										    ConvBertForMultipleChoice,
										    ConvBertForQuestionAnswering,
										    ConvBertForSequenceClassification,
										    ConvBertForTokenClassification,
										    ConvBertLayer,
										    ConvBertModel,
										    ConvBertPreTrainedModel,
										    load_tf_weights_in_convbert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_convbert import (
										    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFConvBertForMaskedLM,
										    TFConvBertForMultipleChoice,
										    TFConvBertForQuestionAnswering,
										    TFConvBertForSequenceClassification,
										    TFConvBertForTokenClassification,
										    TFConvBertLayer,
										    TFConvBertModel,
										    TFConvBertPreTrainedModel,
										)


else:
					import sys

					__snake_case      :      Dict             =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





