


"""simple docstring"""




import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class  __snake_case      (lowerCamelCase ,		lowerCamelCase ,		lowerCamelCase     ):




							@register_to_config
							def __init__(    self:						Dict    ,       A_:						int    ,       A_:						int    ,       A_:						int    ,       A_:						float    ,       A_:						int    ,       A_:						int    ,       A_:						int    ,       A_:						int    ,       A_:						str    ,       A_:						bool = False    ,       ):
										super().__init__()

										__lowerCamelCase        =			nn.Linear(A_    ,       A_    ,       bias=A_      )

										__lowerCamelCase        =			nn.Embedding(A_    ,       A_      )
										__lowerCamelCase        =			False

										__lowerCamelCase        =			nn.Dropout(p=A_      )

										__lowerCamelCase        =			TaConfig(
										    d_model=A_    ,       num_heads=A_    ,       d_kv=A_    ,       d_ff=A_    ,       feed_forward_proj=A_    ,       dropout_rate=A_    ,       is_decoder=A_    ,       is_encoder_decoder=A_    ,       )
										__lowerCamelCase        =			nn.ModuleList()
										for lyr_num in range(A_      ):
													__lowerCamelCase        =			TaBlock(A_      )
													self.encoders.append(A_      )

										__lowerCamelCase        =			TaLayerNorm(A_      )
										__lowerCamelCase        =			nn.Dropout(p=A_      )




							def      __a      (    self:						str    ,       A_:						Dict    ,       A_:						List[Any]      ):
										__lowerCamelCase        =			self.input_proj(A_      )

										# terminal relative positional encodings
										__lowerCamelCase        =			encoder_inputs.shape[1]
										__lowerCamelCase        =			torch.arange(A_    ,       device=encoder_inputs.device      )

										__lowerCamelCase        =			encoder_inputs_mask.sum(-1      )
										__lowerCamelCase        =			torch.roll(input_positions.unsqueeze(0      )    ,       tuple(seq_lens.tolist()      )    ,       dims=0      )
										x += self.position_encoding(A_      )

										__lowerCamelCase        =			self.dropout_pre(A_      )

										# inverted the attention mask
										__lowerCamelCase        =			encoder_inputs.size()
										__lowerCamelCase        =			self.get_extended_attention_mask(A_    ,       A_      )

										for lyr in self.encoders:
													__lowerCamelCase        =			lyr(A_    ,       A_      )[0]
										__lowerCamelCase        =			self.layer_norm(A_      )

										return self.dropout_post(A_      ), encoder_inputs_mask








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__magic_name__	:   Optional[Any]          =							{
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__	:   List[Any]          =							[
					    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'FlaubertForMultipleChoice',
					    'FlaubertForQuestionAnswering',
					    'FlaubertForQuestionAnsweringSimple',
					    'FlaubertForSequenceClassification',
					    'FlaubertForTokenClassification',
					    'FlaubertModel',
					    'FlaubertWithLMHeadModel',
					    'FlaubertPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__	:   Tuple          =							[
					    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFFlaubertForMultipleChoice',
					    'TFFlaubertForQuestionAnsweringSimple',
					    'TFFlaubertForSequenceClassification',
					    'TFFlaubertForTokenClassification',
					    'TFFlaubertModel',
					    'TFFlaubertPreTrainedModel',
					    'TFFlaubertWithLMHeadModel',
					]


if TYPE_CHECKING:
					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
					from .tokenization_flaubert import FlaubertTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flaubert import (
										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaubertForMultipleChoice,
										    FlaubertForQuestionAnswering,
										    FlaubertForQuestionAnsweringSimple,
										    FlaubertForSequenceClassification,
										    FlaubertForTokenClassification,
										    FlaubertModel,
										    FlaubertPreTrainedModel,
										    FlaubertWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_flaubert import (
										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFlaubertForMultipleChoice,
										    TFFlaubertForQuestionAnsweringSimple,
										    TFFlaubertForSequenceClassification,
										    TFFlaubertForTokenClassification,
										    TFFlaubertModel,
										    TFFlaubertPreTrainedModel,
										    TFFlaubertWithLMHeadModel,
										)

else:
					import sys

					__magic_name__	:   List[str]          =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




