

"""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     __a   (      snake_case__,    snake_case__,    snake_case__    ):

			"""simple docstring"""

			@register_to_config
			def __init__(							self     :					Union[str, Any]						,  lowercase_     :					int						,  lowercase_     :					int						,  lowercase_     :					int						,  lowercase_     :					float						,  lowercase_     :					int						,  lowercase_     :					int						,  lowercase_     :					int						,  lowercase_     :					int						,  lowercase_     :					str						,  lowercase_     :					bool = False						,  ):
								super().__init__()

								UpperCamelCase__		:    Union[str, Any]         =nn.Linear(lowercase_						,  lowercase_						,  bias=lowercase_						)

								UpperCamelCase__		:    str         =nn.Embedding(lowercase_						,  lowercase_						)
								UpperCamelCase__		:    Union[str, Any]         =False

								UpperCamelCase__		:    Any         =nn.Dropout(p=lowercase_						)

								UpperCamelCase__		:    List[str]         =TaConfig(
								    d_model=lowercase_						,  num_heads=lowercase_						,  d_kv=lowercase_						,  d_ff=lowercase_						,  feed_forward_proj=lowercase_						,  dropout_rate=lowercase_						,  is_decoder=lowercase_						,  is_encoder_decoder=lowercase_						,  )
								UpperCamelCase__		:    str         =nn.ModuleList()
								for lyr_num in range(lowercase_						):
													UpperCamelCase__		:    Tuple         =TaBlock(lowercase_						)
													self.encoders.append(lowercase_						)

								UpperCamelCase__		:    List[Any]         =TaLayerNorm(lowercase_						)
								UpperCamelCase__		:    Any         =nn.Dropout(p=lowercase_						)






			def    _lowerCAmelCase			(							self     :					Dict						,  lowercase_     :					List[str]						,  lowercase_     :					int						):
								UpperCamelCase__		:    Tuple         =self.input_proj(lowercase_						)

								# terminal relative positional encodings
								UpperCamelCase__		:    Optional[Any]         =encoder_inputs.shape[1]
								UpperCamelCase__		:    Dict         =torch.arange(lowercase_						,  device=encoder_inputs.device						)

								UpperCamelCase__		:    Union[str, Any]         =encoder_inputs_mask.sum(-1						)
								UpperCamelCase__		:    Union[str, Any]         =torch.roll(input_positions.unsqueeze(0						)						,  tuple(seq_lens.tolist()						)						,  dims=0						)
								x += self.position_encoding(lowercase_						)

								UpperCamelCase__		:    Optional[Any]         =self.dropout_pre(lowercase_						)

								# inverted the attention mask
								UpperCamelCase__		:    Dict         =encoder_inputs.size()
								UpperCamelCase__		:    Dict         =self.get_extended_attention_mask(lowercase_						,  lowercase_						)

								for lyr in self.encoders:
													UpperCamelCase__		:    Dict         =lyr(lowercase_						,  lowercase_						)[0]
								UpperCamelCase__		:    Dict         =self.layer_norm(lowercase_						)

								return self.dropout_post(lowercase_						), encoder_inputs_mask





"""simple docstring"""





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = {
    """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Any        = ["""XLMProphetNetTokenizer"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Optional[Any]        = [
				    """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """XLMProphetNetDecoder""",
				    """XLMProphetNetEncoder""",
				    """XLMProphetNetForCausalLM""",
				    """XLMProphetNetForConditionalGeneration""",
				    """XLMProphetNetModel""",
				    """XLMProphetNetPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlm_prophetnet import (
								    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLMProphetNetDecoder,
								    XLMProphetNetEncoder,
								    XLMProphetNetForCausalLM,
								    XLMProphetNetForConditionalGeneration,
								    XLMProphetNetModel,
								    XLMProphetNetPreTrainedModel,
								)

else:
				import sys

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


