

"""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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE       :  List[Any]        = {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Optional[Any]        = ["""T5Tokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  str        = ["""T5TokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """T5EncoderModel""",
				    """T5ForConditionalGeneration""",
				    """T5Model""",
				    """T5PreTrainedModel""",
				    """load_tf_weights_in_t5""",
				    """T5ForQuestionAnswering""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFT5EncoderModel""",
				    """TFT5ForConditionalGeneration""",
				    """TFT5Model""",
				    """TFT5PreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Any        = [
				    """FlaxT5EncoderModel""",
				    """FlaxT5ForConditionalGeneration""",
				    """FlaxT5Model""",
				    """FlaxT5PreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta import TaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta_fast import TaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ta import (
								    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TaEncoderModel,
								    TaForConditionalGeneration,
								    TaForQuestionAnswering,
								    TaModel,
								    TaPreTrainedModel,
								    load_tf_weights_in_ta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ta import (
								    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFTaEncoderModel,
								    TFTaForConditionalGeneration,
								    TFTaModel,
								    TFTaPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_ta import (
								    FlaxTaEncoderModel,
								    FlaxTaForConditionalGeneration,
								    FlaxTaModel,
								    FlaxTaPreTrainedModel,
								)


else:
				import sys

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


