
"""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,
)


lowercase		:						int =					{"configuration_t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config", "T5OnnxConfig"]}

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

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						List[Any] =					["T5TokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						List[Any] =					[
			    "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:
			lowercase		:						Tuple =					[
			    "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFT5EncoderModel",
			    "TFT5ForConditionalGeneration",
			    "TFT5Model",
			    "TFT5PreTrainedModel",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						List[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

			lowercase		:						Tuple =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


"""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  (						_lowercase ,				_lowercase ,				_lowercase       ):






							@register_to_config
							def __init__(					self						:			str						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			float						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			str						, snake_case__						:			bool = False						, ):






												"""simple docstring"""



												super().__init__()

												__a:						Optional[Any]		    =					nn.Linear(snake_case__						, snake_case__						, bias=snake_case__    )

												__a:						Optional[Any]		    =					nn.Embedding(snake_case__						, snake_case__    )
												__a:						Any		    =					False

												__a:						Any		    =					nn.Dropout(p=snake_case__    )

												__a:						List[str]		    =					TaConfig(
												    d_model=snake_case__						, num_heads=snake_case__						, d_kv=snake_case__						, d_ff=snake_case__						, feed_forward_proj=snake_case__						, dropout_rate=snake_case__						, is_decoder=snake_case__						, is_encoder_decoder=snake_case__						, )
												__a:						Optional[int]		    =					nn.ModuleList()
												for lyr_num in range(snake_case__    ):
																	__a:						Union[str, Any]		    =					TaBlock(snake_case__    )
																	self.encoders.append(snake_case__    )

												__a:						List[str]		    =					TaLayerNorm(snake_case__    )
												__a:						Optional[int]		    =					nn.Dropout(p=snake_case__    )






							def 							__lowercase  (					self						:			str						, snake_case__						:			Optional[Any]						, snake_case__						:			int    ):






												"""simple docstring"""



												__a:						str		    =					self.input_proj(snake_case__    )

												# terminal relative positional encodings
												__a:						Optional[int]		    =					encoder_inputs.shape[1]
												__a:						List[Any]		    =					torch.arange(snake_case__						, device=encoder_inputs.device    )

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

												__a:						Any		    =					self.dropout_pre(snake_case__    )

												# inverted the attention mask
												__a:						Any		    =					encoder_inputs.size()
												__a:						str		    =					self.get_extended_attention_mask(snake_case__						, snake_case__    )

												for lyr in self.encoders:
																	__a:						Any		    =					lyr(snake_case__						, snake_case__    )[0]
												__a:						int		    =					self.layer_norm(snake_case__    )

												return self.dropout_post(snake_case__    ), encoder_inputs_mask
