"""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,
)


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

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

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

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

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	[
				    "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

				__lowerCamelCase									=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase									=	logging.get_logger(__name__)

__lowerCamelCase									=	{
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    str    				=		'vilt'





						def __init__(							self		,__UpperCAmelCase=3_05_22		,__UpperCAmelCase=2		,__UpperCAmelCase=2		,__UpperCAmelCase=40		,__UpperCAmelCase=7_68		,__UpperCAmelCase=12		,__UpperCAmelCase=12		,__UpperCAmelCase=30_72		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=1e-12		,__UpperCAmelCase=3_84		,__UpperCAmelCase=32		,__UpperCAmelCase=3		,__UpperCAmelCase=True		,__UpperCAmelCase=-1		,__UpperCAmelCase=False		,__UpperCAmelCase=-1		,**__UpperCAmelCase		,)       ->					Tuple:
													super().__init__(tie_word_embeddings=__UpperCAmelCase		,**__UpperCAmelCase  )

													A__						  =				vocab_size
													A__						  =				type_vocab_size
													A__						  =				modality_type_vocab_size
													A__						  =				max_position_embeddings

													A__						  =				hidden_size
													A__						  =				num_hidden_layers
													A__						  =				num_attention_heads
													A__						  =				intermediate_size
													A__						  =				hidden_act
													A__						  =				hidden_dropout_prob
													A__						  =				attention_probs_dropout_prob
													A__						  =				initializer_range
													A__						  =				layer_norm_eps

													A__						  =				image_size
													A__						  =				patch_size
													A__						  =				num_channels
													A__						  =				qkv_bias
													A__						  =				max_image_length
													A__						  =				num_images





