




"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__A							=     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''GPTJForCausalLM''',
							    '''GPTJForQuestionAnswering''',
							    '''GPTJForSequenceClassification''',
							    '''GPTJModel''',
							    '''GPTJPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    '''TFGPTJForCausalLM''',
							    '''TFGPTJForQuestionAnswering''',
							    '''TFGPTJForSequenceClassification''',
							    '''TFGPTJModel''',
							    '''TFGPTJPreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    '''FlaxGPTJForCausalLM''',
							    '''FlaxGPTJModel''',
							    '''FlaxGPTJPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gptj import (
														    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTJForCausalLM,
														    GPTJForQuestionAnswering,
														    GPTJForSequenceClassification,
														    GPTJModel,
														    GPTJPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_gptj import (
														    TFGPTJForCausalLM,
														    TFGPTJForQuestionAnswering,
														    TFGPTJForSequenceClassification,
														    TFGPTJModel,
														    TFGPTJPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
							import sys

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







"""simple docstring"""

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


__A							=     logging.get_logger(__name__)

__A							=     {
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}
class      lowerCamelCase__      (					lowerCamelCase_     ):
			a__	: Optional[int]        =       """vilt"""


			def __init__(       self			,	SCREAMING_SNAKE_CASE=30_522			,	SCREAMING_SNAKE_CASE=2			,	SCREAMING_SNAKE_CASE=2			,	SCREAMING_SNAKE_CASE=40			,	SCREAMING_SNAKE_CASE=768			,	SCREAMING_SNAKE_CASE=12			,	SCREAMING_SNAKE_CASE=12			,	SCREAMING_SNAKE_CASE=3_072			,	SCREAMING_SNAKE_CASE="gelu"			,	SCREAMING_SNAKE_CASE=0.0			,	SCREAMING_SNAKE_CASE=0.0			,	SCREAMING_SNAKE_CASE=0.02			,	SCREAMING_SNAKE_CASE=1E-12			,	SCREAMING_SNAKE_CASE=384			,	SCREAMING_SNAKE_CASE=32			,	SCREAMING_SNAKE_CASE=3			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=-1			,	SCREAMING_SNAKE_CASE=False			,	SCREAMING_SNAKE_CASE=-1			,	**SCREAMING_SNAKE_CASE			,	):



					"""simple docstring"""







					super().__init__(tie_word_embeddings=SCREAMING_SNAKE_CASE			,	**SCREAMING_SNAKE_CASE							)

					snake_case						:  str        =			vocab_size
					snake_case						:  Optional[Any]        =			type_vocab_size
					snake_case						:  List[str]        =			modality_type_vocab_size
					snake_case						:  Optional[Any]        =			max_position_embeddings

					snake_case						:  Tuple        =			hidden_size
					snake_case						:  List[str]        =			num_hidden_layers
					snake_case						:  int        =			num_attention_heads
					snake_case						:  Optional[int]        =			intermediate_size
					snake_case						:  List[Any]        =			hidden_act
					snake_case						:  List[str]        =			hidden_dropout_prob
					snake_case						:  Union[str, Any]        =			attention_probs_dropout_prob
					snake_case						:  Any        =			initializer_range
					snake_case						:  List[Any]        =			layer_norm_eps

					snake_case						:  Any        =			image_size
					snake_case						:  str        =			patch_size
					snake_case						:  Optional[int]        =			num_channels
					snake_case						:  List[str]        =			qkv_bias
					snake_case						:  Optional[int]        =			max_image_length
					snake_case						:  Any        =			num_images

