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


a__							:	Optional[int]  						=			logging.get_logger(__name__)

a__							:	List[Any]  						=			{
    '''google/realm-cc-news-pretrained-embedder''': (
        '''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'''
    ),
    '''google/realm-cc-news-pretrained-encoder''': (
        '''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'''
    ),
    '''google/realm-cc-news-pretrained-scorer''': (
        '''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'''
    ),
    '''google/realm-cc-news-pretrained-openqa''': (
        '''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'''
    ),
    '''google/realm-orqa-nq-openqa''': '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json''',
    '''google/realm-orqa-nq-reader''': '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json''',
    '''google/realm-orqa-wq-openqa''': '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json''',
    '''google/realm-orqa-wq-reader''': '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json''',
    # See all REALM models at https://huggingface.co/models?filter=realm
}






class   a_    (       _lowerCAmelCase				):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE    : str		=   """realm"""






       def __init__(						self							,    _lowerCamelCase=3_0522							,    _lowerCamelCase=768							,    _lowerCamelCase=128							,    _lowerCamelCase=12							,    _lowerCamelCase=12							,    _lowerCamelCase=8							,    _lowerCamelCase=3072							,    _lowerCamelCase="gelu_new"							,    _lowerCamelCase=0.1							,    _lowerCamelCase=0.1							,    _lowerCamelCase=512							,    _lowerCamelCase=2							,    _lowerCamelCase=0.0_2							,    _lowerCamelCase=1e-12							,    _lowerCamelCase=256							,    _lowerCamelCase=10							,    _lowerCamelCase=1e-3							,    _lowerCamelCase=5							,    _lowerCamelCase=320							,    _lowerCamelCase=1335_3718							,    _lowerCamelCase=5000							,    _lowerCamelCase=1							,    _lowerCamelCase=0							,    _lowerCamelCase=2							,    **_lowerCamelCase							,    ) ->Any:
           super().__init__(pad_token_id=_lowerCamelCase							,    bos_token_id=_lowerCamelCase							,    eos_token_id=_lowerCamelCase							,    **_lowerCamelCase					)

           # Common config
           SCREAMING_SNAKE_CASE							:							List[Any]								=      vocab_size
           SCREAMING_SNAKE_CASE							:							Tuple								=      max_position_embeddings
           SCREAMING_SNAKE_CASE							:							List[str]								=      hidden_size
           SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      retriever_proj_size
           SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      num_hidden_layers
           SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      num_attention_heads
           SCREAMING_SNAKE_CASE							:							Optional[Any]								=      num_candidates
           SCREAMING_SNAKE_CASE							:							Any								=      intermediate_size
           SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      hidden_act
           SCREAMING_SNAKE_CASE							:							Optional[int]								=      hidden_dropout_prob
           SCREAMING_SNAKE_CASE							:							Dict								=      attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE							:							Optional[Any]								=      initializer_range
           SCREAMING_SNAKE_CASE							:							Any								=      type_vocab_size
           SCREAMING_SNAKE_CASE							:							Dict								=      layer_norm_eps

           # Reader config
           SCREAMING_SNAKE_CASE							:							Optional[Any]								=      span_hidden_size
           SCREAMING_SNAKE_CASE							:							Optional[int]								=      max_span_width
           SCREAMING_SNAKE_CASE							:							List[str]								=      reader_layer_norm_eps
           SCREAMING_SNAKE_CASE							:							List[Any]								=      reader_beam_size
           SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      reader_seq_len

           # Retrieval config
           SCREAMING_SNAKE_CASE							:							List[str]								=      num_block_records
           SCREAMING_SNAKE_CASE							:							List[Any]								=      searcher_beam_size





"""simple docstring"""
from typing import TYPE_CHECKING

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


__a      =				{
    "configuration_altclip": [
        "ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AltCLIPConfig",
        "AltCLIPTextConfig",
        "AltCLIPVisionConfig",
    ],
    "processing_altclip": ["AltCLIPProcessor"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "AltCLIPPreTrainedModel",
						    "AltCLIPModel",
						    "AltCLIPTextModel",
						    "AltCLIPVisionModel",
						]


if TYPE_CHECKING:
						from .configuration_altclip import (
						    ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    AltCLIPConfig,
						    AltCLIPTextConfig,
						    AltCLIPVisionConfig,
						)
						from .processing_altclip import AltCLIPProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_altclip import (
												    ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    AltCLIPModel,
												    AltCLIPPreTrainedModel,
												    AltCLIPTextModel,
												    AltCLIPVisionModel,
												)


else:
						import sys

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