


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__magic_name__       					=      logging.get_logger(__name__)

__magic_name__       					=      {
    'flaubert/flaubert_small_cased': 'https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json',
    'flaubert/flaubert_base_uncased': 'https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json',
    'flaubert/flaubert_base_cased': 'https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json',
    'flaubert/flaubert_large_cased': 'https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json',
}





class    lowerCAmelCase__				(			A__       ):






							"""simple docstring"""
							__UpperCAmelCase   :		Dict  					=	'''flaubert'''
							__UpperCAmelCase   :		List[str]  					=	{
							    '''hidden_size''': '''emb_dim''',
							    '''num_attention_heads''': '''n_heads''',
							    '''num_hidden_layers''': '''n_layers''',
							    '''n_words''': '''vocab_size''',  # For backward compatibility
							}




							def __init__(   self				,							a_=False				,							a_=0.0				,							a_=3_0145				,							a_=2048				,							a_=12				,							a_=16				,							a_=0.1				,							a_=0.1				,							a_=True				,							a_=False				,							a_=False				,							a_=False				,							a_=1				,							a_=True				,							a_=512				,							a_=2048**-0.5				,							a_=1E-12				,							a_=0.02				,							a_=0				,							a_=1				,							a_=2				,							a_=3				,							a_=5				,							a_=True				,							a_="first"				,							a_=True				,							a_=None				,							a_=True				,							a_=0.1				,							a_=5				,							a_=5				,							a_=0				,							a_=0				,							a_=2				,							a_=0				,							**a_				,							):
								lowerCamelCase_			:							Union[str, Any]      =       pre_norm
								lowerCamelCase_			:							Union[str, Any]      =       layerdrop
								lowerCamelCase_			:							Union[str, Any]      =       vocab_size
								lowerCamelCase_			:							int      =       emb_dim
								lowerCamelCase_			:							List[str]      =       n_layers
								lowerCamelCase_			:							List[str]      =       n_heads
								lowerCamelCase_			:							str      =       dropout
								lowerCamelCase_			:							List[str]      =       attention_dropout
								lowerCamelCase_			:							Union[str, Any]      =       gelu_activation
								lowerCamelCase_			:							Tuple      =       sinusoidal_embeddings
								lowerCamelCase_			:							Tuple      =       causal
								lowerCamelCase_			:							int      =       asm
								lowerCamelCase_			:							Optional[int]      =       n_langs
								lowerCamelCase_			:							Any      =       use_lang_emb
								lowerCamelCase_			:							Dict      =       layer_norm_eps
								lowerCamelCase_			:							List[Any]      =       bos_index
								lowerCamelCase_			:							Dict      =       eos_index
								lowerCamelCase_			:							List[str]      =       pad_index
								lowerCamelCase_			:							Optional[int]      =       unk_index
								lowerCamelCase_			:							Optional[int]      =       mask_index
								lowerCamelCase_			:							int      =       is_encoder
								lowerCamelCase_			:							Tuple      =       max_position_embeddings
								lowerCamelCase_			:							List[str]      =       embed_init_std
								lowerCamelCase_			:							Optional[Any]      =       init_std
								lowerCamelCase_			:							List[Any]      =       summary_type
								lowerCamelCase_			:							Any      =       summary_use_proj
								lowerCamelCase_			:							int      =       summary_activation
								lowerCamelCase_			:							Tuple      =       summary_proj_to_labels
								lowerCamelCase_			:							Optional[Any]      =       summary_first_dropout
								lowerCamelCase_			:							Union[str, Any]      =       start_n_top
								lowerCamelCase_			:							List[Any]      =       end_n_top
								lowerCamelCase_			:							Dict      =       mask_token_id
								lowerCamelCase_			:							Any      =       lang_id

								if "n_words" in kwargs:
									lowerCamelCase_			:							Dict      =       kwargs["n_words"]

								super().__init__(pad_token_id=a_				,							bos_token_id=a_				,							**a_     )





class    lowerCAmelCase__				(			A__       ):






							"""simple docstring"""




							@property
							def 					_UpperCamelCase			(   self     ):
								if self.task == "multiple-choice":
									lowerCamelCase_			:							Union[str, Any]      =       {0: "batch", 1: "choice", 2: "sequence"}
								else:
									lowerCamelCase_			:							List[str]      =       {0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								    ]     )





from typing import TYPE_CHECKING

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


__magic_name__       					=      {'''configuration_detr''': ['''DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetrConfig''', '''DetrOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      ['''DetrFeatureExtractor''']
							__magic_name__       					=      ['''DetrImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      [
							    '''DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''DetrForObjectDetection''',
							    '''DetrForSegmentation''',
							    '''DetrModel''',
							    '''DetrPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_detr import DetrFeatureExtractor
														from .image_processing_detr import DetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_detr import (
														    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DetrForObjectDetection,
														    DetrForSegmentation,
														    DetrModel,
														    DetrPreTrainedModel,
														)

else:
							import sys

							__magic_name__       					=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

