






'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase_					:			Tuple     						=				logging.get_logger(__name__)

lowerCamelCase_					:			List[Any]     						=				{
    '''squeezebert/squeezebert-uncased''': (
        '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'''
    ),
    '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json''',
    '''squeezebert/squeezebert-mnli-headless''': (
        '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'''
    ),
}
class  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE						):


			'''simple docstring'''





			__a				:				Dict         =       SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
			__a				:				List[str]         =       "squeezebert"






			def __init__( self				:    Union[str, Any]			,   lowercase				:    int=3_0_5_2_2			,   lowercase				:    Any=7_6_8			,   lowercase				:    Dict=1_2			,   lowercase				:    Dict=1_2			,   lowercase				:    Tuple=3_0_7_2			,   lowercase				:    Union[str, Any]="gelu"			,   lowercase				:    Any=0.1			,   lowercase				:    Union[str, Any]=0.1			,   lowercase				:    Union[str, Any]=5_1_2			,   lowercase				:    List[str]=2			,   lowercase				:    Optional[int]=0.0_2			,   lowercase				:    List[str]=1e-12			,   lowercase				:    Union[str, Any]=0			,   lowercase				:    Optional[Any]=7_6_8			,   lowercase				:    Tuple=4			,   lowercase				:    Optional[int]=4			,   lowercase				:    List[str]=4			,   lowercase				:    Tuple=1			,   lowercase				:    Tuple=4			,   lowercase				:    Any=4			,   **lowercase				:    Union[str, Any]			,   )				->     Optional[int]:

									'''simple docstring'''







									super().__init__(pad_token_id=lowercase			,   **lowercase      )

									UpperCamelCase__       =			vocab_size
									UpperCamelCase__       =			hidden_size
									UpperCamelCase__       =			num_hidden_layers
									UpperCamelCase__       =			num_attention_heads
									UpperCamelCase__       =			hidden_act
									UpperCamelCase__       =			intermediate_size
									UpperCamelCase__       =			hidden_dropout_prob
									UpperCamelCase__       =			attention_probs_dropout_prob
									UpperCamelCase__       =			max_position_embeddings
									UpperCamelCase__       =			type_vocab_size
									UpperCamelCase__       =			initializer_range
									UpperCamelCase__       =			layer_norm_eps
									UpperCamelCase__       =			embedding_size
									UpperCamelCase__       =			q_groups
									UpperCamelCase__       =			k_groups
									UpperCamelCase__       =			v_groups
									UpperCamelCase__       =			post_attention_groups
									UpperCamelCase__       =			intermediate_groups
									UpperCamelCase__       =			output_groups
class  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE						):


			'''simple docstring'''





			@property
			def 					A   ( self				:    List[str]      )				->     Mapping[str, Mapping[int, str]]:

									'''simple docstring'''







									if self.task == "multiple-choice":
															UpperCamelCase__       =			{0: """batch""", 1: """choice""", 2: """sequence"""}
									else:
															UpperCamelCase__       =			{0: """batch""", 1: """sequence"""}
									return OrderedDict(
									    [
									        ("""input_ids""", dynamic_axis),
									        ("""attention_mask""", dynamic_axis),
									        ("""token_type_ids""", dynamic_axis),
									    ]      )








'''simple docstring'''


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE						):


			'''simple docstring'''





			__a				:				Union[List[PIL.Image.Image], np.ndarray]
			__a				:				Optional[List[bool]]


if is_transformers_available() and is_torch_available():
					from .modeling_roberta_series import RobertaSeriesModelWithTransformation
					from .pipeline_alt_diffusion import AltDiffusionPipeline
					from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
