






from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE__				     =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__				     =			{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}






class 			_UpperCamelCase(       __lowerCamelCase		):
		__SCREAMING_SNAKE_CASE  :       Union[str, Any]							=							'''xlm-roberta-xl'''

		def __init__(					self		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    Dict=2_5_0_8_8_0    ,       SCREAMING_SNAKE_CASE__		:    Any=2_5_6_0    ,       SCREAMING_SNAKE_CASE__		:    List[Any]=3_6    ,       SCREAMING_SNAKE_CASE__		:    Tuple=3_2    ,       SCREAMING_SNAKE_CASE__		:    str=1_0_2_4_0    ,       SCREAMING_SNAKE_CASE__		:    Dict="gelu"    ,       SCREAMING_SNAKE_CASE__		:    str=0.1    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]=0.1    ,       SCREAMING_SNAKE_CASE__		:    Dict=5_1_4    ,       SCREAMING_SNAKE_CASE__		:    List[str]=1    ,       SCREAMING_SNAKE_CASE__		:    Dict=0.02    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]=1e-05    ,       SCREAMING_SNAKE_CASE__		:    int=1    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]=0    ,       SCREAMING_SNAKE_CASE__		:    List[str]=2    ,       SCREAMING_SNAKE_CASE__		:    int="absolute"    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]=True    ,       SCREAMING_SNAKE_CASE__		:    int=None    ,       **SCREAMING_SNAKE_CASE__		:    List[Any]    ,       ):



					'''simple docstring'''



					super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__    ,       bos_token_id=SCREAMING_SNAKE_CASE__    ,       eos_token_id=SCREAMING_SNAKE_CASE__    ,       **SCREAMING_SNAKE_CASE__    )
					__a						:  Optional[int]									=     vocab_size
					__a						:  int									=     hidden_size
					__a						:  Any									=     num_hidden_layers
					__a						:  Optional[Any]									=     num_attention_heads
					__a						:  Any									=     hidden_act
					__a						:  int									=     intermediate_size
					__a						:  int									=     hidden_dropout_prob
					__a						:  Dict									=     attention_probs_dropout_prob
					__a						:  int									=     max_position_embeddings
					__a						:  Tuple									=     type_vocab_size
					__a						:  Any									=     initializer_range
					__a						:  Any									=     layer_norm_eps
					__a						:  List[str]									=     position_embedding_type
					__a						:  Dict									=     use_cache
					__a						:  Dict									=     classifier_dropout






class 			_UpperCamelCase(       __lowerCamelCase		):

		@property
		def    __lowerCAmelCase  (					self		:    List[Any]    ):



					'''simple docstring'''



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












from dataclasses import dataclass
from enum import Enum
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
class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				42
	_A													=				42


if is_transformers_available() and is_torch_available():
			from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


