

from collections import OrderedDict
from typing import Mapping

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


__snake_case    :    List[Any]         =     logging.get_logger(__name__)

__snake_case    :    Tuple         =     {
    """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     __SCREAMING_SNAKE_CASE					(   __lowerCamelCase):
						_SCREAMING_SNAKE_CASE	:       int   =					'xlm-roberta-xl'






						def __init__(	self     ,						_UpperCamelCase=25_08_80     ,						_UpperCamelCase=25_60     ,						_UpperCamelCase=36     ,						_UpperCamelCase=32     ,						_UpperCamelCase=1_02_40     ,						_UpperCamelCase="gelu"     ,						_UpperCamelCase=0.1     ,						_UpperCamelCase=0.1     ,						_UpperCamelCase=5_14     ,						_UpperCamelCase=1     ,						_UpperCamelCase=0.02     ,						_UpperCamelCase=1E-05     ,						_UpperCamelCase=1     ,						_UpperCamelCase=0     ,						_UpperCamelCase=2     ,						_UpperCamelCase="absolute"     ,						_UpperCamelCase=True     ,						_UpperCamelCase=None     ,						**_UpperCamelCase     ,						):






												"""simple docstring"""
												super().__init__(pad_token_id=UpperCamelCase_     ,						bos_token_id=UpperCamelCase_     ,						eos_token_id=UpperCamelCase_     ,						**UpperCamelCase_   )
												lowerCAmelCase__    						=						vocab_size
												lowerCAmelCase__    						=						hidden_size
												lowerCAmelCase__    						=						num_hidden_layers
												lowerCAmelCase__    						=						num_attention_heads
												lowerCAmelCase__    						=						hidden_act
												lowerCAmelCase__    						=						intermediate_size
												lowerCAmelCase__    						=						hidden_dropout_prob
												lowerCAmelCase__    						=						attention_probs_dropout_prob
												lowerCAmelCase__    						=						max_position_embeddings
												lowerCAmelCase__    						=						type_vocab_size
												lowerCAmelCase__    						=						initializer_range
												lowerCAmelCase__    						=						layer_norm_eps
												lowerCAmelCase__    						=						position_embedding_type
												lowerCAmelCase__    						=						use_cache
												lowerCAmelCase__    						=						classifier_dropout




class     __SCREAMING_SNAKE_CASE					(   __lowerCamelCase):






						@property
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												if self.task == "multiple-choice":
																		lowerCAmelCase__    						=						{0: 'batch', 1: 'choice', 2: 'sequence'}
												else:
																		lowerCAmelCase__    						=						{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


__snake_case    :    Tuple         =     {"""configuration_glpn""": ["""GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GLPNConfig"""]}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Dict         =     ["""GLPNFeatureExtractor"""]
					__snake_case    :    Optional[Any]         =     ["""GLPNImageProcessor"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Tuple         =     [
					    """GLPN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """GLPNForDepthEstimation""",
					    """GLPNLayer""",
					    """GLPNModel""",
					    """GLPNPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__snake_case    :    Optional[int]         =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





