






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


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

__snake_case    :    Any         =     {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}


class     __SCREAMING_SNAKE_CASE					(   __lowercase):
						_SCREAMING_SNAKE_CASE	:       int   =					'''megatron-bert'''






						def __init__(	self     ,						_UpperCamelCase=2_90_56     ,						_UpperCamelCase=10_24     ,						_UpperCamelCase=24     ,						_UpperCamelCase=16     ,						_UpperCamelCase=40_96     ,						_UpperCamelCase="gelu"     ,						_UpperCamelCase=0.1     ,						_UpperCamelCase=0.1     ,						_UpperCamelCase=5_12     ,						_UpperCamelCase=2     ,						_UpperCamelCase=0.02     ,						_UpperCamelCase=1E-12     ,						_UpperCamelCase=0     ,						_UpperCamelCase="absolute"     ,						_UpperCamelCase=True     ,						**_UpperCamelCase     ,						):






												"""simple docstring"""
												super().__init__(pad_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













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__)





