



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


__snake_case    :		str  =logging.get_logger(__name__)

__snake_case    :		List[str]  ={
    'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     ="""transfo-xl"""
		snake_case_     =["""mems"""]
		snake_case_     ={
		    """n_token""": """vocab_size""",
		    """hidden_size""": """d_model""",
		    """num_attention_heads""": """n_head""",
		    """num_hidden_layers""": """n_layer""",
		}



		def __init__(self		,__lowerCamelCase=26_77_35		,__lowerCamelCase=[2_00_00, 4_00_00, 20_00_00]		,__lowerCamelCase=10_24		,__lowerCamelCase=10_24		,__lowerCamelCase=16		,__lowerCamelCase=64		,__lowerCamelCase=40_96		,__lowerCamelCase=4		,__lowerCamelCase=False		,__lowerCamelCase=18		,__lowerCamelCase=16_00		,__lowerCamelCase=10_00		,__lowerCamelCase=True		,__lowerCamelCase=True		,__lowerCamelCase=0		,__lowerCamelCase=-1		,__lowerCamelCase=True		,__lowerCamelCase=0.1		,__lowerCamelCase=0.0		,__lowerCamelCase=True		,__lowerCamelCase="normal"		,__lowerCamelCase=0.01		,__lowerCamelCase=0.01		,__lowerCamelCase=0.02		,__lowerCamelCase=1e-5		,__lowerCamelCase=0		,**__lowerCamelCase		,)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					vocab_size
						lowerCAmelCase__					:    Tuple												=					[]
						self.cutoffs.extend(__lowerCamelCase			)
						if proj_share_all_but_first:
										lowerCAmelCase__					:    Optional[Any]												=					[False] + [True] * len(self.cutoffs			)
						else:
										lowerCAmelCase__					:    str												=					[False] + [False] * len(self.cutoffs			)
						lowerCAmelCase__					:    str												=					d_model
						lowerCAmelCase__					:    List[str]												=					d_embed
						lowerCAmelCase__					:    Optional[Any]												=					d_head
						lowerCAmelCase__					:    str												=					d_inner
						lowerCAmelCase__					:    List[Any]												=					div_val
						lowerCAmelCase__					:    Optional[int]												=					pre_lnorm
						lowerCAmelCase__					:    Any												=					n_layer
						lowerCAmelCase__					:    int												=					n_head
						lowerCAmelCase__					:    Optional[int]												=					mem_len
						lowerCAmelCase__					:    Tuple												=					same_length
						lowerCAmelCase__					:    Optional[Any]												=					attn_type
						lowerCAmelCase__					:    Tuple												=					clamp_len
						lowerCAmelCase__					:    List[str]												=					sample_softmax
						lowerCAmelCase__					:    Dict												=					adaptive
						lowerCAmelCase__					:    str												=					dropout
						lowerCAmelCase__					:    List[str]												=					dropatt
						lowerCAmelCase__					:    List[str]												=					untie_r
						lowerCAmelCase__					:    str												=					init
						lowerCAmelCase__					:    Union[str, Any]												=					init_range
						lowerCAmelCase__					:    Optional[int]												=					proj_init_std
						lowerCAmelCase__					:    Optional[Any]												=					init_std
						lowerCAmelCase__					:    Dict												=					layer_norm_epsilon
						super().__init__(eos_token_id=__lowerCamelCase		,**__lowerCamelCase			)



		@property
		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit."""			)
						return -1



		@max_position_embeddings.setter
		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      int:

						"""simple docstring"""






						raise NotImplementedError(
						    f"""The model {self.model_type} is one of the few models that has no sequence length limit."""			)






from typing import TYPE_CHECKING

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


__snake_case    :		str  ={
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		Tuple  =['ConditionalDetrFeatureExtractor']
							__snake_case    :		Union[str, Any]  =['ConditionalDetrImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		List[str]  =[
							    'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ConditionalDetrForObjectDetection',
							    'ConditionalDetrForSegmentation',
							    'ConditionalDetrModel',
							    'ConditionalDetrPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_conditional_detr import (
							    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    ConditionalDetrConfig,
							    ConditionalDetrOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
														from .image_processing_conditional_detr import ConditionalDetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_conditional_detr import (
														    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ConditionalDetrForObjectDetection,
														    ConditionalDetrForSegmentation,
														    ConditionalDetrModel,
														    ConditionalDetrPreTrainedModel,
														)

else:
							import sys

							__snake_case    :		str  =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

