


from typing import Mapping

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


__magic_name__       					=      logging.get_logger(__name__)





class    lowerCAmelCase__				(			__lowerCamelCase       ):






							"""simple docstring"""
							__UpperCAmelCase   :		int  					=	'''mt5'''
							__UpperCAmelCase   :		List[Any]  					=	['''past_key_values''']




							def __init__(   self				,							a_=25_0112				,							a_=512				,							a_=64				,							a_=1024				,							a_=8				,							a_=None				,							a_=6				,							a_=32				,							a_=128				,							a_=0.1				,							a_=1E-6				,							a_=1.0				,							a_="gated-gelu"				,							a_=True				,							a_=True				,							a_="T5Tokenizer"				,							a_=False				,							a_=0				,							a_=1				,							a_=0				,							**a_				,							):
								super().__init__(
								    is_encoder_decoder=a_				,							tokenizer_class=a_				,							tie_word_embeddings=a_				,							pad_token_id=a_				,							eos_token_id=a_				,							decoder_start_token_id=a_				,							**a_				,							)
								lowerCamelCase_			:							Any      =       vocab_size
								lowerCamelCase_			:							Union[str, Any]      =       d_model
								lowerCamelCase_			:							str      =       d_kv
								lowerCamelCase_			:							List[Any]      =       d_ff
								lowerCamelCase_			:							Union[str, Any]      =       num_layers
								lowerCamelCase_			:							List[Any]      =       (
								    num_decoder_layers if num_decoder_layers is not None else self.num_layers
								)  # default = symmetry
								lowerCamelCase_			:							Dict      =       num_heads
								lowerCamelCase_			:							Any      =       relative_attention_num_buckets
								lowerCamelCase_			:							Union[str, Any]      =       relative_attention_max_distance
								lowerCamelCase_			:							Union[str, Any]      =       dropout_rate
								lowerCamelCase_			:							Any      =       layer_norm_epsilon
								lowerCamelCase_			:							int      =       initializer_factor
								lowerCamelCase_			:							List[Any]      =       feed_forward_proj
								lowerCamelCase_			:							str      =       use_cache

								lowerCamelCase_			:							List[str]      =       self.feed_forward_proj.split("-"     )
								lowerCamelCase_			:							int      =       act_info[-1]
								lowerCamelCase_			:							List[Any]      =       act_info[0] == "gated"

								if len(a_     ) > 1 and act_info[0] != "gated" or len(a_     ) > 2:
									raise ValueError(
									    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
									    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
									    "'gated-gelu' or 'relu'"     )

								# for backwards compatibility
								if feed_forward_proj == "gated-gelu":
									lowerCamelCase_			:							List[str]      =       "gelu_new"




							@property
							def 					_UpperCamelCase			(   self     ):
								return self.d_model




							@property
							def 					_UpperCamelCase			(   self     ):
								return self.num_heads




							@property
							def 					_UpperCamelCase			(   self     ):
								return self.num_layers





class    lowerCAmelCase__				(			__lowerCamelCase       ):






							"""simple docstring"""




							@property
							# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
							def 					_UpperCamelCase			(   self     ):
								lowerCamelCase_			:							str      =       {
								    "input_ids": {0: "batch", 1: "encoder_sequence"},
								    "attention_mask": {0: "batch", 1: "encoder_sequence"},
								}
								if self.use_past:
									lowerCamelCase_			:							List[str]      =       "past_encoder_sequence + sequence"
									lowerCamelCase_			:							List[str]      =       {0: "batch"}
									lowerCamelCase_			:							List[str]      =       {0: "batch", 1: "past_decoder_sequence + sequence"}
								else:
									lowerCamelCase_			:							Optional[int]      =       {0: "batch", 1: "decoder_sequence"}
									lowerCamelCase_			:							int      =       {0: "batch", 1: "decoder_sequence"}

								if self.use_past:
									self.fill_with_past_key_values_(a_				,							direction="inputs"     )

								return common_inputs




							@property
							# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
							def 					_UpperCamelCase			(   self     ):
								return 13




							@property
							def 					_UpperCamelCase			(   self     ):
								return 5E-4





from typing import TYPE_CHECKING

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


__magic_name__       					=      {'''configuration_detr''': ['''DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetrConfig''', '''DetrOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      ['''DetrFeatureExtractor''']
							__magic_name__       					=      ['''DetrImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      [
							    '''DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''DetrForObjectDetection''',
							    '''DetrForSegmentation''',
							    '''DetrModel''',
							    '''DetrPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_detr import DetrFeatureExtractor
														from .image_processing_detr import DetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_detr import (
														    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DetrForObjectDetection,
														    DetrForSegmentation,
														    DetrModel,
														    DetrPreTrainedModel,
														)

else:
							import sys

							__magic_name__       					=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

