
"""simple docstring"""
from typing import Mapping

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


lowercase		:    str			=       logging.get_logger(__name__)




class  _snake_case	(    _lowercase							):
							SCREAMING_SNAKE_CASE		  =    '''mt5'''
							SCREAMING_SNAKE_CASE		  =    ['''past_key_values''']






							def __init__(       self    :      List[str]    ,			snake_case__    :      Union[str, Any]=2_5_0_1_1_2    ,			snake_case__    :      int=5_1_2    ,			snake_case__    :      Optional[Any]=6_4    ,			snake_case__    :      Optional[Any]=1_0_2_4    ,			snake_case__    :      int=8    ,			snake_case__    :      int=None    ,			snake_case__    :      Union[str, Any]=6    ,			snake_case__    :      Any=3_2    ,			snake_case__    :      List[str]=1_2_8    ,			snake_case__    :      List[str]=0.1    ,			snake_case__    :      Dict=1e-6    ,			snake_case__    :      List[str]=1.0    ,			snake_case__    :      List[Any]="gated-gelu"    ,			snake_case__    :      Optional[Any]=True    ,			snake_case__    :      List[Any]=True    ,			snake_case__    :      List[str]="T5Tokenizer"    ,			snake_case__    :      Dict=False    ,			snake_case__    :      Optional[Any]=0    ,			snake_case__    :      Optional[Any]=1    ,			snake_case__    :      Optional[Any]=0    ,			**snake_case__    :      Optional[Any]    ,			):






												"""simple docstring"""



												super().__init__(
												    is_encoder_decoder=snake_case__    ,			tokenizer_class=snake_case__    ,			tie_word_embeddings=snake_case__    ,			pad_token_id=snake_case__    ,			eos_token_id=snake_case__    ,			decoder_start_token_id=snake_case__    ,			**snake_case__    ,			)
												__a:    Optional[Any]		     =       vocab_size
												__a:    Optional[int]		     =       d_model
												__a:    Union[str, Any]		     =       d_kv
												__a:    str		     =       d_ff
												__a:    Optional[Any]		     =       num_layers
												__a:    List[Any]		     =       (
												    num_decoder_layers if num_decoder_layers is not None else self.num_layers
												)  # default = symmetry
												__a:    int		     =       num_heads
												__a:    Union[str, Any]		     =       relative_attention_num_buckets
												__a:    Any		     =       relative_attention_max_distance
												__a:    List[str]		     =       dropout_rate
												__a:    Tuple		     =       layer_norm_epsilon
												__a:    Optional[int]		     =       initializer_factor
												__a:    List[str]		     =       feed_forward_proj
												__a:    int		     =       use_cache

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

												if len(snake_case__     ) > 1 and act_info[0] != "gated" or len(snake_case__     ) > 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":
																	__a:    List[str]		     =       """gelu_new"""






							@property
							def 				__lowercase	(       self    :      Union[str, Any]     ):






												"""simple docstring"""



												return self.d_model






							@property
							def 				__lowercase	(       self    :      Optional[int]     ):






												"""simple docstring"""



												return self.num_heads






							@property
							def 				__lowercase	(       self    :      Optional[int]     ):






												"""simple docstring"""



												return self.num_layers






class  _snake_case	(    _lowercase							):






							@property
							# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
							def 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												__a:    List[str]		     =       {
												    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
												    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
												}
												if self.use_past:
																	__a:    Union[str, Any]		     =       """past_encoder_sequence + sequence"""
																	__a:    str		     =       {0: """batch"""}
																	__a:    Union[str, Any]		     =       {0: """batch""", 1: """past_decoder_sequence + sequence"""}
												else:
																	__a:    Any		     =       {0: """batch""", 1: """decoder_sequence"""}
																	__a:    Dict		     =       {0: """batch""", 1: """decoder_sequence"""}

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

												return common_inputs






							@property
							# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
							def 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												return 1_3






							@property
							def 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												return 5e-4


"""simple docstring"""
from typing import TYPE_CHECKING

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


lowercase		:    Dict			=       {
    'configuration_mobilenet_v1': [
        'MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV1Config',
        'MobileNetV1OnnxConfig',
    ],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    Optional[int]			=       ['MobileNetV1FeatureExtractor']
			lowercase		:    Union[str, Any]			=       ['MobileNetV1ImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    Tuple			=       [
			    'MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'MobileNetV1ForImageClassification',
			    'MobileNetV1Model',
			    'MobileNetV1PreTrainedModel',
			    'load_tf_weights_in_mobilenet_v1',
			]


if TYPE_CHECKING:
			from .configuration_mobilenet_va import (
			    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileNetVaConfig,
			    MobileNetVaOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
						from .image_processing_mobilenet_va import MobileNetVaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilenet_va import (
						    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileNetVaForImageClassification,
						    MobileNetVaModel,
						    MobileNetVaPreTrainedModel,
						    load_tf_weights_in_mobilenet_va,
						)


else:
			import sys

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