
'''simple docstring'''







from typing import Mapping

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


_lowercase :  Optional[int]    							=       logging.get_logger(__name__)

_lowercase :  Any    							=       {
    "google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}






class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     '''umt5'''
					lowerCAmelCase_        =     ['''past_key_values''']






					def __init__(   self				,							__SCREAMING_SNAKE_CASE=25_01_12				,							__SCREAMING_SNAKE_CASE=5_12				,							__SCREAMING_SNAKE_CASE=64				,							__SCREAMING_SNAKE_CASE=10_24				,							__SCREAMING_SNAKE_CASE=8				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=6				,							__SCREAMING_SNAKE_CASE=32				,							__SCREAMING_SNAKE_CASE=1_28				,							__SCREAMING_SNAKE_CASE=0.1				,							__SCREAMING_SNAKE_CASE=1E-6				,							__SCREAMING_SNAKE_CASE=1.0				,							__SCREAMING_SNAKE_CASE="gated-gelu"				,							__SCREAMING_SNAKE_CASE=True				,							__SCREAMING_SNAKE_CASE=True				,							__SCREAMING_SNAKE_CASE="T5Tokenizer"				,							__SCREAMING_SNAKE_CASE=True				,							__SCREAMING_SNAKE_CASE=0				,							__SCREAMING_SNAKE_CASE=1				,							__SCREAMING_SNAKE_CASE=0				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





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

											lowercase_	:     Optional[Any]													=       self.feed_forward_proj.split('''-'''		)
											lowercase_	:     List[str]													=       act_info[-1]
											lowercase_	:     List[str]													=       act_info[0] == '''gated'''

											if len(__SCREAMING_SNAKE_CASE		) > 1 and act_info[0] != "gated" or len(__SCREAMING_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\''''		)

											if feed_forward_proj == "gated-gelu":
																	lowercase_	:     int													=       '''gelu_new'''






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self.d_model






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self.num_heads






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self.num_layers







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					@property
					# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     str													=       {
											    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
											    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
											}
											if self.use_past:
																	lowercase_	:     str													=       '''past_encoder_sequence + sequence'''
																	lowercase_	:     Optional[int]													=       {0: '''batch'''}
																	lowercase_	:     List[str]													=       {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
											else:
																	lowercase_	:     List[Any]													=       {0: '''batch''', 1: '''decoder_sequence'''}
																	lowercase_	:     Optional[Any]													=       {0: '''batch''', 1: '''decoder_sequence'''}

											if self.use_past:
																	self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE				,							direction='''inputs'''		)

											return common_inputs






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


											"""simple docstring"""





											return 13






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return 5E-4





'''simple docstring'''







from typing import TYPE_CHECKING

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


_lowercase :  Union[str, Any]    							=       {
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  Optional[int]    							=       ["Mask2FormerImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  str    							=       [
							    "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "Mask2FormerForUniversalSegmentation",
							    "Mask2FormerModel",
							    "Mask2FormerPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_maskaformer import MaskaFormerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_maskaformer import (
														    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MaskaFormerForUniversalSegmentation,
														    MaskaFormerModel,
														    MaskaFormerPreTrainedModel,
														)


else:
							import sys

							_lowercase :  Dict    							=       _LazyModule(__name__, globals()["__file__"], _import_structure)



