





from typing import Mapping

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


_lowerCamelCase      							=   logging.get_logger(__name__)

_lowerCamelCase      							=   {
    '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       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   """umt5"""
      UpperCAmelCase__							=   ["""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__=True   ,				a__=0   ,				a__=1   ,				a__=0   ,				**a__   ,				):



             """simple docstring"""





             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				:  int						=		vocab_size
             _lowerCamelCase				:  int						=		d_model
             _lowerCamelCase				:  List[Any]						=		d_kv
             _lowerCamelCase				:  Dict						=		d_ff
             _lowerCamelCase				:  List[str]						=		num_layers
             _lowerCamelCase				:  Union[str, Any]						=		(
                 num_decoder_layers if num_decoder_layers is not None else self.num_layers
             )  # default = symmetry
             _lowerCamelCase				:  List[Any]						=		num_heads
             _lowerCamelCase				:  Tuple						=		relative_attention_num_buckets
             _lowerCamelCase				:  int						=		relative_attention_max_distance
             _lowerCamelCase				:  int						=		dropout_rate
             _lowerCamelCase				:  Dict						=		layer_norm_epsilon
             _lowerCamelCase				:  Dict						=		initializer_factor
             _lowerCamelCase				:  Dict						=		feed_forward_proj
             _lowerCamelCase				:  int						=		use_cache

             _lowerCamelCase				:  List[str]						=		self.feed_forward_proj.split('''-''')
             _lowerCamelCase				:  Optional[int]						=		act_info[-1]
             _lowerCamelCase				:  List[str]						=		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\'''')

             if feed_forward_proj == "gated-gelu":
                    _lowerCamelCase				:  Any						=		'''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       __A							(   lowerCamelCase__	):




      """simple docstring"""







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



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		{
                 '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
                 '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
             }
             if self.use_past:
                    _lowerCamelCase				:  Dict						=		'''past_encoder_sequence + sequence'''
                    _lowerCamelCase				:  Union[str, Any]						=		{0: '''batch'''}
                    _lowerCamelCase				:  Dict						=		{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
             else:
                    _lowerCamelCase				:  Dict						=		{0: '''batch''', 1: '''decoder_sequence'''}
                    _lowerCamelCase				:  Union[str, Any]						=		{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 					__snake_case      (		self):



             """simple docstring"""





             return 13



      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return 5e-4








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase      							=   {
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase      							=   [
           'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CLIPSegModel',
           'CLIPSegPreTrainedModel',
           'CLIPSegTextModel',
           'CLIPSegVisionModel',
           'CLIPSegForImageSegmentation',
       ]

if TYPE_CHECKING:
       from .configuration_clipseg import (
           CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
           CLIPSegConfig,
           CLIPSegTextConfig,
           CLIPSegVisionConfig,
       )
       from .processing_clipseg import CLIPSegProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_clipseg import (
                  CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CLIPSegForImageSegmentation,
                  CLIPSegModel,
                  CLIPSegPreTrainedModel,
                  CLIPSegTextModel,
                  CLIPSegVisionModel,
              )

else:
       import sys

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

