




from typing import Mapping

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


A_      			=       logging.get_logger(__name__)

A_      			=       {
    "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       lowercase(  _UpperCAmelCase				):







 '''simple docstring'''





 lowercase__				      =      "umt5"
 lowercase__				      =      ["past_key_values"]


 def __init__(       self:	Any,   a_:	Union[str, Any]=250_112,   a_:	Tuple=512,   a_:	List[Any]=64,   a_:	str=1_024,   a_:	List[str]=8,   a_:	Dict=None,   a_:	Optional[int]=6,   a_:	int=32,   a_:	Dict=128,   a_:	List[Any]=0.1,   a_:	List[Any]=1E-6,   a_:	List[str]=1.0,   a_:	Union[str, Any]="gated-gelu",   a_:	Union[str, Any]=True,   a_:	Dict=True,   a_:	Tuple="T5Tokenizer",   a_:	List[str]=True,   a_:	int=0,   a_:	int=1,   a_:	int=0,   **a_:	Optional[Any],   ):




   '''simple docstring'''



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

   _snake_case							:	Any			=						self.feed_forward_proj.split("""-"""	)
   _snake_case							:	Tuple			=						act_info[-1]
   _snake_case							:	Optional[Any]			=						act_info[0] == '''gated'''

   if len(lowerCamelCase_	) > 1 and act_info[0] != "gated" or len(lowerCamelCase_	) > 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":
     _snake_case							:	Any			=						'''gelu_new'''


 @property
 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   return self.d_model


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




   '''simple docstring'''



   return self.num_heads


 @property
 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   return self.num_layers







class       lowercase(  _UpperCAmelCase				):







 '''simple docstring'''





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




   '''simple docstring'''



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

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

   return common_inputs


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




   '''simple docstring'''



   return 13


 @property
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   return 5E-4








"""simple docstring"""


from ...processing_utils import ProcessorMixin






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      ["image_processor", "feature_extractor"]
 lowercase__				      =      "TvltImageProcessor"
 lowercase__				      =      "TvltFeatureExtractor"


 def __init__(       self:	Dict,   a_:	Union[str, Any],   a_:	Union[str, Any]	):




   '''simple docstring'''



   super().__init__(image_processor=a_,   feature_extractor=a_	)

   _snake_case							:	Any			=						image_processor
   _snake_case							:	Dict			=						feature_extractor


 def __call__(       self:	int,   a_:	str=None,   a_:	Tuple=None,   a_:	Dict=None,   a_:	str=None,   a_:	Optional[int]=False,   a_:	Tuple=False,   *a_:	List[str],   **a_:	int,   ):




   '''simple docstring'''



   if images is None and audio is None:
     raise ValueError("""You need to specify either an `images` or `audio` input to process."""	)

   _snake_case							:	Optional[int]			=						None
   if images is not None:
     _snake_case							:	Tuple			=						self.image_processor(a_,   mask_pixel=a_,   *a_,   **a_	)
   if images_mixed is not None:
     _snake_case							:	Optional[int]			=						self.image_processor(a_,   is_mixed=a_,   *a_,   **a_	)
   if audio is not None:
     _snake_case							:	Any			=						self.feature_extractor(
         a_,   *a_,   sampling_rate=a_,   mask_audio=a_,   **a_	)

   _snake_case							:	List[str]			=						{}
   if audio is not None:
     output_dict.update(a_	)
   if images is not None:
     output_dict.update(a_	)
   if images_mixed_dict is not None:
     output_dict.update(a_	)
   return output_dict


 @property
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.image_processor.model_input_names
   _snake_case							:	List[str]			=						self.feature_extractor.model_input_names
   return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names	)	)


