

'''simple docstring'''
from typing import Mapping

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


_UpperCAmelCase  :		List[str]  =		logging.get_logger(__name__)

_UpperCAmelCase  :		str  =		{
    '''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 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):
 UpperCamelCase__     =       'umt5'
 UpperCamelCase__     =       ['past_key_values']







 def __init__(       self  , snake_case_=25_01_12  , snake_case_=5_12  , snake_case_=64  , snake_case_=10_24  , snake_case_=8  , snake_case_=None  , snake_case_=6  , snake_case_=32  , snake_case_=1_28  , snake_case_=0.1  , snake_case_=1E-6  , snake_case_=1.0  , snake_case_="gated-gelu"  , snake_case_=True  , snake_case_=True  , snake_case_="T5Tokenizer"  , snake_case_=True  , snake_case_=0  , snake_case_=1  , snake_case_=0  , **snake_case_  , ):
   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_  , )
   lowercase									=vocab_size
   lowercase									=d_model
   lowercase									=d_kv
   lowercase									=d_ff
   lowercase									=num_layers
   lowercase									=(
       num_decoder_layers if num_decoder_layers is not None else self.num_layers
   )  # default = symmetry
   lowercase									=num_heads
   lowercase									=relative_attention_num_buckets
   lowercase									=relative_attention_max_distance
   lowercase									=dropout_rate
   lowercase									=layer_norm_epsilon
   lowercase									=initializer_factor
   lowercase									=feed_forward_proj
   lowercase									=use_cache

   lowercase									=self.feed_forward_proj.split('''-'''   )
   lowercase									=act_info[-1]
   lowercase									=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\''''   )

   if feed_forward_proj == "gated-gelu":
     lowercase									='''gelu_new'''







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







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







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

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):







 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def 						_A(       self   ):
   lowercase									={
       '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
       '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
   }
   if self.use_past:
     lowercase									='''past_encoder_sequence + sequence'''
     lowercase									={0: '''batch'''}
     lowercase									={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
   else:
     lowercase									={0: '''batch''', 1: '''decoder_sequence'''}
     lowercase									={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 						_A(       self   ):
   return 13







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







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

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase     :						Dict						       =  {'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase     :						int						       =  ['''LayoutXLMTokenizer''']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase     :						Any						       =  ['''LayoutXLMTokenizerFast''']

if TYPE_CHECKING:
       from .processing_layoutxlm import LayoutXLMProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm import LayoutXLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
       import sys

       __UpperCamelCase     :						Dict						       =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





