
from typing import Mapping

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


UpperCAmelCase__	:  Any      =   logging.get_logger(__name__)






class UpperCAmelCase     (		lowerCAmelCase__     ):





   '''simple docstring'''
   __UpperCamelCase	:					Any				       =					"""mt5"""
   __UpperCamelCase	:					Optional[int]				       =					["""past_key_values"""]


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

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

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

      # for backwards compatibility
      if feed_forward_proj == "gated-gelu":
         _A:						Optional[int]	    = '''gelu_new'''


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

      """simple docstring"""
      return self.d_model


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

      """simple docstring"""
      return self.num_heads


   @property
   def 				__magic_name__       (      self  :							str  ):

      """simple docstring"""
      return self.num_layers






class UpperCAmelCase     (		lowerCAmelCase__     ):





   '''simple docstring'''


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

      """simple docstring"""
      _A:						int	    = {
          '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
          '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
      }
      if self.use_past:
         _A:						int	    = '''past_encoder_sequence + sequence'''
         _A:						Union[str, Any]	    = {0: '''batch'''}
         _A:						Tuple	    = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
      else:
         _A:						Union[str, Any]	    = {0: '''batch''', 1: '''decoder_sequence'''}
         _A:						Dict	    = {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 				__magic_name__       (      self  :							Optional[int]  ):

      """simple docstring"""
      return 1_3


   @property
   def 				__magic_name__       (      self  :							int  ):

      """simple docstring"""
      return 5e-4





import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


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






class UpperCAmelCase     (		SCREAMING_SNAKE_CASE__     ):





   '''simple docstring'''


   def __init__(      self  :							str    ,    *lowerCAmelCase_  :							Union[str, Any]    ,    **lowerCAmelCase_  :							Union[str, Any]  ):

      """simple docstring"""
      warnings.warn(
          '''The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
          ''' Please use ConvNextImageProcessor instead.'''    ,    lowerCAmelCase_    ,    )
      super().__init__(*lowerCAmelCase_    ,    **lowerCAmelCase_  )



