





from typing import Mapping

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


snake_case__      :							Dict									=  logging.get_logger(__name__)





class A_      (    lowerCAmelCase__     ):
   lowerCAmelCase__       =				"""mt5"""
   lowerCAmelCase__       =				["""past_key_values"""]




   def __init__(self			:Optional[int]					,     _UpperCamelCase			:List[str]=25_0112					,     _UpperCamelCase			:Dict=512					,     _UpperCamelCase			:List[str]=64					,     _UpperCamelCase			:Any=1024					,     _UpperCamelCase			:Optional[int]=8					,     _UpperCamelCase			:Dict=None					,     _UpperCamelCase			:Dict=6					,     _UpperCamelCase			:Any=32					,     _UpperCamelCase			:Dict=128					,     _UpperCamelCase			:Tuple=0.1					,     _UpperCamelCase			:str=1e-6					,     _UpperCamelCase			:List[str]=1.0					,     _UpperCamelCase			:Optional[int]="gated-gelu"					,     _UpperCamelCase			:Union[str, Any]=True					,     _UpperCamelCase			:List[Any]=True					,     _UpperCamelCase			:str="T5Tokenizer"					,     _UpperCamelCase			:str=False					,     _UpperCamelCase			:List[str]=0					,     _UpperCamelCase			:Optional[int]=1					,     _UpperCamelCase			:List[str]=0					,     **_UpperCamelCase			:List[str]					,     )->      Optional[int]:
         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      =       vocab_size
         __A      =       d_model
         __A      =       d_kv
         __A      =       d_ff
         __A      =       num_layers
         __A      =       (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         __A      =       num_heads
         __A      =       relative_attention_num_buckets
         __A      =       relative_attention_max_distance
         __A      =       dropout_rate
         __A      =       layer_norm_epsilon
         __A      =       initializer_factor
         __A      =       feed_forward_proj
         __A      =       use_cache

         __A      =       self.feed_forward_proj.split('''-'''				)
         __A      =       act_info[-1]
         __A      =       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      =       '''gelu_new'''




   @property
   def _lowerCAmelCase   (self			:List[str]				)->      List[Any]:
         return self.d_model




   @property
   def _lowerCAmelCase   (self			:Union[str, Any]				)->      Tuple:
         return self.num_heads






   @property
   def _lowerCAmelCase   (self			:str				)->      str:
         return self.num_layers





class A_      (    lowerCAmelCase__     ):




   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
   def _lowerCAmelCase   (self			:str				)->      Mapping[str, Mapping[int, str]]:
         __A      =       {
             '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
             '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
         }
         if self.use_past:
               __A      =       '''past_encoder_sequence + sequence'''
               __A      =       {0: '''batch'''}
               __A      =       {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         else:
               __A      =       {0: '''batch''', 1: '''decoder_sequence'''}
               __A      =       {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 _lowerCAmelCase   (self			:Optional[int]				)->      int:
         return 13






   @property
   def _lowerCAmelCase   (self			:int				)->      float:
         return 5e-4












from unittest.mock import patch

import datasets
from datasets import Dataset



def _a       (			)			->		Optional[Any]:







      '''simple docstring'''






      __A      =       Dataset.from_dict({'''col_1''': [3, 2, 0, 1]}				)

      with patch('''tqdm.auto.tqdm'''				) as mock_tqdm:
            datasets.disable_progress_bar()
            dset.map(lambda lowerCamelCase				: {"col_2": x["col_1"] + 1}				)
            mock_tqdm.assert_not_called()

            mock_tqdm.reset_mock()

            datasets.enable_progress_bar()
            dset.map(lambda lowerCamelCase				: {"col_2": x["col_1"] + 1}				)
            mock_tqdm.assert_called()





