'''simple docstring'''

from typing import Mapping

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


_A      :				Tuple 				=    logging.get_logger(__name__)







class     _lowercase					(    UpperCAmelCase__      ):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE					:	Optional[Any]				     = """mt5"""
 _SCREAMING_SNAKE_CASE					:	Union[str, Any]				     = ["""past_key_values"""]
 def __init__(				self       :     Optional[int]				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]=25_01_12				,	SCREAMING_SNAKE_CASE__       :     int=5_12				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]=64				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]=10_24				,	SCREAMING_SNAKE_CASE__       :     str=8				,	SCREAMING_SNAKE_CASE__       :     int=None				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]=6				,	SCREAMING_SNAKE_CASE__       :     List[str]=32				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]=1_28				,	SCREAMING_SNAKE_CASE__       :     Optional[int]=0.1				,	SCREAMING_SNAKE_CASE__       :     Tuple=1e-6				,	SCREAMING_SNAKE_CASE__       :     Tuple=1.0				,	SCREAMING_SNAKE_CASE__       :     int="gated-gelu"				,	SCREAMING_SNAKE_CASE__       :     List[Any]=True				,	SCREAMING_SNAKE_CASE__       :     List[str]=True				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]="T5Tokenizer"				,	SCREAMING_SNAKE_CASE__       :     List[Any]=False				,	SCREAMING_SNAKE_CASE__       :     List[Any]=0				,	SCREAMING_SNAKE_CASE__       :     Optional[int]=1				,	SCREAMING_SNAKE_CASE__       :     List[str]=0				,	**SCREAMING_SNAKE_CASE__       :     int				,	)  ->   str:
  super().__init__(
      is_encoder_decoder=SCREAMING_SNAKE_CASE__				,	tokenizer_class=SCREAMING_SNAKE_CASE__				,	tie_word_embeddings=SCREAMING_SNAKE_CASE__				,	pad_token_id=SCREAMING_SNAKE_CASE__				,	eos_token_id=SCREAMING_SNAKE_CASE__				,	decoder_start_token_id=SCREAMING_SNAKE_CASE__				,	**SCREAMING_SNAKE_CASE__				,	)
  __lowerCAmelCase    							=						vocab_size
  __lowerCAmelCase    							=						d_model
  __lowerCAmelCase    							=						d_kv
  __lowerCAmelCase    							=						d_ff
  __lowerCAmelCase    							=						num_layers
  __lowerCAmelCase    							=						(
      num_decoder_layers if num_decoder_layers is not None else self.num_layers
  )  # default = symmetry
  __lowerCAmelCase    							=						num_heads
  __lowerCAmelCase    							=						relative_attention_num_buckets
  __lowerCAmelCase    							=						relative_attention_max_distance
  __lowerCAmelCase    							=						dropout_rate
  __lowerCAmelCase    							=						layer_norm_epsilon
  __lowerCAmelCase    							=						initializer_factor
  __lowerCAmelCase    							=						feed_forward_proj
  __lowerCAmelCase    							=						use_cache

  __lowerCAmelCase    							=						self.feed_forward_proj.split("""-"""							)
  __lowerCAmelCase    							=						act_info[-1]
  __lowerCAmelCase    							=						act_info[0] == """gated"""

  if len(SCREAMING_SNAKE_CASE__							) > 1 and act_info[0] != "gated" or len(SCREAMING_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'"""							)

  # for backwards compatibility
  if feed_forward_proj == "gated-gelu":
   __lowerCAmelCase    							=						"""gelu_new"""
 @property
 def a (				self       :     Optional[int]							)  ->   List[Any]:
  return self.d_model
 @property
 def a (				self       :     Tuple							)  ->   Optional[int]:
  return self.num_heads





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







class     _lowercase					(    UpperCAmelCase__      ):


 '''simple docstring'''






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

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

  return common_inputs
 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def a (				self       :     Tuple							)  ->   int:
  return 13





 @property
 def a (				self       :     Optional[Any]							)  ->   float:
  return 5e-4







'''simple docstring'''

from unittest.mock import patch

import datasets
from datasets import Dataset







def 					UpperCamelCase_						( ) ->					Dict:






 '''simple docstring'''

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

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

  mock_tqdm.reset_mock()

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






