


from typing import Mapping

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


__UpperCAmelCase      			=  logging.get_logger(__name__)

__UpperCAmelCase      			=  {
    """google/long-t5-local-base""": """https://huggingface.co/google/long-t5-local-base/blob/main/config.json""",
    """google/long-t5-local-large""": """https://huggingface.co/google/long-t5-local-large/blob/main/config.json""",
    """google/long-t5-tglobal-base""": """https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json""",
    """google/long-t5-tglobal-large""": """https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json""",
}



class 							lowercase__(						snake_case__			):






      '''simple docstring'''







      snake_case__       =					'''longt5'''
      snake_case__       =					['''past_key_values''']
      snake_case__       =					{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}




      def __init__(		self   ,			__SCREAMING_SNAKE_CASE=3_21_28   ,			__SCREAMING_SNAKE_CASE=5_12   ,			__SCREAMING_SNAKE_CASE=64   ,			__SCREAMING_SNAKE_CASE=20_48   ,			__SCREAMING_SNAKE_CASE=6   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE=8   ,			__SCREAMING_SNAKE_CASE=1_27   ,			__SCREAMING_SNAKE_CASE=16   ,			__SCREAMING_SNAKE_CASE=32   ,			__SCREAMING_SNAKE_CASE=1_28   ,			__SCREAMING_SNAKE_CASE=0.1   ,			__SCREAMING_SNAKE_CASE=1E-6   ,			__SCREAMING_SNAKE_CASE=1.0   ,			__SCREAMING_SNAKE_CASE="relu"   ,			__SCREAMING_SNAKE_CASE=True   ,			__SCREAMING_SNAKE_CASE="local"   ,			__SCREAMING_SNAKE_CASE=True   ,			__SCREAMING_SNAKE_CASE=0   ,			__SCREAMING_SNAKE_CASE=1   ,			**__SCREAMING_SNAKE_CASE   ,			)					->						Optional[Any]:







         """simple docstring"""







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

         UpperCamelCase__ :			str 						=self.feed_forward_proj.split("-")
         UpperCamelCase__ :			Dict 						=act_info[-1]
         UpperCamelCase__ :			List[str] 						=act_info[0] == "gated"

         if len(A_) > 1 and act_info[0] != "gated" or len(A_) > 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":
            UpperCamelCase__ :			Dict 						="gelu_new"

         super().__init__(
             pad_token_id=A_   ,			eos_token_id=A_   ,			is_encoder_decoder=A_   ,			**A_   ,			)




class 							lowercase__(						snake_case__			):






      '''simple docstring'''







      @property
      def    UpperCAmelCase							(		self)					->						List[str]:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						={
             "input_ids": {0: "batch", 1: "encoder_sequence"},
             "attention_mask": {0: "batch", 1: "encoder_sequence"},
         }
         if self.use_past:
            UpperCamelCase__ :			Any 						="past_encoder_sequence + sequence"
            UpperCamelCase__ :			List[Any] 						={0: "batch"}
            UpperCamelCase__ :			Union[str, Any] 						={0: "batch", 1: "past_decoder_sequence + sequence"}
         else:
            UpperCamelCase__ :			Dict 						={0: "batch", 1: "decoder_sequence"}
            UpperCamelCase__ :			Any 						={0: "batch", 1: "decoder_sequence"}

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

         return common_inputs




      @property
      def    UpperCAmelCase							(		self)					->						List[str]:







         """simple docstring"""







         return 13




import unittest
from timeit import timeit







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   int:

     """simple docstring"""







     _lowerCAmelCase :       List[str]	   =     first_num if first_num >= second_num else second_num
     _lowerCAmelCase :       List[str]	   =     max_num
     while (common_mult % first_num > 0) or (common_mult % second_num > 0):
          common_mult += max_num
     return common_mult







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   int:

     """simple docstring"""







     return b if a == 0 else greatest_common_divisor(b % a		,				SCREAMING_SNAKE_CASE					)







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   int:

     """simple docstring"""







     return first_num // greatest_common_divisor(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					) * second_num







def 						_snake_case		(     )							->   str:

     """simple docstring"""







     _lowerCAmelCase :       List[Any]	   =     (
         "from __main__ import least_common_multiple_slow, least_common_multiple_fast"
     )
     print(
         "least_common_multiple_slow():"		,				timeit("least_common_multiple_slow(1000, 999)"		,				setup=SCREAMING_SNAKE_CASE					)		,				)
     print(
         "least_common_multiple_fast():"		,				timeit("least_common_multiple_fast(1000, 999)"		,				setup=SCREAMING_SNAKE_CASE					)		,				)





class A__     (			unittest.TestCase			):



 """simple docstring"""







 _lowercase  :       Union[str, Any]  			=    (
     (10, 20),
     (13, 15),
     (4, 31),
     (10, 42),
     (43, 34),
     (5, 12),
     (12, 25),
     (10, 25),
     (6, 9),
 )
 _lowercase  :       Dict  			=    (20, 195, 124, 210, 1_462, 60, 300, 50, 18)

 def  __magic_name__							(		self			:						int							):






      '''simple docstring'''




      for i, (first_num, second_num) in enumerate(self.test_inputs							):
           _lowerCAmelCase :       Optional[int]	   =     least_common_multiple_slow(A_  ,    A_							)
           _lowerCAmelCase :       Optional[int]	   =     least_common_multiple_fast(A_  ,    A_							)
           with self.subTest(i=A_							):
                self.assertEqual(A_  ,    self.expected_results[i]							)
                self.assertEqual(A_  ,    self.expected_results[i]							)


if __name__ == "__main__":
  benchmark()
  unittest.main()






