





'''simple docstring'''

from typing import Mapping

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


lowercase_         =			logging.get_logger(__name__)

lowercase_         =			{
    """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  a_					(    snake_case_	):

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


    def __init__(		self			,		A=3_2128			,		A=512			,		A=64			,		A=2048			,		A=6			,		A=None			,		A=8			,		A=127			,		A=16			,		A=32			,		A=128			,		A=0.1			,		A=1e-6			,		A=1.0			,		A="relu"			,		A=True			,		A="local"			,		A=True			,		A=0			,		A=1			,		**A			,		)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      vocab_size
        _SCREAMING_SNAKE_CASE     =      d_model
        _SCREAMING_SNAKE_CASE     =      d_kv
        _SCREAMING_SNAKE_CASE     =      d_ff
        _SCREAMING_SNAKE_CASE     =      num_layers
        # default = symmetry
        _SCREAMING_SNAKE_CASE     =      num_decoder_layers if num_decoder_layers is not None else self.num_layers
        _SCREAMING_SNAKE_CASE     =      num_heads
        _SCREAMING_SNAKE_CASE     =      local_radius
        _SCREAMING_SNAKE_CASE     =      global_block_size
        _SCREAMING_SNAKE_CASE     =      relative_attention_num_buckets
        _SCREAMING_SNAKE_CASE     =      relative_attention_max_distance
        _SCREAMING_SNAKE_CASE     =      dropout_rate
        _SCREAMING_SNAKE_CASE     =      layer_norm_epsilon
        _SCREAMING_SNAKE_CASE     =      initializer_factor
        _SCREAMING_SNAKE_CASE     =      feed_forward_proj
        _SCREAMING_SNAKE_CASE     =      encoder_attention_type
        _SCREAMING_SNAKE_CASE     =      use_cache

        _SCREAMING_SNAKE_CASE     =      self.feed_forward_proj.split("""-"""	)
        _SCREAMING_SNAKE_CASE     =      act_info[-1]
        _SCREAMING_SNAKE_CASE     =      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":
            _SCREAMING_SNAKE_CASE     =      """gelu_new"""

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




class  a_					(    snake_case_	):

    '''simple docstring'''


    @property
    def       snake_case_(		self	)		->       Mapping[str, Mapping[int, str]]:
        _SCREAMING_SNAKE_CASE     =      {
            """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
            """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
        }
        if self.use_past:
            _SCREAMING_SNAKE_CASE     =      """past_encoder_sequence + sequence"""
            _SCREAMING_SNAKE_CASE     =      {0: """batch"""}
            _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """past_decoder_sequence + sequence"""}
        else:
            _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """decoder_sequence"""}
            _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """decoder_sequence"""}

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

        return common_inputs





    @property
    def       snake_case_(		self	)		->       int:
        return 13










'''simple docstring'''

import timeit


class  a_					:

    '''simple docstring'''


    def __init__(		self			,		A	)		->       Optional[int]:
        if isinstance(A			,		A	):  # Initializes a matrix identical to the one provided.
            _SCREAMING_SNAKE_CASE     =      arg
            _SCREAMING_SNAKE_CASE     =      len(A	)
        else:  # Initializes a square matrix of the given size and set values to zero.
            _SCREAMING_SNAKE_CASE     =      arg
            _SCREAMING_SNAKE_CASE     =      [[0 for _ in range(self.n	)] for _ in range(self.n	)]





    def __mul__(		self			,		A	)		->       Optional[int]:
        _SCREAMING_SNAKE_CASE     =      Matrix(self.n	)
        for i in range(self.n	):
            for j in range(self.n	):
                for k in range(self.n	):
                    matrix.t[i][j] += self.t[i][k] * b.t[k][j]
        return matrix





def lowerCamelCase						(					__lowerCamelCase			:   List[str] ,	__lowerCamelCase			:   Optional[int]   )						->int:
    _SCREAMING_SNAKE_CASE     =      Matrix([[1, 0], [0, 1]]   )
    while b > 0:
        if b & 1:
            matrix *= a
        a *= a
        b >>= 1
    return matrix





def lowerCamelCase						(					__lowerCamelCase			:   List[Any] ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   Tuple   )						->Dict:
    # Trivial Cases
    if n == 1:
        return fa
    elif n == 2:
        return fa
    _SCREAMING_SNAKE_CASE     =      Matrix([[1, 1], [1, 0]]   )
    _SCREAMING_SNAKE_CASE     =      modular_exponentiation(__lowerCamelCase ,	n - 2   )
    return fa * matrix.t[0][0] + fa * matrix.t[0][1]





def lowerCamelCase						(					__lowerCamelCase			:   str ,	__lowerCamelCase			:   List[Any] ,	__lowerCamelCase			:   Tuple   )						->str:
    # Trivial Cases
    if n == 1:
        return fa
    elif n == 2:
        return fa

    _SCREAMING_SNAKE_CASE     =      fa
    _SCREAMING_SNAKE_CASE     =      fa
    n -= 2

    while n > 0:
        _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      fn_a + fn_a, fn_a
        n -= 1

    return fn_a





def lowerCamelCase						(					)						->str:
    _SCREAMING_SNAKE_CASE     =      """
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
"""
    _SCREAMING_SNAKE_CASE     =      """fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"""
    _SCREAMING_SNAKE_CASE     =      timeit.timeit(setup=__lowerCamelCase ,	stmt=__lowerCamelCase ,	number=100   )
    print("""With matrix exponentiation the average execution time is """ ,	exec_time / 100   )
    return exec_time





def lowerCamelCase						(					)						->int:
    _SCREAMING_SNAKE_CASE     =      """
from random import randint
from __main__ import simple_fibonacci
"""
    _SCREAMING_SNAKE_CASE     =      """simple_fibonacci(randint(1,70000), 1, 1)"""
    _SCREAMING_SNAKE_CASE     =      timeit.timeit(setup=__lowerCamelCase ,	stmt=__lowerCamelCase ,	number=100   )
    print(
        """Without matrix exponentiation the average execution time is """ ,	exec_time / 100   )
    return exec_time





def lowerCamelCase						(					)						->List[str]:
    matrix_exponentiation_time()
    simple_fibonacci_time()


if __name__ == "__main__":
      main()



