
from typing import Mapping

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


__A				=logging.get_logger(__name__)

__A				={
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = 'umt5'
	lowerCAmelCase__         = ['past_key_values']



	def __init__(    self						,  lowercase=250112						,  lowercase=512						,  lowercase=64						,  lowercase=1024						,  lowercase=8						,  lowercase=None						,  lowercase=6						,  lowercase=32						,  lowercase=128						,  lowercase=0.1						,  lowercase=1e-6						,  lowercase=1.0						,  lowercase="gated-gelu"						,  lowercase=True						,  lowercase=True						,  lowercase="T5Tokenizer"						,  lowercase=True						,  lowercase=0						,  lowercase=1						,  lowercase=0						,  **lowercase						,  )				->       List[Any]:
					super().__init__(
					    is_encoder_decoder=lowercase						,  tokenizer_class=lowercase						,  tie_word_embeddings=lowercase						,  pad_token_id=lowercase						,  eos_token_id=lowercase						,  decoder_start_token_id=lowercase						,  **lowercase						,  )
					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(lowercase ) > 1 and act_info[0] != "gated" or len(lowercase ) > 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'" )

					if feed_forward_proj == "gated-gelu":
									lowerCamelCase_						=			"gelu_new"



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					return self.d_model



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					return self.num_heads



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




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
	def     SCREAMING_SNAKE_CASE_(    self )				->       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_(lowercase						,  direction="inputs" )

					return common_inputs



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



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









"""simple docstring"""



def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list						)   ->Any:



    '''simple docstring'''







    _enforce_args(snake_case_				,snake_case_						)
    if n == 0:
        return 0
    __A    :						int						 =     float('''-inf'''						)
    for i in range(1				,n + 1						):
        __A    :						Union[str, Any]						 =     max(
            snake_case_				,prices[i - 1] + naive_cut_rod_recursive(n - i				,snake_case_						)						)

    return max_revue







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list						)   ->int:



    '''simple docstring'''







    _enforce_args(snake_case_				,snake_case_						)
    __A    :						Dict						 =     [float('''-inf'''						) for _ in range(n + 1						)]
    return _top_down_cut_rod_recursive(snake_case_				,snake_case_				,snake_case_						)







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list				,snake_case_  :  list						)   ->Any:



    '''simple docstring'''







    if max_rev[n] >= 0:
        return max_rev[n]
    elif n == 0:
        return 0
    else:
        __A    :						Any						 =     float('''-inf'''						)
        for i in range(1				,n + 1						):
            __A    :						Union[str, Any]						 =     max(
                snake_case_				,prices[i - 1] + _top_down_cut_rod_recursive(n - i				,snake_case_				,snake_case_						)				,)

        __A    :						Any						 =     max_revenue

    return max_rev[n]







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list						)   ->Any:



    '''simple docstring'''







    _enforce_args(snake_case_				,snake_case_						)

    # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
    # length 0.
    __A    :						Union[str, Any]						 =     [float('''-inf'''						) for _ in range(n + 1						)]
    __A    :						List[Any]						 =     0

    for i in range(1				,n + 1						):
        __A    :						Union[str, Any]						 =     max_rev[i]
        for j in range(1				,i + 1						):
            __A    :						str						 =     max(snake_case_				,prices[j - 1] + max_rev[i - j]						)

        __A    :						List[str]						 =     max_revenue_i

    return max_rev[n]







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list						)   ->Union[str, Any]:



    '''simple docstring'''







    if n < 0:
        __A    :						Union[str, Any]						 =     F"""n must be greater than or equal to 0. Got n = {n}"""
        raise ValueError(snake_case_						)

    if n > len(snake_case_						):
        __A    :						List[Any]						 =     (
            '''Each integral piece of rod must have a corresponding price. '''
            F"""Got n = {n} but length of prices = {len(snake_case_						)}"""
        )
        raise ValueError(snake_case_						)







def 		__lowercase     (   )   ->str:



    '''simple docstring'''







    __A    :						Any						 =     [6, 10, 12, 15, 20, 23]
    __A    :						Union[str, Any]						 =     len(snake_case_						)

    # the best revenue comes from cutting the rod into 6 pieces, each
    # of length 1 resulting in a revenue of 6 * 6 = 36.
    __A    :						str						 =     36

    __A    :						Any						 =     top_down_cut_rod(snake_case_				,snake_case_						)
    __A    :						Any						 =     bottom_up_cut_rod(snake_case_				,snake_case_						)
    __A    :						Optional[Any]						 =     naive_cut_rod_recursive(snake_case_				,snake_case_						)

    assert expected_max_revenue == max_rev_top_down
    assert max_rev_top_down == max_rev_bottom_up
    assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
       main()




