




"""simple docstring"""




from typing import Mapping

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


a_            =   logging.get_logger(__name__)

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

					__lowercase	:       int				=   self.feed_forward_proj.split('''-'''   )
					__lowercase	:       Union[str, Any]				=   act_info[-1]
					__lowercase	:       Any				=   act_info[0] == '''gated'''

					if len(UpperCamelCase_   ) > 1 and act_info[0] != "gated" or len(UpperCamelCase_   ) > 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":
								__lowercase	:       Any				=   '''gelu_new'''
		@property
		def 					_lowerCamelCase    (		self   )		->   int:
					return self.d_model
		@property
		def 					_lowerCamelCase    (		self   )		->   Dict:
					return self.num_heads
		@property
		def 					_lowerCamelCase    (		self   )		->   str:
					return self.num_layers


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

					if self.use_past:
								self.fill_with_past_key_values_(UpperCamelCase_							,		direction='''inputs'''   )

					return common_inputs
		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
		def 					_lowerCamelCase    (		self   )		->   int:
					return 13
		@property
		def 					_lowerCamelCase    (		self   )		->   float:
					return 5E-4









"""simple docstring"""




a_            =   1_0_0_0







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return x * x







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase = 4					):
			assert callable(
			    __UpperCamelCase					), f"""the function(object) passed should be callable your input : {function}"""
			assert isinstance(__UpperCamelCase					,							(float, int)					), f"""a should be float or integer your input : {a}"""
			assert isinstance(function(__UpperCamelCase					)					,							(float, int)					), (
			    "the function should return integer or float return type of your function, "
			    f"""{type(__UpperCamelCase					)}"""
			)
			assert isinstance(__UpperCamelCase					,							(float, int)					), f"""b should be float or integer your input : {b}"""
			assert (
			    isinstance(__UpperCamelCase					,							__UpperCamelCase					) and precision > 0
			), f"""precision should be positive integer your input : {precision}"""

			# just applying the formula of simpson for approximate integration written in
			# mentioned article in first comment of this file and above this function

			__lowercase	:       Optional[Any]				=   (b - a) / N_STEPS
			__lowercase	:       Optional[int]				=   function(__UpperCamelCase					) + function(__UpperCamelCase					)

			for i in range(1					,							__UpperCamelCase					):
						__lowercase	:       Union[str, Any]				=   a + h * i
						result += function(__UpperCamelCase					) * (4 if i % 2 else 2)

			result *= h / 3
			return round(__UpperCamelCase					,							__UpperCamelCase					)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



