


"""simple docstring"""







from typing import Mapping

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


a__    :							Dict            =					logging.get_logger(__name__)

a__    :							Optional[Any]            =					{
    '''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 			UpperCamelCase_     (			A__):

	"""simple docstring"""





	snake_case__    :  str    =      "umt5"
	snake_case__    :  Union[str, Any]    =      ["past_key_values"]




	def __init__(			self      :    Optional[Any]      ,      UpperCAmelCase__      :    Union[str, Any]=2_5_0_1_1_2      ,      UpperCAmelCase__      :    Union[str, Any]=5_1_2      ,      UpperCAmelCase__      :    Optional[Any]=6_4      ,      UpperCAmelCase__      :    Any=1_0_2_4      ,      UpperCAmelCase__      :    List[str]=8      ,      UpperCAmelCase__      :    Optional[int]=None      ,      UpperCAmelCase__      :    str=6      ,      UpperCAmelCase__      :    List[Any]=3_2      ,      UpperCAmelCase__      :    List[Any]=1_2_8      ,      UpperCAmelCase__      :    List[str]=0.1      ,      UpperCAmelCase__      :    List[str]=1E-6      ,      UpperCAmelCase__      :    Union[str, Any]=1.0      ,      UpperCAmelCase__      :    Any="gated-gelu"      ,      UpperCAmelCase__      :    Dict=True      ,      UpperCAmelCase__      :    int=True      ,      UpperCAmelCase__      :    Optional[Any]="T5Tokenizer"      ,      UpperCAmelCase__      :    int=True      ,      UpperCAmelCase__      :    Any=0      ,      UpperCAmelCase__      :    Optional[int]=1      ,      UpperCAmelCase__      :    Optional[int]=0      ,      **UpperCAmelCase__      :    Dict      ,      )	->     Tuple:
						super().__init__(
						    is_encoder_decoder=__A      ,      tokenizer_class=__A      ,      tie_word_embeddings=__A      ,      pad_token_id=__A      ,      eos_token_id=__A      ,      decoder_start_token_id=__A      ,      **__A      ,      )
						__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
						__SCREAMING_SNAKE_CASE						       =    (
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						__SCREAMING_SNAKE_CASE						       =    num_heads
						__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						       =    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'"       )

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




	@property
	def   UpperCAmelCase_				(			self      :    Any       )	->     int:
						return self.d_model




	@property
	def   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     List[str]:
						return self.num_heads




	@property
	def   UpperCAmelCase_				(			self      :    List[str]       )	->     str:
						return self.num_layers




class 			UpperCamelCase_     (			A__):

	"""simple docstring"""





	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
	def   UpperCAmelCase_				(			self      :    Optional[int]       )	->     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
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
	def   UpperCAmelCase_				(			self      :    Dict       )	->     int:
						return 1_3




	@property
	def   UpperCAmelCase_				(			self      :    str       )	->     float:
						return 5E-4




"""simple docstring"""







from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					return math.sqrt(sum(pow(a - b  ,						2       ) for a, b in zip(lowerCAmelCase_  ,						lowerCAmelCase_       )       )       )




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					if dataset.ndim != value_array.ndim:
										__SCREAMING_SNAKE_CASE						       =    (
										    "Wrong input data's dimensions... "
										    f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
										)
										raise ValueError(lowerCAmelCase_       )

					try:
										if dataset.shape[1] != value_array.shape[1]:
															__SCREAMING_SNAKE_CASE						       =    (
															    "Wrong input data's shape... "
															    f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
															)
															raise ValueError(lowerCAmelCase_       )
					except IndexError:
										if dataset.ndim != value_array.ndim:
															raise TypeError("Wrong shape"       )

					if dataset.dtype != value_array.dtype:
										__SCREAMING_SNAKE_CASE						       =    (
										    "Input data have different datatype... "
										    f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
										)
										raise TypeError(lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    []

					for value in value_array:
										__SCREAMING_SNAKE_CASE						       =    euclidean(lowerCAmelCase_  ,						dataset[0]       )
										__SCREAMING_SNAKE_CASE						       =    dataset[0].tolist()

										for dataset_value in dataset[1:]:
															__SCREAMING_SNAKE_CASE						       =    euclidean(lowerCAmelCase_  ,						lowerCAmelCase_       )

															if dist > temp_dist:
																				__SCREAMING_SNAKE_CASE						       =    temp_dist
																				__SCREAMING_SNAKE_CASE						       =    dataset_value.tolist()

										answer.append([vector, dist]       )

					return answer




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					return np.dot(lowerCAmelCase_  ,						lowerCAmelCase_       ) / (norm(lowerCAmelCase_       ) * norm(lowerCAmelCase_       ))


if __name__ == "__main__":
		import doctest

		doctest.testmod()
