




from __future__ import annotations

import math

SCREAMING_SNAKE_CASE:     List[str]									= '''2020.9.26'''
SCREAMING_SNAKE_CASE:     List[Any]									= '''xcodz-dot, cclaus, dhruvmanila'''


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	tuple[float, float]:
		if not all(isinstance(lowerCAmelCase    ,						(float, int) ) for val in locals().values() ):
				SCREAMING_SNAKE_CASE_					      =							F'''Input values must either be float or int: {list(locals().values() )}'''
				raise TypeError(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							((x * distance) / (z + distance)) * scale
		SCREAMING_SNAKE_CASE_					      =							((y * distance) / (z + distance)) * scale
		return projected_x, projected_y


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	tuple[float, float, float]:
		if not isinstance(lowerCAmelCase    ,						lowerCAmelCase ):
				raise TypeError('Axis must be a str' )
		SCREAMING_SNAKE_CASE_					      =							locals()
		del input_variables["axis"]
		if not all(isinstance(lowerCAmelCase    ,						(float, int) ) for val in input_variables.values() ):
				SCREAMING_SNAKE_CASE_					      =							(
				    'Input values except axis must either be float or int: '
				    F'''{list(input_variables.values() )}'''
				)
				raise TypeError(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							(angle % 360) / 450 * 180 / math.pi
		if axis == "z":
				SCREAMING_SNAKE_CASE_					      =							x * math.cos(lowerCAmelCase ) - y * math.sin(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							y * math.cos(lowerCAmelCase ) + x * math.sin(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							z
		elif axis == "x":
				SCREAMING_SNAKE_CASE_					      =							y * math.cos(lowerCAmelCase ) - z * math.sin(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							z * math.cos(lowerCAmelCase ) + y * math.sin(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							x
		elif axis == "y":
				SCREAMING_SNAKE_CASE_					      =							x * math.cos(lowerCAmelCase ) - z * math.sin(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							z * math.cos(lowerCAmelCase ) + x * math.sin(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							y
		else:
				raise ValueError('not a valid axis, choose one of \'x\', \'y\', \'z\'' )

		return new_x, new_y, new_z


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(f"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
						print(f"""{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }""")





from typing import Mapping

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


SCREAMING_SNAKE_CASE:     List[Any]									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     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    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      ="umt5"
						lowerCAmelCase__			      =["past_key_values"]



						def __init__(			self	:   Optional[Any]					,							snake_case__	:   Union[str, Any]=25_01_12					,							snake_case__	:   Optional[int]=5_12					,							snake_case__	:   Optional[Any]=64					,							snake_case__	:   str=10_24					,							snake_case__	:   Dict=8					,							snake_case__	:   Union[str, Any]=None					,							snake_case__	:   List[Any]=6					,							snake_case__	:   List[Any]=32					,							snake_case__	:   List[Any]=1_28					,							snake_case__	:   Optional[int]=0.1					,							snake_case__	:   List[str]=1e-6					,							snake_case__	:   Optional[Any]=1.0					,							snake_case__	:   Optional[int]="gated-gelu"					,							snake_case__	:   Any=True					,							snake_case__	:   List[Any]=True					,							snake_case__	:   List[str]="T5Tokenizer"					,							snake_case__	:   List[str]=True					,							snake_case__	:   Tuple=0					,							snake_case__	:   Optional[int]=1					,							snake_case__	:   List[str]=0					,							**snake_case__	:   Any					,							):

								"""simple docstring"""

								super().__init__(
								    is_encoder_decoder=snake_case__					,							tokenizer_class=snake_case__					,							tie_word_embeddings=snake_case__					,							pad_token_id=snake_case__					,							eos_token_id=snake_case__					,							decoder_start_token_id=snake_case__					,							**snake_case__					,							)
								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(snake_case__  ) > 1 and act_info[0] != "gated" or len(snake_case__  ) > 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 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								return self.d_model



						@property
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								return self.num_heads






						@property
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								return self.num_layers



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):



						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								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_(snake_case__					,							direction='inputs'  )

								return common_inputs



						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								return 13






						@property
						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								return 5e-4