


from typing import Mapping

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


lowerCAmelCase	=							logging.get_logger(__name__)

lowerCAmelCase	=							{
    '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 	_a							(      UpperCamelCase__      ):






							"""simple docstring"""




							_lowercase     :      Dict  				=			'''umt5'''
							_lowercase     :      Union[str, Any]  				=			['''past_key_values''']

							def __init__(							self:     int   ,						UpperCamelCase_:     Any=250_112   ,						UpperCamelCase_:     Optional[Any]=512   ,						UpperCamelCase_:     Union[str, Any]=64   ,						UpperCamelCase_:     Any=1_024   ,						UpperCamelCase_:     Any=8   ,						UpperCamelCase_:     Optional[int]=None   ,						UpperCamelCase_:     Union[str, Any]=6   ,						UpperCamelCase_:     Optional[int]=32   ,						UpperCamelCase_:     List[Any]=128   ,						UpperCamelCase_:     Optional[Any]=0.1   ,						UpperCamelCase_:     Union[str, Any]=1E-6   ,						UpperCamelCase_:     Optional[int]=1.0   ,						UpperCamelCase_:     Tuple="gated-gelu"   ,						UpperCamelCase_:     Optional[Any]=True   ,						UpperCamelCase_:     List[str]=True   ,						UpperCamelCase_:     Union[str, Any]="T5Tokenizer"   ,						UpperCamelCase_:     Tuple=True   ,						UpperCamelCase_:     Optional[Any]=0   ,						UpperCamelCase_:     Any=1   ,						UpperCamelCase_:     int=0   ,						**UpperCamelCase_:     List[Any]   ,						)      ->       Dict:





									"""simple docstring"""







									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__						=				vocab_size
									lowercase__						=				d_model
									lowercase__						=				d_kv
									lowercase__						=				d_ff
									lowercase__						=				num_layers
									lowercase__						=				(
									    num_decoder_layers if num_decoder_layers is not None else self.num_layers
									)  # default = symmetry
									lowercase__						=				num_heads
									lowercase__						=				relative_attention_num_buckets
									lowercase__						=				relative_attention_max_distance
									lowercase__						=				dropout_rate
									lowercase__						=				layer_norm_epsilon
									lowercase__						=				initializer_factor
									lowercase__						=				feed_forward_proj
									lowercase__						=				use_cache

									lowercase__						=				self.feed_forward_proj.split('''-'''							)
									lowercase__						=				act_info[-1]
									lowercase__						=				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\''''							)

									if feed_forward_proj == "gated-gelu":
											lowercase__						=				'''gelu_new'''

							@property
							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       Optional[Any]:





									"""simple docstring"""







									return self.d_model

							@property
							def 				lowerCamelCase_			(							self:     Any							)      ->       Optional[int]:





									"""simple docstring"""







									return self.num_heads


							@property
							def 				lowerCamelCase_			(							self:     str							)      ->       Optional[int]:





									"""simple docstring"""







									return self.num_layers


class 	_a							(      UpperCamelCase__      ):






							"""simple docstring"""




							@property
							# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       Mapping[str, Mapping[int, str]]:





									"""simple docstring"""







									lowercase__						=				{
									    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
									    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
									}
									if self.use_past:
											lowercase__						=				'''past_encoder_sequence + sequence'''
											lowercase__						=				{0: '''batch'''}
											lowercase__						=				{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
									else:
											lowercase__						=				{0: '''batch''', 1: '''decoder_sequence'''}
											lowercase__						=				{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:     Any							)      ->       int:





									"""simple docstring"""







									return 13


							@property
							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       float:





									"""simple docstring"""







									return 5E-4






def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				len(SCREAMING_SNAKE_CASE					)
		lowercase__						=				[]
		for i in range(len(SCREAMING_SNAKE_CASE					) - pat_len + 1					):
				lowercase__						=				True
				for j in range(SCREAMING_SNAKE_CASE					):
						if s[i + j] != pattern[j]:
								lowercase__						=				False
								break
				if match_found:
						position.append(SCREAMING_SNAKE_CASE					)
		return position


if __name__ == "__main__":
			assert naive_pattern_search('ABCDEFG', 'DE') == [3]
			print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))


