



from typing import Mapping

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


lowercase_								=     logging.get_logger(__name__)

lowercase_								=     {
    """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 				_snake_case			(					lowercase__):
				UpperCamelCase__				:					Tuple     		="""umt5"""
				UpperCamelCase__				:					Optional[int]     		=["""past_key_values"""]




				def __init__(						self					:						Dict,					__lowercase					:						str=25_0112,					__lowercase					:						Tuple=512,					__lowercase					:						str=64,					__lowercase					:						List[Any]=1024,					__lowercase					:						Dict=8,					__lowercase					:						List[str]=None,					__lowercase					:						List[str]=6,					__lowercase					:						Dict=32,					__lowercase					:						Optional[int]=128,					__lowercase					:						int=0.1,					__lowercase					:						Dict=1e-6,					__lowercase					:						List[str]=1.0,					__lowercase					:						int="gated-gelu",					__lowercase					:						List[str]=True,					__lowercase					:						Optional[int]=True,					__lowercase					:						Optional[int]="T5Tokenizer",					__lowercase					:						List[str]=True,					__lowercase					:						Any=0,					__lowercase					:						int=1,					__lowercase					:						List[Any]=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,					)
							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(__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":
										lowercase__					  =				"gelu_new"




				@property
				def 							A__      (						self					:						int						):
							return self.d_model




				@property
				def 							A__      (						self					:						int						):
							return self.num_heads




				@property
				def 							A__      (						self					:						str						):
							return self.num_layers
class 				_snake_case			(					lowercase__):




				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
				def 							A__      (						self					:						Dict						):
							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_(__lowercase,					direction="inputs"						)

							return common_inputs




				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
				def 							A__      (						self					:						Optional[Any]						):
							return 13




				@property
				def 							A__      (						self					:						Optional[int]						):
							return 5e-4









def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			lowercase__  ,       lowercase__					  =				len(SCREAMING_SNAKE_CASE_  ), len(grid[0]  )
			if (
			    min(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ) < 0
			    or row == row_length
			    or col == col_length
			    or (row, col) in visit
			    or grid[row][col] == 1
			):
						return 0
			if row == row_length - 1 and col == col_length - 1:
						return 1

			visit.add((row, col)  )

			lowercase__					  =				0
			count += depth_first_search(SCREAMING_SNAKE_CASE_						,  row + 1						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			count += depth_first_search(SCREAMING_SNAKE_CASE_						,  row - 1						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			count += depth_first_search(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  col + 1						,  SCREAMING_SNAKE_CASE_  )
			count += depth_first_search(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  col - 1						,  SCREAMING_SNAKE_CASE_  )

			visit.remove((row, col)  )
			return count


if __name__ == "__main__":
							import doctest

							doctest.testmod()




