






"""simple docstring"""



from typing import Mapping

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


A__	:    int			 = logging.get_logger(__name__)

A__	:    Optional[int]			 = {
    '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__      (							snake_case__       ):
			_UpperCAmelCase		:Dict             =			"umt5"
			_UpperCAmelCase		:str             =			["past_key_values"]


			def __init__(				self					:  Any       ,	snake_case__					:  int=25_0112       ,	snake_case__					:  str=512       ,	snake_case__					:  Dict=64       ,	snake_case__					:  Optional[Any]=1024       ,	snake_case__					:  List[str]=8       ,	snake_case__					:  Any=None       ,	snake_case__					:  Dict=6       ,	snake_case__					:  Tuple=32       ,	snake_case__					:  List[str]=128       ,	snake_case__					:  int=0.1       ,	snake_case__					:  Optional[Any]=1E-6       ,	snake_case__					:  Optional[Any]=1.0       ,	snake_case__					:  Tuple="gated-gelu"       ,	snake_case__					:  Dict=True       ,	snake_case__					:  Optional[int]=True       ,	snake_case__					:  Optional[Any]="T5Tokenizer"       ,	snake_case__					:  List[str]=True       ,	snake_case__					:  Optional[int]=0       ,	snake_case__					:  str=1       ,	snake_case__					:  Any=0       ,	**snake_case__					:  Optional[Any]       ,	):
										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__       ,	)
										lowerCamelCase_	:							List[Any]									=vocab_size
										lowerCamelCase_	:							Dict									=d_model
										lowerCamelCase_	:							Union[str, Any]									=d_kv
										lowerCamelCase_	:							List[Any]									=d_ff
										lowerCamelCase_	:							Tuple									=num_layers
										lowerCamelCase_	:							List[str]									=(
										    num_decoder_layers if num_decoder_layers is not None else self.num_layers
										)  # default = symmetry
										lowerCamelCase_	:							Dict									=num_heads
										lowerCamelCase_	:							int									=relative_attention_num_buckets
										lowerCamelCase_	:							str									=relative_attention_max_distance
										lowerCamelCase_	:							Optional[int]									=dropout_rate
										lowerCamelCase_	:							Dict									=layer_norm_epsilon
										lowerCamelCase_	:							int									=initializer_factor
										lowerCamelCase_	:							List[Any]									=feed_forward_proj
										lowerCamelCase_	:							Dict									=use_cache

										lowerCamelCase_	:							List[str]									=self.feed_forward_proj.split("-" )
										lowerCamelCase_	:							List[Any]									=act_info[-1]
										lowerCamelCase_	:							Optional[int]									=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":
																	lowerCamelCase_	:							int									="gelu_new"


			@property
			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										return self.d_model


			@property
			def      UpperCAmelCase__						(				self					:  str ):
										return self.num_heads


			@property
			def      UpperCAmelCase__						(				self					:  Dict ):
										return self.num_layers







class        lowercase__      (							snake_case__       ):


			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										lowerCamelCase_	:							List[str]									={
										    "input_ids": {0: "batch", 1: "encoder_sequence"},
										    "attention_mask": {0: "batch", 1: "encoder_sequence"},
										}
										if self.use_past:
																	lowerCamelCase_	:							List[str]									="past_encoder_sequence + sequence"
																	lowerCamelCase_	:							Optional[Any]									={0: "batch"}
																	lowerCamelCase_	:							int									={0: "batch", 1: "past_decoder_sequence + sequence"}
										else:
																	lowerCamelCase_	:							List[Any]									={0: "batch", 1: "decoder_sequence"}
																	lowerCamelCase_	:							List[Any]									={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      UpperCAmelCase__						(				self					:  Any ):
										return 13


			@property
			def      UpperCAmelCase__						(				self					:  int ):
										return 5E-4











"""simple docstring"""



def 			_snake_case    (					lowerCamelCase__     :   Optional[Any]  )    ->     Optional[int]:
							if not head:
														return True
							# split the list to two parts
							lowerCamelCase_							,						lowerCamelCase_	:							Union[str, Any]									=head.next, head
							while fast and fast.next:
														lowerCamelCase_	:							Optional[Any]									=fast.next.next
														lowerCamelCase_	:							str									=slow.next
							lowerCamelCase_	:							Tuple									=slow.next
							lowerCamelCase_	:							Any									=None  # Don't forget here! But forget still works!
							# reverse the second part
							lowerCamelCase_	:							List[str]									=None
							while second:
														lowerCamelCase_	:							Any									=second.next
														lowerCamelCase_	:							Union[str, Any]									=node
														lowerCamelCase_	:							Union[str, Any]									=second
														lowerCamelCase_	:							Optional[Any]									=nxt
							# compare two parts
							# second part has the same or one less node
							while node:
														if node.val != head.val:
																					return False
														lowerCamelCase_	:							List[str]									=node.next
														lowerCamelCase_	:							Optional[Any]									=head.next
							return True

def 			_snake_case    (					lowerCamelCase__     :   str  )    ->     Optional[int]:
							if not head or not head.next:
														return True

							# 1. Get the midpoint (slow)
							lowerCamelCase_	:							List[str]									=head
							while fast and fast.next:
														lowerCamelCase_							,						lowerCamelCase_	:							Union[str, Any]									=fast.next.next, slow.next

							# 2. Push the second half into the stack
							lowerCamelCase_	:							List[Any]									=[slow.val]
							while slow.next:
														lowerCamelCase_	:							List[Any]									=slow.next
														stack.append(slow.val  )

							# 3. Comparison
							while stack:
														if stack.pop() != cur.val:
																					return False
														lowerCamelCase_	:							Union[str, Any]									=cur.next

							return True

def 			_snake_case    (					lowerCamelCase__     :   Dict  )    ->     Optional[Any]:
							if not head or not head.next:
														return True
							lowerCamelCase_	:							Union[str, Any]									={}
							lowerCamelCase_	:							List[Any]									=0
							while head:
														if head.val in d:
																					d[head.val].append(lowerCamelCase__  )
														else:
																					lowerCamelCase_	:							List[str]									=[pos]
														lowerCamelCase_	:							Optional[int]									=head.next
														pos += 1
							lowerCamelCase_	:							Union[str, Any]									=pos - 1
							lowerCamelCase_	:							Optional[int]									=0
							for v in d.values():
														if len(lowerCamelCase__  ) % 2 != 0:
																					middle += 1
														else:
																					lowerCamelCase_	:							Optional[Any]									=0
																					for i in range(0    ,						len(lowerCamelCase__  )  ):
																												if v[i] + v[len(lowerCamelCase__  ) - 1 - step] != checksum:
																																			return False
																												step += 1
														if middle > 1:
																					return False
							return True



