



"""simple docstring"""


from typing import Mapping

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


snake_case_								=			logging.get_logger(__name__)







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				"""mt5"""
						__UpperCamelCase													=				["""past_key_values"""]






						def __init__(   self					:Any  ,    lowercase_					:Optional[Any]=25_01_12  ,    lowercase_					:List[str]=5_12  ,    lowercase_					:Any=64  ,    lowercase_					:List[str]=10_24  ,    lowercase_					:int=8  ,    lowercase_					:int=None  ,    lowercase_					:int=6  ,    lowercase_					:Any=32  ,    lowercase_					:List[Any]=1_28  ,    lowercase_					:int=0.1  ,    lowercase_					:List[str]=1E-6  ,    lowercase_					:Optional[Any]=1.0  ,    lowercase_					:Union[str, Any]="gated-gelu"  ,    lowercase_					:Optional[int]=True  ,    lowercase_					:Tuple=True  ,    lowercase_					:Union[str, Any]="T5Tokenizer"  ,    lowercase_					:Tuple=False  ,    lowercase_					:str=0  ,    lowercase_					:Optional[Any]=1  ,    lowercase_					:Optional[int]=0  ,    **lowercase_					:Tuple  ,    )							->     List[str]:
												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_  ,    )
												UpperCAmelCase			 =					vocab_size
												UpperCAmelCase			 =					d_model
												UpperCAmelCase			 =					d_kv
												UpperCAmelCase			 =					d_ff
												UpperCAmelCase			 =					num_layers
												UpperCAmelCase			 =					(
												    num_decoder_layers if num_decoder_layers is not None else self.num_layers
												)  # default = symmetry
												UpperCAmelCase			 =					num_heads
												UpperCAmelCase			 =					relative_attention_num_buckets
												UpperCAmelCase			 =					relative_attention_max_distance
												UpperCAmelCase			 =					dropout_rate
												UpperCAmelCase			 =					layer_norm_epsilon
												UpperCAmelCase			 =					initializer_factor
												UpperCAmelCase			 =					feed_forward_proj
												UpperCAmelCase			 =					use_cache

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

												# for backwards compatibility
												if feed_forward_proj == "gated-gelu":
																		UpperCAmelCase			 =					'gelu_new'






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






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






						@property
						def       UpperCAmelCase__	(   self					:List[Any]   )							->     Dict:
												return self.num_layers







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






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






						@property
						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     float:
												return 5E-4









from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    bool     ):






				'''simple docstring'''






				if not inplace:
								_lowerCAmelCase  						=ma + ma
				else:
								ma += ma

				return ma

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    List[int]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=-1 * len(lowercase__     )
				_lowerCAmelCase  						=list(range(len(tensor.shape[:zero_index]     )     )     )
				return tensor.permute(first_inds + [zero_index + i for i in inds]     )

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    int     ):






				'''simple docstring'''






				return t.reshape(t.shape[:-no_dims] + (-1,)     )

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int       ,     lowercase__     :    float = 1e-4     ):






				'''simple docstring'''






				_lowerCAmelCase  						=mask.expand(*value.shape     )
				return torch.sum(mask * value       ,     dim=lowercase__     ) / (eps + torch.sum(lowercase__       ,     dim=lowercase__     ))

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.types.Number = 2.3_1_2_5       ,     lowercase__     :    torch.types.Number = 2_1.6_8_7_5       ,     lowercase__     :    int = 64     ):






				'''simple docstring'''






				_lowerCAmelCase  						=torch.linspace(lowercase__       ,     lowercase__       ,     no_bins - 1       ,     device=pts.device     )
				_lowerCAmelCase  						=torch.sqrt(torch.sum((pts.unsqueeze(-2     ) - pts.unsqueeze(-3     )) ** 2       ,     dim=-1     )     )
				return torch.bucketize(lowercase__       ,     lowercase__     )

def 		snake_case_   (				lowercase__     :    Callable[[list], Any]       ,     lowercase__     :    List[dict]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=dicts[0]
				_lowerCAmelCase  						={}
				for k, v in first.items():
								_lowerCAmelCase  						=[d[k] for d in dicts]
								if isinstance(lowercase__       ,     lowercase__     ):
												_lowerCAmelCase  						=dict_multimap(lowercase__       ,     lowercase__     )
								else:
												_lowerCAmelCase  						=fn(lowercase__     )

				return new_dict

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor     ):






				'''simple docstring'''






				_lowerCAmelCase  						=v_bins.view(((1,) * len(x.shape     )) + (len(lowercase__     ),)     )
				_lowerCAmelCase  						=x[..., None] - reshaped_bins
				_lowerCAmelCase  						=torch.argmin(torch.abs(lowercase__     )       ,     dim=-1     )
				return nn.functional.one_hot(lowercase__       ,     num_classes=len(lowercase__     )     ).float()

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int = 0       ,     lowercase__     :    int = 0     ):






				'''simple docstring'''






				_lowerCAmelCase  						=[]
				for i, s in enumerate(data.shape[:no_batch_dims]     ):
								_lowerCAmelCase  						=torch.arange(lowercase__     )
								_lowerCAmelCase  						=r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape     ) - i - 1)))     )
								ranges.append(lowercase__     )

				_lowerCAmelCase  						=[slice(lowercase__     ) for _ in range(len(data.shape     ) - no_batch_dims     )]
				_lowerCAmelCase  						=inds
				ranges.extend(lowercase__     )
				# Matt note: Editing this to get around the behaviour of using a list as an array index changing
				# in recent Numpy versions
				return data[tuple(lowercase__     )]


__SCREAMING_SNAKE_CASE	:							str           =  TypeVar('''T''')

def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    Dict[Any, Union[dict, list, tuple, T]]       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				_lowerCAmelCase  						={}
				for k, v in dic.items():
								if isinstance(lowercase__       ,     lowercase__     ):
												_lowerCAmelCase  						=dict_map(lowercase__       ,     lowercase__       ,     lowercase__     )
								else:
												_lowerCAmelCase  						=tree_map(lowercase__       ,     lowercase__       ,     lowercase__     )

				return new_dict

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    T       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    dict       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    list       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    tuple       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

def 		snake_case_   (				lowercase__     :    Optional[Any]       ,     lowercase__     :    str       ,     lowercase__     :    Optional[int]     ):






				'''simple docstring'''






				if isinstance(lowercase__       ,     lowercase__     ):
								return dict_map(lowercase__       ,     lowercase__       ,     lowercase__     )
				elif isinstance(lowercase__       ,     lowercase__     ):
								return [tree_map(lowercase__       ,     lowercase__       ,     lowercase__     ) for x in tree]
				elif isinstance(lowercase__       ,     lowercase__     ):
								return tuple(tree_map(lowercase__       ,     lowercase__       ,     lowercase__     ) for x in tree     )
				elif isinstance(lowercase__       ,     lowercase__     ):
								return fn(lowercase__     )
				else:
								print(type(lowercase__     )     )
								raise ValueError("""Not supported"""     )





__SCREAMING_SNAKE_CASE	:							Any           =  partial(tree_map, leaf_type=torch.Tensor)

