





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


UpperCAmelCase_  							=				logging.get_logger(__name__)

UpperCAmelCase_  							=				{
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							List[Any]     	=	"gpt_neo"
   a		:							int     	=	["past_key_values"]
   a		:							Optional[Any]     	=	{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}




   def __init__(					self,	__magic_name__=50257,	__magic_name__=2048,	__magic_name__=2048,	__magic_name__=24,	__magic_name__=[[["global", "local"], 12]],	__magic_name__=16,	__magic_name__=None,	__magic_name__=256,	__magic_name__="gelu_new",	__magic_name__=0.0,	__magic_name__=0.0,	__magic_name__=0.0,	__magic_name__=0.1,	__magic_name__=1E-5,	__magic_name__=0.02,	__magic_name__=True,	__magic_name__=50256,	__magic_name__=50256,	**__magic_name__,	)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   vocab_size
       UpperCamelCase__							:       Any      =   max_position_embeddings
       UpperCamelCase__							:       int      =   hidden_size
       UpperCamelCase__							:       Any      =   num_layers
       UpperCamelCase__							:       Dict      =   num_heads
       UpperCamelCase__							:       List[Any]      =   intermediate_size
       UpperCamelCase__							:       Optional[int]      =   window_size
       UpperCamelCase__							:       Tuple      =   activation_function
       UpperCamelCase__							:       List[str]      =   resid_dropout
       UpperCamelCase__							:       List[Any]      =   embed_dropout
       UpperCamelCase__							:       List[str]      =   attention_dropout
       UpperCamelCase__							:       List[Any]      =   classifier_dropout
       UpperCamelCase__							:       Any      =   layer_norm_epsilon
       UpperCamelCase__							:       Dict      =   initializer_range
       UpperCamelCase__							:       Optional[Any]      =   use_cache

       UpperCamelCase__							:       List[str]      =   bos_token_id
       UpperCamelCase__							:       Optional[int]      =   eos_token_id

       UpperCamelCase__							:       Optional[Any]      =   attention_types
       UpperCamelCase__							:       Tuple      =   self.expand_attention_types_params(__magic_name__							)

       if len(self.attention_layers							) != self.num_layers:
           raise ValueError(
               '''Configuration for convolutional module is incorrect. '''
               '''It is required that `len(config.attention_layers)` == `config.num_layers` '''
               f"but is `len(config.attention_layers) = {len(self.attention_layers							)}`, "
               f"`config.num_layers = {self.num_layers}`. "
               '''`config.attention_layers` is prepared using `config.attention_types`. '''
               '''Please verify the value of `config.attention_types` argument.'''							)

       super().__init__(bos_token_id=__magic_name__,	eos_token_id=__magic_name__,	**__magic_name__							)




   @staticmethod
   def    UpperCamelCase__						(					__magic_name__							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       Any      =   []
       for item in attention_types:
           for _ in range(item[1]							):
               attentions.extend(item[0]							)
       return attentions




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Any	,     __UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    Optional[int]   )   ->			List[str]:
    import torch

    UpperCamelCase__							:       Any      =   input.size()
    UpperCamelCase__							:       Tuple      =   len(__UpperCAmelCase   )
    UpperCamelCase__							:       List[str]      =   shape[dimension]

    UpperCamelCase__							:       List[Any]      =   torch.arange(0	,     __UpperCAmelCase	,     __UpperCAmelCase   )
    UpperCamelCase__							:       Optional[Any]      =   torch.div(sizedim - size	,     __UpperCAmelCase	,     rounding_mode='''floor'''   ) + 1
    UpperCamelCase__							:       List[Any]      =   torch.arange(__UpperCAmelCase   ) + low_indices[:min_length][:, None]

    UpperCamelCase__							:       str      =   [slice(__UpperCAmelCase   )] * rank
    UpperCamelCase__							:       List[Any]      =   indices
    UpperCamelCase__							:       int      =   input[s]

    UpperCamelCase__							:       Any      =   list(range(0	,     rank + 1   )   )
    perm.append(perm.pop(dimension + 1   )   )

    return sliced.permute(__UpperCAmelCase   )




def 	lowerCAmelCase_					(						__UpperCAmelCase:    str	,     __UpperCAmelCase:    Dict   )   ->			Union[str, Any]:
    import torch

    UpperCamelCase__							:       int      =   torch.arange(1	,     __UpperCAmelCase   )
    UpperCamelCase__							:       List[Any]      =   torch.remainder(__UpperCAmelCase	,     __UpperCAmelCase   )
    UpperCamelCase__							:       List[str]      =   remainders == 0
    UpperCamelCase__							:       List[Any]      =   candidates[divisor_indices]
    UpperCamelCase__							:       str      =   torch.max(__UpperCAmelCase   )
    return largest_divisor, torch.div(__UpperCAmelCase	,     __UpperCAmelCase	,     rounding_mode='''floor'''   )






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   @property
   def    UpperCamelCase__						(					self							)				->   Mapping[str, Mapping[int, str]]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}							)
       if self.use_past:
           self.fill_with_past_key_values_(__magic_name__,	direction='''inputs'''							)
           UpperCamelCase__							:       int      =   {0: '''batch''', 1: '''past_sequence + sequence'''}
       else:
           UpperCamelCase__							:       Union[str, Any]      =   {0: '''batch''', 1: '''sequence'''}

       return common_inputs




   @property
   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       return self._config.num_heads




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = -1,	__magic_name__ = -1,	__magic_name__ = False,	__magic_name__ = None,	)				->   Mapping[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   super(__magic_name__,	self							).generate_dummy_inputs(
           __magic_name__,	batch_size=__magic_name__,	seq_length=__magic_name__,	is_pair=__magic_name__,	framework=__magic_name__							)

       # We need to order the input in the way they appears in the forward()
       UpperCamelCase__							:       List[str]      =   OrderedDict({'''input_ids''': common_inputs['''input_ids''']}							)

       # Need to add the past_keys
       if self.use_past:
           if not is_torch_available():
               raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
           else:
               import torch

               UpperCamelCase__     ,UpperCamelCase__							:       Tuple      =   common_inputs['''input_ids'''].shape
               # Not using the same length for past_key_values
               UpperCamelCase__							:       int      =   seqlen + 2
               UpperCamelCase__							:       List[str]      =   (
                   batch,
                   self.num_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // self.num_attention_heads,
               )
               UpperCamelCase__							:       Optional[int]      =   [
                   (torch.zeros(__magic_name__							), torch.zeros(__magic_name__							)) for _ in range(self.num_layers							)
               ]

       UpperCamelCase__							:       Dict      =   common_inputs['''attention_mask''']
       if self.use_past:
           UpperCamelCase__							:       int      =   ordered_inputs['''attention_mask'''].dtype
           UpperCamelCase__							:       Dict      =   torch.cat(
               [ordered_inputs['''attention_mask'''], torch.ones(__magic_name__,	__magic_name__,	dtype=__magic_name__							)],	dim=1							)

       return ordered_inputs




   @property
   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       return 13








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


UpperCAmelCase_  							=				logging.get_logger(__name__)






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Any     	=	"upernet"




   def __init__(					self,	__magic_name__=None,	__magic_name__=512,	__magic_name__=0.02,	__magic_name__=[1, 2, 3, 6],	__magic_name__=True,	__magic_name__=0.4,	__magic_name__=384,	__magic_name__=256,	__magic_name__=1,	__magic_name__=False,	__magic_name__=255,	**__magic_name__,	)				->   Dict:






       """simple docstring"""
       super().__init__(**__magic_name__							)

       if backbone_config is None:
           logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''							)
           UpperCamelCase__							:       str      =   CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']							)
       elif isinstance(__magic_name__,	__magic_name__							):
           UpperCamelCase__							:       Tuple      =   backbone_config.get('''model_type'''							)
           UpperCamelCase__							:       List[str]      =   CONFIG_MAPPING[backbone_model_type]
           UpperCamelCase__							:       Optional[int]      =   config_class.from_dict(__magic_name__							)

       UpperCamelCase__							:       Dict      =   backbone_config
       UpperCamelCase__							:       int      =   hidden_size
       UpperCamelCase__							:       int      =   initializer_range
       UpperCamelCase__							:       Dict      =   pool_scales
       UpperCamelCase__							:       Tuple      =   use_auxiliary_head
       UpperCamelCase__							:       Tuple      =   auxiliary_loss_weight
       UpperCamelCase__							:       Optional[int]      =   auxiliary_in_channels
       UpperCamelCase__							:       Optional[Any]      =   auxiliary_channels
       UpperCamelCase__							:       Any      =   auxiliary_num_convs
       UpperCamelCase__							:       Union[str, Any]      =   auxiliary_concat_input
       UpperCamelCase__							:       List[str]      =   loss_ignore_index




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   copy.deepcopy(self.__dict__							)
       UpperCamelCase__							:       int      =   self.backbone_config.to_dict()
       UpperCamelCase__							:       Any      =   self.__class__.model_type
       return output

