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      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Tuple      =			'gpt_neo'
   __UpperCAmelCase : Any      =			['past_key_values']
   __UpperCAmelCase : List[Any]      =			{'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


   def __init__(self     :						Optional[Any]       ,  __UpperCAmelCase     :						Any=5_0_2_5_7       ,  __UpperCAmelCase     :						List[Any]=2_0_4_8       ,  __UpperCAmelCase     :						int=2_0_4_8       ,  __UpperCAmelCase     :						Tuple=2_4       ,  __UpperCAmelCase     :						List[str]=[[["global", "local"], 1_2]]       ,  __UpperCAmelCase     :						Union[str, Any]=1_6       ,  __UpperCAmelCase     :						Union[str, Any]=None       ,  __UpperCAmelCase     :						List[Any]=2_5_6       ,  __UpperCAmelCase     :						List[Any]="gelu_new"       ,  __UpperCAmelCase     :						Optional[Any]=0.0       ,  __UpperCAmelCase     :						Optional[int]=0.0       ,  __UpperCAmelCase     :						Any=0.0       ,  __UpperCAmelCase     :						int=0.1       ,  __UpperCAmelCase     :						str=1E-5       ,  __UpperCAmelCase     :						List[str]=0.02       ,  __UpperCAmelCase     :						Optional[Any]=True       ,  __UpperCAmelCase     :						Optional[int]=5_0_2_5_6       ,  __UpperCAmelCase     :						int=5_0_2_5_6       ,  **__UpperCAmelCase     :						str       ,  )						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  vocab_size
         UpperCAmelCase__								=  max_position_embeddings
         UpperCAmelCase__								=  hidden_size
         UpperCAmelCase__								=  num_layers
         UpperCAmelCase__								=  num_heads
         UpperCAmelCase__								=  intermediate_size
         UpperCAmelCase__								=  window_size
         UpperCAmelCase__								=  activation_function
         UpperCAmelCase__								=  resid_dropout
         UpperCAmelCase__								=  embed_dropout
         UpperCAmelCase__								=  attention_dropout
         UpperCAmelCase__								=  classifier_dropout
         UpperCAmelCase__								=  layer_norm_epsilon
         UpperCAmelCase__								=  initializer_range
         UpperCAmelCase__								=  use_cache

         UpperCAmelCase__								=  bos_token_id
         UpperCAmelCase__								=  eos_token_id

         UpperCAmelCase__								=  attention_types
         UpperCAmelCase__								=  self.expand_attention_types_params(__UpperCAmelCase		)

         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=__UpperCAmelCase       ,  eos_token_id=__UpperCAmelCase       ,  **__UpperCAmelCase		)





   @staticmethod
   def 			lowercase_      (__UpperCAmelCase     :						List[str]		)						->				Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  []
         for item in attention_types:
               for _ in range(item[1]		):
                     attentions.extend(item[0]		)
         return attentions




def    lowerCAmelCase_ (     __A,  __A,  __A,  __A							)     ->				int:







      '''simple docstring'''


      import torch

      UpperCAmelCase__								=  input.size()
      UpperCAmelCase__								=  len(__A							)
      UpperCAmelCase__								=  shape[dimension]

      UpperCAmelCase__								=  torch.arange(0,  __A,  __A							)
      UpperCAmelCase__								=  torch.div(sizedim - size,  __A,  rounding_mode="floor"							) + 1
      UpperCAmelCase__								=  torch.arange(__A							) + low_indices[:min_length][:, None]

      UpperCAmelCase__								=  [slice(__A							)] * rank
      UpperCAmelCase__								=  indices
      UpperCAmelCase__								=  input[s]

      UpperCAmelCase__								=  list(range(0,  rank + 1							)							)
      perm.append(perm.pop(dimension + 1							)							)

      return sliced.permute(__A							)




def    lowerCAmelCase_ (     __A,  __A							)     ->				Any:







      '''simple docstring'''


      import torch

      UpperCAmelCase__								=  torch.arange(1,  __A							)
      UpperCAmelCase__								=  torch.remainder(__A,  __A							)
      UpperCAmelCase__								=  remainders == 0
      UpperCAmelCase__								=  candidates[divisor_indices]
      UpperCAmelCase__								=  torch.max(__A							)
      return largest_divisor, torch.div(__A,  __A,  rounding_mode="floor"							)







class      A							(				UpperCAmelCase_     ):


   @property
   def 			lowercase_      (self     :						Any		)						->				Mapping[str, Mapping[int, str]]:
         """simple docstring"""



         UpperCAmelCase__								=  OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
         if self.use_past:
               self.fill_with_past_key_values_(__UpperCAmelCase       ,  direction="inputs"		)
               UpperCAmelCase__								=  {0: "batch", 1: "past_sequence + sequence"}
         else:
               UpperCAmelCase__								=  {0: "batch", 1: "sequence"}

         return common_inputs


   @property
   def 			lowercase_      (self     :						List[Any]		)						->				int:
         """simple docstring"""



         return self._config.num_heads


   def 			lowercase_      (self     :						str       ,  __UpperCAmelCase     :						PreTrainedTokenizer       ,  __UpperCAmelCase     :						int = -1       ,  __UpperCAmelCase     :						int = -1       ,  __UpperCAmelCase     :						bool = False       ,  __UpperCAmelCase     :						Optional[TensorType] = None       ,  )						->				Mapping[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  super(__UpperCAmelCase       ,  self		).generate_dummy_inputs(
             __UpperCAmelCase       ,  batch_size=__UpperCAmelCase       ,  seq_length=__UpperCAmelCase       ,  is_pair=__UpperCAmelCase       ,  framework=__UpperCAmelCase		)

         # We need to order the input in the way they appears in the forward()
         UpperCAmelCase__								=  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__								=  common_inputs["input_ids"].shape
                     # Not using the same length for past_key_values
                     UpperCAmelCase__								=  seqlen + 2
                     UpperCAmelCase__								=  (
                         batch,
                         self.num_attention_heads,
                         past_key_values_length,
                         self._config.hidden_size // self.num_attention_heads,
                     )
                     UpperCAmelCase__								=  [
                         (torch.zeros(__UpperCAmelCase		), torch.zeros(__UpperCAmelCase		)) for _ in range(self.num_layers		)
                     ]

         UpperCAmelCase__								=  common_inputs["attention_mask"]
         if self.use_past:
               UpperCAmelCase__								=  ordered_inputs["attention_mask"].dtype
               UpperCAmelCase__								=  torch.cat(
                   [ordered_inputs["attention_mask"], torch.ones(__UpperCAmelCase       ,  __UpperCAmelCase       ,  dtype=__UpperCAmelCase		)]       ,  dim=1		)

         return ordered_inputs





   @property
   def 			lowercase_      (self     :						Optional[Any]		)						->				int:
         """simple docstring"""



         return 1_3







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__      		= logging.get_logger(__name__)

lowerCAmelCase__      		= {
    'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= "vit_mae"


     def __init__(						self ,						__lowerCamelCase=7_6_8 ,						__lowerCamelCase=1_2 ,						__lowerCamelCase=1_2 ,						__lowerCamelCase=3_0_7_2 ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=1e-12 ,						__lowerCamelCase=2_2_4 ,						__lowerCamelCase=1_6 ,						__lowerCamelCase=3 ,						__lowerCamelCase=True ,						__lowerCamelCase=1_6 ,						__lowerCamelCase=5_1_2 ,						__lowerCamelCase=8 ,						__lowerCamelCase=2_0_4_8 ,						__lowerCamelCase=0.7_5 ,						__lowerCamelCase=False ,						**__lowerCamelCase ,						)			->				int:
            super().__init__(**__lowerCamelCase)

            _A      :       int				    = hidden_size
            _A      :       List[str]				    = num_hidden_layers
            _A      :       List[Any]				    = num_attention_heads
            _A      :       Optional[Any]				    = intermediate_size
            _A      :       Optional[int]				    = hidden_act
            _A      :       List[Any]				    = hidden_dropout_prob
            _A      :       List[Any]				    = attention_probs_dropout_prob
            _A      :       Union[str, Any]				    = initializer_range
            _A      :       str				    = layer_norm_eps
            _A      :       Any				    = image_size
            _A      :       int				    = patch_size
            _A      :       int				    = num_channels
            _A      :       Dict				    = qkv_bias
            _A      :       Tuple				    = decoder_num_attention_heads
            _A      :       Tuple				    = decoder_hidden_size
            _A      :       List[str]				    = decoder_num_hidden_layers
            _A      :       Optional[Any]				    = decoder_intermediate_size
            _A      :       List[str]				    = mask_ratio
            _A      :       Union[str, Any]				    = norm_pix_loss
