'''simple docstring'''
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


a=  logging.get_logger(__name__)

a=  {
    '''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"""






     SCREAMING_SNAKE_CASE__		   =						'''gpt_neo'''
     SCREAMING_SNAKE_CASE__		   =						['''past_key_values''']
     SCREAMING_SNAKE_CASE__		   =						{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}







     def __init__(		self     ,			_lowerCamelCase=5_0_2_5_7     ,			_lowerCamelCase=2_0_4_8     ,			_lowerCamelCase=2_0_4_8     ,			_lowerCamelCase=2_4     ,			_lowerCamelCase=[[["global", "local"], 1_2]]     ,			_lowerCamelCase=1_6     ,			_lowerCamelCase=None     ,			_lowerCamelCase=2_5_6     ,			_lowerCamelCase="gelu_new"     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=1E-5     ,			_lowerCamelCase=0.0_2     ,			_lowerCamelCase=True     ,			_lowerCamelCase=5_0_2_5_6     ,			_lowerCamelCase=5_0_2_5_6     ,			**_lowerCamelCase     ,			):
         __UpperCamelCase	:	str		    =  vocab_size
         __UpperCamelCase	:	Optional[int]		    =  max_position_embeddings
         __UpperCamelCase	:	Any		    =  hidden_size
         __UpperCamelCase	:	Dict		    =  num_layers
         __UpperCamelCase	:	Tuple		    =  num_heads
         __UpperCamelCase	:	Union[str, Any]		    =  intermediate_size
         __UpperCamelCase	:	int		    =  window_size
         __UpperCamelCase	:	int		    =  activation_function
         __UpperCamelCase	:	Tuple		    =  resid_dropout
         __UpperCamelCase	:	Optional[Any]		    =  embed_dropout
         __UpperCamelCase	:	List[Any]		    =  attention_dropout
         __UpperCamelCase	:	str		    =  classifier_dropout
         __UpperCamelCase	:	Union[str, Any]		    =  layer_norm_epsilon
         __UpperCamelCase	:	List[Any]		    =  initializer_range
         __UpperCamelCase	:	List[Any]		    =  use_cache

         __UpperCamelCase	:	Optional[int]		    =  bos_token_id
         __UpperCamelCase	:	int		    =  eos_token_id

         __UpperCamelCase	:	Union[str, Any]		    =  attention_types
         __UpperCamelCase	:	Optional[int]		    =  self.expand_attention_types_params(_lowerCamelCase    )

         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=_lowerCamelCase     ,			eos_token_id=_lowerCamelCase     ,			**_lowerCamelCase    )







     @staticmethod
     def lowerCAmelCase     (		_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  []
         for item in attention_types:
             for _ in range(item[1]    ):
                 attentions.extend(item[0]    )
         return attentions







def  _UpperCamelCase (     _a	:	List[str]				,     _a	:	int				,     _a	:	List[Any]				,     _a	:	Optional[int]			):




    """simple docstring"""



    import torch

    __UpperCamelCase	:	List[str]		    =  input.size()
    __UpperCamelCase	:	Optional[Any]		    =  len(_a			)
    __UpperCamelCase	:	Optional[Any]		    =  shape[dimension]

    __UpperCamelCase	:	Dict		    =  torch.arange(0				,     _a				,     _a			)
    __UpperCamelCase	:	Union[str, Any]		    =  torch.div(sizedim - size				,     _a				,     rounding_mode='floor'			) + 1
    __UpperCamelCase	:	Dict		    =  torch.arange(_a			) + low_indices[:min_length][:, None]

    __UpperCamelCase	:	Union[str, Any]		    =  [slice(_a			)] * rank
    __UpperCamelCase	:	Optional[Any]		    =  indices
    __UpperCamelCase	:	List[str]		    =  input[s]

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

    return sliced.permute(_a			)







def  _UpperCamelCase (     _a	:	Any				,     _a	:	str			):




    """simple docstring"""



    import torch

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






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     @property
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[str]		    =  OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}    )
         if self.use_past:
             self.fill_with_past_key_values_(_lowerCamelCase     ,			direction='inputs'    )
             __UpperCamelCase	:	Any		    =  {0: 'batch', 1: 'past_sequence + sequence'}
         else:
             __UpperCamelCase	:	Tuple		    =  {0: 'batch', 1: 'sequence'}

         return common_inputs







     @property
     def lowerCAmelCase     (		self    ):
         return self._config.num_heads







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = -1     ,			_lowerCamelCase = -1     ,			_lowerCamelCase = False     ,			_lowerCamelCase = None     ,			):
         __UpperCamelCase	:	List[Any]		    =  super(_lowerCamelCase     ,			self    ).generate_dummy_inputs(
             _lowerCamelCase     ,			batch_size=_lowerCamelCase     ,			seq_length=_lowerCamelCase     ,			is_pair=_lowerCamelCase     ,			framework=_lowerCamelCase    )

         # We need to order the input in the way they appears in the forward()
         __UpperCamelCase	:	Tuple		    =  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	:	List[str]		    =  common_inputs['input_ids'].shape
                 # Not using the same length for past_key_values
                 __UpperCamelCase	:	Any		    =  seqlen + 2
                 __UpperCamelCase	:	List[Any]		    =  (
                     batch,
                     self.num_attention_heads,
                     past_key_values_length,
                     self._config.hidden_size // self.num_attention_heads,
                 )
                 __UpperCamelCase	:	int		    =  [
                     (torch.zeros(_lowerCamelCase    ), torch.zeros(_lowerCamelCase    )) for _ in range(self.num_layers    )
                 ]

         __UpperCamelCase	:	Union[str, Any]		    =  common_inputs['attention_mask']
         if self.use_past:
             __UpperCamelCase	:	Tuple		    =  ordered_inputs['attention_mask'].dtype
             __UpperCamelCase	:	str		    =  torch.cat(
                 [ordered_inputs['attention_mask'], torch.ones(_lowerCamelCase     ,			_lowerCamelCase     ,			dtype=_lowerCamelCase    )]     ,			dim=1    )

         return ordered_inputs







     @property
     def lowerCAmelCase     (		self    ):
         return 1_3




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a=  {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  [
      '''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''ViTMSNModel''',
      '''ViTMSNForImageClassification''',
      '''ViTMSNPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_msn import (
        VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMSNForImageClassification,
        ViTMSNModel,
        ViTMSNPreTrainedModel,
    )

else:
  import sys

  a=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



