





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


__lowerCamelCase       :     Optional[Any]   =     logging.get_logger(__name__)

__lowerCamelCase       :     Dict   =     {
    """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__   (       __snake_case			):
      _UpperCAmelCase	:int						    =   'gpt_neo'
      _UpperCAmelCase	:int						    =   ['past_key_values']
      _UpperCAmelCase	:int						    =   {'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}





      def __init__( self       ,							A_=5_0257       ,							A_=2048       ,							A_=2048       ,							A_=24       ,							A_=[[["global", "local"], 12]]       ,							A_=16       ,							A_=None       ,							A_=256       ,							A_="gelu_new"       ,							A_=0.0       ,							A_=0.0       ,							A_=0.0       ,							A_=0.1       ,							A_=1e-5       ,							A_=0.02       ,							A_=True       ,							A_=5_0256       ,							A_=5_0256       ,							**A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   vocab_size
       UpperCamelCase	:					Union[str, Any]       	=   max_position_embeddings
       UpperCamelCase	:					List[Any]       	=   hidden_size
       UpperCamelCase	:					str       	=   num_layers
       UpperCamelCase	:					Any       	=   num_heads
       UpperCamelCase	:					Optional[int]       	=   intermediate_size
       UpperCamelCase	:					int       	=   window_size
       UpperCamelCase	:					Any       	=   activation_function
       UpperCamelCase	:					List[Any]       	=   resid_dropout
       UpperCamelCase	:					List[str]       	=   embed_dropout
       UpperCamelCase	:					str       	=   attention_dropout
       UpperCamelCase	:					Optional[Any]       	=   classifier_dropout
       UpperCamelCase	:					List[Any]       	=   layer_norm_epsilon
       UpperCamelCase	:					Any       	=   initializer_range
       UpperCamelCase	:					Union[str, Any]       	=   use_cache

       UpperCamelCase	:					Optional[Any]       	=   bos_token_id
       UpperCamelCase	:					List[Any]       	=   eos_token_id

       UpperCamelCase	:					Optional[int]       	=   attention_types
       UpperCamelCase	:					Any       	=   self.expand_attention_types_params(A_   )

       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=A_       ,							eos_token_id=A_       ,							**A_   )





      @staticmethod
      def        __UpperCamelCase( A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   []
       for item in attention_types:
        for _ in range(item[1]   ):
         attentions.extend(item[0]   )
       return attentions



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> Dict:
 import torch

 UpperCamelCase	:					List[str]       	=   input.size()
 UpperCamelCase	:					Any       	=   len(_lowerCAmelCase     )
 UpperCamelCase	:					Dict       	=   shape[dimension]

 UpperCamelCase	:					Optional[Any]       	=   torch.arange(0							,   _lowerCAmelCase							,   _lowerCAmelCase     )
 UpperCamelCase	:					str       	=   torch.div(sizedim - size							,   _lowerCAmelCase							,   rounding_mode="floor"     ) + 1
 UpperCamelCase	:					Union[str, Any]       	=   torch.arange(_lowerCAmelCase     ) + low_indices[:min_length][:, None]

 UpperCamelCase	:					Dict       	=   [slice(_lowerCAmelCase     )] * rank
 UpperCamelCase	:					Dict       	=   indices
 UpperCamelCase	:					str       	=   input[s]

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

 return sliced.permute(_lowerCAmelCase     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> Union[str, Any]:
 import torch

 UpperCamelCase	:					Optional[int]       	=   torch.arange(1							,   _lowerCAmelCase     )
 UpperCamelCase	:					str       	=   torch.remainder(_lowerCAmelCase							,   _lowerCAmelCase     )
 UpperCamelCase	:					Optional[int]       	=   remainders == 0
 UpperCamelCase	:					Tuple       	=   candidates[divisor_indices]
 UpperCamelCase	:					int       	=   torch.max(_lowerCAmelCase     )
 return largest_divisor, torch.div(_lowerCAmelCase							,   _lowerCAmelCase							,   rounding_mode="floor"     )



class 					A__   (       __snake_case			):





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}   )
       if self.use_past:
        self.fill_with_past_key_values_(A_       ,							direction="inputs"   )
        UpperCamelCase	:					Optional[int]       	=   {0: "batch", 1: "past_sequence + sequence"}
       else:
        UpperCamelCase	:					str       	=   {0: "batch", 1: "sequence"}

       return common_inputs





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return self._config.num_heads





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   super(A_       ,							self   ).generate_dummy_inputs(
           A_       ,							batch_size=A_       ,							seq_length=A_       ,							is_pair=A_       ,							framework=A_   )

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

       UpperCamelCase	:					Any       	=   common_inputs["attention_mask"]
       if self.use_past:
        UpperCamelCase	:					Any       	=   ordered_inputs["attention_mask"].dtype
        UpperCamelCase	:					List[Any]       	=   torch.cat(
            [ordered_inputs["attention_mask"], torch.ones(A_       ,							A_       ,							dtype=A_   )]       ,							dim=1   )

       return ordered_inputs





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return 13







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCamelCase       :     str   =     logging.get_logger(__name__)

__lowerCamelCase       :     str   =     {
    """facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}



class 					A__   (       __snake_case       ,   __snake_case			):
      _UpperCAmelCase	:Optional[int]						    =   'convnextv2'





      def __init__( self       ,							A_=3       ,							A_=4       ,							A_=4       ,							A_=None       ,							A_=None       ,							A_="gelu"       ,							A_=0.02       ,							A_=1e-12       ,							A_=0.0       ,							A_=224       ,							A_=None       ,							A_=None       ,							**A_       ,							):
       '''simple docstring'''




       super().__init__(**A_   )

       UpperCamelCase	:					Dict       	=   num_channels
       UpperCamelCase	:					Union[str, Any]       	=   patch_size
       UpperCamelCase	:					Union[str, Any]       	=   num_stages
       UpperCamelCase	:					List[Any]       	=   [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
       UpperCamelCase	:					List[str]       	=   [3, 3, 9, 3] if depths is None else depths
       UpperCamelCase	:					Dict       	=   hidden_act
       UpperCamelCase	:					Union[str, Any]       	=   initializer_range
       UpperCamelCase	:					Tuple       	=   layer_norm_eps
       UpperCamelCase	:					str       	=   drop_path_rate
       UpperCamelCase	:					List[str]       	=   image_size
       UpperCamelCase	:					List[str]       	=   ["stem"] + [F"""stage{idx}""" for idx in range(1       ,							len(self.depths   ) + 1   )]
       UpperCamelCase	,    UpperCamelCase	:					str       	=   get_aligned_output_features_output_indices(
           out_features=A_       ,							out_indices=A_       ,							stage_names=self.stage_names   )
