"""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


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				(		lowerCAmelCase_   ):
    _snake_case     :  str     							=							'gpt_neo'
    _snake_case     :  List[Any]     							=							['past_key_values']
    _snake_case     :  int     							=							{'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


    def __init__(					self	:  int   ,				__lowerCAmelCase	:  Dict=5_0257   ,				__lowerCAmelCase	:  List[str]=2048   ,				__lowerCAmelCase	:  List[str]=2048   ,				__lowerCAmelCase	:  int=24   ,				__lowerCAmelCase	:  Optional[int]=[[["global", "local"], 12]]   ,				__lowerCAmelCase	:  Dict=16   ,				__lowerCAmelCase	:  str=None   ,				__lowerCAmelCase	:  str=256   ,				__lowerCAmelCase	:  List[Any]="gelu_new"   ,				__lowerCAmelCase	:  List[str]=0.0   ,				__lowerCAmelCase	:  Optional[int]=0.0   ,				__lowerCAmelCase	:  Any=0.0   ,				__lowerCAmelCase	:  Union[str, Any]=0.1   ,				__lowerCAmelCase	:  Optional[int]=1e-5   ,				__lowerCAmelCase	:  Union[str, Any]=0.02   ,				__lowerCAmelCase	:  Optional[Any]=True   ,				__lowerCAmelCase	:  List[Any]=5_0256   ,				__lowerCAmelCase	:  List[Any]=5_0256   ,				**__lowerCAmelCase	:  Optional[int]   ,				):
      _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(__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	:  str					):
      _UpperCAmelCase						 =      []
      for item in attention_types:
        for _ in range(item[1]					):
          attentions.extend(item[0]					)
      return attentions






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  import torch

  _UpperCAmelCase						 =      input.size()
  _UpperCAmelCase						 =      len(lowercase		)
  _UpperCAmelCase						 =      shape[dimension]

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

  _UpperCAmelCase						 =      [slice(lowercase		)] * rank
  _UpperCAmelCase						 =      indices
  _UpperCAmelCase						 =      input[s]

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

  return sliced.permute(lowercase		)






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  import torch

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


class        a				(		lowerCAmelCase_   ):


    @property
    def      lowerCAmelCase_  (					self	:  Tuple					):
      _UpperCAmelCase						 =      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}					)
      if self.use_past:
        self.fill_with_past_key_values_(__lowerCAmelCase   ,				direction="""inputs"""					)
        _UpperCAmelCase						 =      {0: """batch""", 1: """past_sequence + sequence"""}
      else:
        _UpperCAmelCase						 =      {0: """batch""", 1: """sequence"""}

      return common_inputs


    @property
    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      return self._config.num_heads


    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  PreTrainedTokenizer   ,				__lowerCAmelCase	:  int = -1   ,				__lowerCAmelCase	:  int = -1   ,				__lowerCAmelCase	:  bool = False   ,				__lowerCAmelCase	:  Optional[TensorType] = None   ,				):
      _UpperCAmelCase						 =      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						 =      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(__lowerCAmelCase					), torch.zeros(__lowerCAmelCase					)) 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(__lowerCAmelCase   ,				__lowerCAmelCase   ,				dtype=__lowerCAmelCase					)]   ,				dim=1					)

      return ordered_inputs


    @property
    def      lowerCAmelCase_  (					self	:  int					):
      return 13

"""simple docstring"""


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


UpperCAmelCase__  		=						logging.get_logger(__name__)

UpperCAmelCase__  		=						{
    """shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class        a				(		lowerCAmelCase_	,							lowerCAmelCase_   ):
    _snake_case     :  Union[str, Any]     							=							'nat'

    _snake_case     :  List[str]     							=							{
        'num_attention_heads': 'num_heads',
        'num_hidden_layers': 'num_layers',
    }


    def __init__(					self	:  List[Any]   ,				__lowerCAmelCase	:  int=4   ,				__lowerCAmelCase	:  Union[str, Any]=3   ,				__lowerCAmelCase	:  Dict=64   ,				__lowerCAmelCase	:  int=[3, 4, 6, 5]   ,				__lowerCAmelCase	:  List[str]=[2, 4, 8, 16]   ,				__lowerCAmelCase	:  Tuple=7   ,				__lowerCAmelCase	:  List[str]=3.0   ,				__lowerCAmelCase	:  int=True   ,				__lowerCAmelCase	:  int=0.0   ,				__lowerCAmelCase	:  Tuple=0.0   ,				__lowerCAmelCase	:  Any=0.1   ,				__lowerCAmelCase	:  List[str]="gelu"   ,				__lowerCAmelCase	:  List[Any]=0.02   ,				__lowerCAmelCase	:  Union[str, Any]=1e-5   ,				__lowerCAmelCase	:  List[Any]=0.0   ,				__lowerCAmelCase	:  Tuple=None   ,				__lowerCAmelCase	:  Optional[int]=None   ,				**__lowerCAmelCase	:  Optional[Any]   ,				):
      super().__init__(**__lowerCAmelCase					)

      _UpperCAmelCase						 =      patch_size
      _UpperCAmelCase						 =      num_channels
      _UpperCAmelCase						 =      embed_dim
      _UpperCAmelCase						 =      depths
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      _UpperCAmelCase						 =      num_heads
      _UpperCAmelCase						 =      kernel_size
      _UpperCAmelCase						 =      mlp_ratio
      _UpperCAmelCase						 =      qkv_bias
      _UpperCAmelCase						 =      hidden_dropout_prob
      _UpperCAmelCase						 =      attention_probs_dropout_prob
      _UpperCAmelCase						 =      drop_path_rate
      _UpperCAmelCase						 =      hidden_act
      _UpperCAmelCase						 =      layer_norm_eps
      _UpperCAmelCase						 =      initializer_range
      # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      _UpperCAmelCase						 =      int(embed_dim * 2 ** (len(__lowerCAmelCase					) - 1)					)
      _UpperCAmelCase						 =      layer_scale_init_value
      _UpperCAmelCase						 =      ["""stem"""] + [f'''stage{idx}''' for idx in range(1   ,				len(__lowerCAmelCase					) + 1					)]
      _UpperCAmelCase ,				_UpperCAmelCase						 =      get_aligned_output_features_output_indices(
          out_features=__lowerCAmelCase   ,				out_indices=__lowerCAmelCase   ,				stage_names=self.stage_names					)
