





'''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 __a (   _snake_case			):
   __UpperCamelCase   : List[Any]	      =	'gpt_neo'
   __UpperCamelCase   : Tuple	      =	['past_key_values']
   __UpperCamelCase   : Optional[int]	      =	{'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}





   def __init__(					self				:			List[Any]				,lowerCamelCase				:			Optional[Any]=5_0257				,lowerCamelCase				:			List[str]=2048				,lowerCamelCase				:			Optional[int]=2048				,lowerCamelCase				:			Optional[Any]=24				,lowerCamelCase				:			str=[[["global", "local"], 12]]				,lowerCamelCase				:			int=16				,lowerCamelCase				:			Union[str, Any]=None				,lowerCamelCase				:			Optional[Any]=256				,lowerCamelCase				:			Union[str, Any]="gelu_new"				,lowerCamelCase				:			List[str]=0.0				,lowerCamelCase				:			List[Any]=0.0				,lowerCamelCase				:			int=0.0				,lowerCamelCase				:			List[str]=0.1				,lowerCamelCase				:			Tuple=1E-5				,lowerCamelCase				:			Optional[int]=0.02				,lowerCamelCase				:			Optional[Any]=True				,lowerCamelCase				:			Dict=5_0256				,lowerCamelCase				:			Tuple=5_0256				,**lowerCamelCase				:			Optional[Any]				,):

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE      							=       bos_token_id
      __SCREAMING_SNAKE_CASE      							=       eos_token_id

      __SCREAMING_SNAKE_CASE      							=       attention_types
      __SCREAMING_SNAKE_CASE      							=       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      UpperCAmelCase__     (					lowerCamelCase				:			List[Any]    ):

      '''simple docstring'''

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





def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Tuple:

   '''simple docstring'''







   import torch

   __SCREAMING_SNAKE_CASE      							=       input.size()
   __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       shape[dimension]

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

   __SCREAMING_SNAKE_CASE      							=       [slice(__UpperCAmelCase      )] * rank
   __SCREAMING_SNAKE_CASE      							=       indices
   __SCREAMING_SNAKE_CASE      							=       input[s]

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

   return sliced.permute(__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					List[Any]:

   '''simple docstring'''







   import torch

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







class __a (   _snake_case			):





   @property
   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

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

      return common_inputs





   @property
   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      return self._config.num_heads





   def      UpperCAmelCase__     (					self				:			str				,lowerCamelCase				:			PreTrainedTokenizer				,lowerCamelCase				:			int = -1				,lowerCamelCase				:			int = -1				,lowerCamelCase				:			bool = False				,lowerCamelCase				:			Optional[TensorType] = None				,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       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()
      __SCREAMING_SNAKE_CASE      							=       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

            __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       common_inputs["""input_ids"""].shape
            # Not using the same length for past_key_values
            __SCREAMING_SNAKE_CASE      							=       seqlen + 2
            __SCREAMING_SNAKE_CASE      							=       (
                batch,
                self.num_attention_heads,
                past_key_values_length,
                self._config.hidden_size // self.num_attention_heads,
            )
            __SCREAMING_SNAKE_CASE      							=       [
                (torch.zeros(lowerCamelCase    ), torch.zeros(lowerCamelCase    )) for _ in range(self.num_layers    )
            ]

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

      return ordered_inputs





   @property
   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      return 13












'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a        =						{"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a        =						[
        "SEW_PRETRAINED_MODEL_ARCHIVE_LIST",
        "SEWForCTC",
        "SEWForSequenceClassification",
        "SEWModel",
        "SEWPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_sew import (
            SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
            SEWForCTC,
            SEWForSequenceClassification,
            SEWModel,
            SEWPreTrainedModel,
        )


else:
    import sys

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





