from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowercase_     :   Union[str, Any] 				=				logging.get_logger(__name__)

lowercase_     :   Dict 				=				{
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      "gptj"
    __a  =      {
        "max_position_embeddings": "n_positions",
        "hidden_size": "n_embd",
        "num_attention_heads": "n_head",
        "num_hidden_layers": "n_layer",
    }
    def __init__(  self , lowerCAmelCase=50400 , lowerCAmelCase=2048 , lowerCAmelCase=4096 , lowerCAmelCase=28 , lowerCAmelCase=16 , lowerCAmelCase=64 , lowerCAmelCase=None , lowerCAmelCase="gelu_new" , lowerCAmelCase=0.0 , lowerCAmelCase=0.0 , lowerCAmelCase=0.0 , lowerCAmelCase=1e-5 , lowerCAmelCase=0.02 , lowerCAmelCase=True , lowerCAmelCase=50256 , lowerCAmelCase=50256 , lowerCAmelCase=False , **lowerCAmelCase , )       ->					str:
     SCREAMING_SNAKE_CASE__:   Tuple=      vocab_size
     SCREAMING_SNAKE_CASE__:   List[str]=      n_positions
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      n_embd
     SCREAMING_SNAKE_CASE__:   Dict=      n_layer
     SCREAMING_SNAKE_CASE__:   List[str]=      n_head
     SCREAMING_SNAKE_CASE__:   List[str]=      n_inner
     SCREAMING_SNAKE_CASE__:   Any=      rotary_dim
     SCREAMING_SNAKE_CASE__:   str=      activation_function
     SCREAMING_SNAKE_CASE__:   str=      resid_pdrop
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      embd_pdrop
     SCREAMING_SNAKE_CASE__:   List[Any]=      attn_pdrop
     SCREAMING_SNAKE_CASE__:   List[str]=      layer_norm_epsilon
     SCREAMING_SNAKE_CASE__:   str=      initializer_range
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      use_cache

     SCREAMING_SNAKE_CASE__:   Tuple=      bos_token_id
     SCREAMING_SNAKE_CASE__:   Tuple=      eos_token_id

     super().__init__(
         bos_token_id=lowerCAmelCase , eos_token_id=lowerCAmelCase , tie_word_embeddings=lowerCAmelCase , **lowerCAmelCase   )






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    def __init__(  self , lowerCAmelCase , lowerCAmelCase = "default" , lowerCAmelCase = None , lowerCAmelCase = False , )       ->					List[Any]:
     super().__init__(lowerCAmelCase , task=lowerCAmelCase , patching_specs=lowerCAmelCase , use_past=lowerCAmelCase   )
     if not getattr(self._config , '''pad_token_id''' , lowerCAmelCase   ):
      # TODO: how to do that better?
      SCREAMING_SNAKE_CASE__:   str=      0
    @property
    def 						UpperCamelCase_				(  self   )       ->					Mapping[str, Mapping[int, str]]:
     SCREAMING_SNAKE_CASE__:   Any=      OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}   )
     if self.use_past:
      self.fill_with_past_key_values_(lowerCAmelCase , direction='''inputs'''   )
      SCREAMING_SNAKE_CASE__:   Any=      {0: '''batch''', 1: '''past_sequence + sequence'''}
     else:
      SCREAMING_SNAKE_CASE__:   Any=      {0: '''batch''', 1: '''sequence'''}

     return common_inputs
    @property
    def 						UpperCamelCase_				(  self   )       ->					int:
     return self._config.n_layer
    @property
    def 						UpperCamelCase_				(  self   )       ->					int:
     return self._config.n_head
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase = -1 , lowerCAmelCase = -1 , lowerCAmelCase = False , lowerCAmelCase = None , )       ->					Mapping[str, Any]:
     SCREAMING_SNAKE_CASE__:   Optional[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()
     SCREAMING_SNAKE_CASE__:   Optional[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

       SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   str=      common_inputs['''input_ids'''].shape
       # Not using the same length for past_key_values
       SCREAMING_SNAKE_CASE__:   List[str]=      seqlen + 2
       SCREAMING_SNAKE_CASE__:   Tuple=      (
           batch,
           self.num_attention_heads,
           past_key_values_length,
           self._config.hidden_size // self.num_attention_heads,
       )
       SCREAMING_SNAKE_CASE__:   Dict=      [
           (torch.zeros(lowerCAmelCase   ), torch.zeros(lowerCAmelCase   )) for _ in range(self.num_layers   )
       ]

     SCREAMING_SNAKE_CASE__:   Dict=      common_inputs['''attention_mask''']
     if self.use_past:
      SCREAMING_SNAKE_CASE__:   List[str]=      ordered_inputs['''attention_mask'''].dtype
      SCREAMING_SNAKE_CASE__:   str=      torch.cat(
          [ordered_inputs['''attention_mask'''], torch.ones(lowerCAmelCase , lowerCAmelCase , dtype=lowerCAmelCase   )] , dim=1   )

     return ordered_inputs



    @property
    def 						UpperCamelCase_				(  self   )       ->					int:
     return 13


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_     :   Dict 				=				{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   str 				=				[
          'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'AutoformerForPrediction',
          'AutoformerModel',
          'AutoformerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_autoformer import (
          AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          AutoformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_autoformer import (
                AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                AutoformerForPrediction,
                AutoformerModel,
                AutoformerPreTrainedModel,
            )

else:
      import sys

      lowercase_     :   Tuple 				=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

