


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


lowerCAmelCase__      		= logging.get_logger(__name__)

lowerCAmelCase__      		= {
    '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__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= "gptj"
     __SCREAMING_SNAKE_CASE  			= {
         "max_position_embeddings": "n_positions",
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }


     def __init__(						self ,						__lowerCamelCase=5_0_4_0_0 ,						__lowerCamelCase=2_0_4_8 ,						__lowerCamelCase=4_0_9_6 ,						__lowerCamelCase=2_8 ,						__lowerCamelCase=1_6 ,						__lowerCamelCase=6_4 ,						__lowerCamelCase=None ,						__lowerCamelCase="gelu_new" ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=1e-5 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=True ,						__lowerCamelCase=5_0_2_5_6 ,						__lowerCamelCase=5_0_2_5_6 ,						__lowerCamelCase=False ,						**__lowerCamelCase ,						)			->				Any:
            _A      :       Dict				    = vocab_size
            _A      :       Any				    = n_positions
            _A      :       List[str]				    = n_embd
            _A      :       Optional[int]				    = n_layer
            _A      :       str				    = n_head
            _A      :       Union[str, Any]				    = n_inner
            _A      :       List[Any]				    = rotary_dim
            _A      :       int				    = activation_function
            _A      :       Dict				    = resid_pdrop
            _A      :       int				    = embd_pdrop
            _A      :       int				    = attn_pdrop
            _A      :       Tuple				    = layer_norm_epsilon
            _A      :       List[Any]				    = initializer_range
            _A      :       Dict				    = use_cache

            _A      :       Any				    = bos_token_id
            _A      :       Optional[int]				    = eos_token_id

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







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase = "default" ,						__lowerCamelCase = None ,						__lowerCamelCase = False ,						)			->				Dict:
            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?
                   _A      :       Dict				    = 0


     @property
     def  _lowerCamelCase (						self)			->				Mapping[str, Mapping[int, str]]:
            _A      :       Optional[int]				    = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
            if self.use_past:
                   self.fill_with_past_key_values_(__lowerCamelCase ,						direction="inputs")
                   _A      :       List[Any]				    = {0: "batch", 1: "past_sequence + sequence"}
            else:
                   _A      :       Dict				    = {0: "batch", 1: "sequence"}

            return common_inputs


     @property
     def  _lowerCamelCase (						self)			->				int:
            return self._config.n_layer


     @property
     def  _lowerCamelCase (						self)			->				int:
            return self._config.n_head


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = -1 ,						__lowerCamelCase = -1 ,						__lowerCamelCase = False ,						__lowerCamelCase = None ,						)			->				Mapping[str, Any]:
            _A      :       Tuple				    = 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()
            _A      :       Union[str, Any]				    = 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

                          _A    ,			_A      :       Optional[Any]				    = common_inputs["input_ids"].shape
                          # Not using the same length for past_key_values
                          _A      :       Optional[int]				    = seqlen + 2
                          _A      :       Optional[Any]				    = (
                              batch,
                              self.num_attention_heads,
                              past_key_values_length,
                              self._config.hidden_size // self.num_attention_heads,
                          )
                          _A      :       List[str]				    = [
                              (torch.zeros(__lowerCamelCase), torch.zeros(__lowerCamelCase)) for _ in range(self.num_layers)
                          ]

            _A      :       List[Any]				    = common_inputs["attention_mask"]
            if self.use_past:
                   _A      :       str				    = ordered_inputs["attention_mask"].dtype
                   _A      :       Union[str, Any]				    = torch.cat(
                       [ordered_inputs["attention_mask"], torch.ones(__lowerCamelCase ,						__lowerCamelCase ,						dtype=__lowerCamelCase)] ,						dim=1)

            return ordered_inputs


     @property
     def  _lowerCamelCase (						self)			->				int:
            return 1_3







from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_				=			logging.get_logger(__name__)

snake_case_				=			{
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[Any]							=    'vit_msn'


      def __init__(self       :      Union[str, Any]				,			a__       :      Optional[Any]=768				,			a__       :      Optional[Any]=12				,			a__       :      Optional[int]=12				,			a__       :      Optional[int]=3072				,			a__       :      Union[str, Any]="gelu"				,			a__       :      str=0.0				,			a__       :      int=0.0				,			a__       :      Optional[Any]=0.0_2				,			a__       :      List[Any]=1E-06				,			a__       :      Optional[int]=224				,			a__       :      str=16				,			a__       :      Optional[Any]=3				,			a__       :      int=True				,			**a__       :      List[Any]				,			):





         """simple docstring"""



         super().__init__(**a__      )

         __snake_case							       =   hidden_size
         __snake_case							       =   num_hidden_layers
         __snake_case							       =   num_attention_heads
         __snake_case							       =   intermediate_size
         __snake_case							       =   hidden_act
         __snake_case							       =   hidden_dropout_prob
         __snake_case							       =   attention_probs_dropout_prob
         __snake_case							       =   initializer_range
         __snake_case							       =   layer_norm_eps
         __snake_case							       =   image_size
         __snake_case							       =   patch_size
         __snake_case							       =   num_channels
         __snake_case							       =   qkv_bias

