
'''simple docstring'''



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


UpperCamelCase__      :		Dict       							=  logging.get_logger(__name__)

UpperCamelCase__      :		List[str]       							=  {
    "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    _a				(_lowerCamelCase):





       """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       ,							A__=5_04_00       ,							A__=20_48       ,							A__=40_96       ,							A__=28       ,							A__=16       ,							A__=64       ,							A__=None       ,							A__="gelu_new"       ,							A__=0.0       ,							A__=0.0       ,							A__=0.0       ,							A__=1E-5       ,							A__=0.02       ,							A__=True       ,							A__=5_02_56       ,							A__=5_02_56       ,							A__=False       ,							**A__       ,							)    ->			Tuple:
          _SCREAMING_SNAKE_CASE      =				vocab_size
          _SCREAMING_SNAKE_CASE      =				n_positions
          _SCREAMING_SNAKE_CASE      =				n_embd
          _SCREAMING_SNAKE_CASE      =				n_layer
          _SCREAMING_SNAKE_CASE      =				n_head
          _SCREAMING_SNAKE_CASE      =				n_inner
          _SCREAMING_SNAKE_CASE      =				rotary_dim
          _SCREAMING_SNAKE_CASE      =				activation_function
          _SCREAMING_SNAKE_CASE      =				resid_pdrop
          _SCREAMING_SNAKE_CASE      =				embd_pdrop
          _SCREAMING_SNAKE_CASE      =				attn_pdrop
          _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

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






class    _a				(_lowerCamelCase):





       """simple docstring"""




       def __init__(							self       ,							A__       ,							A__ = "default"       ,							A__ = None       ,							A__ = False       ,							)    ->			int:
          super().__init__(A__       ,							task=A__       ,							patching_specs=A__       ,							use_past=A__      )
          if not getattr(self._config       ,							"""pad_token_id"""       ,							A__      ):
             # TODO: how to do that better?
             _SCREAMING_SNAKE_CASE      =				0






       @property
       def        UpperCamelCase				(							self      )    ->			Mapping[str, Mapping[int, str]]:
          _SCREAMING_SNAKE_CASE      =				OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
          if self.use_past:
             self.fill_with_past_key_values_(A__       ,							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      )    ->			int:
          return self._config.n_layer






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






       def        UpperCamelCase				(							self       ,							A__       ,							A__ = -1       ,							A__ = -1       ,							A__ = False       ,							A__ = None       ,							)    ->			Mapping[str, Any]:
          _SCREAMING_SNAKE_CASE      =				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()
          _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(A__      ), torch.zeros(A__      )) 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(A__       ,							A__       ,							dtype=A__      )]       ,							dim=1      )

          return ordered_inputs






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








'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


UpperCamelCase__      :		int       							=  {"tokenization_herbert": ["HerbertTokenizer"]}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__      :		Tuple       							=  ["HerbertTokenizerFast"]


if TYPE_CHECKING:
     from .tokenization_herbert import HerbertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_herbert_fast import HerbertTokenizerFast

else:
     import sys

     UpperCamelCase__      :		Tuple       							=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






