


"""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


__snake_case		=       logging.get_logger(__name__)

__snake_case		=       {
    "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				):

     '''simple docstring'''
     A_       :	List[str]    =		'gptj'
     A_       :	List[Any]    =		{
         'max_position_embeddings': 'n_positions',
         'hidden_size': 'n_embd',
         'num_attention_heads': 'n_head',
         'num_hidden_layers': 'n_layer',
     }


     def __init__(      self    ,				__UpperCAmelCase=50400    ,				__UpperCAmelCase=2048    ,				__UpperCAmelCase=4096    ,				__UpperCAmelCase=28    ,				__UpperCAmelCase=16    ,				__UpperCAmelCase=64    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase="gelu_new"    ,				__UpperCAmelCase=0.0    ,				__UpperCAmelCase=0.0    ,				__UpperCAmelCase=0.0    ,				__UpperCAmelCase=1e-5    ,				__UpperCAmelCase=0.02    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=50256    ,				__UpperCAmelCase=50256    ,				__UpperCAmelCase=False    ,				**__UpperCAmelCase    ,				)       ->							Optional[Any]:
          _a    	=				vocab_size
          _a    	=				n_positions
          _a    	=				n_embd
          _a    	=				n_layer
          _a    	=				n_head
          _a    	=				n_inner
          _a    	=				rotary_dim
          _a    	=				activation_function
          _a    	=				resid_pdrop
          _a    	=				embd_pdrop
          _a    	=				attn_pdrop
          _a    	=				layer_norm_epsilon
          _a    	=				initializer_range
          _a    	=				use_cache

          _a    	=				bos_token_id
          _a    	=				eos_token_id

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







class     __lowerCamelCase  (					_UpperCAmelCase				):

     '''simple docstring'''


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = "default"    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = False    ,				)       ->							Optional[int]:
          super().__init__(lowercase_    ,				task=lowercase_    ,				patching_specs=lowercase_    ,				use_past=lowercase_							)
          if not getattr(self._config    ,				'''pad_token_id'''    ,				lowercase_							):
               # TODO: how to do that better?
               _a    	=				0


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a    	=				OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}							)
          if self.use_past:
               self.fill_with_past_key_values_(lowercase_    ,				direction='''inputs'''							)
               _a    	=				{0: """batch""", 1: """past_sequence + sequence"""}
          else:
               _a    	=				{0: """batch""", 1: """sequence"""}

          return common_inputs


     @property
     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          return self._config.n_layer


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          return self._config.n_head


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = -1    ,				__UpperCAmelCase = -1    ,				__UpperCAmelCase = False    ,				__UpperCAmelCase = None    ,				)       ->							Any:
          _a    	=				super(lowercase_    ,				self							).generate_dummy_inputs(
              lowercase_    ,				batch_size=lowercase_    ,				seq_length=lowercase_    ,				is_pair=lowercase_    ,				framework=lowercase_							)

          # We need to order the input in the way they appears in the forward()
          _a    	=				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    	=				common_inputs["""input_ids"""].shape
                    # Not using the same length for past_key_values
                    _a    	=				seqlen + 2
                    _a    	=				(
                        batch,
                        self.num_attention_heads,
                        past_key_values_length,
                        self._config.hidden_size // self.num_attention_heads,
                    )
                    _a    	=				[
                        (torch.zeros(lowercase_							), torch.zeros(lowercase_							)) for _ in range(self.num_layers							)
                    ]

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

          return ordered_inputs





     @property
     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          return 13



"""simple docstring"""






from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)