
"""simple docstring"""



import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCAmelCase__        =					logging.get_logger(__name__)
def    _UpperCAmelCase (					__lowerCamelCase  :			int						)	->   Union[str, Any]:
  _snake_case   	=			R'''\w+[.]\d+'''
  _snake_case   	=			re.findall(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE						)
  for pat in pats:
    _snake_case   	=			key.replace(_SCREAMING_SNAKE_CASE					,   '''_'''.join(pat.split('''.'''						)						)						)
  return key
def    _UpperCAmelCase (					__lowerCamelCase  :			Optional[int]					,   __lowerCamelCase  :			List[str]					,   __lowerCamelCase  :			int						)	->   List[str]:
  _snake_case   	=			pt_tuple_key[:-1] + ('''scale''',)
  if (
      any('''norm''' in str_ for str_ in pt_tuple_key						)
      and (pt_tuple_key[-1] == "bias")
      and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
      and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
  ):
    _snake_case   	=			pt_tuple_key[:-1] + ('''scale''',)
    return renamed_pt_tuple_key, pt_tensor
  elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
    _snake_case   	=			pt_tuple_key[:-1] + ('''scale''',)
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
    _snake_case   	=			pt_tuple_key[:-1] + ('''embedding''',)
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  _snake_case   	=			pt_tuple_key[:-1] + ('''kernel''',)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
    _snake_case   	=			pt_tensor.transpose(2					,   3					,   1					,   0						)
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  _snake_case   	=			pt_tuple_key[:-1] + ('''kernel''',)
  if pt_tuple_key[-1] == "weight":
    _snake_case   	=			pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  _snake_case   	=			pt_tuple_key[:-1] + ('''weight''',)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  _snake_case   	=			pt_tuple_key[:-1] + ('''bias''',)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor

def    _UpperCAmelCase (					__lowerCamelCase  :			List[str]					,   __lowerCamelCase  :			Optional[int]					,   __lowerCamelCase  :			Optional[Any]=42						)	->   Union[str, Any]:
  _snake_case   	=			{k: v.numpy() for k, v in pt_state_dict.items()}

  # Step 2: Since the model is stateless, get random Flax params
  _snake_case   	=			flax_model.init_weights(PRNGKey(_SCREAMING_SNAKE_CASE						)						)

  _snake_case   	=			flatten_dict(_SCREAMING_SNAKE_CASE						)
  _snake_case   	=			{}

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    _snake_case   	=			rename_key(_SCREAMING_SNAKE_CASE						)
    _snake_case   	=			tuple(renamed_pt_key.split('''.'''						)						)

    # Correctly rename weight parameters
    _snake_case	,   _snake_case   	=			rename_key_and_reshape_tensor(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE						)

    if flax_key in random_flax_state_dict:
      if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
        raise ValueError(
            f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
            f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''						)

        # also add unexpected weight so that warning is thrown
    _snake_case   	=			jnp.asarray(_SCREAMING_SNAKE_CASE						)

  return unflatten_dict(_SCREAMING_SNAKE_CASE						)










from ... import PretrainedConfig


__A							:			Optional[Any]         =			{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
     __magic_name__   =			'nezha'




     def __init__(    self				,     snake_case_=2_1128				,     snake_case_=768				,     snake_case_=12				,     snake_case_=12				,     snake_case_=3072				,     snake_case_="gelu"				,     snake_case_=0.1				,     snake_case_=0.1				,     snake_case_=512				,     snake_case_=64				,     snake_case_=2				,     snake_case_=0.02				,     snake_case_=1E-12				,     snake_case_=0.1				,     snake_case_=0				,     snake_case_=2				,     snake_case_=3				,     snake_case_=True				,     **snake_case_				,     ):
         super().__init__(pad_token_id=snake_case_				,     bos_token_id=snake_case_				,     eos_token_id=snake_case_				,     **snake_case_							)

         _A     =							vocab_size
         _A     =							hidden_size
         _A     =							num_hidden_layers
         _A     =							num_attention_heads
         _A     =							hidden_act
         _A     =							intermediate_size
         _A     =							hidden_dropout_prob
         _A     =							attention_probs_dropout_prob
         _A     =							max_position_embeddings
         _A     =							max_relative_position
         _A     =							type_vocab_size
         _A     =							initializer_range
         _A     =							layer_norm_eps
         _A     =							classifier_dropout
         _A     =							use_cache





