



"""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


lowercase__		:				str          =	logging.get_logger(__name__)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Dict						)						->   Union[str, Any]:

  """simple docstring"""




  lowerCAmelCase_   :				str								= R'\w+[.]\d+'
  lowerCAmelCase_   :				List[str]								= re.findall(lowerCAmelCase__    ,   lowerCAmelCase__						)
  for pat in pats:
    lowerCAmelCase_   :				int								= key.replace(lowerCAmelCase__    ,   '_'.join(pat.split('.'						)						)						)
  return key

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[Any]    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				Optional[int]						)						->   Union[str, Any]:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= 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)
  ):
    lowerCAmelCase_   :				List[str]								= 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:
    lowerCAmelCase_   :				Optional[int]								= 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:
    lowerCAmelCase_   :				int								= pt_tuple_key[:-1] + ('embedding',)
    return renamed_pt_tuple_key, pt_tensor

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

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

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

  # old PyTorch layer norm bias
  lowerCAmelCase_   :				Dict								= 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__      :				Any    ,   lowerCAmelCase__      :				Any    ,   lowerCAmelCase__      :				Dict=42						)						->   Union[str, Any]:

  """simple docstring"""




  lowerCAmelCase_   :				List[Any]								= {k: v.numpy() for k, v in pt_state_dict.items()}

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

  lowerCAmelCase_   :				Optional[int]								= flatten_dict(lowerCAmelCase__						)
  lowerCAmelCase_   :				Any								= {}

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    lowerCAmelCase_   :				Tuple								= rename_key(lowerCAmelCase__						)
    lowerCAmelCase_   :				List[Any]								= tuple(renamed_pt_key.split('.'						)						)

    # Correctly rename weight parameters
    lowerCAmelCase_						,lowerCAmelCase_   :				Tuple								= rename_key_and_reshape_tensor(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)

    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
    lowerCAmelCase_   :				int								= jnp.asarray(lowerCAmelCase__						)

  return unflatten_dict(lowerCAmelCase__						)










"""simple docstring"""







from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate







@dataclass(frozen=lowercase_  )
class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     field(default="""summarization""",					metadata={"""include_in_asdict_even_if_is_default""": True}  )
      _SCREAMING_SNAKE_CASE  				=     Features({"""text""": Value("""string"""  )}  )
      _SCREAMING_SNAKE_CASE  				=     Features({"""summary""": Value("""string"""  )}  )
      _SCREAMING_SNAKE_CASE  				=     "text"
      _SCREAMING_SNAKE_CASE  				=     "summary"



      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        return {self.text_column: "text", self.summary_column: "summary"}





