




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

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

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






    """simple docstring"""





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





    @property
    def 							a__				(				self  )			->		Dict[str, str]:
         return {self.text_column: "text", self.summary_column: "summary"}





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


__a          =			logging.get_logger(__name__)







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


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







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Dict       =      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)
     ):
          UpperCAmelCase_      :							List[Any]       =      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:
          UpperCAmelCase_      :							Any       =      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:
          UpperCAmelCase_      :							Tuple       =      pt_tuple_key[:-1] + ('''embedding''',)
          return renamed_pt_tuple_key, pt_tensor

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

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

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

     # old PyTorch layer norm bias
     UpperCAmelCase_      :							Tuple       =      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 lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=42				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      {k: v.numpy() for k, v in pt_state_dict.items()}

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

     UpperCAmelCase_      :							List[Any]       =      flatten_dict(_lowercase				)
     UpperCAmelCase_      :							int       =      {}

     # Need to change some parameters name to match Flax names
     for pt_key, pt_tensor in pt_state_dict.items():
          UpperCAmelCase_      :							Optional[int]       =      rename_key(_lowercase				)
          UpperCAmelCase_      :							List[str]       =      tuple(renamed_pt_key.split('''.'''				)				)

          # Correctly rename weight parameters
          UpperCAmelCase_,   UpperCAmelCase_      :							Any       =      rename_key_and_reshape_tensor(_lowercase			,						_lowercase			,						_lowercase				)

          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
          UpperCAmelCase_      :							int       =      jnp.asarray(_lowercase				)

     return unflatten_dict(_lowercase				)