


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


_SCREAMING_SNAKE_CASE      :			Optional[int]      				=   logging.get_logger(__name__)
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    R"""\w+[.]\d+"""
     snake_case					=    re.findall(UpperCamelCase_							,UpperCamelCase_    )
     for pat in pats:
          snake_case					=    key.replace(UpperCamelCase_							,'''_'''.join(pat.split('''.'''    )    )    )
     return key
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     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__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=42    ):



     """simple docstring"""






     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(UpperCamelCase_    )    )

     snake_case					=    flatten_dict(UpperCamelCase_    )
     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(UpperCamelCase_    )
          snake_case					=    tuple(renamed_pt_key.split('''.'''    )    )

          # Correctly rename weight parameters
          snake_case					=    rename_key_and_reshape_tensor(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )

          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(UpperCamelCase_    )

     return unflatten_dict(UpperCamelCase_    )








import re

from filelock import FileLock


try:
 import nltk

 _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   True
except (ImportError, ModuleNotFoundError):
 _SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   False

if NLTK_AVAILABLE:
 with FileLock(".lock") as lock:
  nltk.download("punkt", quiet=True)
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     re.sub('''<n>'''							,''''''							,UpperCamelCase_    )  # remove pegasus newline char
     assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
     return "\n".join(nltk.sent_tokenize(UpperCamelCase_    )    )




