




'''simple docstring'''



from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


UpperCAmelCase_							:	Tuple	     =		logging.get_logger(__name__)







def       _UpperCamelCase			(_lowerCamelCase				:  int ,						_lowerCamelCase				:  List[str]						)-> Union[str, Any]:




     '''simple docstring'''





     try:
          with open(_lowerCamelCase ,						'''rb'''						) as flax_state_f:
               __snake_case            =	from_bytes(_lowerCamelCase ,						flax_state_f.read()						)
     except UnpicklingError as e:
          try:
               with open(_lowerCamelCase						) as f:
                    if f.read().startswith('''version'''						):
                         raise OSError(
                             '''You seem to have cloned a repository without having git-lfs installed. Please'''
                             ''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
                             ''' folder you cloned.'''						)
                    else:
                         raise ValueError from e
          except (UnicodeDecodeError, ValueError):
               raise EnvironmentError(f'''Unable to convert {model_file} to Flax deserializable object. '''						)

     return load_flax_weights_in_pytorch_model(_lowerCamelCase ,						_lowerCamelCase						)







def       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  Union[str, Any]						)-> Any:




     '''simple docstring'''





     try:
          import torch  # noqa: F401
     except ImportError:
          logger.error(
              '''Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'''
              ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
              ''' instructions.'''						)
          raise

     # check if we have bf16 weights
     __snake_case            =	flatten_dict(jax.tree_util.tree_map(lambda _lowerCamelCase						: x.dtype == jnp.bfloataa ,						_lowerCamelCase						)						).values()
     if any(_lowerCamelCase						):
          # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

          # and bf16 is not fully supported in PT yet.
          logger.warning(
              '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
              '''before loading those in PyTorch model.'''						)
          __snake_case            =	jax.tree_util.tree_map(
              lambda _lowerCamelCase						: params.astype(np.floataa						) if params.dtype == jnp.bfloataa else params ,						_lowerCamelCase						)

     __snake_case            =	''''''

     __snake_case            =	flatten_dict(_lowerCamelCase ,						sep='''.'''						)
     __snake_case            =	pt_model.state_dict()

     # keep track of unexpected & missing keys
     __snake_case            =	[]
     __snake_case            =	set(pt_model_dict.keys()						)

     for flax_key_tuple, flax_tensor in flax_state_dict.items():
          __snake_case            =	flax_key_tuple.split('''.'''						)

          if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
               __snake_case            =	flax_key_tuple_array[:-1] + ['''weight''']
               __snake_case            =	jnp.transpose(_lowerCamelCase ,						(3, 2, 0, 1)						)
          elif flax_key_tuple_array[-1] == "kernel":
               __snake_case            =	flax_key_tuple_array[:-1] + ['''weight''']
               __snake_case            =	flax_tensor.T
          elif flax_key_tuple_array[-1] == "scale":
               __snake_case            =	flax_key_tuple_array[:-1] + ['''weight''']

          if "time_embedding" not in flax_key_tuple_array:
               for i, flax_key_tuple_string in enumerate(_lowerCamelCase						):
                    __snake_case            =	(
                        flax_key_tuple_string.replace('''_0''' ,						'''.0'''						)
                        .replace('''_1''' ,						'''.1'''						)
                        .replace('''_2''' ,						'''.2'''						)
                        .replace('''_3''' ,						'''.3'''						)
                        .replace('''_4''' ,						'''.4'''						)
                        .replace('''_5''' ,						'''.5'''						)
                        .replace('''_6''' ,						'''.6'''						)
                        .replace('''_7''' ,						'''.7'''						)
                        .replace('''_8''' ,						'''.8'''						)
                        .replace('''_9''' ,						'''.9'''						)
                    )

          __snake_case            =	'''.'''.join(_lowerCamelCase						)

          if flax_key in pt_model_dict:
               if flax_tensor.shape != pt_model_dict[flax_key].shape:
                    raise ValueError(
                        f'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
                        f'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'''						)
               else:
                    # add weight to pytorch dict
                    __snake_case            =	np.asarray(_lowerCamelCase						) if not isinstance(_lowerCamelCase ,						np.ndarray						) else flax_tensor
                    __snake_case            =	torch.from_numpy(_lowerCamelCase						)
                    # remove from missing keys
                    missing_keys.remove(_lowerCamelCase						)
          else:
               # weight is not expected by PyTorch model
               unexpected_keys.append(_lowerCamelCase						)

     pt_model.load_state_dict(_lowerCamelCase						)

     # re-transform missing_keys to list
     __snake_case            =	list(_lowerCamelCase						)

     if len(_lowerCamelCase						) > 0:
          logger.warning(
              '''Some weights of the Flax model were not used when initializing the PyTorch model'''
              f''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
              f''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
              ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
              f''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
              ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
              ''' FlaxBertForSequenceClassification model).'''						)
     if len(_lowerCamelCase						) > 0:
          logger.warning(
              f'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
              f''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
              ''' use it for predictions and inference.'''						)

     return pt_model











'''simple docstring'''

import re

from filelock import FileLock


try:
							import nltk

							__lowerCAmelCase		     =  True
except (ImportError, ModuleNotFoundError):
							__lowerCAmelCase		     =  False

if NLTK_AVAILABLE:
							with FileLock('''.lock''') as lock:
														nltk.download('''punkt''', quiet=True)



def 	__lowerCamelCase	( lowerCAmelCase_    )     -> str:
		re.sub('<n>'		,     ''		,     lowerCAmelCase_    )  # 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(lowerCAmelCase_    )    )




