


'''simple docstring'''







import os
from pickle import UnpicklingError
from typing import Dict, Tuple

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

import transformers

from .utils import logging


A__:    str					    =       logging.get_logger(__name__)


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Tuple    ,_UpperCAmelCase	:	int    ,_UpperCAmelCase	:	Union[str, Any]    ,_UpperCAmelCase	:	List[Any]=False  )  ->			Dict:






       try:
              import torch  # noqa: F401
       except ImportError:
              logger.error(
                  """Loading a PyTorch model in Flax, 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

       if not is_sharded:
              _a    : Optional[Any]     					=os.path.abspath(__a  )
              logger.info(F"Loading PyTorch weights from {pt_path}"  )

              _a    : Tuple     					=torch.load(__a    ,map_location="""cpu"""  )
              logger.info(F"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()  ):,} parameters."  )

              _a    : List[Any]     					=convert_pytorch_state_dict_to_flax(__a    ,__a  )
       else:
              # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
              _a    : Union[str, Any]     					=convert_pytorch_sharded_state_dict_to_flax(__a    ,__a  )
       return flax_state_dict


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Optional[int]    ,_UpperCAmelCase	:	List[Any]    ,_UpperCAmelCase	:	List[str]    ,_UpperCAmelCase	:	Union[str, Any]    ,)  ->			(Tuple[str], np.ndarray):






       def is_key_or_prefix_key_in_dict(_UpperCAmelCase	:	List[Any]  ) -> bool:
              return len(set(__a  ) & {key, (model_prefix,) + key}  ) > 0

       # layer norm
       _a    : Dict     					=pt_tuple_key[:-1] + ('''scale''',)
       if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__a  ):
              return renamed_pt_tuple_key, pt_tensor

       # batch norm layer mean
       _a    : Dict     					=pt_tuple_key[:-1] + ('''mean''',)
       if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__a  ):
              return renamed_pt_tuple_key, pt_tensor

       # batch norm layer var
       _a    : Optional[Any]     					=pt_tuple_key[:-1] + ('''var''',)
       if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__a  ):
              return renamed_pt_tuple_key, pt_tensor

       # embedding
       _a    : Optional[int]     					=pt_tuple_key[:-1] + ('''embedding''',)
       if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__a  ):
              return renamed_pt_tuple_key, pt_tensor

       # conv layer
       _a    : Tuple     					=pt_tuple_key[:-1] + ('''kernel''',)
       if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__a  ):
              _a    : int     					=pt_tensor.transpose(2    ,3    ,1    ,0  )
              return renamed_pt_tuple_key, pt_tensor

       # linear layer
       _a    : Tuple     					=pt_tuple_key[:-1] + ('''kernel''',)
       if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__a  ):
              _a    : str     					=pt_tensor.T
              return renamed_pt_tuple_key, pt_tensor

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

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

       # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
       _a    : Tuple     					=None
       if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
              _a    : Tuple     					=pt_tuple_key[-2] + '''_g'''
       elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
              _a    : Union[str, Any]     					=pt_tuple_key[-2] + '''_v'''
       if name is not None:
              _a    : Tuple     					=pt_tuple_key[:-3] + (name,)
              return renamed_pt_tuple_key, pt_tensor

       return pt_tuple_key, pt_tensor


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Tuple    ,_UpperCAmelCase	:	Any  )  ->			Tuple:
       _a    : Optional[Any]     					={k: v.numpy() for k, v in pt_state_dict.items()}

       _a    : List[str]     					=flax_model.base_model_prefix

       # use params dict if the model contains batch norm layers
       if "params" in flax_model.params:
              _a    : Optional[Any]     					=flax_model.params['''params''']
       else:
              _a    : Optional[Any]     					=flax_model.params
       _a    : Union[str, Any]     					=flatten_dict(__a  )

       # add batch_stats keys,values to dict
       if "batch_stats" in flax_model.params:
              _a    : List[str]     					=flatten_dict(flax_model.params["""batch_stats"""]  )
              random_flax_state_dict.update(__a  )

       _a    : Any     					={}

       _a    : Optional[int]     					=(model_prefix not in flax_model_params) and (
           model_prefix in {k.split("""."""  )[0] for k in pt_state_dict.keys()}
       )
       _a    : Union[str, Any]     					=(model_prefix in flax_model_params) and (
           model_prefix not in {k.split("""."""  )[0] for k in pt_state_dict.keys()}
       )

       # Need to change some parameters name to match Flax names
       for pt_key, pt_tensor in pt_state_dict.items():
              _a    : int     					=tuple(pt_key.split("""."""  )  )

              # remove base model prefix if necessary
              _a    : Union[str, Any]     					=pt_tuple_key[0] == model_prefix
              if load_model_with_head_into_base_model and has_base_model_prefix:
                     _a    : List[str]     					=pt_tuple_key[1:]

              # Correctly rename weight parameters
              _a    : int     					=rename_key_and_reshape_tensor(
                  __a    ,__a    ,__a    ,__a  )

              # add model prefix if necessary
              _a    : int     					=(model_prefix,) + flax_key in random_flax_state_dict
              if load_base_model_into_model_with_head and require_base_model_prefix:
                     _a    : Optional[Any]     					=(model_prefix,) + flax_key

              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}."  )

        # add batch stats if the model contains batchnorm layers
              if "batch_stats" in flax_model.params:
                     if "mean" in flax_key[-1] or "var" in flax_key[-1]:
                            _a    : Union[str, Any]     					=jnp.asarray(__a  )
                            continue
                     # remove num_batches_tracked key
                     if "num_batches_tracked" in flax_key[-1]:
                            flax_state_dict.pop(__a    ,__a  )
                            continue

                     # also add unexpected weight so that warning is thrown
                     _a    : Tuple     					=jnp.asarray(__a  )

              else:
                     # also add unexpected weight so that warning is thrown
                     _a    : Union[str, Any]     					=jnp.asarray(__a  )

       return unflatten_dict(__a  )


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Tuple    ,_UpperCAmelCase	:	Dict  )  ->			List[str]:
       import torch

       # Load the index
       _a    : int     					={}
       for shard_file in shard_filenames:
              # load using msgpack utils
              _a    : List[Any]     					=torch.load(__a  )
              _a    : List[Any]     					={k: v.numpy() for k, v in pt_state_dict.items()}

              _a    : List[Any]     					=flax_model.base_model_prefix

              # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
              if "batch_stats" in flax_model.params:
                     _a    : int     					=flax_model.params['''params''']

                     _a    : Union[str, Any]     					=flatten_dict(__a  )
                     random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]  )  )
              else:
                     _a    : Dict     					=flax_model.params
                     _a    : int     					=flatten_dict(__a  )

              _a    : List[str]     					=(model_prefix not in flax_model_params) and (
                  model_prefix in {k.split("""."""  )[0] for k in pt_state_dict.keys()}
              )
              _a    : int     					=(model_prefix in flax_model_params) and (
                  model_prefix not in {k.split("""."""  )[0] for k in pt_state_dict.keys()}
              )
              # Need to change some parameters name to match Flax names
              for pt_key, pt_tensor in pt_state_dict.items():
                     _a    : str     					=tuple(pt_key.split("""."""  )  )

                     # remove base model prefix if necessary
                     _a    : Dict     					=pt_tuple_key[0] == model_prefix
                     if load_model_with_head_into_base_model and has_base_model_prefix:
                            _a    : Dict     					=pt_tuple_key[1:]

                     # Correctly rename weight parameters
                     _a    : Optional[Any]     					=rename_key_and_reshape_tensor(
                         __a    ,__a    ,__a    ,__a  )
                     # add model prefix if necessary
                     _a    : Any     					=(model_prefix,) + flax_key in random_flax_state_dict
                     if load_base_model_into_model_with_head and require_base_model_prefix:
                            _a    : List[str]     					=(model_prefix,) + flax_key

                     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}."  )

            # add batch stats if the model contains batchnorm layers
                     if "batch_stats" in flax_model.params:
                            if "mean" in flax_key[-1]:
                                   _a    : List[Any]     					=jnp.asarray(__a  )
                                   continue
                            if "var" in flax_key[-1]:
                                   _a    : List[Any]     					=jnp.asarray(__a  )
                                   continue
                            # remove num_batches_tracked key
                            if "num_batches_tracked" in flax_key[-1]:
                                   flax_state_dict.pop(__a    ,__a  )
                                   continue

                            # also add unexpected weight so that warning is thrown
                            _a    : str     					=jnp.asarray(__a  )

                     else:
                            # also add unexpected weight so that warning is thrown
                            _a    : List[Any]     					=jnp.asarray(__a  )
       return unflatten_dict(__a  )


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Optional[Any]    ,_UpperCAmelCase	:	int  )  ->			Tuple:
       _a    : Tuple     					=os.path.abspath(__a  )
       logger.info(F"Loading Flax weights from {flax_checkpoint_path}"  )

       # import correct flax class
       _a    : str     					=getattr(__a    ,"""Flax""" + model.__class__.__name__  )

       # load flax weight dict
       with open(__a    ,"""rb"""  ) as state_f:
              try:
                     _a    : Tuple     					=from_bytes(__a    ,state_f.read()  )
              except UnpicklingError:
                     raise EnvironmentError(F"Unable to convert {flax_checkpoint_path} to Flax deserializable object. "  )

       return load_flax_weights_in_pytorch_model(__a    ,__a  )


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Dict    ,_UpperCAmelCase	:	List[Any]  )  ->			int:






       try:
              import torch  # noqa: F401
       except ImportError:
              logger.error(
                  """Loading a 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
       _a    : List[Any]     					=flatten_dict(jax.tree_util.tree_map(lambda _UpperCAmelCase  : x.dtype == jnp.bfloataa    ,__a  )  ).values()
       if any(__a  ):
              # convert all weights to fp32 if the 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."""  )
              _a    : str     					=jax.tree_util.tree_map(
                  lambda _UpperCAmelCase  : params.astype(np.floataa  ) if params.dtype == jnp.bfloataa else params    ,__a  )

       _a    : Optional[Any]     					=flatten_dict(__a  )
       _a    : str     					=pt_model.state_dict()

       _a    : List[Any]     					=(pt_model.base_model_prefix in flax_state) and (
           pt_model.base_model_prefix not in {k.split("""."""  )[0] for k in pt_model_dict.keys()}
       )
       _a    : Optional[int]     					=(pt_model.base_model_prefix not in flax_state) and (
           pt_model.base_model_prefix in {k.split("""."""  )[0] for k in pt_model_dict.keys()}
       )

       # keep track of unexpected & missing keys
       _a    : Optional[Any]     					=[]
       _a    : str     					=set(pt_model_dict.keys()  )

       for flax_key_tuple, flax_tensor in flax_state_dict.items():
              _a    : Tuple     					=flax_key_tuple[0] == pt_model.base_model_prefix
              _a    : int     					='''.'''.join((pt_model.base_model_prefix,) + flax_key_tuple  ) in pt_model_dict

              # adapt flax_key to prepare for loading from/to base model only
              if load_model_with_head_into_base_model and has_base_model_prefix:
                     _a    : str     					=flax_key_tuple[1:]
              elif load_base_model_into_model_with_head and require_base_model_prefix:
                     _a    : List[str]     					=(pt_model.base_model_prefix,) + flax_key_tuple

              # rename flax weights to PyTorch format
              if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__a  ) not in pt_model_dict:
                     # conv layer
                     _a    : Any     					=flax_key_tuple[:-1] + ('''weight''',)
                     _a    : Tuple     					=jnp.transpose(__a    ,(3, 2, 0, 1)  )
              elif flax_key_tuple[-1] == "kernel" and ".".join(__a  ) not in pt_model_dict:
                     # linear layer
                     _a    : Dict     					=flax_key_tuple[:-1] + ('''weight''',)
                     _a    : Tuple     					=flax_tensor.T
              elif flax_key_tuple[-1] in ["scale", "embedding"]:
                     _a    : Union[str, Any]     					=flax_key_tuple[:-1] + ('''weight''',)

              # adding batch stats from flax batch norm to pt
              elif "mean" in flax_key_tuple[-1]:
                     _a    : Optional[Any]     					=flax_key_tuple[:-1] + ('''running_mean''',)
              elif "var" in flax_key_tuple[-1]:
                     _a    : List[Any]     					=flax_key_tuple[:-1] + ('''running_var''',)

              if "batch_stats" in flax_state:
                     _a    : Optional[int]     					='''.'''.join(flax_key_tuple[1:]  )  # Remove the params/batch_stats header
              else:
                     _a    : Any     					='''.'''.join(__a  )

              # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
              _a    : Dict     					={}
              # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
              for key in pt_model_dict:
                     _a    : Dict     					=key.split("""."""  )
                     _a    : Union[str, Any]     					=None
                     if key_components[-3::2] == ["parametrizations", "original0"]:
                            _a    : Union[str, Any]     					=key_components[-2] + '''_g'''
                     elif key_components[-3::2] == ["parametrizations", "original1"]:
                            _a    : List[Any]     					=key_components[-2] + '''_v'''
                     if name is not None:
                            _a    : Dict     					=key_components[:-3] + [name]
                            _a    : Optional[int]     					='''.'''.join(__a  )
                            _a    : Any     					=key

              if flax_key in special_pt_names:
                     _a    : Tuple     					=special_pt_names[flax_key]

              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
                            _a    : Dict     					=np.asarray(__a  ) if not isinstance(__a    ,np.ndarray  ) else flax_tensor
                            _a    : Optional[int]     					=torch.from_numpy(__a  )
                            # remove from missing keys
                            missing_keys.remove(__a  )
              else:
                     # weight is not expected by PyTorch model
                     unexpected_keys.append(__a  )

       pt_model.load_state_dict(__a  )

       # re-transform missing_keys to list
       _a    : List[str]     					=list(__a  )

       if len(__a  ) > 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)."""  )
       else:
              logger.warning(F"All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"  )
       if len(__a  ) > 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."""  )
       else:
              logger.warning(
                  F"All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"
                  """If your task is similar to the task the model of the checkpoint was trained on, """
                  F"you can already use {pt_model.__class__.__name__} for predictions without further training."  )

       return pt_model






'''simple docstring'''
import re

from filelock import FileLock


try:
		import nltk

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

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


def 			a      (			__a   )  ->	str:



					'''simple docstring'''






					re.sub('''<n>''' ,					'''''' ,					__a   )  # 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(__a   )   )