




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


lowerCamelCase    :		Optional[int]			   =       logging.get_logger(__name__)

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Any     ,							lowercase     :	int     ,							lowercase     :	Tuple     ,							lowercase     :	str=False					):


      '''simple docstring'''



      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:
            lowerCamelCase_							=   os.path.abspath(lowercase					)
            logger.info(f"""Loading PyTorch weights from {pt_path}"""					)

            lowerCamelCase_							=   torch.load(lowercase     ,							map_location='cpu'					)
            logger.info(f"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()					):,} parameters."""					)

            lowerCamelCase_							=   convert_pytorch_state_dict_to_flax(lowercase     ,							lowercase					)
      else:
            # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
            lowerCamelCase_							=   convert_pytorch_sharded_state_dict_to_flax(lowercase     ,							lowercase					)
      return flax_state_dict

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Tuple[str]     ,							lowercase     :	np.ndarray     ,							lowercase     :	Dict[str, jnp.ndarray]     ,							lowercase     :	str     ,							):


      '''simple docstring'''



      def is_key_or_prefix_key_in_dict(lowercase     :	Tuple[str]					) -> bool:
            return len(set(lowercase					) & {key, (model_prefix,) + key}					) > 0

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

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

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

      # embedding
      lowerCamelCase_							=   pt_tuple_key[:-1] + ('embedding',)
      if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(lowercase					):
            return renamed_pt_tuple_key, pt_tensor

      # conv layer
      lowerCamelCase_							=   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(lowercase					):
            lowerCamelCase_							=   pt_tensor.transpose(2     ,							3     ,							1     ,							0					)
            return renamed_pt_tuple_key, pt_tensor

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

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

      # old PyTorch layer norm bias
      lowerCamelCase_							=   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
      lowerCamelCase_							=   None
      if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
            lowerCamelCase_							=   pt_tuple_key[-2] + '_g'
      elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
            lowerCamelCase_							=   pt_tuple_key[-2] + '_v'
      if name is not None:
            lowerCamelCase_							=   pt_tuple_key[:-3] + (name,)
            return renamed_pt_tuple_key, pt_tensor

      return pt_tuple_key, pt_tensor

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Optional[Any]     ,							lowercase     :	Dict					):


      '''simple docstring'''



      lowerCamelCase_							=   {k: v.numpy() for k, v in pt_state_dict.items()}

      lowerCamelCase_							=   flax_model.base_model_prefix

      # use params dict if the model contains batch norm layers
      if "params" in flax_model.params:
            lowerCamelCase_							=   flax_model.params['params']
      else:
            lowerCamelCase_							=   flax_model.params
      lowerCamelCase_							=   flatten_dict(lowercase					)

      # add batch_stats keys,values to dict
      if "batch_stats" in flax_model.params:
            lowerCamelCase_							=   flatten_dict(flax_model.params['batch_stats']					)
            random_flax_state_dict.update(lowercase					)

      lowerCamelCase_							=   {}

      lowerCamelCase_							=   (model_prefix not in flax_model_params) and (
          model_prefix in {k.split('.'					)[0] for k in pt_state_dict.keys()}
      )
      lowerCamelCase_							=   (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():
            lowerCamelCase_							=   tuple(pt_key.split('.'					)					)

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

            # Correctly rename weight parameters
            lowerCamelCase_					,    lowerCamelCase_							=   rename_key_and_reshape_tensor(
                lowercase     ,							lowercase     ,							lowercase     ,							lowercase					)

            # add model prefix if necessary
            lowerCamelCase_							=   (model_prefix,) + flax_key in random_flax_state_dict
            if load_base_model_into_model_with_head and require_base_model_prefix:
                  lowerCamelCase_							=   (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]:
                        lowerCamelCase_							=   jnp.asarray(lowercase					)
                        continue
                  # remove num_batches_tracked key
                  if "num_batches_tracked" in flax_key[-1]:
                        flax_state_dict.pop(lowercase     ,							lowercase					)
                        continue

                  # also add unexpected weight so that warning is thrown
                  lowerCamelCase_							=   jnp.asarray(lowercase					)

            else:
                  # also add unexpected weight so that warning is thrown
                  lowerCamelCase_							=   jnp.asarray(lowercase					)

      return unflatten_dict(lowercase					)

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Optional[int]     ,							lowercase     :	Tuple					):


      '''simple docstring'''



      import torch

      # Load the index
      lowerCamelCase_							=   {}
      for shard_file in shard_filenames:
            # load using msgpack utils
            lowerCamelCase_							=   torch.load(lowercase					)
            lowerCamelCase_							=   {k: v.numpy() for k, v in pt_state_dict.items()}

            lowerCamelCase_							=   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:
                  lowerCamelCase_							=   flax_model.params['params']

                  lowerCamelCase_							=   flatten_dict(lowercase					)
                  random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats']					)					)
            else:
                  lowerCamelCase_							=   flax_model.params
                  lowerCamelCase_							=   flatten_dict(lowercase					)

            lowerCamelCase_							=   (model_prefix not in flax_model_params) and (
                model_prefix in {k.split('.'					)[0] for k in pt_state_dict.keys()}
            )
            lowerCamelCase_							=   (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():
                  lowerCamelCase_							=   tuple(pt_key.split('.'					)					)

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

                  # Correctly rename weight parameters
                  lowerCamelCase_					,    lowerCamelCase_							=   rename_key_and_reshape_tensor(
                      lowercase     ,							lowercase     ,							lowercase     ,							lowercase					)
                  # add model prefix if necessary
                  lowerCamelCase_							=   (model_prefix,) + flax_key in random_flax_state_dict
                  if load_base_model_into_model_with_head and require_base_model_prefix:
                        lowerCamelCase_							=   (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]:
                              lowerCamelCase_							=   jnp.asarray(lowercase					)
                              continue
                        if "var" in flax_key[-1]:
                              lowerCamelCase_							=   jnp.asarray(lowercase					)
                              continue
                        # remove num_batches_tracked key
                        if "num_batches_tracked" in flax_key[-1]:
                              flax_state_dict.pop(lowercase     ,							lowercase					)
                              continue

                        # also add unexpected weight so that warning is thrown
                        lowerCamelCase_							=   jnp.asarray(lowercase					)

                  else:
                        # also add unexpected weight so that warning is thrown
                        lowerCamelCase_							=   jnp.asarray(lowercase					)
      return unflatten_dict(lowercase					)

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	int     ,							lowercase     :	int					):


      '''simple docstring'''



      lowerCamelCase_							=   os.path.abspath(lowercase					)
      logger.info(f"""Loading Flax weights from {flax_checkpoint_path}"""					)

      # import correct flax class
      lowerCamelCase_							=   getattr(lowercase     ,							'Flax' + model.__class__.__name__					)

      # load flax weight dict
      with open(lowercase     ,							'rb'					) as state_f:
            try:
                  lowerCamelCase_							=   from_bytes(lowercase     ,							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(lowercase     ,							lowercase					)






def       _SCREAMING_SNAKE_CASE      (							lowercase     :	List[Any]     ,							lowercase     :	Optional[int]					):


      '''simple docstring'''



      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
      lowerCamelCase_							=   flatten_dict(jax.tree_util.tree_map(lambda lowercase					: x.dtype == jnp.bfloataa     ,							lowercase					)					).values()
      if any(lowercase					):
            # 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.'					)
            lowerCamelCase_							=   jax.tree_util.tree_map(
                lambda lowercase					: params.astype(np.floataa					) if params.dtype == jnp.bfloataa else params     ,							lowercase					)

      lowerCamelCase_							=   flatten_dict(lowercase					)
      lowerCamelCase_							=   pt_model.state_dict()

      lowerCamelCase_							=   (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()}
      )
      lowerCamelCase_							=   (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
      lowerCamelCase_							=   []
      lowerCamelCase_							=   set(pt_model_dict.keys()					)

      for flax_key_tuple, flax_tensor in flax_state_dict.items():
            lowerCamelCase_							=   flax_key_tuple[0] == pt_model.base_model_prefix
            lowerCamelCase_							=   '.'.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:
                  lowerCamelCase_							=   flax_key_tuple[1:]
            elif load_base_model_into_model_with_head and require_base_model_prefix:
                  lowerCamelCase_							=   (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(lowercase					) not in pt_model_dict:
                  # conv layer
                  lowerCamelCase_							=   flax_key_tuple[:-1] + ('weight',)
                  lowerCamelCase_							=   jnp.transpose(lowercase     ,							(3, 2, 0, 1)					)
            elif flax_key_tuple[-1] == "kernel" and ".".join(lowercase					) not in pt_model_dict:
                  # linear layer
                  lowerCamelCase_							=   flax_key_tuple[:-1] + ('weight',)
                  lowerCamelCase_							=   flax_tensor.T
            elif flax_key_tuple[-1] in ["scale", "embedding"]:
                  lowerCamelCase_							=   flax_key_tuple[:-1] + ('weight',)

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

            if "batch_stats" in flax_state:
                  lowerCamelCase_							=   '.'.join(flax_key_tuple[1:]					)  # Remove the params/batch_stats header
            else:
                  lowerCamelCase_							=   '.'.join(lowercase					)

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

            if flax_key in special_pt_names:
                  lowerCamelCase_							=   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
                        lowerCamelCase_							=   np.asarray(lowercase					) if not isinstance(lowercase     ,							np.ndarray					) else flax_tensor
                        lowerCamelCase_							=   torch.from_numpy(lowercase					)
                        # remove from missing keys
                        missing_keys.remove(lowercase					)
            else:
                  # weight is not expected by PyTorch model
                  unexpected_keys.append(lowercase					)

      pt_model.load_state_dict(lowercase					)

      # re-transform missing_keys to list
      lowerCamelCase_							=   list(lowercase					)

      if len(lowercase					) > 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(lowercase					) > 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









from ..utils import DummyObject, requires_backends







class   A(						metaclass=UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						['''keras_nlp''']

 def __init__(    self :  Optional[int]						,							*A_ :  Any						,							**A_ :  Dict  )       ->     Optional[int]:

       """simple docstring"""






       requires_backends(self						,							['keras_nlp']  )



