

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           =					logging.get_logger(__name__)




def 			UpperCAmelCase__				(			_A      : List[str]	,      _A      : Dict	,      _A      : Optional[int]	,      _A      : Any=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:
    a__  =os.path.abspath(_lowerCamelCase							)
    logger.info(F"""Loading PyTorch weights from {pt_path}"""							)

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

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




def 			UpperCAmelCase__				(			_A      : Tuple[str]	,      _A      : np.ndarray	,      _A      : Dict[str, jnp.ndarray]	,      _A      : str	,      ):





  '''simple docstring'''







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

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

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

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

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

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

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

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

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

  return pt_tuple_key, pt_tensor




def 			UpperCAmelCase__				(			_A      : List[Any]	,      _A      : Dict							):





  '''simple docstring'''







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

  a__  =flax_model.base_model_prefix

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

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

  a__  ={}

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

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

    # Correctly rename weight parameters
    a__,      a__  =rename_key_and_reshape_tensor(
        _lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase							)

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

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

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

  return unflatten_dict(_lowerCamelCase							)




def 			UpperCAmelCase__				(			_A      : Tuple	,      _A      : List[str]							):





  '''simple docstring'''







  import torch

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

    a__  =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__  =flax_model.params['''params''']

      a__  =flatten_dict(_lowerCamelCase							)
      random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']							)							)
    else:
      a__  =flax_model.params
      a__  =flatten_dict(_lowerCamelCase							)

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

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

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

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

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




def 			UpperCAmelCase__				(			_A      : Optional[Any]	,      _A      : Union[str, Any]							):





  '''simple docstring'''







  a__  =os.path.abspath(_lowerCamelCase							)
  logger.info(F"""Loading Flax weights from {flax_checkpoint_path}"""							)

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

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




def 			UpperCAmelCase__				(			_A      : List[Any]	,      _A      : str							):





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

  a__  =flatten_dict(_lowerCamelCase							)
  a__  =pt_model.state_dict()

  a__  =(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__  =(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__  =[]
  a__  =set(pt_model_dict.keys()							)

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

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

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

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

    if flax_key in special_pt_names:
      a__  =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__  =np.asarray(_lowerCamelCase							) if not isinstance(_lowerCamelCase	,      np.ndarray							) else flax_tensor
        a__  =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
  a__  =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).'''							)
  else:
    logger.warning(F"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"""							)
  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.'''							)
  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 importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


_SCREAMING_SNAKE_CASE					:				Any					    =						importlib.util.find_spec('''s3fs''') is not None

if _has_safs:
  from .safilesystem import SaFileSystem  # noqa: F401

_SCREAMING_SNAKE_CASE					:				List[compression.BaseCompressedFileFileSystem]					    =						[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
  if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
    warnings.warn(F'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
  fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)







def      lowerCamelCase__       (							_lowerCamelCase       :		str )      ->				str:
  if "://" in dataset_path:
    lowerCamelCase_         =						dataset_path.split('://' )[1]
  return dataset_path







def      lowerCamelCase__       (							_lowerCamelCase       :		fsspec.AbstractFileSystem )      ->				bool:
  if fs is not None and fs.protocol != "file":
    return True
  else:
    return False







def      lowerCamelCase__       (							_lowerCamelCase       :		fsspec.AbstractFileSystem		,				_lowerCamelCase       :		str		,				_lowerCamelCase       :		str )      ->				int:
  lowerCamelCase_         =						not is_remote_filesystem(_lowerCamelCase )
  if is_local:
    # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
    shutil.move(fs._strip_protocol(_lowerCamelCase )		,				fs._strip_protocol(_lowerCamelCase ) )
  else:
    fs.mv(_lowerCamelCase		,				_lowerCamelCase		,				recursive=_lowerCamelCase )







def      lowerCamelCase__       (							)      ->				None:
  if hasattr(fsspec.asyn		,				'reset_lock' ):
    # for future fsspec>2022.05.0
    fsspec.asyn.reset_lock()
  else:
    lowerCamelCase_         =						None
    lowerCamelCase_         =						None
    lowerCamelCase_         =						threading.Lock()




