


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


_snake_case             =     logging.get_logger(__name__)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=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 :					List[str]    =				os.path.abspath(SCREAMING_SNAKE_CASE_	)
    logger.info(f"""Loading PyTorch weights from {pt_path}"""	)

    lowerCamelCase :					List[str]    =				torch.load(SCREAMING_SNAKE_CASE_      ,						map_location="cpu"	)
    logger.info(f"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()	):,} parameters."""	)

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						):


  '''simple docstring'''

  def is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE_	) -> bool:
    return len(set(SCREAMING_SNAKE_CASE_	) & {key, (model_prefix,) + key}	) > 0

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

  # batch norm layer mean
  lowerCamelCase :					Union[str, Any]    =				pt_tuple_key[:-1] + ("mean",)
  if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE_	):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer var
  lowerCamelCase :					List[Any]    =				pt_tuple_key[:-1] + ("var",)
  if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE_	):
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  lowerCamelCase :					Dict    =				pt_tuple_key[:-1] + ("embedding",)
  if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE_	):
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  lowerCamelCase :					Any    =				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(SCREAMING_SNAKE_CASE_	):
    lowerCamelCase :					Optional[int]    =				pt_tensor.transpose(2      ,						3      ,						1      ,						0	)
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  lowerCamelCase :					List[Any]    =				pt_tuple_key[:-1] + ("kernel",)
  if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE_	):
    lowerCamelCase :					List[str]    =				pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

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

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

  return pt_tuple_key, pt_tensor






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

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

  lowerCamelCase :					Dict    =				flax_model.base_model_prefix

  # use params dict if the model contains batch norm layers
  if "params" in flax_model.params:
    lowerCamelCase :					Dict    =				flax_model.params["params"]
  else:
    lowerCamelCase :					Any    =				flax_model.params
  lowerCamelCase :					Tuple    =				flatten_dict(SCREAMING_SNAKE_CASE_	)

  # add batch_stats keys,values to dict
  if "batch_stats" in flax_model.params:
    lowerCamelCase :					Union[str, Any]    =				flatten_dict(flax_model.params["batch_stats"]	)
    random_flax_state_dict.update(SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					str    =				{}

  lowerCamelCase :					Optional[Any]    =				(model_prefix not in flax_model_params) and (
      model_prefix in {k.split("."	)[0] for k in pt_state_dict.keys()}
  )
  lowerCamelCase :					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():
    lowerCamelCase :					str    =				tuple(pt_key.split("."	)	)

    # remove base model prefix if necessary
    lowerCamelCase :					Tuple    =				pt_tuple_key[0] == model_prefix
    if load_model_with_head_into_base_model and has_base_model_prefix:
      lowerCamelCase :					List[Any]    =				pt_tuple_key[1:]

    # Correctly rename weight parameters
    lowerCamelCase    ,	lowerCamelCase :					Dict    =				rename_key_and_reshape_tensor(
        SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)

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

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

    else:
      # also add unexpected weight so that warning is thrown
      lowerCamelCase :					int    =				jnp.asarray(SCREAMING_SNAKE_CASE_	)

  return unflatten_dict(SCREAMING_SNAKE_CASE_	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  import torch

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

    lowerCamelCase :					str    =				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 :					str    =				flax_model.params["params"]

      lowerCamelCase :					List[Any]    =				flatten_dict(SCREAMING_SNAKE_CASE_	)
      random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]	)	)
    else:
      lowerCamelCase :					str    =				flax_model.params
      lowerCamelCase :					Union[str, Any]    =				flatten_dict(SCREAMING_SNAKE_CASE_	)

    lowerCamelCase :					List[Any]    =				(model_prefix not in flax_model_params) and (
        model_prefix in {k.split("."	)[0] for k in pt_state_dict.keys()}
    )
    lowerCamelCase :					str    =				(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 :					List[str]    =				tuple(pt_key.split("."	)	)

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

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

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

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Tuple    =				os.path.abspath(SCREAMING_SNAKE_CASE_	)
  logger.info(f"""Loading Flax weights from {flax_checkpoint_path}"""	)

  # import correct flax class
  lowerCamelCase :					int    =				getattr(SCREAMING_SNAKE_CASE_      ,						"Flax" + model.__class__.__name__	)

  # load flax weight dict
  with open(SCREAMING_SNAKE_CASE_      ,						"rb"	) as state_f:
    try:
      lowerCamelCase :					Tuple    =				from_bytes(SCREAMING_SNAKE_CASE_      ,						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(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)







def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''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 :					int    =				flatten_dict(jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE_	: x.dtype == jnp.bfloataa      ,						SCREAMING_SNAKE_CASE_	)	).values()
  if any(SCREAMING_SNAKE_CASE_	):
    # 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 :					List[Any]    =				jax.tree_util.tree_map(
        lambda SCREAMING_SNAKE_CASE_	: params.astype(np.floataa	) if params.dtype == jnp.bfloataa else params      ,						SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					Union[str, Any]    =				flatten_dict(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Optional[Any]    =				pt_model.state_dict()

  lowerCamelCase :					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()}
  )
  lowerCamelCase :					str    =				(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 :					int    =				[]
  lowerCamelCase :					str    =				set(pt_model_dict.keys()	)

  for flax_key_tuple, flax_tensor in flax_state_dict.items():
    lowerCamelCase :					Any    =				flax_key_tuple[0] == pt_model.base_model_prefix
    lowerCamelCase :					str    =				".".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 :					Tuple    =				flax_key_tuple[1:]
    elif load_base_model_into_model_with_head and require_base_model_prefix:
      lowerCamelCase :					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(SCREAMING_SNAKE_CASE_	) not in pt_model_dict:
      # conv layer
      lowerCamelCase :					str    =				flax_key_tuple[:-1] + ("weight",)
      lowerCamelCase :					Optional[Any]    =				jnp.transpose(SCREAMING_SNAKE_CASE_      ,						(3, 2, 0, 1)	)
    elif flax_key_tuple[-1] == "kernel" and ".".join(SCREAMING_SNAKE_CASE_	) not in pt_model_dict:
      # linear layer
      lowerCamelCase :					List[str]    =				flax_key_tuple[:-1] + ("weight",)
      lowerCamelCase :					int    =				flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
      lowerCamelCase :					List[Any]    =				flax_key_tuple[:-1] + ("weight",)

    # adding batch stats from flax batch norm to pt
    elif "mean" in flax_key_tuple[-1]:
      lowerCamelCase :					int    =				flax_key_tuple[:-1] + ("running_mean",)
    elif "var" in flax_key_tuple[-1]:
      lowerCamelCase :					Union[str, Any]    =				flax_key_tuple[:-1] + ("running_var",)

    if "batch_stats" in flax_state:
      lowerCamelCase :					Tuple    =				".".join(flax_key_tuple[1:]	)  # Remove the params/batch_stats header
    else:
      lowerCamelCase :					List[Any]    =				".".join(SCREAMING_SNAKE_CASE_	)

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

    if flax_key in special_pt_names:
      lowerCamelCase :					Optional[Any]    =				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 :					Dict    =				np.asarray(SCREAMING_SNAKE_CASE_	) if not isinstance(SCREAMING_SNAKE_CASE_      ,						np.ndarray	) else flax_tensor
        lowerCamelCase :					Tuple    =				torch.from_numpy(SCREAMING_SNAKE_CASE_	)
        # remove from missing keys
        missing_keys.remove(SCREAMING_SNAKE_CASE_	)
    else:
      # weight is not expected by PyTorch model
      unexpected_keys.append(SCREAMING_SNAKE_CASE_	)

  pt_model.load_state_dict(SCREAMING_SNAKE_CASE_	)

  # re-transform missing_keys to list
  lowerCamelCase :					int    =				list(SCREAMING_SNAKE_CASE_	)

  if len(SCREAMING_SNAKE_CASE_	) > 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(SCREAMING_SNAKE_CASE_	) > 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







def 		lowercase_(  SCREAMING_SNAKE_CASE_ = 4000000	):


  '''simple docstring'''

  lowerCamelCase :					Any    =				[0, 1]
  lowerCamelCase :					Union[str, Any]    =				0
  while fib[i] <= n:
    fib.append(fib[i] + fib[i + 1]	)
    if fib[i + 2] > n:
      break
    i += 1
  lowerCamelCase :					Union[str, Any]    =				0
  for j in range(len(SCREAMING_SNAKE_CASE_	) - 1	):
    if fib[j] % 2 == 0:
      total += fib[j]

  return total


if __name__ == "__main__":
   print(f'''{solution() = }''')



