



"""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


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

def     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[Any]   ,       lowerCAmelCase__				:    Optional[Any]   ,       lowerCAmelCase__				:    List[Any]   ,       lowerCAmelCase__				:    Any=False						)						->       List[Any]:

  """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_       :    int								=			os.path.abspath(SCREAMING_SNAKE_CASE_						)
    logger.info(f"Loading PyTorch weights from {pt_path}"						)

    lowerCAmelCase_       :    Optional[int]								=			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_       :    Optional[int]								=			convert_pytorch_sharded_state_dict_to_flax(SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_						)
  return flax_state_dict

def     UpperCamelCase_   (   lowerCAmelCase__				:    str   ,       lowerCAmelCase__				:    str   ,       lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    Any   ,       )						->       (Tuple[str], np.ndarray):

  """simple docstring"""




  def is_key_or_prefix_key_in_dict(lowerCAmelCase__				:    Optional[Any]						) -> 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_       :    int								=			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_       :    Tuple								=			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_       :    int								=			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_       :    Union[str, 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_       :    str								=			pt_tensor.transpose(2   ,       3   ,       1   ,       0						)
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  lowerCAmelCase_       :    Union[str, 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_       :    str								=			pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

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

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

  return pt_tuple_key, pt_tensor

def     UpperCamelCase_   (   lowerCAmelCase__				:    str   ,       lowerCAmelCase__				:    Dict						)						->       int:

  """simple docstring"""




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

  lowerCAmelCase_       :    Optional[Any]								=			flax_model.base_model_prefix

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

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

  lowerCAmelCase_       :    Tuple								=			{}

  lowerCAmelCase_       :    int								=			(model_prefix not in flax_model_params) and (
      model_prefix in {k.split('.'						)[0] for k in pt_state_dict.keys()}
  )
  lowerCAmelCase_       :    Dict								=			(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[Any]								=			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_       :    Union[str, Any]								=			pt_tuple_key[1:]

    # Correctly rename weight parameters
    lowerCAmelCase_						,lowerCAmelCase_       :    List[Any]								=			rename_key_and_reshape_tensor(
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_						)

    # add model prefix if necessary
    lowerCAmelCase_       :    int								=			(model_prefix,) + flax_key in random_flax_state_dict
    if load_base_model_into_model_with_head and require_base_model_prefix:
      lowerCAmelCase_       :    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] or "var" in flax_key[-1]:
        lowerCAmelCase_       :    Dict								=			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_       :    Tuple								=			jnp.asarray(SCREAMING_SNAKE_CASE_						)

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

  return unflatten_dict(SCREAMING_SNAKE_CASE_						)

def     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[Any]   ,       lowerCAmelCase__				:    Tuple						)						->       Any:

  """simple docstring"""




  import torch

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

    lowerCAmelCase_       :    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:
      lowerCAmelCase_       :    Any								=			flax_model.params['params']

      lowerCAmelCase_       :    Tuple								=			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_       :    Any								=			(model_prefix not in flax_model_params) and (
        model_prefix in {k.split('.'						)[0] for k in pt_state_dict.keys()}
    )
    lowerCAmelCase_       :    Dict								=			(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_       :    Union[str, Any]								=			tuple(pt_key.split('.'						)						)

      # remove base model prefix if necessary
      lowerCAmelCase_       :    Union[str, Any]								=			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_       :    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]:
          lowerCAmelCase_       :    Union[str, Any]								=			jnp.asarray(SCREAMING_SNAKE_CASE_						)
          continue
        if "var" in flax_key[-1]:
          lowerCAmelCase_       :    int								=			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_       :    List[str]								=			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     UpperCamelCase_   (   lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    Any						)						->       Any:

  """simple docstring"""




  lowerCAmelCase_       :    Union[str, Any]								=			os.path.abspath(SCREAMING_SNAKE_CASE_						)
  logger.info(f"Loading Flax weights from {flax_checkpoint_path}"						)

  # import correct flax class
  lowerCAmelCase_       :    str								=			getattr(SCREAMING_SNAKE_CASE_   ,       'Flax' + model.__class__.__name__						)

  # load flax weight dict
  with open(SCREAMING_SNAKE_CASE_   ,       'rb'						) as state_f:
    try:
      lowerCAmelCase_       :    Dict								=			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     UpperCamelCase_   (   lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    int						)						->       Dict:

  """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_       :    List[str]								=			flatten_dict(jax.tree_util.tree_map(lambda lowerCAmelCase__						: 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[str]								=			jax.tree_util.tree_map(
        lambda lowerCAmelCase__						: params.astype(np.floataa						) if params.dtype == jnp.bfloataa else params   ,       SCREAMING_SNAKE_CASE_						)

  lowerCAmelCase_       :    List[Any]								=			flatten_dict(SCREAMING_SNAKE_CASE_						)
  lowerCAmelCase_       :    Union[str, Any]								=			pt_model.state_dict()

  lowerCAmelCase_       :    Tuple								=			(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_       :    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
  lowerCAmelCase_       :    Optional[Any]								=			[]
  lowerCAmelCase_       :    List[Any]								=			set(pt_model_dict.keys()						)

  for flax_key_tuple, flax_tensor in flax_state_dict.items():
    lowerCAmelCase_       :    Optional[Any]								=			flax_key_tuple[0] == pt_model.base_model_prefix
    lowerCAmelCase_       :    Tuple								=			'.'.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_       :    Optional[int]								=			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_       :    Union[str, Any]								=			flax_key_tuple[:-1] + ('weight',)
      lowerCAmelCase_       :    Tuple								=			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_       :    Optional[int]								=			flax_key_tuple[:-1] + ('weight',)
      lowerCAmelCase_       :    List[Any]								=			flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
      lowerCAmelCase_       :    Any								=			flax_key_tuple[:-1] + ('weight',)

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

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

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

    if flax_key in special_pt_names:
      lowerCAmelCase_       :    str								=			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_       :    Optional[Any]								=			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_       :    Tuple								=			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










"""simple docstring"""







import re

def     UpperCamelCase_   (   lowerCAmelCase__				:    str						)						->       bool:

  """simple docstring"""




  lowerCAmelCase_       :    str								=			re.compile(
      R'^(?:0|94|\+94|0{2}94)' R'7(0|1|2|4|5|6|7|8)' R'(-| |)' R'\d{7}$'						)

  return bool(re.search(lowerCAmelCase__   ,       lowerCAmelCase__						)						)


if __name__ == "__main__":
     lowercase__		:    Optional[int]				   =     """0094702343221"""

     print(is_sri_lankan_phone_number(phone))





