






"""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


_UpperCamelCase					      =  logging.get_logger(__name__)

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_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:
            UpperCAmelCase          =	os.path.abspath(_snake_case							)
            logger.info(F'''Loading PyTorch weights from {pt_path}'''							)

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

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

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	):







      """simple docstring"""






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

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

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

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

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

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

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

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

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

      return pt_tuple_key, pt_tensor

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






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

      UpperCAmelCase          =	flax_model.base_model_prefix

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

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

      UpperCAmelCase          =	{}

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

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

            # Correctly rename weight parameters
            UpperCAmelCase      , UpperCAmelCase          =	rename_key_and_reshape_tensor(
                _snake_case				,	_snake_case				,	_snake_case				,	_snake_case							)

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

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

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

      return unflatten_dict(_snake_case							)

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      import torch

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

            UpperCAmelCase          =	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:
                  UpperCAmelCase          =	flax_model.params["""params"""]

                  UpperCAmelCase          =	flatten_dict(_snake_case							)
                  random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]							)							)
            else:
                  UpperCAmelCase          =	flax_model.params
                  UpperCAmelCase          =	flatten_dict(_snake_case							)

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

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

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

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

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

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	os.path.abspath(_snake_case							)
      logger.info(F'''Loading Flax weights from {flax_checkpoint_path}'''							)

      # import correct flax class
      UpperCAmelCase          =	getattr(_snake_case				,	"""Flax""" + model.__class__.__name__							)

      # load flax weight dict
      with open(_snake_case				,	"""rb"""							) as state_f:
            try:
                  UpperCAmelCase          =	from_bytes(_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(_snake_case				,	_snake_case							)






def _a			(		_snake_case				,	_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
      UpperCAmelCase          =	flatten_dict(jax.tree_util.tree_map(lambda _snake_case							: x.dtype == jnp.bfloataa				,	_snake_case							)							).values()
      if any(_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."""							)
            UpperCAmelCase          =	jax.tree_util.tree_map(
                lambda _snake_case							: params.astype(np.floataa							) if params.dtype == jnp.bfloataa else params				,	_snake_case							)

      UpperCAmelCase          =	flatten_dict(_snake_case							)
      UpperCAmelCase          =	pt_model.state_dict()

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

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

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

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

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

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

      pt_model.load_state_dict(_snake_case							)

      # re-transform missing_keys to list
      UpperCAmelCase          =	list(_snake_case							)

      if len(_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(_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"""







_UpperCamelCase					      =  [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCamelCase					      =  [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCamelCase					      =  {
    0: """Sunday""",
    1: """Monday""",
    2: """Tuesday""",
    3: """Wednesday""",
    4: """Thursday""",
    5: """Friday""",
    6: """Saturday""",
}

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      assert len(str(_snake_case							)							) > 2, "year should be in YYYY format"
      assert 1 <= month <= 12, "month should be between 1 to 12"
      assert 1 <= day <= 31, "day should be between 1 to 31"

      # Doomsday algorithm:
      UpperCAmelCase          =	year // 100
      UpperCAmelCase          =	(5 * (century % 4) + 2) % 7
      UpperCAmelCase          =	year % 100
      UpperCAmelCase          =	centurian % 12
      UpperCAmelCase          =	(
          (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
      ) % 7
      UpperCAmelCase          =	(
          DOOMSDAY_NOT_LEAP[month - 1]
          if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
          else DOOMSDAY_LEAP[month - 1]
      )
      UpperCAmelCase          =	(dooms_day + day - day_anchor) % 7
      return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
  import doctest

  doctest.testmod()



