

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


A_       : str         =			logging.get_logger(__name__)

def 						UpperCamelCase					(lowercase_: Optional[Any]    ,      lowercase_: Union[str, Any]    ,      lowercase_: Dict    ,      lowercase_: Tuple=False					)     ->      str:
	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__							:    Any        =		os.path.abspath(__lowerCamelCase					)
		logger.info(f"""Loading PyTorch weights from {pt_path}"""					)

		A__							:    Any        =		torch.load(__lowerCamelCase    ,      map_location="""cpu"""					)
		logger.info(f"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()					):,} parameters."""					)

		A__							:    Union[str, Any]        =		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__							:    Any        =		convert_pytorch_sharded_state_dict_to_flax(__lowerCamelCase    ,      __lowerCamelCase					)
	return flax_state_dict

def 						UpperCamelCase					(lowercase_: Union[str, Any]    ,      lowercase_: List[Any]    ,      lowercase_: List[Any]    ,      lowercase_: Union[str, Any]    ,      )     ->      Union[str, Any]:

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

	# layer norm
	A__							:    Optional[Any]        =		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__							:    List[Any]        =		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__							:    List[Any]        =		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__							:    Dict        =		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__							:    List[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(__lowerCamelCase					):
		A__							:    Optional[int]        =		pt_tensor.transpose(2    ,      3    ,      1    ,      0					)
		return renamed_pt_tuple_key, pt_tensor

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

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

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

	return pt_tuple_key, pt_tensor

def 						UpperCamelCase					(lowercase_: Tuple    ,      lowercase_: int					)     ->      Tuple:
	# convert pytorch tensor to numpy
	A__							:    List[Any]        =		{k: v.numpy() for k, v in pt_state_dict.items()}

	A__							:    str        =		flax_model.base_model_prefix

	# use params dict if the model contains batch norm layers
	if "params" in flax_model.params:
		A__							:    int        =		flax_model.params["params"]
	else:
		A__							:    int        =		flax_model.params
	A__							:    Tuple        =		flatten_dict(__lowerCamelCase					)

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

	A__							:    int        =		{}

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

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

		# Correctly rename weight parameters
		A__							:    List[Any]        =		rename_key_and_reshape_tensor(
		    __lowerCamelCase    ,      __lowerCamelCase    ,      __lowerCamelCase    ,      __lowerCamelCase					)

		# add model prefix if necessary
		A__							:    List[str]        =		(model_prefix,) + flax_key in random_flax_state_dict
		if load_base_model_into_model_with_head and require_base_model_prefix:
			A__							:    Tuple        =		(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__							:    Union[str, Any]        =		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__							:    Tuple        =		jnp.asarray(__lowerCamelCase					)

		else:
			# also add unexpected weight so that warning is thrown
			A__							:    Any        =		jnp.asarray(__lowerCamelCase					)

	return unflatten_dict(__lowerCamelCase					)

def 						UpperCamelCase					(lowercase_: List[str]    ,      lowercase_: Tuple					)     ->      Optional[Any]:
	import torch

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

		A__							:    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:
			A__							:    Dict        =		flax_model.params["params"]

			A__							:    Optional[Any]        =		flatten_dict(__lowerCamelCase					)
			random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]					)					)
		else:
			A__							:    Union[str, Any]        =		flax_model.params
			A__							:    str        =		flatten_dict(__lowerCamelCase					)

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

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

			# Correctly rename weight parameters
			A__							:    str        =		rename_key_and_reshape_tensor(
			    __lowerCamelCase    ,      __lowerCamelCase    ,      __lowerCamelCase    ,      __lowerCamelCase					)
			# add model prefix if necessary
			A__							:    Dict        =		(model_prefix,) + flax_key in random_flax_state_dict
			if load_base_model_into_model_with_head and require_base_model_prefix:
				A__							:    Optional[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]:
					A__							:    Tuple        =		jnp.asarray(__lowerCamelCase					)
					continue
				if "var" in flax_key[-1]:
					A__							:    str        =		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__							:    Tuple        =		jnp.asarray(__lowerCamelCase					)

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

def 						UpperCamelCase					(lowercase_: int    ,      lowercase_: List[Any]					)     ->      Dict:
	A__							:    Dict        =		os.path.abspath(__lowerCamelCase					)
	logger.info(f"""Loading Flax weights from {flax_checkpoint_path}"""					)

	# import correct flax class
	A__							:    List[Any]        =		getattr(__lowerCamelCase    ,      """Flax""" + model.__class__.__name__					)

	# load flax weight dict
	with open(__lowerCamelCase    ,      """rb"""					) as state_f:
		try:
			A__							:    List[Any]        =		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					(lowercase_: Union[str, Any]    ,      lowercase_: Optional[int]					)     ->      List[str]:

	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__							:    Optional[Any]        =		flatten_dict(jax.tree_util.tree_map(lambda lowercase_					: 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__							:    str        =		jax.tree_util.tree_map(
		    lambda lowercase_					: params.astype(np.floataa					) if params.dtype == jnp.bfloataa else params    ,      __lowerCamelCase					)

	A__							:    Dict        =		flatten_dict(__lowerCamelCase					)
	A__							:    Dict        =		pt_model.state_dict()

	A__							:    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()}
	)
	A__							:    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
	A__							:    List[Any]        =		[]
	A__							:    List[Any]        =		set(pt_model_dict.keys()					)

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

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

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

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

		if flax_key in special_pt_names:
			A__							:    Union[str, 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
				A__							:    Tuple        =		np.asarray(__lowerCamelCase					) if not isinstance(__lowerCamelCase    ,      np.ndarray					) else flax_tensor
				A__							:    Optional[int]        =		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__							:    Any        =		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"""





from __future__ import annotations







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   if (voltage, current, resistance).count(0  ) != 1:
      raise ValueError("One and only one argument must be 0"  )
   if resistance < 0:
      raise ValueError("Resistance cannot be negative"  )
   if voltage == 0:
      return {"voltage": float(current * resistance  )}
   elif current == 0:
      return {"current": voltage / resistance}
   elif resistance == 0:
      return {"resistance": voltage / current}
   else:
      raise ValueError("Exactly one argument must be 0"  )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


