



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    :						List[str]  =						logging.get_logger(__name__)
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase=False       )		->		Union[str, 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:
												A__	:							int						    = os.path.abspath(__lowercase       )
												logger.info(F'Loading PyTorch weights from {pt_path}'       )

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

												A__	:							int						    = convert_pytorch_state_dict_to_flax(__lowercase							,     __lowercase       )
						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(__lowercase							,     __lowercase       )
						return flax_state_dict
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     )		->		(Tuple[str], np.ndarray):







						"""simple docstring"""

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

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

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

						# embedding
						A__	:							List[str]						    = pt_tuple_key[:-1] + ("embedding",)
						if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__lowercase       ):
												return renamed_pt_tuple_key, pt_tensor

						# conv layer
						A__	:							int						    = 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(__lowercase       ):
												A__	:							Tuple						    = 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(__lowercase       ):
												A__	:							str						    = pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

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

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

						return pt_tuple_key, pt_tensor
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		Union[str, Any]:







						"""simple docstring"""

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

						A__	:							Union[str, Any]						    = flax_model.base_model_prefix

						# use params dict if the model contains batch norm layers
						if "params" in flax_model.params:
												A__	:							List[str]						    = flax_model.params["params"]
						else:
												A__	:							Union[str, Any]						    = flax_model.params
						A__	:							Union[str, Any]						    = flatten_dict(__lowercase       )

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

						A__	:							int						    = {}

						A__	:							Tuple						    = (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__	:							Dict						    = tuple(pt_key.split("."       )       )

												# remove base model prefix if necessary
												A__	:							List[Any]						    = 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__    ,       A__	:							List[str]						    = rename_key_and_reshape_tensor(
												    __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )

												# add model prefix if necessary
												A__	:							Union[str, Any]						    = (model_prefix,) + flax_key in random_flax_state_dict
												if load_base_model_into_model_with_head and require_base_model_prefix:
																		A__	:							Dict						    = (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__	:							Optional[Any]						    = jnp.asarray(__lowercase       )
																								continue
																		# remove num_batches_tracked key
																		if "num_batches_tracked" in flax_key[-1]:
																								flax_state_dict.pop(__lowercase							,     __lowercase       )
																								continue

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

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

						return unflatten_dict(__lowercase       )
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		int:







						"""simple docstring"""

						import torch

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

												A__	:							Optional[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__	:							Optional[Any]						    = flax_model.params["params"]

																		A__	:							List[str]						    = flatten_dict(__lowercase       )
																		random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]       )       )
												else:
																		A__	:							List[Any]						    = flax_model.params
																		A__	:							Optional[int]						    = flatten_dict(__lowercase       )

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

																								# also add unexpected weight so that warning is thrown
																								A__	:							Tuple						    = jnp.asarray(__lowercase       )

																		else:
																								# also add unexpected weight so that warning is thrown
																								A__	:							int						    = jnp.asarray(__lowercase       )
						return unflatten_dict(__lowercase       )
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		List[str]:







						"""simple docstring"""

						A__	:							int						    = os.path.abspath(__lowercase       )
						logger.info(F'Loading Flax weights from {flax_checkpoint_path}'       )

						# import correct flax class
						A__	:							Tuple						    = getattr(__lowercase							,     "Flax" + model.__class__.__name__       )

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




def 	snake_case__       (					__lowercase							,     __lowercase       )		->		Union[str, Any]:







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

						A__	:							Union[str, Any]						    = flatten_dict(__lowercase       )
						A__	:							List[Any]						    = pt_model.state_dict()

						A__	:							int						    = (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__	:							Any						    = (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__	:							Optional[Any]						    = []
						A__	:							str						    = set(pt_model_dict.keys()       )

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

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

												if "batch_stats" in flax_state:
																		A__	:							Tuple						    = ".".join(flax_key_tuple[1:]       )  # Remove the params/batch_stats header
												else:
																		A__	:							str						    = ".".join(__lowercase       )

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

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

						pt_model.load_state_dict(__lowercase       )

						# re-transform missing_keys to list
						A__	:							Any						    = list(__lowercase       )

						if len(__lowercase       ) > 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(__lowercase       ) > 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 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase = 0							,     __lowercase = 0       )		->		int:







						"""simple docstring"""

						A__	:							Tuple						    = right or len(__lowercase       ) - 1
						if left > right:
												return -1
						elif list_data[left] == key:
												return left
						elif list_data[right] == key:
												return right
						else:
												return search(__lowercase							,     __lowercase							,     left + 1							,     right - 1       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()