


"""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    :     int   						=		logging.get_logger(__name__)
def      lowerCamelCase       (     _UpperCamelCase		:      Any       ,			_UpperCamelCase		:      int       ,			_UpperCamelCase		:      Optional[Any]       ,			_UpperCamelCase		:      Dict=False							)		->       int:

							'''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   :						Union[str, Any]      							=					os.path.abspath(_UpperCamelCase							)
														logger.info(f'''Loading PyTorch weights from {pt_path}'''							)

														__UpperCAmelCase   :						Tuple      							=					torch.load(_UpperCamelCase       ,			map_location="""cpu"""							)
														logger.info(f'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()							):,} parameters.'''							)

														__UpperCAmelCase   :						int      							=					convert_pytorch_state_dict_to_flax(_UpperCamelCase       ,			_UpperCamelCase							)
							else:
														# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
														__UpperCAmelCase   :						int      							=					convert_pytorch_sharded_state_dict_to_flax(_UpperCamelCase       ,			_UpperCamelCase							)
							return flax_state_dict
def      lowerCamelCase       (     _UpperCamelCase		:      Tuple[str]       ,			_UpperCamelCase		:      np.ndarray       ,			_UpperCamelCase		:      Dict[str, jnp.ndarray]       ,			_UpperCamelCase		:      str       ,			)		->       (Tuple[str], np.ndarray):

							'''simple docstring'''



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

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

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

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

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

							# conv layer
							__UpperCAmelCase   :						List[str]      							=					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(_UpperCamelCase							):
														__UpperCAmelCase   :						Tuple      							=					pt_tensor.transpose(2       ,			3       ,			1       ,			0							)
														return renamed_pt_tuple_key, pt_tensor

							# linear layer
							__UpperCAmelCase   :						int      							=					pt_tuple_key[:-1] + ("""kernel""",)
							if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_UpperCamelCase							):
														__UpperCAmelCase   :						Optional[Any]      							=					pt_tensor.T
														return renamed_pt_tuple_key, pt_tensor

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

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

							return pt_tuple_key, pt_tensor
def      lowerCamelCase       (     _UpperCamelCase		:      Optional[Any]       ,			_UpperCamelCase		:      List[str]							)		->       str:

							'''simple docstring'''



							__UpperCAmelCase   :						Optional[Any]      							=					{k: v.numpy() for k, v in pt_state_dict.items()}

							__UpperCAmelCase   :						List[Any]      							=					flax_model.base_model_prefix

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

							# add batch_stats keys,values to dict
							if "batch_stats" in flax_model.params:
														__UpperCAmelCase   :						List[str]      							=					flatten_dict(flax_model.params["""batch_stats"""]							)
														random_flax_state_dict.update(_UpperCamelCase							)

							__UpperCAmelCase   :						Optional[int]      							=					{}

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

														# remove base model prefix if necessary
														__UpperCAmelCase   :						List[str]      							=					pt_tuple_key[0] == model_prefix
														if load_model_with_head_into_base_model and has_base_model_prefix:
																					__UpperCAmelCase   :						int      							=					pt_tuple_key[1:]

														# Correctly rename weight parameters
														__UpperCAmelCase							,__UpperCAmelCase   :						Any      							=					rename_key_and_reshape_tensor(
														    _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase							)

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

																					# also add unexpected weight so that warning is thrown
																					__UpperCAmelCase   :						List[str]      							=					jnp.asarray(_UpperCamelCase							)

														else:
																					# also add unexpected weight so that warning is thrown
																					__UpperCAmelCase   :						Optional[int]      							=					jnp.asarray(_UpperCamelCase							)

							return unflatten_dict(_UpperCamelCase							)
def      lowerCamelCase       (     _UpperCamelCase		:      Tuple       ,			_UpperCamelCase		:      List[str]							)		->       Any:

							'''simple docstring'''



							import torch

							# Load the index
							__UpperCAmelCase   :						Dict      							=					{}
							for shard_file in shard_filenames:
														# load using msgpack utils
														__UpperCAmelCase   :						Any      							=					torch.load(_UpperCamelCase							)
														__UpperCAmelCase   :						Tuple      							=					{k: v.numpy() for k, v in pt_state_dict.items()}

														__UpperCAmelCase   :						Tuple      							=					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   :						int      							=					flax_model.params["""params"""]

																					__UpperCAmelCase   :						Tuple      							=					flatten_dict(_UpperCamelCase							)
																					random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]							)							)
														else:
																					__UpperCAmelCase   :						Any      							=					flax_model.params
																					__UpperCAmelCase   :						str      							=					flatten_dict(_UpperCamelCase							)

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

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

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

																												# also add unexpected weight so that warning is thrown
																												__UpperCAmelCase   :						List[str]      							=					jnp.asarray(_UpperCamelCase							)

																					else:
																												# also add unexpected weight so that warning is thrown
																												__UpperCAmelCase   :						Union[str, Any]      							=					jnp.asarray(_UpperCamelCase							)
							return unflatten_dict(_UpperCamelCase							)
def      lowerCamelCase       (     _UpperCamelCase		:      Optional[Any]       ,			_UpperCamelCase		:      int							)		->       int:

							'''simple docstring'''



							__UpperCAmelCase   :						Optional[int]      							=					os.path.abspath(_UpperCamelCase							)
							logger.info(f'''Loading Flax weights from {flax_checkpoint_path}'''							)

							# import correct flax class
							__UpperCAmelCase   :						Optional[Any]      							=					getattr(_UpperCamelCase       ,			"""Flax""" + model.__class__.__name__							)

							# load flax weight dict
							with open(_UpperCamelCase       ,			"""rb"""							) as state_f:
														try:
																					__UpperCAmelCase   :						int      							=					from_bytes(_UpperCamelCase       ,			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(_UpperCamelCase       ,			_UpperCamelCase							)
def      lowerCamelCase       (     _UpperCamelCase		:      List[Any]       ,			_UpperCamelCase		:      int							)		->       List[str]:

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

							__UpperCAmelCase   :						str      							=					flatten_dict(_UpperCamelCase							)
							__UpperCAmelCase   :						str      							=					pt_model.state_dict()

							__UpperCAmelCase   :						Optional[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()}
							)
							__UpperCAmelCase   :						Union[str, 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
							__UpperCAmelCase   :						List[str]      							=					[]
							__UpperCAmelCase   :						List[Any]      							=					set(pt_model_dict.keys()							)

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

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

														if "batch_stats" in flax_state:
																					__UpperCAmelCase   :						Union[str, Any]      							=					""".""".join(flax_key_tuple[1:]							)  # Remove the params/batch_stats header
														else:
																					__UpperCAmelCase   :						str      							=					""".""".join(_UpperCamelCase							)

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

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

							pt_model.load_state_dict(_UpperCamelCase							)

							# re-transform missing_keys to list
							__UpperCAmelCase   :						Optional[Any]      							=					list(_UpperCamelCase							)

							if len(_UpperCamelCase							) > 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(_UpperCamelCase							) > 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 os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
							import ctypes
							import msvcrt  # noqa





							class      lowerCamelCase__   (     ctypes.Structure				):







												"""simple docstring"""


												__a	       =							[("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def      lowerCamelCase       (     )		->       Optional[int]:

							'''simple docstring'''



							if os.name == "nt":
														__UpperCAmelCase   :						Dict      							=					CursorInfo()
														__UpperCAmelCase   :						Any      							=					ctypes.windll.kernelaa.GetStdHandle(-1_1							)
														ctypes.windll.kernelaa.GetConsoleCursorInfo(_UpperCamelCase       ,			ctypes.byref(_UpperCamelCase							)							)
														__UpperCAmelCase   :						Tuple      							=					False
														ctypes.windll.kernelaa.SetConsoleCursorInfo(_UpperCamelCase       ,			ctypes.byref(_UpperCamelCase							)							)
							elif os.name == "posix":
														sys.stdout.write("""\033[?25l"""							)
														sys.stdout.flush()
def      lowerCamelCase       (     )		->       Optional[int]:

							'''simple docstring'''



							if os.name == "nt":
														__UpperCAmelCase   :						str      							=					CursorInfo()
														__UpperCAmelCase   :						int      							=					ctypes.windll.kernelaa.GetStdHandle(-1_1							)
														ctypes.windll.kernelaa.GetConsoleCursorInfo(_UpperCamelCase       ,			ctypes.byref(_UpperCamelCase							)							)
														__UpperCAmelCase   :						Union[str, Any]      							=					True
														ctypes.windll.kernelaa.SetConsoleCursorInfo(_UpperCamelCase       ,			ctypes.byref(_UpperCamelCase							)							)
							elif os.name == "posix":
														sys.stdout.write("""\033[?25h"""							)
														sys.stdout.flush()
@contextmanager
def      lowerCamelCase       (     )		->       str:

							'''simple docstring'''



							try:
														hide_cursor()
														yield
							finally:
														show_cursor()
