



'''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__:     Union[str, Any]										=    logging.get_logger(__name__)


def 				snake_case_  (      _lowerCAmelCase						:		Any     ,   _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		Dict     ,   _lowerCAmelCase						:		Optional[Any]=False						)   ->     Optional[Any]:
							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				: Optional[int]											=	os.path.abspath(_lowerCAmelCase						)
														logger.info(f"""Loading PyTorch weights from {pt_path}"""						)

														UpperCAmelCase				: Union[str, Any]											=	torch.load(_lowerCAmelCase     ,   map_location='''cpu'''						)
														logger.info(f"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()						):,} parameters."""						)

														UpperCAmelCase				: 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
														UpperCAmelCase				: int											=	convert_pytorch_sharded_state_dict_to_flax(_lowerCAmelCase     ,   _lowerCAmelCase						)
							return flax_state_dict


def 				snake_case_  (      _lowerCAmelCase						:		Tuple[str]     ,   _lowerCAmelCase						:		np.ndarray     ,   _lowerCAmelCase						:		Dict[str, jnp.ndarray]     ,   _lowerCAmelCase						:		str     ,   )   ->     (Tuple[str], np.ndarray):

							def is_key_or_prefix_key_in_dict(_lowerCAmelCase						:		Tuple[str]						) -> bool:
														return len(set(_lowerCAmelCase						) & {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(_lowerCAmelCase						):
														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(_lowerCAmelCase						):
														return renamed_pt_tuple_key, pt_tensor

							# batch norm layer var
							UpperCAmelCase				: Tuple											=	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
							UpperCAmelCase				: Optional[int]											=	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
							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(_lowerCAmelCase						):
														UpperCAmelCase				: List[str]											=	pt_tensor.transpose(2     ,   3     ,   1     ,   0						)
														return renamed_pt_tuple_key, pt_tensor

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

							# old PyTorch layer norm weight
							UpperCAmelCase				: 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
							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				: Any											=	None
							if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
														UpperCAmelCase				: List[Any]											=	pt_tuple_key[-2] + '''_g'''
							elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
														UpperCAmelCase				: Optional[Any]											=	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 				snake_case_  (      _lowerCAmelCase						:		List[Any]     ,   _lowerCAmelCase						:		Tuple						)   ->     int:
							# convert pytorch tensor to numpy
							UpperCAmelCase				: Dict											=	{k: v.numpy() for k, v in pt_state_dict.items()}

							UpperCAmelCase				: str											=	flax_model.base_model_prefix

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

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

							UpperCAmelCase				: Tuple											=	{}

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

														# remove base model prefix if necessary
														UpperCAmelCase				: Union[str, Any]											=	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				: Tuple											=	rename_key_and_reshape_tensor(
														    _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						)

														# add model prefix if necessary
														UpperCAmelCase				: List[str]											=	(model_prefix,) + flax_key in random_flax_state_dict
														if load_base_model_into_model_with_head and require_base_model_prefix:
																					UpperCAmelCase				: Optional[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] or "var" in flax_key[-1]:
																												UpperCAmelCase				: Dict											=	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
																					UpperCAmelCase				: Tuple											=	jnp.asarray(_lowerCAmelCase						)

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

							return unflatten_dict(_lowerCAmelCase						)


def 				snake_case_  (      _lowerCAmelCase						:		Dict     ,   _lowerCAmelCase						:		str						)   ->     Dict:
							import torch

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

														UpperCAmelCase				: 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:
																					UpperCAmelCase				: int											=	flax_model.params['''params''']

																					UpperCAmelCase				: List[str]											=	flatten_dict(_lowerCAmelCase						)
																					random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']						)						)
														else:
																					UpperCAmelCase				: int											=	flax_model.params
																					UpperCAmelCase				: Dict											=	flatten_dict(_lowerCAmelCase						)

														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				: 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():
																					UpperCAmelCase				: Dict											=	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				: Any											=	pt_tuple_key[1:]

																					# Correctly rename weight parameters
																					UpperCAmelCase							,	UpperCAmelCase				: Any											=	rename_key_and_reshape_tensor(
																					    _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						)
																					# add model prefix if necessary
																					UpperCAmelCase				: List[Any]											=	(model_prefix,) + flax_key in random_flax_state_dict
																					if load_base_model_into_model_with_head and require_base_model_prefix:
																												UpperCAmelCase				: 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]:
																																			UpperCAmelCase				: str											=	jnp.asarray(_lowerCAmelCase						)
																																			continue
																												if "var" in flax_key[-1]:
																																			UpperCAmelCase				: List[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
																												UpperCAmelCase				: int											=	jnp.asarray(_lowerCAmelCase						)

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


def 				snake_case_  (      _lowerCAmelCase						:		Optional[int]     ,   _lowerCAmelCase						:		Optional[Any]						)   ->     List[Any]:
							UpperCAmelCase				: int											=	os.path.abspath(_lowerCAmelCase						)
							logger.info(f"""Loading Flax weights from {flax_checkpoint_path}"""						)

							# import correct flax class
							UpperCAmelCase				: List[str]											=	getattr(_lowerCAmelCase     ,   '''Flax''' + model.__class__.__name__						)

							# load flax weight dict
							with open(_lowerCAmelCase     ,   '''rb'''						) as state_f:
														try:
																					UpperCAmelCase				: List[str]											=	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 				snake_case_  (      _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		Optional[int]						)   ->     int:

							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 _lowerCAmelCase						: 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.'''						)
														UpperCAmelCase				: Dict											=	jax.tree_util.tree_map(
														    lambda _lowerCAmelCase						: params.astype(np.floataa						) if params.dtype == jnp.bfloataa else params     ,   _lowerCAmelCase						)

							UpperCAmelCase				: Optional[int]											=	flatten_dict(_lowerCAmelCase						)
							UpperCAmelCase				: int											=	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				: List[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				: Any											=	[]
							UpperCAmelCase				: Union[str, 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				: Union[str, 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:
																					UpperCAmelCase				: int											=	flax_key_tuple[1:]
														elif load_base_model_into_model_with_head and require_base_model_prefix:
																					UpperCAmelCase				: Tuple											=	(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
																					UpperCAmelCase				: Union[str, Any]											=	flax_key_tuple[:-1] + ('''weight''',)
																					UpperCAmelCase				: str											=	jnp.transpose(_lowerCAmelCase     ,   (3, 2, 0, 1)						)
														elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCAmelCase						) not in pt_model_dict:
																					# linear layer
																					UpperCAmelCase				: Union[str, Any]											=	flax_key_tuple[:-1] + ('''weight''',)
																					UpperCAmelCase				: Any											=	flax_tensor.T
														elif flax_key_tuple[-1] in ["scale", "embedding"]:
																					UpperCAmelCase				: Dict											=	flax_key_tuple[:-1] + ('''weight''',)

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

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

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






import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


UpperCamelCase__:     Dict										=    logging.get_logger(__name__)

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  Optional[Any]	,    *__snake_case    :  List[str]	,    **__snake_case    :  str	)    ->	None:
								warnings.warn(
								    '''The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
								    ''' use BeitImageProcessor instead.'''	,    __snake_case	,    )
								super().__init__(*__snake_case	,    **__snake_case	)





