import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
							import bitsandbytes as bnb

from copy import deepcopy


_lowerCamelCase   :				Tuple     			=						logging.getLogger(__name__)





def 		__lowerCamelCase							(UpperCAmelCase__       :							torch.nn.Module ,	UpperCAmelCase__       :							BnbQuantizationConfig ,	UpperCAmelCase__       :							Union[str, os.PathLike] = None ,	UpperCAmelCase__       :							Optional[Dict[str, Union[int, str, torch.device]]] = None ,	UpperCAmelCase__       :							Optional[List[str]] = None ,	UpperCAmelCase__       :							Optional[Dict[Union[int, str], Union[int, str]]] = None ,	UpperCAmelCase__       :							Optional[Union[str, os.PathLike]] = None ,	UpperCAmelCase__       :							bool = False ,	):
					SCREAMING_SNAKE_CASE           =    bnb_quantization_config.load_in_abit
					SCREAMING_SNAKE_CASE           =    bnb_quantization_config.load_in_abit

					if load_in_abit and not is_abit_bnb_available():
										raise ImportError(
										    "You have a version of `bitsandbytes` that is not compatible with 8bit quantization,"
										    " make sure you have the latest version of `bitsandbytes` installed."					)
					if load_in_abit and not is_abit_bnb_available():
										raise ValueError(
										    "You have a version of `bitsandbytes` that is not compatible with 4bit quantization,"
										    "make sure you have the latest version of `bitsandbytes` installed."					)

					SCREAMING_SNAKE_CASE           =    []
					# custom device map
					if isinstance(UpperCAmelCase__ ,	UpperCAmelCase__					) and len(device_map.keys()					) > 1:
										SCREAMING_SNAKE_CASE           =    [key for key, value in device_map.items() if value in ["disk", "cpu"]]

					# We keep some modules such as the lm_head in their original dtype for numerical stability reasons
					if bnb_quantization_config.skip_modules is None:
										SCREAMING_SNAKE_CASE           =    get_keys_to_not_convert(UpperCAmelCase__					)

					# add cpu modules to skip modules only for 4-bit modules
					if load_in_abit:
										bnb_quantization_config.skip_modules.extend(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    bnb_quantization_config.skip_modules

					# We add the modules we want to keep in full precision
					if bnb_quantization_config.keep_in_fpaa_modules is None:
										SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    bnb_quantization_config.keep_in_fpaa_modules
					modules_to_not_convert.extend(UpperCAmelCase__					)

					# compatibility with peft
					SCREAMING_SNAKE_CASE           =    load_in_abit
					SCREAMING_SNAKE_CASE           =    load_in_abit

					SCREAMING_SNAKE_CASE           =    get_parameter_device(UpperCAmelCase__					)
					if model_device.type != "meta":
										# quantization of an already loaded model
										logger.warning(
										    "It is not recommended to quantize a loaded model. "
										    "The model should be instantiated under the `init_empty_weights` context manager."					)
										SCREAMING_SNAKE_CASE           =    replace_with_bnb_layers(UpperCAmelCase__ ,	UpperCAmelCase__ ,	modules_to_not_convert=UpperCAmelCase__					)
										# convert param to the right dtype
										SCREAMING_SNAKE_CASE           =    bnb_quantization_config.torch_dtype
										for name, param in model.state_dict().items():
															if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules					):
																				param.to(torch.floataa					)
																				if param.dtype != torch.floataa:
																									SCREAMING_SNAKE_CASE           =    name.replace(".weight" ,	""					).replace(".bias" ,	""					)
																									SCREAMING_SNAKE_CASE           =    getattr(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
																									if param is not None:
																														param.to(torch.floataa					)
															elif torch.is_floating_point(UpperCAmelCase__					):
																				param.to(UpperCAmelCase__					)
										if model_device.type == "cuda":
															# move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
															model.cuda(torch.cuda.current_device()					)
															torch.cuda.empty_cache()
										elif torch.cuda.is_available():
															model.to(torch.cuda.current_device()					)
										else:
															raise RuntimeError("No GPU found. A GPU is needed for quantization."					)
										logger.info(
										    F"The model device type is {model_device.type}. However, cuda is needed for quantization."
										    "We move the model to cuda."					)
										return model

					elif weights_location is None:
										raise RuntimeError(
										    F"`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} "					)

					else:
										with init_empty_weights():
															SCREAMING_SNAKE_CASE           =    replace_with_bnb_layers(
															    UpperCAmelCase__ ,	UpperCAmelCase__ ,	modules_to_not_convert=UpperCAmelCase__					)

										SCREAMING_SNAKE_CASE           =    get_quantized_model_device_map(
										    UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	max_memory=UpperCAmelCase__ ,	no_split_module_classes=UpperCAmelCase__ ,	)
										if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
															SCREAMING_SNAKE_CASE           =    True

										SCREAMING_SNAKE_CASE           =    any(x in list(device_map.values()					) for x in ["cpu", "disk"]					)

										load_checkpoint_in_model(
										    UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	dtype=bnb_quantization_config.torch_dtype ,	offload_folder=UpperCAmelCase__ ,	offload_state_dict=UpperCAmelCase__ ,	keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules ,	offload_abit_bnb=load_in_abit and offload ,	)
										return dispatch_model(UpperCAmelCase__ ,	device_map=UpperCAmelCase__ ,	offload_dir=UpperCAmelCase__					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							str=None ,	UpperCAmelCase__       :							str=None ,	UpperCAmelCase__       :							Dict=None					):
					if device_map is None:
										if torch.cuda.is_available():
															SCREAMING_SNAKE_CASE           =    {"": torch.cuda.current_device()}
										else:
															raise RuntimeError("No GPU found. A GPU is needed for quantization."					)
										logger.info("The device_map was not initialized." "Setting device_map to `{'':torch.cuda.current_device()}`."					)

					if isinstance(UpperCAmelCase__ ,	UpperCAmelCase__					):
										if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
															raise ValueError(
															    "If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or "
															    "'sequential'."					)

										SCREAMING_SNAKE_CASE           =    {}
										special_dtypes.update(
										    {
										        name: bnb_quantization_config.torch_dtype
										        for name, _ in model.named_parameters()
										        if any(m in name for m in bnb_quantization_config.skip_modules					)
										    }					)
										special_dtypes.update(
										    {
										        name: torch.floataa
										        for name, _ in model.named_parameters()
										        if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules					)
										    }					)

										SCREAMING_SNAKE_CASE           =    {}
										SCREAMING_SNAKE_CASE           =    special_dtypes
										SCREAMING_SNAKE_CASE           =    no_split_module_classes
										SCREAMING_SNAKE_CASE           =    bnb_quantization_config.target_dtype

										# get max_memory for each device.
										if device_map != "sequential":
															SCREAMING_SNAKE_CASE           =    get_balanced_memory(
															    UpperCAmelCase__ ,	low_zero=(device_map == "balanced_low_0") ,	max_memory=UpperCAmelCase__ ,	**UpperCAmelCase__ ,	)

										SCREAMING_SNAKE_CASE           =    max_memory
										SCREAMING_SNAKE_CASE           =    infer_auto_device_map(UpperCAmelCase__ ,	**UpperCAmelCase__					)

					if isinstance(UpperCAmelCase__ ,	UpperCAmelCase__					):
										# check if don't have any quantized module on the cpu
										SCREAMING_SNAKE_CASE           =    bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

										SCREAMING_SNAKE_CASE           =    {
										    key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
										}
										for device in ["cpu", "disk"]:
															if device in device_map_without_some_modules.values():
																				if bnb_quantization_config.load_in_abit:
																									raise ValueError(
																									    "\n                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n                        these modules in `torch_dtype`, you need to pass a custom `device_map` to\n                        `load_and_quantize_model`. Check\n                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n                        for more details.\n                        "					)
																				else:
																									logger.info(
																									    "Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit"					)
										del device_map_without_some_modules
					return device_map





def 		__lowerCamelCase							(UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							Optional[int]=None ,	UpperCAmelCase__       :							Tuple=None					):

					if modules_to_not_convert is None:
										SCREAMING_SNAKE_CASE           =    []

					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    _replace_with_bnb_layers(
					    UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
					if not has_been_replaced:
										logger.warning(
										    "You are loading your model in 8bit or 4bit but no linear modules were found in your model."
										    " this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers."
										    " Please double check your model architecture, or submit an issue on github if you think this is"
										    " a bug."					)
					return model





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							Union[str, Any]=None ,	UpperCAmelCase__       :							str=None ,	):
					SCREAMING_SNAKE_CASE           =    False
					for name, module in model.named_children():
										if current_key_name is None:
															SCREAMING_SNAKE_CASE           =    []
										current_key_name.append(UpperCAmelCase__					)
										if isinstance(UpperCAmelCase__ ,	nn.Linear					) and name not in modules_to_not_convert:
															# Check if the current key is not in the `modules_to_not_convert`
															SCREAMING_SNAKE_CASE           =    ".".join(UpperCAmelCase__					)
															SCREAMING_SNAKE_CASE           =    True
															for key in modules_to_not_convert:
																				if (
																				    (key in current_key_name_str) and (key + "." in current_key_name_str)
																				) or key == current_key_name_str:
																									SCREAMING_SNAKE_CASE           =    False
																									break
															if proceed:
																				# Load bnb module with empty weight and replace ``nn.Linear` module
																				if bnb_quantization_config.load_in_abit:
																									SCREAMING_SNAKE_CASE           =    bnb.nn.LinearabitLt(
																									    module.in_features ,	module.out_features ,	module.bias is not None ,	has_fpaa_weights=UpperCAmelCase__ ,	threshold=bnb_quantization_config.llm_inta_threshold ,	)
																				elif bnb_quantization_config.load_in_abit:
																									SCREAMING_SNAKE_CASE           =    bnb.nn.Linearabit(
																									    module.in_features ,	module.out_features ,	module.bias is not None ,	bnb_quantization_config.bnb_abit_compute_dtype ,	compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant ,	quant_type=bnb_quantization_config.bnb_abit_quant_type ,	)
																				else:
																									raise ValueError("load_in_8bit and load_in_4bit can't be both False"					)
																				SCREAMING_SNAKE_CASE           =    module.weight.data
																				if module.bias is not None:
																									SCREAMING_SNAKE_CASE           =    module.bias.data
																				bnb_module.requires_grad_(UpperCAmelCase__					)
																				setattr(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
																				SCREAMING_SNAKE_CASE           =    True
										if len(list(module.children()					)					) > 0:
															SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    _replace_with_bnb_layers(
															    UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
															SCREAMING_SNAKE_CASE           =    has_been_replaced | _has_been_replaced
										# Remove the last key for recursion
										current_key_name.pop(-1					)
					return model, has_been_replaced





def 		__lowerCamelCase							(UpperCAmelCase__       :							Union[str, Any]					):
					# Create a copy of the model
					with init_empty_weights():
										SCREAMING_SNAKE_CASE           =    deepcopy(UpperCAmelCase__					)  # this has 0 cost since it is done inside `init_empty_weights` context manager`

					SCREAMING_SNAKE_CASE           =    find_tied_parameters(UpperCAmelCase__					)
					# For compatibility with Accelerate < 0.18
					if isinstance(UpperCAmelCase__ ,	UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    sum(list(tied_params.values()					) ,	[]					) + list(tied_params.keys()					)
					else:
										SCREAMING_SNAKE_CASE           =    sum(UpperCAmelCase__ ,	[]					)
					SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					) > 0

					# Check if it is a base model
					SCREAMING_SNAKE_CASE           =    False
					if hasattr(UpperCAmelCase__ ,	"base_model_prefix"					):
										SCREAMING_SNAKE_CASE           =    not hasattr(UpperCAmelCase__ ,	model.base_model_prefix					)

					# Ignore this for base models (BertModel, GPT2Model, etc.)
					if (not has_tied_params) and is_base_model:
										return []

					# otherwise they have an attached head
					SCREAMING_SNAKE_CASE           =    list(model.named_children()					)
					SCREAMING_SNAKE_CASE           =    [list_modules[-1][0]]

					# add last module together with tied weights
					SCREAMING_SNAKE_CASE           =    set(UpperCAmelCase__					) - set(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    list(set(UpperCAmelCase__					)					) + list(UpperCAmelCase__					)

					# remove ".weight" from the keys
					SCREAMING_SNAKE_CASE           =    [".weight", ".bias"]
					SCREAMING_SNAKE_CASE           =    []
					for name in list_untouched:
										for name_to_remove in names_to_remove:
															if name_to_remove in name:
																				SCREAMING_SNAKE_CASE           =    name.replace(UpperCAmelCase__ ,	""					)
										filtered_module_names.append(UpperCAmelCase__					)

					return filtered_module_names





def 		__lowerCamelCase							(UpperCAmelCase__       :							int					):
					for m in model.modules():
										if isinstance(UpperCAmelCase__ ,	bnb.nn.Linearabit					):
															return True
					return False





def 		__lowerCamelCase							(UpperCAmelCase__       :							nn.Module					):
					return next(parameter.parameters()					).device





def 		__lowerCamelCase							(UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[int]					):
					# if it is not quantized, we quantize and offload the quantized weights and the SCB stats
					if fpaa_statistics is None:
										set_module_tensor_to_device(UpperCAmelCase__ ,	UpperCAmelCase__ ,	0 ,	dtype=UpperCAmelCase__ ,	value=UpperCAmelCase__					)
										SCREAMING_SNAKE_CASE           =    param_name
										SCREAMING_SNAKE_CASE           =    model
										if "." in tensor_name:
															SCREAMING_SNAKE_CASE           =    tensor_name.split("."					)
															for split in splits[:-1]:
																				SCREAMING_SNAKE_CASE           =    getattr(UpperCAmelCase__ ,	UpperCAmelCase__					)
																				if new_module is None:
																									raise ValueError(F"{module} has no attribute {split}."					)
																				SCREAMING_SNAKE_CASE           =    new_module
															SCREAMING_SNAKE_CASE           =    splits[-1]
										# offload weights
										SCREAMING_SNAKE_CASE           =    False
										offload_weight(module._parameters[tensor_name] ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	index=UpperCAmelCase__					)
										if hasattr(module._parameters[tensor_name] ,	"SCB"					):
															offload_weight(
															    module._parameters[tensor_name].SCB ,	param_name.replace("weight" ,	"SCB"					) ,	UpperCAmelCase__ ,	index=UpperCAmelCase__ ,	)
					else:
										offload_weight(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	index=UpperCAmelCase__					)
										offload_weight(UpperCAmelCase__ ,	param_name.replace("weight" ,	"SCB"					) ,	UpperCAmelCase__ ,	index=UpperCAmelCase__					)

					set_module_tensor_to_device(UpperCAmelCase__ ,	UpperCAmelCase__ ,	"meta" ,	dtype=UpperCAmelCase__ ,	value=torch.empty(*param.size()					)					)





from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


_lowerCamelCase   :				Optional[Any]     			=						logging.get_logger(__name__)





def 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							Dict					):
					try:
										with open(UpperCAmelCase__ ,	"rb"					) as flax_state_f:
															SCREAMING_SNAKE_CASE           =    from_bytes(UpperCAmelCase__ ,	flax_state_f.read()					)
					except UnpicklingError as e:
										try:
															with open(UpperCAmelCase__					) as f:
																				if f.read().startswith("version"					):
																									raise OSError(
																									    "You seem to have cloned a repository without having git-lfs installed. Please"
																									    " install git-lfs and run `git lfs install` followed by `git lfs pull` in the"
																									    " folder you cloned."					)
																				else:
																									raise ValueError from e
										except (UnicodeDecodeError, ValueError):
															raise EnvironmentError(F"Unable to convert {model_file} to Flax deserializable object. "					)

					return load_flax_weights_in_pytorch_model(UpperCAmelCase__ ,	UpperCAmelCase__					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[Any]					):

					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    "Loading 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
					SCREAMING_SNAKE_CASE           =    flatten_dict(jax.tree_util.tree_map(lambda UpperCAmelCase__					: x.dtype == jnp.bfloataa ,	UpperCAmelCase__					)					).values()
					if any(UpperCAmelCase__					):
										# convert all weights to fp32 if they 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."					)
										SCREAMING_SNAKE_CASE           =    jax.tree_util.tree_map(
										    lambda UpperCAmelCase__					: params.astype(np.floataa					) if params.dtype == jnp.bfloataa else params ,	UpperCAmelCase__					)

					SCREAMING_SNAKE_CASE           =    ""

					SCREAMING_SNAKE_CASE           =    flatten_dict(UpperCAmelCase__ ,	sep="."					)
					SCREAMING_SNAKE_CASE           =    pt_model.state_dict()

					# keep track of unexpected & missing keys
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    set(pt_model_dict.keys()					)

					for flax_key_tuple, flax_tensor in flax_state_dict.items():
										SCREAMING_SNAKE_CASE           =    flax_key_tuple.split("."					)

										if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
															SCREAMING_SNAKE_CASE           =    flax_key_tuple_array[:-1] + ["weight"]
															SCREAMING_SNAKE_CASE           =    jnp.transpose(UpperCAmelCase__ ,	(3, 2, 0, 1)					)
										elif flax_key_tuple_array[-1] == "kernel":
															SCREAMING_SNAKE_CASE           =    flax_key_tuple_array[:-1] + ["weight"]
															SCREAMING_SNAKE_CASE           =    flax_tensor.T
										elif flax_key_tuple_array[-1] == "scale":
															SCREAMING_SNAKE_CASE           =    flax_key_tuple_array[:-1] + ["weight"]

										if "time_embedding" not in flax_key_tuple_array:
															for i, flax_key_tuple_string in enumerate(UpperCAmelCase__					):
																				SCREAMING_SNAKE_CASE           =    (
																				    flax_key_tuple_string.replace("_0" ,	".0"					)
																				    .replace("_1" ,	".1"					)
																				    .replace("_2" ,	".2"					)
																				    .replace("_3" ,	".3"					)
																				    .replace("_4" ,	".4"					)
																				    .replace("_5" ,	".5"					)
																				    .replace("_6" ,	".6"					)
																				    .replace("_7" ,	".7"					)
																				    .replace("_8" ,	".8"					)
																				    .replace("_9" ,	".9"					)
																				)

										SCREAMING_SNAKE_CASE           =    ".".join(UpperCAmelCase__					)

										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
																				SCREAMING_SNAKE_CASE           =    np.asarray(UpperCAmelCase__					) if not isinstance(UpperCAmelCase__ ,	np.ndarray					) else flax_tensor
																				SCREAMING_SNAKE_CASE           =    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
					SCREAMING_SNAKE_CASE           =    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)."					)
					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."					)

					return pt_model




