



import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
	import bitsandbytes as bnb
	import torch
	import torch.nn as nn

	from ..pytorch_utils import ConvaD

if is_accelerate_available():
	from accelerate import init_empty_weights
	from accelerate.utils import find_tied_parameters

UpperCAmelCase							 =					logging.get_logger(__name__)

def 			A_      (	__a     :				int ,		__a     :				Union[str, Any] ,		__a     :				Optional[Any] ,		__a     :				str=None ,		__a     :				Optional[Any]=None		):

				"""simple docstring"""
				# Recurse if needed
				if "." in tensor_name:
								a__				      =		tensor_name.split("""."""		)
								for split in splits[:-1]:
												a__				      =		getattr(__a ,		__a		)
												if new_module is None:
																raise ValueError(F'''{module} has no attribute {split}.'''		)
												a__				      =		new_module
								a__				      =		splits[-1]

				if tensor_name not in module._parameters and tensor_name not in module._buffers:
								raise ValueError(F'''{module} does not have a parameter or a buffer named {tensor_name}.'''		)
				a__				      =		tensor_name in module._buffers
				a__				      =		getattr(__a ,		__a		)

				if old_value.device == torch.device("""meta"""		) and device not in ["meta", torch.device("""meta"""		)] and value is None:
								raise ValueError(F'''{tensor_name} is on the meta device, we need a `value` to put in on {device}.'''		)

				a__				      =		False
				a__				      =		False
				if is_buffer or not is_bitsandbytes_available():
								a__				      =		False
								a__				      =		False
				else:
								a__				      =		hasattr(bnb.nn ,		"""Params4bit"""		) and isinstance(module._parameters[tensor_name] ,		bnb.nn.Paramsabit		)
								a__				      =		isinstance(module._parameters[tensor_name] ,		bnb.nn.IntaParams		)

				if is_abit or is_abit:
								a__				      =		module._parameters[tensor_name]
								if param.device.type != "cuda":
												if value is None:
																a__				      =		old_value.to(__a		)
												elif isinstance(__a ,		torch.Tensor		):
																a__				      =		value.to("""cpu"""		)
																if value.dtype == torch.inta:
																				a__				      =		version.parse(importlib.metadata.version("""bitsandbytes"""		)		) > version.parse(
																				    """0.37.2"""		)
																				if not is_abit_serializable:
																								raise ValueError(
																								    """Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. """
																								    """Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`."""		)
												else:
																a__				      =		torch.tensor(__a ,		device="""cpu"""		)

												# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
												# Since weights are saved in the correct "orientation", we skip transposing when loading.
												if issubclass(module.source_cls ,		__a		) and fpaa_statistics is None:
																a__				      =		new_value.T

												a__				      =		old_value.__dict__
												if is_abit:
																a__				      =		bnb.nn.IntaParams(__a ,		requires_grad=__a ,		**__a		).to(__a		)
												elif is_abit:
																a__				      =		bnb.nn.Paramsabit(__a ,		requires_grad=__a ,		**__a		).to(__a		)

												a__				      =		new_value
												if fpaa_statistics is not None:
																setattr(module.weight ,		"""SCB""" ,		fpaa_statistics.to(__a		)		)

				else:
								if value is None:
												a__				      =		old_value.to(__a		)
								elif isinstance(__a ,		torch.Tensor		):
												a__				      =		value.to(__a		)
								else:
												a__				      =		torch.tensor(__a ,		device=__a		)

								if is_buffer:
												a__				      =		new_value
								else:
												a__				      =		nn.Parameter(__a ,		requires_grad=old_value.requires_grad		)
												a__				      =		new_value

def 			A_      (	__a     :				Optional[Any] ,		__a     :				List[Any]=None ,		__a     :				Any=None ,		__a     :				List[Any]=None ,		__a     :				Optional[int]=False		):

				"""simple docstring"""
				for name, module in model.named_children():
								if current_key_name is None:
												a__				      =		[]
								current_key_name.append(__a		)

								if (isinstance(__a ,		nn.Linear		) or isinstance(__a ,		__a		)) and name not in modules_to_not_convert:
												# Check if the current key is not in the `modules_to_not_convert`
												if not any(key in """.""".join(__a		) for key in modules_to_not_convert		):
																with init_empty_weights():
																				if isinstance(__a ,		__a		):
																								a__      ,			a__				      =		module.weight.shape
																				else:
																								a__				      =		module.in_features
																								a__				      =		module.out_features

																				if quantization_config.quantization_method() == "llm_int8":
																								a__				      =		bnb.nn.LinearabitLt(
																								    __a ,		__a ,		module.bias is not None ,		has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight ,		threshold=quantization_config.llm_inta_threshold ,		)
																								a__				      =		True
																				else:
																								if (
																								    quantization_config.llm_inta_skip_modules is not None
																								    and name in quantization_config.llm_inta_skip_modules
																								):
																												pass
																								else:
																												a__				      =		bnb.nn.Linearabit(
																												    __a ,		__a ,		module.bias is not None ,		quantization_config.bnb_abit_compute_dtype ,		compress_statistics=quantization_config.bnb_abit_use_double_quant ,		quant_type=quantization_config.bnb_abit_quant_type ,		)
																												a__				      =		True
                    # Store the module class in case we need to transpose the weight later
																				a__				      =		type(__a		)
																				# Force requires grad to False to avoid unexpected errors
																				model._modules[name].requires_grad_(__a		)
								if len(list(module.children()		)		) > 0:
												a__      ,			a__				      =		_replace_with_bnb_linear(
												    __a ,		__a ,		__a ,		__a ,		has_been_replaced=__a ,		)
								# Remove the last key for recursion
								current_key_name.pop(-1		)
				return model, has_been_replaced

def 			A_      (	__a     :				Union[str, Any] ,		__a     :				Any=None ,		__a     :				List[str]=None ,		__a     :				Union[str, Any]=None		):

				"""simple docstring"""
				a__				      =		["""lm_head"""] if modules_to_not_convert is None else modules_to_not_convert
				a__      ,			a__				      =		_replace_with_bnb_linear(
				    __a ,		__a ,		__a ,		__a		)

				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."""
								    """ Please double check your model architecture, or submit an issue on github if you think this is"""
								    """ a bug."""		)

				return model

def 			A_      (	*__a     :				Any ,		**__a     :				List[Any]		):

				"""simple docstring"""
				warnings.warn(
				    """`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead""" ,		__a ,		)
				return replace_with_bnb_linear(*__a ,		**__a		)

def 			A_      (	*__a     :				Optional[Any] ,		**__a     :				Any		):

				"""simple docstring"""
				warnings.warn(
				    """`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead""" ,		__a ,		)
				return set_module_quantized_tensor_to_device(*__a ,		**__a		)




def 			A_      (	__a     :				Optional[int]		):

				"""simple docstring"""
				a__				      =		deepcopy(__a		)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
				tied_model.tie_weights()

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

				# Check if it is a base model
				a__				      =		not hasattr(__a ,		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
				a__				      =		list(model.named_children()		)
				a__				      =		[list_modules[-1][0]]

				# add last module together with tied weights
				a__				      =		set(__a		) - set(__a		)
				a__				      =		list(set(__a		)		) + list(__a		)

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

				return filtered_module_names






# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


UpperCAmelCase							 =					"""3"""

print("""Python version:""", sys.version)
print("""transformers version:""", transformers.__version__)

try:
	import torch

	print("""Torch version:""", torch.__version__)
	print("""Cuda available:""", torch.cuda.is_available())
	print("""Cuda version:""", torch.version.cuda)
	print("""CuDNN version:""", torch.backends.cudnn.version())
	print("""Number of GPUs available:""", torch.cuda.device_count())
	print("""NCCL version:""", torch.cuda.nccl.version())
except ImportError:
	print("""Torch version:""", None)

try:
	import deepspeed

	print("""DeepSpeed version:""", deepspeed.__version__)
except ImportError:
	print("""DeepSpeed version:""", None)

try:
	import tensorflow as tf

	print("""TensorFlow version:""", tf.__version__)
	print("""TF GPUs available:""", bool(tf.config.list_physical_devices("""GPU""")))
	print("""Number of TF GPUs available:""", len(tf.config.list_physical_devices("""GPU""")))
except ImportError:
	print("""TensorFlow version:""", None)

