



"""simple docstring"""







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_   						=   logging.getLogger(__name__)



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase = None						,     __lowerCamelCase = None						,     __lowerCamelCase = None						,     __lowerCamelCase = None						,     __lowerCamelCase = None						,     __lowerCamelCase = False						,     )							->      int:
		lowercase__   :    Dict				=			bnb_quantization_config.load_in_abit
		lowercase__   :    int				=			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.'''	)

		lowercase__   :    List[str]				=			[]
		# custom device map
		if isinstance(__lowerCamelCase						,     __lowerCamelCase	) and len(device_map.keys()	) > 1:
				lowercase__   :    str				=			[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:
				lowercase__   :    Any				=			get_keys_to_not_convert(__lowerCamelCase	)

		# add cpu modules to skip modules only for 4-bit modules
		if load_in_abit:
				bnb_quantization_config.skip_modules.extend(__lowerCamelCase	)
		lowercase__   :    Union[str, Any]				=			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:
				lowercase__   :    Union[str, Any]				=			[]
		lowercase__   :    Optional[Any]				=			bnb_quantization_config.keep_in_fpaa_modules
		modules_to_not_convert.extend(__lowerCamelCase	)

		# compatibility with peft
		lowercase__   :    Optional[Any]				=			load_in_abit
		lowercase__   :    Optional[Any]				=			load_in_abit

		lowercase__   :    str				=			get_parameter_device(__lowerCamelCase	)
		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.'''	)
				lowercase__   :    Dict				=			replace_with_bnb_layers(__lowerCamelCase						,     __lowerCamelCase						,     modules_to_not_convert=__lowerCamelCase	)
				# convert param to the right dtype
				lowercase__   :    Dict				=			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:
										lowercase__   :    Any				=			name.replace('''.weight'''						,     ''''''	).replace('''.bias'''						,     ''''''	)
										lowercase__   :    Dict				=			getattr(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)
										if param is not None:
												param.to(torch.floataa	)
						elif torch.is_floating_point(__lowerCamelCase	):
								param.to(__lowerCamelCase	)
				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():
						lowercase__   :    int				=			replace_with_bnb_layers(
						    __lowerCamelCase						,     __lowerCamelCase						,     modules_to_not_convert=__lowerCamelCase	)

				lowercase__   :    List[str]				=			get_quantized_model_device_map(
				    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     max_memory=__lowerCamelCase						,     no_split_module_classes=__lowerCamelCase						,     )
				if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
						lowercase__   :    List[str]				=			True

				lowercase__   :    int				=			any(x in list(device_map.values()	) for x in ['''cpu''', '''disk''']	)

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



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase=None						,     __lowerCamelCase=None						,     __lowerCamelCase=None	)							->      int:
		if device_map is None:
				if torch.cuda.is_available():
						lowercase__   :    Optional[int]				=			{'''''': 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(__lowerCamelCase						,     __lowerCamelCase	):
				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\'.'''	)

				lowercase__   :    List[str]				=			{}
				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	)
				    }	)

				lowercase__   :    List[str]				=			{}
				lowercase__   :    Tuple				=			special_dtypes
				lowercase__   :    Optional[int]				=			no_split_module_classes
				lowercase__   :    int				=			bnb_quantization_config.target_dtype

				# get max_memory for each device.
				if device_map != "sequential":
						lowercase__   :    Optional[Any]				=			get_balanced_memory(
						    __lowerCamelCase						,     low_zero=(device_map == '''balanced_low_0''')						,     max_memory=__lowerCamelCase						,     **__lowerCamelCase						,     )

				lowercase__   :    int				=			max_memory
				lowercase__   :    List[Any]				=			infer_auto_device_map(__lowerCamelCase						,     **__lowerCamelCase	)

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

				lowercase__   :    Union[str, Any]				=			{
				    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(
										    '''
                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
                        these modules in `torch_dtype`, you need to pass a custom `device_map` to
                        `load_and_quantize_model`. Check
                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
                        for more details.
                        '''	)
								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      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase=None						,     __lowerCamelCase=None	)							->      str:

		if modules_to_not_convert is None:
				lowercase__   :    Optional[int]				=			[]

		lowercase__		,			lowercase__   :    Optional[Any]				=			_replace_with_bnb_layers(
		    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)
		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      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase=None						,     __lowerCamelCase=None						,     )							->      int:
		lowercase__   :    Optional[Any]				=			False
		for name, module in model.named_children():
				if current_key_name is None:
						lowercase__   :    Optional[int]				=			[]
				current_key_name.append(__lowerCamelCase	)
				if isinstance(__lowerCamelCase						,     nn.Linear	) and name not in modules_to_not_convert:
						# Check if the current key is not in the `modules_to_not_convert`
						lowercase__   :    Union[str, Any]				=			'''.'''.join(__lowerCamelCase	)
						lowercase__   :    Dict				=			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:
										lowercase__   :    int				=			False
										break
						if proceed:
								# Load bnb module with empty weight and replace ``nn.Linear` module
								if bnb_quantization_config.load_in_abit:
										lowercase__   :    Optional[int]				=			bnb.nn.LinearabitLt(
										    module.in_features						,     module.out_features						,     module.bias is not None						,     has_fpaa_weights=__lowerCamelCase						,     threshold=bnb_quantization_config.llm_inta_threshold						,     )
								elif bnb_quantization_config.load_in_abit:
										lowercase__   :    Dict				=			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'''	)
								lowercase__   :    List[str]				=			module.weight.data
								if module.bias is not None:
										lowercase__   :    Dict				=			module.bias.data
								bnb_module.requires_grad_(__lowerCamelCase	)
								setattr(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)
								lowercase__   :    Tuple				=			True
				if len(list(module.children()	)	) > 0:
						lowercase__		,			lowercase__   :    int				=			_replace_with_bnb_layers(
						    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)
						lowercase__   :    Union[str, Any]				=			has_been_replaced | _has_been_replaced
				# Remove the last key for recursion
				current_key_name.pop(-1	)
		return model, has_been_replaced



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      List[str]:
		# Create a copy of the model
		with init_empty_weights():
				lowercase__   :    int				=			deepcopy(__lowerCamelCase	)  # this has 0 cost since it is done inside `init_empty_weights` context manager`

		lowercase__   :    Optional[Any]				=			find_tied_parameters(__lowerCamelCase	)
		# For compatibility with Accelerate < 0.18
		if isinstance(__lowerCamelCase						,     __lowerCamelCase	):
				lowercase__   :    List[str]				=			sum(list(tied_params.values()	)						,     []	) + list(tied_params.keys()	)
		else:
				lowercase__   :    Optional[int]				=			sum(__lowerCamelCase						,     []	)
		lowercase__   :    List[str]				=			len(__lowerCamelCase	) > 0

		# Check if it is a base model
		lowercase__   :    Any				=			False
		if hasattr(__lowerCamelCase						,     '''base_model_prefix'''	):
				lowercase__   :    Optional[int]				=			not hasattr(__lowerCamelCase						,     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
		lowercase__   :    int				=			list(model.named_children()	)
		lowercase__   :    List[Any]				=			[list_modules[-1][0]]

		# add last module together with tied weights
		lowercase__   :    Union[str, Any]				=			set(__lowerCamelCase	) - set(__lowerCamelCase	)
		lowercase__   :    List[str]				=			list(set(__lowerCamelCase	)	) + list(__lowerCamelCase	)

		# remove ".weight" from the keys
		lowercase__   :    int				=			['''.weight''', '''.bias''']
		lowercase__   :    Optional[Any]				=			[]
		for name in list_untouched:
				for name_to_remove in names_to_remove:
						if name_to_remove in name:
								lowercase__   :    str				=			name.replace(__lowerCamelCase						,     ''''''	)
				filtered_module_names.append(__lowerCamelCase	)

		return filtered_module_names



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Dict:
		for m in model.modules():
				if isinstance(__lowerCamelCase						,     bnb.nn.Linearabit	):
						return True
		return False



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Dict:
		return next(parameter.parameters()	).device





def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      List[str]:
		# 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(__lowerCamelCase						,     __lowerCamelCase						,     0						,     dtype=__lowerCamelCase						,     value=__lowerCamelCase	)
				lowercase__   :    Any				=			param_name
				lowercase__   :    List[Any]				=			model
				if "." in tensor_name:
						lowercase__   :    Dict				=			tensor_name.split('''.'''	)
						for split in splits[:-1]:
								lowercase__   :    int				=			getattr(__lowerCamelCase						,     __lowerCamelCase	)
								if new_module is None:
										raise ValueError(f"""{module} has no attribute {split}."""	)
								lowercase__   :    int				=			new_module
						lowercase__   :    str				=			splits[-1]
				# offload weights
				lowercase__   :    Any				=			False
				offload_weight(module._parameters[tensor_name]						,     __lowerCamelCase						,     __lowerCamelCase						,     index=__lowerCamelCase	)
				if hasattr(module._parameters[tensor_name]						,     '''SCB'''	):
						offload_weight(
						    module._parameters[tensor_name].SCB						,     param_name.replace('''weight'''						,     '''SCB'''	)						,     __lowerCamelCase						,     index=__lowerCamelCase						,     )
		else:
				offload_weight(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     index=__lowerCamelCase	)
				offload_weight(__lowerCamelCase						,     param_name.replace('''weight'''						,     '''SCB'''	)						,     __lowerCamelCase						,     index=__lowerCamelCase	)

		set_module_tensor_to_device(__lowerCamelCase						,     __lowerCamelCase						,     '''meta'''						,     dtype=__lowerCamelCase						,     value=torch.empty(*param.size()	)	)





"""simple docstring"""







import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCAmelCase_   						=   version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
							from nltk import word_tokenize


lowerCAmelCase_   						=   '\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n'

lowerCAmelCase_   						=   '\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n'

lowerCAmelCase_   						=   '\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    \'meteor\': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric(\'meteor\')\n    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]\n    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results["meteor"], 4))\n    0.6944\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,_KWARGS_DESCRIPTION    )
class        __A       (	datasets.Metric    ):







		'''simple docstring'''





		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		str:






				"""simple docstring"""



				return datasets.MetricInfo(
				    description=_DESCRIPTION			,citation=_CITATION			,inputs_description=_KWARGS_DESCRIPTION			,features=datasets.Features(
				        {
				            '''predictions''': datasets.Value('''string'''			,id='''sequence'''						),
				            '''references''': datasets.Value('''string'''			,id='''sequence'''						),
				        }						)			,codebase_urls=['''https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py''']			,reference_urls=[
				        '''https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score''',
				        '''https://en.wikipedia.org/wiki/METEOR''',
				    ]			,)







		def 					UpperCAmelCase    (	self						:  str			,_snake_case						:  Dict						)					->		Dict:






				"""simple docstring"""



				import nltk

				nltk.download('''wordnet'''						)
				if NLTK_VERSION >= version.Version('''3.6.5'''						):
						nltk.download('''punkt'''						)
				if NLTK_VERSION >= version.Version('''3.6.6'''						):
						nltk.download('''omw-1.4'''						)







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  Dict			,_snake_case						:  List[str]			,_snake_case						:  Tuple=0.9			,_snake_case						:  Optional[int]=3			,_snake_case						:  Union[str, Any]=0.5						)					->		List[str]:






				"""simple docstring"""



				if NLTK_VERSION >= version.Version('''3.6.5'''						):
						lowercase__   :    int				=			[
						    meteor_score.single_meteor_score(
						        word_tokenize(_snake_case						)			,word_tokenize(_snake_case						)			,alpha=_snake_case			,beta=_snake_case			,gamma=_snake_case						)
						    for ref, pred in zip(_snake_case			,_snake_case						)
						]
				else:
						lowercase__   :    Tuple				=			[
						    meteor_score.single_meteor_score(_snake_case			,_snake_case			,alpha=_snake_case			,beta=_snake_case			,gamma=_snake_case						)
						    for ref, pred in zip(_snake_case			,_snake_case						)
						]

				return {"meteor": np.mean(_snake_case						)}
