

'''simple docstring'''


from jiwer import compute_measures

import datasets


lowerCAmelCase__										= "\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

lowerCAmelCase__										= "\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

lowerCAmelCase__										= "\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,					_KWARGS_DESCRIPTION )
class 					lowercase						(  datasets.Metric ):


							def 						_snake_case							(			self)							->				Optional[int]:
								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/jitsi/jiwer/']    ,			reference_urls=[
								        'https://en.wikipedia.org/wiki/Word_error_rate',
								    ]    ,			)





							def 						_snake_case							(			self    ,			_snake_case=None    ,			_snake_case=None    ,			_snake_case=False)							->				Union[str, Any]:
								if concatenate_texts:
									return compute_measures(_snake_case    ,			_snake_case)["wer"]
								else:
									UpperCAmelCase_		:      List[Any]       	=					0
									UpperCAmelCase_		:      Any       	=					0
									for prediction, reference in zip(_snake_case    ,			_snake_case):
										UpperCAmelCase_		:      Any       	=					compute_measures(_snake_case    ,			_snake_case)
										incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
										total += measures["substitutions"] + measures["deletions"] + measures["hits"]
									return incorrect / total









'''simple docstring'''


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

lowerCAmelCase__										= logging.get_logger(__name__)







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase=None   ,UpperCamelCase=None  )    ->    Any:
	# Recurse if needed
	if "." in tensor_name:
		UpperCAmelCase_		:      Tuple       	=					tensor_name.split('.'  )
		for split in splits[:-1]:
			UpperCAmelCase_		:      Optional[int]       	=					getattr(UpperCamelCase   ,UpperCamelCase  )
			if new_module is None:
				raise ValueError(f"""{module} has no attribute {split}."""  )
			UpperCAmelCase_		:      Union[str, Any]       	=					new_module
		UpperCAmelCase_		:      int       	=					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}."""  )
	UpperCAmelCase_		:      Tuple       	=					tensor_name in module._buffers
	UpperCAmelCase_		:      Any       	=					getattr(UpperCamelCase   ,UpperCamelCase  )

	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}."""  )

	UpperCAmelCase_		:      Any       	=					False
	UpperCAmelCase_		:      Any       	=					False
	if is_buffer or not is_bitsandbytes_available():
		UpperCAmelCase_		:      Dict       	=					False
		UpperCAmelCase_		:      Tuple       	=					False
	else:
		UpperCAmelCase_		:      List[str]       	=					hasattr(bnb.nn   ,'Params4bit'  ) and isinstance(module._parameters[tensor_name]   ,bnb.nn.Paramsabit  )
		UpperCAmelCase_		:      str       	=					isinstance(module._parameters[tensor_name]   ,bnb.nn.IntaParams  )

	if is_abit or is_abit:
		UpperCAmelCase_		:      Union[str, Any]       	=					module._parameters[tensor_name]
		if param.device.type != "cuda":
			if value is None:
				UpperCAmelCase_		:      List[str]       	=					old_value.to(UpperCamelCase  )
			elif isinstance(UpperCamelCase   ,torch.Tensor  ):
				UpperCAmelCase_		:      Any       	=					value.to('cpu'  )
				if value.dtype == torch.inta:
					UpperCAmelCase_		:      Dict       	=					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:
				UpperCAmelCase_		:      List[Any]       	=					torch.tensor(UpperCamelCase   ,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   ,UpperCamelCase  ) and fpaa_statistics is None:
				UpperCAmelCase_		:      Dict       	=					new_value.T

			UpperCAmelCase_		:      Tuple       	=					old_value.__dict__
			if is_abit:
				UpperCAmelCase_		:      Any       	=					bnb.nn.IntaParams(UpperCamelCase   ,requires_grad=UpperCamelCase   ,**UpperCamelCase  ).to(UpperCamelCase  )
			elif is_abit:
				UpperCAmelCase_		:      Optional[Any]       	=					bnb.nn.Paramsabit(UpperCamelCase   ,requires_grad=UpperCamelCase   ,**UpperCamelCase  ).to(UpperCamelCase  )

			UpperCAmelCase_		:      Optional[Any]       	=					new_value
			if fpaa_statistics is not None:
				setattr(module.weight   ,'SCB'   ,fpaa_statistics.to(UpperCamelCase  )  )

	else:
		if value is None:
			UpperCAmelCase_		:      str       	=					old_value.to(UpperCamelCase  )
		elif isinstance(UpperCamelCase   ,torch.Tensor  ):
			UpperCAmelCase_		:      Tuple       	=					value.to(UpperCamelCase  )
		else:
			UpperCAmelCase_		:      str       	=					torch.tensor(UpperCamelCase   ,device=UpperCamelCase  )

		if is_buffer:
			UpperCAmelCase_		:      Dict       	=					new_value
		else:
			UpperCAmelCase_		:      Optional[int]       	=					nn.Parameter(UpperCamelCase   ,requires_grad=old_value.requires_grad  )
			UpperCAmelCase_		:      Optional[int]       	=					new_value







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase=None   ,UpperCamelCase=None   ,UpperCamelCase=None   ,UpperCamelCase=False  )    ->    Tuple:
	for name, module in model.named_children():
		if current_key_name is None:
			UpperCAmelCase_		:      Optional[int]       	=					[]
		current_key_name.append(UpperCamelCase  )

		if (isinstance(UpperCamelCase   ,nn.Linear  ) or isinstance(UpperCamelCase   ,UpperCamelCase  )) 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(UpperCamelCase  ) for key in modules_to_not_convert  ):
				with init_empty_weights():
					if isinstance(UpperCamelCase   ,UpperCamelCase  ):
						UpperCAmelCase_	,    UpperCAmelCase_		:      Optional[Any]       	=					module.weight.shape
					else:
						UpperCAmelCase_		:      Dict       	=					module.in_features
						UpperCAmelCase_		:      Any       	=					module.out_features

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







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase=None   ,UpperCamelCase=None   ,UpperCamelCase=None  )    ->    Optional[int]:
	UpperCAmelCase_		:      Union[str, Any]       	=					['lm_head'] if modules_to_not_convert is None else modules_to_not_convert
	UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					_replace_with_bnb_linear(
	    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.'
		    ' Please double check your model architecture, or submit an issue on github if you think this is'
		    ' a bug.'  )

	return model







def 						SCREAMING_SNAKE_CASE(  *UpperCamelCase   ,**UpperCamelCase  )    ->    Tuple:
	warnings.warn(
	    '`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead'   ,UpperCamelCase   ,)
	return replace_with_bnb_linear(*UpperCamelCase   ,**UpperCamelCase  )







def 						SCREAMING_SNAKE_CASE(  *UpperCamelCase   ,**UpperCamelCase  )    ->    List[Any]:
	warnings.warn(
	    '`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead'   ,UpperCamelCase   ,)
	return set_module_quantized_tensor_to_device(*UpperCamelCase   ,**UpperCamelCase  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    int:
	UpperCAmelCase_		:      Union[str, Any]       	=					deepcopy(UpperCamelCase  )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
	tied_model.tie_weights()

	UpperCAmelCase_		:      Tuple       	=					find_tied_parameters(UpperCamelCase  )
	# For compatibility with Accelerate < 0.18
	if isinstance(UpperCamelCase   ,UpperCamelCase  ):
		UpperCAmelCase_		:      Dict       	=					sum(list(tied_params.values()  )   ,[]  ) + list(tied_params.keys()  )
	else:
		UpperCAmelCase_		:      Union[str, Any]       	=					sum(UpperCamelCase   ,[]  )
	UpperCAmelCase_		:      Union[str, Any]       	=					len(UpperCamelCase  ) > 0

	# Check if it is a base model
	UpperCAmelCase_		:      List[Any]       	=					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
	UpperCAmelCase_		:      str       	=					list(model.named_children()  )
	UpperCAmelCase_		:      Optional[int]       	=					[list_modules[-1][0]]

	# add last module together with tied weights
	UpperCAmelCase_		:      Any       	=					set(UpperCamelCase  ) - set(UpperCamelCase  )
	UpperCAmelCase_		:      Optional[int]       	=					list(set(UpperCamelCase  )  ) + list(UpperCamelCase  )

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

	return filtered_module_names






