



import datasets


a_   :Optional[int]        =      "\\n@InProceedings{conneau2018xnli,\n  author = \"Conneau, Alexis\n                 and Rinott, Ruty\n                 and Lample, Guillaume\n                 and Williams, Adina\n                 and Bowman, Samuel R.\n                 and Schwenk, Holger\n                 and Stoyanov, Veselin\",\n  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",\n  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods\n               in Natural Language Processing\",\n  year = \"2018\",\n  publisher = \"Association for Computational Linguistics\",\n  location = \"Brussels, Belgium\",\n}\n"

a_   :List[Any]        =      "\\nXNLI is a subset of a few thousand examples from MNLI which has been translated\ninto a 14 different languages (some low-ish resource). As with MNLI, the goal is\nto predict textual entailment (does sentence A imply/contradict/neither sentence\nB) and is a classification task (given two sentences, predict one of three\nlabels).\n"

a_   :str        =      "\nComputes XNLI score which is just simple accuracy.\nArgs:\n    predictions: Predicted labels.\n    references: Ground truth labels.\nReturns:\n    'accuracy': accuracy\nExamples:\n\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> xnli_metric = datasets.load_metric(\"xnli\")\n    >>> results = xnli_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n"
def        lowercase_							(A : Any		,					A : Union[str, Any]      ):
       return (preds == labels).mean()





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






     """simple docstring"""


     def    lowercase_   (       self  :  List[str]					)   ->int:
            return datasets.MetricInfo(
                description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
                    {
                        'predictions': datasets.Value('int64' if self.config_name != 'sts-b' else 'float32'					),
                        'references': datasets.Value('int64' if self.config_name != 'sts-b' else 'float32'					),
                    }					), codebase_urls=[], reference_urls=[], format='numpy', )






     def    lowercase_   (       self  :  List[str], _snake_case  :  Dict, _snake_case  :  Optional[Any]					)   ->List[str]:
            return {"accuracy": simple_accuracy(_snake_case, _snake_case					)}











import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
a_   :List[Any]        =      logging.get_logger(__name__)

a_   :List[Any]        =      {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
a_   :List[Any]        =      [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]
def        lowercase_							(A : Dict      ):
       snake_case__							:	Optional[Any]   	=	{}
       with open(A		,					'r'      ) as file:
              for line_number, line in enumerate(A      ):
                     snake_case__							:	Dict   	=	line.strip()
                     if line:
                            snake_case__							:	int   	=	line.split()
                            snake_case__							:	List[str]   	=	line_number
                            snake_case__							:	Dict   	=	words[0]
                            snake_case__							:	Optional[Any]   	=	value
       return result
def        lowercase_							(A : int		,					A : int		,					A : Optional[int]		,					A : Optional[Any]		,					A : Tuple      ):
       for attribute in key.split('.'      ):
              snake_case__							:	Optional[int]   	=	getattr(A		,					A      )

       snake_case__							:	Union[str, Any]   	=	None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(A      ):
                     snake_case__							:	List[str]   	=	PARAM_MAPPING[full_name.split('.'      )[-1]]
                     snake_case__							:	Dict   	=	'param'

       if weight_type is not None and weight_type != "param":
              snake_case__							:	Union[str, Any]   	=	getattr(A		,					A      ).shape
       elif weight_type is not None and weight_type == "param":
              snake_case__							:	Optional[int]   	=	hf_pointer
              for attribute in hf_param_name.split('.'      ):
                     snake_case__							:	Optional[Any]   	=	getattr(A		,					A      )
              snake_case__							:	Dict   	=	shape_pointer.shape

              # let's reduce dimension
              snake_case__							:	List[Any]   	=	value[0]
       else:
              snake_case__							:	Union[str, Any]   	=	hf_pointer.shape

       if hf_shape != value.shape:
              raise ValueError(
                  F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
                  F''' {value.shape} for {full_name}'''      )

       if weight_type == "weight":
              snake_case__							:	Any   	=	value
       elif weight_type == "weight_g":
              snake_case__							:	List[Any]   	=	value
       elif weight_type == "weight_v":
              snake_case__							:	Any   	=	value
       elif weight_type == "bias":
              snake_case__							:	List[Any]   	=	value
       elif weight_type == "param":
              for attribute in hf_param_name.split('.'      ):
                     snake_case__							:	int   	=	getattr(A		,					A      )
              snake_case__							:	Optional[int]   	=	value
       else:
              snake_case__							:	Optional[Any]   	=	value

       logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''      )
def        lowercase_							(A : Tuple		,					A : List[Any]		,					A : int		,					A : str		,					A : Tuple      ):
       snake_case__							:	Optional[int]   	=	None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(A      ):
                     snake_case__							:	List[str]   	=	PARAM_MAPPING[full_name.split('.'      )[-1]]
                     snake_case__							:	str   	=	'param'

       if weight_type is not None and weight_type != "param":
              snake_case__							:	int   	=	'.'.join([key, weight_type]      )
       elif weight_type is not None and weight_type == "param":
              snake_case__							:	Any   	=	'.'.join([key, hf_param_name]      )
       else:
              snake_case__							:	Dict   	=	key

       snake_case__							:	List[str]   	=	value if 'lm_head' in full_key else value[0]


a_   :List[str]        =      {
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}
def        lowercase_							(A : str		,					A : Optional[Any]		,					A : Optional[Any]=None		,					A : List[str]=None      ):
       snake_case__							:	Optional[int]   	=	False
       for key, mapped_key in MAPPING.items():
              snake_case__							:	Tuple   	=	'wav2vec2.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
              if key in name or key.split('w2v_model.'      )[-1] == name.split('.'      )[0]:
                     snake_case__							:	Optional[int]   	=	True
                     if "*" in mapped_key:
                            snake_case__							:	List[Any]   	=	name.split(A      )[0].split('.'      )[-2]
                            snake_case__							:	Union[str, Any]   	=	mapped_key.replace('*'		,					A      )
                     if "weight_g" in name:
                            snake_case__							:	Tuple   	=	'weight_g'
                     elif "weight_v" in name:
                            snake_case__							:	List[str]   	=	'weight_v'
                     elif "bias" in name:
                            snake_case__							:	Dict   	=	'bias'
                     elif "weight" in name:
                            # TODO: don't match quantizer.weight_proj
                            snake_case__							:	Optional[int]   	=	'weight'
                     else:
                            snake_case__							:	str   	=	None
                     if hf_dict is not None:
                            rename_dict(A		,					A		,					A		,					A		,					A      )
                     else:
                            set_recursively(A		,					A		,					A		,					A		,					A      )
                     return is_used
       return is_used
def        lowercase_							(A : Optional[Any]		,					A : Dict		,					A : Optional[int]      ):
       snake_case__							:	Dict   	=	[]
       snake_case__							:	Tuple   	=	fairseq_model.state_dict()

       snake_case__							:	str   	=	hf_model.wavaveca.feature_extractor

       for name, value in fairseq_dict.items():
              snake_case__							:	str   	=	False
              if "conv_layers" in name:
                     load_conv_layer(
                         A		,					A		,					A		,					A		,					hf_model.config.feat_extract_norm == 'group'		,					)
                     snake_case__							:	Any   	=	True
              else:
                     snake_case__							:	Dict   	=	load_wavaveca_layer(A		,					A		,					A      )
              if not is_used:
                     unused_weights.append(A      )

       logger.warning(F'''Unused weights: {unused_weights}'''      )
def        lowercase_							(A : Dict		,					A : Optional[Any]		,					A : Tuple		,					A : str		,					A : List[str]      ):
       snake_case__							:	List[Any]   	=	full_name.split('conv_layers.'      )[-1]
       snake_case__							:	List[str]   	=	name.split('.'      )
       snake_case__							:	List[Any]   	=	int(items[0]      )
       snake_case__							:	str   	=	int(items[1]      )

       if type_id == 0:
              if "bias" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''      )
                     snake_case__							:	Any   	=	value
                     logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''      )
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''      )
                     snake_case__							:	str   	=	value
                     logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''      )
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
              if "bias" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''      )
                     snake_case__							:	str   	=	value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''      )
                     snake_case__							:	int   	=	value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
       else:
              unused_weights.append(A      )
@torch.no_grad()
def        lowercase_							(A : Union[str, Any]		,					A : str		,					A : Tuple=None		,					A : List[str]=None		,					A : Any=True		,					A : Optional[int]=False      ):
       if config_path is not None:
              snake_case__							:	List[Any]   	=	WavaVecaConfig.from_pretrained(A      )
       else:
              snake_case__							:	List[Any]   	=	WavaVecaConfig()

       if is_seq_class:
              snake_case__							:	Dict   	=	read_txt_into_dict(A      )
              snake_case__							:	Any   	=	idalabel
              snake_case__							:	Union[str, Any]   	=	WavaVecaForSequenceClassification(A      )
              snake_case__							:	Any   	=	WavaVecaFeatureExtractor(
                  feature_size=1		,					sampling_rate=1_6_0_0_0		,					padding_value=0		,					do_normalize=A		,					return_attention_mask=A		,					)
              feature_extractor.save_pretrained(A      )

       elif is_finetuned:
              if dict_path:
                     snake_case__							:	str   	=	Dictionary.load(A      )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     snake_case__							:	List[str]   	=	target_dict.pad_index
                     snake_case__							:	Optional[int]   	=	target_dict.bos_index
                     snake_case__							:	Optional[int]   	=	target_dict.eos_index
                     snake_case__							:	List[Any]   	=	len(target_dict.symbols      )
                     snake_case__							:	str   	=	os.path.join(A		,					'vocab.json'      )
                     if not os.path.isdir(A      ):
                            logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(A      )      )
                            return
                     os.makedirs(A		,					exist_ok=A      )
                     snake_case__							:	Optional[Any]   	=	target_dict.indices

                     # fairseq has the <pad> and <s> switched
                     snake_case__							:	Optional[Any]   	=	0
                     snake_case__							:	Union[str, Any]   	=	1
                     with open(A		,					'w'		,					encoding='utf-8'      ) as vocab_handle:
                            json.dump(A		,					A      )
                     snake_case__							:	List[Any]   	=	WavaVecaCTCTokenizer(
                         A		,					unk_token=target_dict.unk_word		,					pad_token=target_dict.pad_word		,					bos_token=target_dict.bos_word		,					eos_token=target_dict.eos_word		,					word_delimiter_token='|'		,					do_lower_case=A		,					)
                     snake_case__							:	str   	=	True if config.feat_extract_norm == 'layer' else False
                     snake_case__							:	Optional[Any]   	=	WavaVecaFeatureExtractor(
                         feature_size=1		,					sampling_rate=1_6_0_0_0		,					padding_value=0		,					do_normalize=A		,					return_attention_mask=A		,					)
                     snake_case__							:	Union[str, Any]   	=	WavaVecaProcessor(feature_extractor=A		,					tokenizer=A      )
                     processor.save_pretrained(A      )

              snake_case__							:	str   	=	WavaVecaForCTC(A      )
       else:
              snake_case__							:	int   	=	WavaVecaForPreTraining(A      )

       if is_finetuned or is_seq_class:
              snake_case__	,				snake_case__	,				snake_case__							:	str   	=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]		,					arg_overrides={'data': '/'.join(dict_path.split('/'      )[:-1]      )}      )
       else:
              snake_case__							:	Tuple   	=	argparse.Namespace(task='audio_pretraining'      )
              snake_case__							:	str   	=	fairseq.tasks.setup_task(A      )

              snake_case__	,				snake_case__	,				snake_case__							:	Any   	=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		,					task=A      )

       snake_case__							:	List[Any]   	=	model[0].eval()

       recursively_load_weights(A		,					A		,					not is_finetuned      )

       hf_wavavec.save_pretrained(A      )


if __name__ == "__main__":
 a_   :List[Any]        =      argparse.ArgumentParser()
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
 parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 parser.add_argument(
     "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
 )
 parser.add_argument(
     "--is_seq_class",
     action="store_true",
     help="Whether the model to convert is a fine-tuned sequence classification model or not",
 )
 a_   :str        =      parser.parse_args()

 a_   :Tuple        =      not args.not_finetuned and not args.is_seq_class
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.dict_path,
     is_finetuned,
     args.is_seq_class,
 )






