'''simple docstring'''





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()





def      lowerCAmelCase_    ( snake_case_	:			str ,   snake_case_	:			str ,   snake_case_	:			str ,   snake_case_	:			PreTrainedTokenizer ,   snake_case_	:			int ,   snake_case_	:			Optional[int] = None ,   )   ->				str:


     '''simple docstring'''




     UpperCAmelCase_	   =       {}

     if train_file is not None:
          UpperCAmelCase_	   =       [train_file]
     if eval_file is not None:
          UpperCAmelCase_	   =       [eval_file]
     if test_file is not None:
          UpperCAmelCase_	   =       [test_file]

     UpperCAmelCase_	   =       datasets.load_dataset("csv" ,   data_files=lowerCamelCase_      )
     UpperCAmelCase_	   =       list(ds[list(files.keys()      )[0]].features.keys()      )
     UpperCAmelCase_	   =       features_name.pop(lowerCamelCase_      )
     UpperCAmelCase_	   =       list(set(ds[list(files.keys()      )[0]][label_name]      )      )
     UpperCAmelCase_	   =       {label: i for i, label in enumerate(lowerCamelCase_      )}
     UpperCAmelCase_	   =       tokenizer.model_input_names
     UpperCAmelCase_	   =       {}

     if len(lowerCamelCase_      ) == 1:
          for k in files.keys():
               UpperCAmelCase_	   =       ds[k].map(
                   lambda snake_case_      : tokenizer.batch_encode_plus(
                       example[features_name[0]] ,   truncation=lowerCamelCase_ ,   max_length=lowerCamelCase_ ,   padding="max_length"      ) ,   batched=lowerCamelCase_ ,   )
     elif len(lowerCamelCase_      ) == 2:
          for k in files.keys():
               UpperCAmelCase_	   =       ds[k].map(
                   lambda snake_case_      : tokenizer.batch_encode_plus(
                       (example[features_name[0]], example[features_name[1]]) ,   truncation=lowerCamelCase_ ,   max_length=lowerCamelCase_ ,   padding="max_length" ,   ) ,   batched=lowerCamelCase_ ,   )

     def gen_train():
          for ex in transformed_ds[datasets.Split.TRAIN]:
               UpperCAmelCase_	   =       {k: v for k, v in ex.items() if k in input_names}
               UpperCAmelCase_	   =       labelaid[ex[label_name]]
               yield (d, label)

     def gen_val():
          for ex in transformed_ds[datasets.Split.VALIDATION]:
               UpperCAmelCase_	   =       {k: v for k, v in ex.items() if k in input_names}
               UpperCAmelCase_	   =       labelaid[ex[label_name]]
               yield (d, label)

     def gen_test():
          for ex in transformed_ds[datasets.Split.TEST]:
               UpperCAmelCase_	   =       {k: v for k, v in ex.items() if k in input_names}
               UpperCAmelCase_	   =       labelaid[ex[label_name]]
               yield (d, label)

     UpperCAmelCase_	   =       (
         tf.data.Dataset.from_generator(
             lowerCamelCase_ ,   ({k: tf.intaa for k in input_names}, tf.intaa) ,   ({k: tf.TensorShape([None]      ) for k in input_names}, tf.TensorShape([]      )) ,   )
         if datasets.Split.TRAIN in transformed_ds
         else None
     )

     if train_ds is not None:
          UpperCAmelCase_	   =       train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]      )      )      )

     UpperCAmelCase_	   =       (
         tf.data.Dataset.from_generator(
             lowerCamelCase_ ,   ({k: tf.intaa for k in input_names}, tf.intaa) ,   ({k: tf.TensorShape([None]      ) for k in input_names}, tf.TensorShape([]      )) ,   )
         if datasets.Split.VALIDATION in transformed_ds
         else None
     )

     if val_ds is not None:
          UpperCAmelCase_	   =       val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]      )      )      )

     UpperCAmelCase_	   =       (
         tf.data.Dataset.from_generator(
             lowerCamelCase_ ,   ({k: tf.intaa for k in input_names}, tf.intaa) ,   ({k: tf.TensorShape([None]      ) for k in input_names}, tf.TensorShape([]      )) ,   )
         if datasets.Split.TEST in transformed_ds
         else None
     )

     if test_ds is not None:
          UpperCAmelCase_	   =       test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]      )      )      )

     return train_ds, val_ds, test_ds, labelaid


SCREAMING_SNAKE_CASE_:					Dict  =logging.getLogger(__name__)




@dataclass
class      __A				:
    a__	:						int	       =  field(metadata={"""help""": """Which column contains the label"""}     )
    a__	:						str	       =  field(default=lowercase__		,     metadata={"""help""": """The path of the training file"""}     )
    a__	:						Optional[str]	       =  field(default=lowercase__		,     metadata={"""help""": """The path of the development file"""}     )
    a__	:						Optional[str]	       =  field(default=lowercase__		,     metadata={"""help""": """The path of the test file"""}     )
    a__	:						int	       =  field(
        default=128		,     metadata={
            """help""": (
                """The maximum total input sequence length after tokenization. Sequences longer """
                """than this will be truncated, sequences shorter will be padded."""
            )
        }		,     )
    a__	:						bool	       =  field(
        default=lowercase__		,     metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )




@dataclass
class      __A				:
    a__	:						str	       =  field(
        metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}     )
    a__	:						Optional[str]	       =  field(
        default=lowercase__		,     metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}     )
    a__	:						Optional[str]	       =  field(
        default=lowercase__		,     metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}     )
    a__	:						bool	       =  field(default=lowercase__		,     metadata={"""help""": """Set this flag to use fast tokenization."""}     )
    # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
    # or just modify its tokenizer_config.json.
    a__	:						Optional[str]	       =  field(
        default=lowercase__		,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}		,     )





def      lowerCAmelCase_    ( )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_	   =       HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)      )
     UpperCAmelCase_	   =       parser.parse_args_into_dataclasses()

     if (
         os.path.exists(training_args.output_dir      )
         and os.listdir(training_args.output_dir      )
         and training_args.do_train
         and not training_args.overwrite_output_dir
     ):
          raise ValueError(
              f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
              " --overwrite_output_dir to overcome."      )

     # Setup logging
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" ,   datefmt="%m/%d/%Y %H:%M:%S" ,   level=logging.INFO ,   )
     logger.info(
         f"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1      )}, """
         f"""16-bits training: {training_args.fpaa}"""      )
     logger.info(f"""Training/evaluation parameters {training_args}"""      )

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     UpperCAmelCase_	   =       AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,   cache_dir=model_args.cache_dir ,   )

     UpperCAmelCase_	   =       get_tfds(
         train_file=data_args.train_file ,   eval_file=data_args.dev_file ,   test_file=data_args.test_file ,   tokenizer=lowerCamelCase_ ,   label_column_id=data_args.label_column_id ,   max_seq_length=data_args.max_seq_length ,   )

     UpperCAmelCase_	   =       AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path ,   num_labels=len(lowerCamelCase_      ) ,   labelaid=lowerCamelCase_ ,   idalabel={id: label for label, id in labelaid.items()} ,   finetuning_task="text-classification" ,   cache_dir=model_args.cache_dir ,   )

     with training_args.strategy.scope():
          UpperCAmelCase_	   =       TFAutoModelForSequenceClassification.from_pretrained(
              model_args.model_name_or_path ,   from_pt=bool(".bin" in model_args.model_name_or_path      ) ,   config=lowerCamelCase_ ,   cache_dir=model_args.cache_dir ,   )

     def compute_metrics(snake_case_	:			EvalPrediction      ) -> Dict:
          UpperCAmelCase_	   =       np.argmax(p.predictions ,   axis=1      )

          return {"acc": (preds == p.label_ids).mean()}

     # Initialize our Trainer
     UpperCAmelCase_	   =       TFTrainer(
         model=lowerCamelCase_ ,   args=lowerCamelCase_ ,   train_dataset=lowerCamelCase_ ,   eval_dataset=lowerCamelCase_ ,   compute_metrics=lowerCamelCase_ ,   )

     # Training
     if training_args.do_train:
          trainer.train()
          trainer.save_model()
          tokenizer.save_pretrained(training_args.output_dir      )

     # Evaluation
     UpperCAmelCase_	   =       {}
     if training_args.do_eval:
          logger.info("*** Evaluate ***"      )
          UpperCAmelCase_	   =       trainer.evaluate()
          UpperCAmelCase_	   =       os.path.join(training_args.output_dir ,   "eval_results.txt"      )

          with open(lowerCamelCase_ ,   "w"      ) as writer:
               logger.info("***** Eval results *****"      )

               for key, value in result.items():
                    logger.info(f"""  {key} = {value}"""      )
                    writer.write(f"""{key} = {value}\n"""      )

               results.update(lowerCamelCase_      )

     return results


if __name__ == "__main__":
    main()





'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:					int  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					Dict  ={
    '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',
    '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',
}
SCREAMING_SNAKE_CASE_:					List[str]  =[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def      lowerCAmelCase_    ( snake_case_	:			str ,   snake_case_	:			List[Any] ,   snake_case_	:			Union[str, Any] ,   snake_case_	:			int ,   snake_case_	:			Any      )   ->				Optional[int]:


     '''simple docstring'''




     for attribute in key.split("."      ):
          UpperCAmelCase_	   =       getattr(snake_case_ ,   snake_case_      )

     if weight_type is not None:
          UpperCAmelCase_	   =       getattr(snake_case_ ,   snake_case_      ).shape
     else:
          UpperCAmelCase_	   =       hf_pointer.shape

     assert hf_shape == value.shape, (
         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":
          UpperCAmelCase_	   =       value
     elif weight_type == "weight_g":
          UpperCAmelCase_	   =       value
     elif weight_type == "weight_v":
          UpperCAmelCase_	   =       value
     elif weight_type == "bias":
          UpperCAmelCase_	   =       value
     else:
          UpperCAmelCase_	   =       value

     logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""      )





def      lowerCAmelCase_    ( snake_case_	:			List[Any] ,   snake_case_	:			List[str]      )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       []
     UpperCAmelCase_	   =       fairseq_model.state_dict()

     UpperCAmelCase_	   =       hf_model.feature_extractor

     # if encoder has different dim to decoder -> use proj_weight
     UpperCAmelCase_	   =       None

     for name, value in fairseq_dict.items():
          UpperCAmelCase_	   =       False
          if "conv_layers" in name:
               load_conv_layer(
                   snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_ ,   hf_model.config.feat_extract_norm == "group" ,   )
               UpperCAmelCase_	   =       True
          elif name.split("."      )[0] == "proj":
               UpperCAmelCase_	   =       fairseq_model.proj
               UpperCAmelCase_	   =       True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split("w2v_model."      )[-1] == name.split("."      )[0]:
                         UpperCAmelCase_	   =       True
                         if "*" in mapped_key:
                              UpperCAmelCase_	   =       name.split(snake_case_      )[0].split("."      )[-2]
                              UpperCAmelCase_	   =       mapped_key.replace("*" ,   snake_case_      )
                         if "weight_g" in name:
                              UpperCAmelCase_	   =       "weight_g"
                         elif "weight_v" in name:
                              UpperCAmelCase_	   =       "weight_v"
                         elif "bias" in name:
                              UpperCAmelCase_	   =       "bias"
                         elif "weight" in name:
                              UpperCAmelCase_	   =       "weight"
                         else:
                              UpperCAmelCase_	   =       None
                         set_recursively(snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_      )
                    continue
          if not is_used:
               unused_weights.append(snake_case_      )

     logger.warning(f"""Unused weights: {unused_weights}"""      )

     return proj_weight





def      lowerCAmelCase_    ( snake_case_	:			Dict ,   snake_case_	:			List[Any] ,   snake_case_	:			str ,   snake_case_	:			Any ,   snake_case_	:			int      )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_	   =       full_name.split("conv_layers."      )[-1]
     UpperCAmelCase_	   =       name.split("."      )
     UpperCAmelCase_	   =       int(items[0]      )
     UpperCAmelCase_	   =       int(items[1]      )

     if type_id == 0:
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                   f"""{full_name} has size {value.shape}, but"""
                   f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
               )
               UpperCAmelCase_	   =       value
               logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                   f"""{full_name} has size {value.shape}, but"""
                   f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
               )
               UpperCAmelCase_	   =       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:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                   f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
                   " found."
               )
               UpperCAmelCase_	   =       value
               logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                   f"""{full_name} has size {value.shape}, but"""
                   f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
               )
               UpperCAmelCase_	   =       value
               logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
     else:
          unused_weights.append(snake_case_      )





def      lowerCAmelCase_    ( snake_case_	:			Any      )   ->				Any:


     '''simple docstring'''




     UpperCAmelCase_   ,			UpperCAmelCase_	   =       emb.weight.shape
     UpperCAmelCase_	   =       nn.Linear(snake_case_ ,   snake_case_ ,   bias=snake_case_      )
     UpperCAmelCase_	   =       emb.weight.data
     return lin_layer





def      lowerCAmelCase_    ( snake_case_	:			int      )   ->				List[str]:


     '''simple docstring'''




     with open(snake_case_ ,   "r" ,   encoding="utf-8"      ) as f:
          UpperCAmelCase_	   =       f.readlines()
          UpperCAmelCase_	   =       [line.split(" "      )[0] for line in lines]

     UpperCAmelCase_	   =       len(snake_case_      )

     UpperCAmelCase_	   =       {
         "<s>": 0,
         "<pad>": 1,
         "</s>": 2,
         "<unk>": 3,
     }

     vocab_dict.update(dict(zip(snake_case_ ,   range(4 ,   num_words + 4      )      )      )      )
     return vocab_dict





@torch.no_grad()
def      lowerCAmelCase_    ( snake_case_	:			Dict ,   snake_case_	:			Tuple ,   snake_case_	:			List[str] ,   snake_case_	:			Dict ,   snake_case_	:			Union[str, Any] ,   snake_case_	:			Tuple ,   snake_case_	:			Any ,   )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_	   =       WavaVecaConfig.from_pretrained(snake_case_      )
     UpperCAmelCase_	   =       SpeechaTextaConfig.from_pretrained(
         snake_case_ ,   vocab_size=snake_case_ ,   decoder_layers=snake_case_ ,   do_stable_layer_norm=snake_case_      )

     UpperCAmelCase_	   =       WavaVecaFeatureExtractor(
         feature_size=1 ,   sampling_rate=1_60_00 ,   padding_value=0 ,   do_normalize=snake_case_ ,   return_attention_mask=snake_case_ ,   )

     UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path] ,   arg_overrides={"data": "/".join(dict_path.split("/"      )[:-1]      )}      )
     UpperCAmelCase_	   =       model[0].eval()

     # set weights for wav2vec2 encoder
     UpperCAmelCase_	   =       WavaVecaModel(snake_case_      )
     UpperCAmelCase_	   =       recursively_load_weights_wavaveca(model.encoder ,   snake_case_      )

     UpperCAmelCase_	   =       SpeechaTextaForCausalLM(snake_case_      )
     UpperCAmelCase_   ,			UpperCAmelCase_	   =       hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() ,   strict=snake_case_      )

     # set output linear layer
     unexpected_keys.remove("embed_out"      )
     UpperCAmelCase_	   =       nn.Parameter(model.decoder.embed_out.detach()      )

     # layer norm is init to identity matrix so leaving it is fine
     logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}"""      )
     logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""      )

     UpperCAmelCase_	   =       SpeechEncoderDecoderModel(encoder=snake_case_ ,   decoder=snake_case_      )
     UpperCAmelCase_	   =       False

     # add projection layer
     UpperCAmelCase_	   =       nn.Parameter(projection_layer.weight      )
     UpperCAmelCase_	   =       nn.Parameter(projection_layer.bias      )

     UpperCAmelCase_	   =       create_vocab_dict(snake_case_      )

     with open(os.path.join(snake_case_ ,   "vocab.json"      ) ,   "w"      ) as fp:
          json.dump(snake_case_ ,   snake_case_      )

     UpperCAmelCase_	   =       SpeechaTextaTokenizer(os.path.join(snake_case_ ,   "vocab.json"      )      )
     tokenizer.save_pretrained(snake_case_      )

     UpperCAmelCase_	   =       hf_wavavec.config.to_dict()
     UpperCAmelCase_	   =       tokenizer.pad_token_id
     UpperCAmelCase_	   =       tokenizer.bos_token_id
     UpperCAmelCase_	   =       tokenizer.eos_token_id
     UpperCAmelCase_	   =       "speech_to_text_2"
     UpperCAmelCase_	   =       "wav2vec2"

     UpperCAmelCase_	   =       SpeechEncoderDecoderConfig.from_dict(snake_case_      )

     hf_wavavec.save_pretrained(snake_case_      )
     feature_extractor.save_pretrained(snake_case_      )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					Optional[int]  =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(
        '--encoder_config_path',
        default='facebook/wav2vec2-large-lv60',
        type=str,
        help='Path to hf encoder wav2vec2 checkpoint config',
    )
    parser.add_argument(
        '--decoder_config_path',
        default='facebook/s2t-small-mustc-en-fr-st',
        type=str,
        help='Path to hf decoder s2t checkpoint config',
    )
    parser.add_argument('--vocab_size', default=1_02_24, type=int, help='Vocab size of decoder')
    parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

    SCREAMING_SNAKE_CASE_:					Dict  =parser.parse_args()
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.dict_path,
        encoder_config_path=args.encoder_config_path,
        decoder_config_path=args.decoder_config_path,
        vocab_size=args.vocab_size,
        num_decoder_layers=args.num_decoder_layers,
    )




