
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_A							=       logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

_A							=       {
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_A							=       {
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_A							=       sorted(arg_to_scheduler.keys())
_A							=       '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''

class 		lowercase_							(							pl.LightningModule					):

 def __init__(   self  ,						__UpperCamelCase  ,						__UpperCamelCase=None  ,						__UpperCamelCase="base"  ,						__UpperCamelCase=None  ,						__UpperCamelCase=None  ,						__UpperCamelCase=None  ,						**__UpperCamelCase  ,						):




        """simple docstring"""


        super().__init__()
        # TODO: move to self.save_hyperparameters()
        # self.save_hyperparameters()
        # can also expand arguments into trainer signature for easier reading

        self.save_hyperparameters(__UpperCamelCase  )
        UpperCamelCase_											=							0
        UpperCamelCase_											=							Path(self.hparams.output_dir  )
        UpperCamelCase_											=							self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
               UpperCamelCase_											=							AutoConfig.from_pretrained(
                   self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path  ,						**({"""num_labels""": num_labels} if num_labels is not None else {})  ,						cache_dir=__UpperCamelCase  ,						**__UpperCamelCase  ,						)
        else:
               UpperCamelCase_											=							config

        UpperCamelCase_											=							("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
        for p in extra_model_params:
               if getattr(self.hparams  ,						__UpperCamelCase  ,						__UpperCamelCase  ):
                      assert hasattr(self.config  ,						__UpperCamelCase  ), f'''model config doesn\'t have a `{p}` attribute'''
                      setattr(self.config  ,						__UpperCamelCase  ,						getattr(self.hparams  ,						__UpperCamelCase  )  )

        if tokenizer is None:
               UpperCamelCase_											=							AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path  ,						cache_dir=__UpperCamelCase  ,						)
        else:
               UpperCamelCase_											=							tokenizer
        UpperCamelCase_											=							MODEL_MODES[mode]
        if model is None:
               UpperCamelCase_											=							self.model_type.from_pretrained(
                   self.hparams.model_name_or_path  ,						from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path  )  ,						config=self.config  ,						cache_dir=__UpperCamelCase  ,						)
        else:
               UpperCamelCase_											=							model

 def lowerCamelCase_					(   self  ,						*__UpperCamelCase  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							self.model_type.from_pretrained(*__UpperCamelCase  ,						**__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							arg_to_scheduler[self.hparams.lr_scheduler]
        UpperCamelCase_											=							get_schedule_func(
            self.opt  ,						num_warmup_steps=self.hparams.warmup_steps  ,						num_training_steps=self.total_steps()  )
        UpperCamelCase_											=							{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
        return scheduler

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.model
        UpperCamelCase_											=							["""bias""", """LayerNorm.weight"""]
        UpperCamelCase_											=							[
            {
                """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay  )],
                """weight_decay""": self.hparams.weight_decay,
            },
            {
                """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay  )],
                """weight_decay""": 0.0,
            },
        ]
        if self.hparams.adafactor:
               UpperCamelCase_											=							Adafactor(
                   __UpperCamelCase  ,						lr=self.hparams.learning_rate  ,						scale_parameter=__UpperCamelCase  ,						relative_step=__UpperCamelCase  )

        else:
               UpperCamelCase_											=							AdamW(
                   __UpperCamelCase  ,						lr=self.hparams.learning_rate  ,						eps=self.hparams.adam_epsilon  )
        UpperCamelCase_											=							optimizer

        UpperCamelCase_											=							self.get_lr_scheduler()

        return [optimizer], [scheduler]

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ):




        """simple docstring"""


        return self.validation_step(__UpperCamelCase  ,						__UpperCamelCase  )

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


        return self.validation_end(__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							max(1  ,						self.hparams.gpus  )  # TODO: consider num_tpu_cores
        UpperCamelCase_											=							self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
        return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


        if stage == "test":
               UpperCamelCase_											=							len(self.test_dataloader().dataset  )
        else:
               UpperCamelCase_											=							self.get_dataloader("""train"""  ,						self.hparams.train_batch_size  ,						shuffle=__UpperCamelCase  )
               UpperCamelCase_											=							len(self.train_dataloader().dataset  )

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase = False  ):




        """simple docstring"""


        raise NotImplementedError("""You must implement this for your task"""  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        return self.train_loader

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        return self.get_dataloader("""dev"""  ,						self.hparams.eval_batch_size  ,						shuffle=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        return self.get_dataloader("""test"""  ,						self.hparams.eval_batch_size  ,						shuffle=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


        return os.path.join(
            self.hparams.data_dir  ,						"""cached_{}_{}_{}""".format(
                __UpperCamelCase  ,						list(filter(__UpperCamelCase  ,						self.hparams.model_name_or_path.split("""/"""  )  )  ).pop()  ,						str(self.hparams.max_seq_length  )  ,						)  ,						)

 @pl.utilities.rank_zero_only
 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							self.output_dir.joinpath("""best_tfmr"""  )
        UpperCamelCase_											=							self.step_count
        self.model.save_pretrained(__UpperCamelCase  )
        self.tokenizer.save_pretrained(__UpperCamelCase  )

 @staticmethod
 def lowerCamelCase_					(   __UpperCamelCase  ,						__UpperCamelCase  ):




        """simple docstring"""


        parser.add_argument(
            """--model_name_or_path"""  ,						default=__UpperCamelCase  ,						type=__UpperCamelCase  ,						required=__UpperCamelCase  ,						help="""Path to pretrained model or model identifier from huggingface.co/models"""  ,						)
        parser.add_argument(
            """--config_name"""  ,						default=""""""  ,						type=__UpperCamelCase  ,						help="""Pretrained config name or path if not the same as model_name"""  )
        parser.add_argument(
            """--tokenizer_name"""  ,						default=__UpperCamelCase  ,						type=__UpperCamelCase  ,						help="""Pretrained tokenizer name or path if not the same as model_name"""  ,						)
        parser.add_argument(
            """--cache_dir"""  ,						default=""""""  ,						type=__UpperCamelCase  ,						help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""  ,						)
        parser.add_argument(
            """--encoder_layerdrop"""  ,						type=__UpperCamelCase  ,						help="""Encoder layer dropout probability (Optional). Goes into model.config"""  ,						)
        parser.add_argument(
            """--decoder_layerdrop"""  ,						type=__UpperCamelCase  ,						help="""Decoder layer dropout probability (Optional). Goes into model.config"""  ,						)
        parser.add_argument(
            """--dropout"""  ,						type=__UpperCamelCase  ,						help="""Dropout probability (Optional). Goes into model.config"""  ,						)
        parser.add_argument(
            """--attention_dropout"""  ,						type=__UpperCamelCase  ,						help="""Attention dropout probability (Optional). Goes into model.config"""  ,						)
        parser.add_argument("""--learning_rate"""  ,						default=5e-5  ,						type=__UpperCamelCase  ,						help="""The initial learning rate for Adam."""  )
        parser.add_argument(
            """--lr_scheduler"""  ,						default="""linear"""  ,						choices=__UpperCamelCase  ,						metavar=__UpperCamelCase  ,						type=__UpperCamelCase  ,						help="""Learning rate scheduler"""  ,						)
        parser.add_argument("""--weight_decay"""  ,						default=0.0  ,						type=__UpperCamelCase  ,						help="""Weight decay if we apply some."""  )
        parser.add_argument("""--adam_epsilon"""  ,						default=1e-8  ,						type=__UpperCamelCase  ,						help="""Epsilon for Adam optimizer."""  )
        parser.add_argument("""--warmup_steps"""  ,						default=0  ,						type=__UpperCamelCase  ,						help="""Linear warmup over warmup_steps."""  )
        parser.add_argument("""--num_workers"""  ,						default=4  ,						type=__UpperCamelCase  ,						help="""kwarg passed to DataLoader"""  )
        parser.add_argument("""--num_train_epochs"""  ,						dest="""max_epochs"""  ,						default=3  ,						type=__UpperCamelCase  )
        parser.add_argument("""--train_batch_size"""  ,						default=3_2  ,						type=__UpperCamelCase  )
        parser.add_argument("""--eval_batch_size"""  ,						default=3_2  ,						type=__UpperCamelCase  )
        parser.add_argument("""--adafactor"""  ,						action="""store_true"""  )



class 		lowercase_							(							pl.Callback					):

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ):




        """simple docstring"""


        if (
            trainer.is_global_zero and trainer.global_rank == 0
        ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
               pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.



class 		lowercase_							(							pl.Callback					):

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							trainer.lr_schedulers[0]["""scheduler"""]
        UpperCamelCase_											=							{f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()  )}
        pl_module.logger.log_metrics(__UpperCamelCase  )

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ):




        """simple docstring"""


        rank_zero_info("""***** Validation results *****"""  )
        UpperCamelCase_											=							trainer.callback_metrics
        # Log results
        for key in sorted(__UpperCamelCase  ):
               if key not in ["log", "progress_bar"]:
                      rank_zero_info("""{} = {}\n""".format(__UpperCamelCase  ,						str(metrics[key]  )  )  )

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ):




        """simple docstring"""


        rank_zero_info("""***** Test results *****"""  )
        UpperCamelCase_											=							trainer.callback_metrics
        # Log and save results to file
        UpperCamelCase_											=							os.path.join(pl_module.hparams.output_dir  ,						"""test_results.txt"""  )
        with open(__UpperCamelCase  ,						"""w"""  ) as writer:
               for key in sorted(__UpperCamelCase  ):
                      if key not in ["log", "progress_bar"]:
                             rank_zero_info("""{} = {}\n""".format(__UpperCamelCase  ,						str(metrics[key]  )  )  )
                             writer.write("""{} = {}\n""".format(__UpperCamelCase  ,						str(metrics[key]  )  )  )




def 					lowerCamelCase__					(    a__	:	Any						,   a__	:	List[str]  )		->						None:
       #  To allow all pl args uncomment the following line
       #  parser = pl.Trainer.add_argparse_args(parser)
       parser.add_argument(
           """--output_dir"""						,   default=a__						,   type=a__						,   required=a__						,   help="""The output directory where the model predictions and checkpoints will be written."""						,   )
       parser.add_argument(
           """--fp16"""						,   action="""store_true"""						,   help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""						,   )

       parser.add_argument(
           """--fp16_opt_level"""						,   type=a__						,   default="""O2"""						,   help=(
               """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
               """See details at https://nvidia.github.io/apex/amp.html"""
           )						,   )
       parser.add_argument("""--n_tpu_cores"""						,   dest="""tpu_cores"""						,   type=a__  )
       parser.add_argument("""--max_grad_norm"""						,   dest="""gradient_clip_val"""						,   default=1.0						,   type=a__						,   help="""Max gradient norm"""  )
       parser.add_argument("""--do_train"""						,   action="""store_true"""						,   help="""Whether to run training."""  )
       parser.add_argument("""--do_predict"""						,   action="""store_true"""						,   help="""Whether to run predictions on the test set."""  )
       parser.add_argument(
           """--gradient_accumulation_steps"""						,   dest="""accumulate_grad_batches"""						,   type=a__						,   default=1						,   help="""Number of updates steps to accumulate before performing a backward/update pass."""						,   )
       parser.add_argument("""--seed"""						,   type=a__						,   default=42						,   help="""random seed for initialization"""  )
       parser.add_argument(
           """--data_dir"""						,   default=a__						,   type=a__						,   required=a__						,   help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""						,   )







def 					lowerCamelCase__					(    a__	:	BaseTransformer						,   a__	:	argparse.Namespace						,   a__	:	List[str]=None						,   a__	:	int=True						,   a__	:	Any=None						,   a__	:	Optional[Any]=[]						,   a__	:	Union[str, Any]=None						,   a__	:	Tuple=None						,   **a__	:	Any						,   )		->						str:
       pl.seed_everything(args.seed  )

       # init model
       UpperCamelCase_											=							Path(model.hparams.output_dir  )
       odir.mkdir(exist_ok=a__  )

       # add custom checkpoints
       if checkpoint_callback is None:
              UpperCamelCase_											=							pl.callbacks.ModelCheckpoint(
                  filepath=args.output_dir						,   prefix="""checkpoint"""						,   monitor="""val_loss"""						,   mode="""min"""						,   save_top_k=1  )
       if early_stopping_callback:
              extra_callbacks.append(a__  )
       if logging_callback is None:
              UpperCamelCase_											=							LoggingCallback()

       UpperCamelCase_											=							{}

       # TODO: remove with PyTorch 1.6 since pl uses native amp
       if args.fpaa:
              UpperCamelCase_											=							16
              # train_params["amp_level"] = args.fp16_opt_level

       if args.gpus > 1:
              UpperCamelCase_											=							"""auto"""  # "ddp"
              UpperCamelCase_											=							"""ddp"""

       UpperCamelCase_											=							args.accumulate_grad_batches
       UpperCamelCase_											=							None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
       UpperCamelCase_											=							"""auto"""

       UpperCamelCase_											=							pl.Trainer.from_argparse_args(
           a__						,   weights_summary=a__						,   callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]						,   logger=a__						,   **a__						,   )

       if args.do_train:
              trainer.fit(a__  )

       return trainer





import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
_A							=       logging.get_logger(__name__)

_A							=       {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''models.0.layer_norm''': '''feature_projection.layer_norm''',
    '''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''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
_A							=       [
    '''lm_head''',
]




def 					lowerCamelCase__					(    a__	:	Tuple						,   a__	:	Any						,   a__	:	Optional[int]						,   a__	:	Optional[Any]						,   a__	:	Tuple  )		->						str:
       for attribute in key.split("""."""  ):
              UpperCamelCase_											=							getattr(a__						,   a__  )

       if weight_type is not None:
              UpperCamelCase_											=							getattr(a__						,   a__  ).shape
       else:
              UpperCamelCase_											=							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":
              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__					(    a__	:	List[str]						,   a__	:	int						,   a__	:	List[str]  )		->						int:
       UpperCamelCase_											=							[]
       UpperCamelCase_											=							fairseq_model.state_dict()

       if not is_headless:
              UpperCamelCase_											=							hf_model.dataavec_audio.feature_extractor
              UpperCamelCase_											=							hf_model.dataavec_audio.encoder.pos_conv_embed

       else:
              UpperCamelCase_											=							hf_model.feature_extractor
              UpperCamelCase_											=							hf_model.encoder.pos_conv_embed

       for name, value in fairseq_dict.items():
              UpperCamelCase_											=							False
              if "conv_layers" in name:
                     load_conv_layer(
                         a__						,   a__						,   a__						,   a__						,   )
                     UpperCamelCase_											=							True
              elif "pos_conv" in name:
                     load_pos_conv_layer(
                         a__						,   a__						,   a__						,   a__						,   )
                     UpperCamelCase_											=							True
              else:
                     for key, mapped_key in MAPPING.items():
                            if not is_headless:
                                   UpperCamelCase_											=							"""data2vec_audio.""" + 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]:
                                   UpperCamelCase_											=							True
                                   if "*" in mapped_key:
                                          UpperCamelCase_											=							name.split(a__  )[0].split("""."""  )[-2]
                                          UpperCamelCase_											=							mapped_key.replace("""*"""						,   a__  )
                                   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:
                                          # TODO: don't match quantizer.weight_proj
                                          UpperCamelCase_											=							"""weight"""
                                   else:
                                          UpperCamelCase_											=							None
                                   set_recursively(a__						,   a__						,   a__						,   a__						,   a__  )
                            continue
              if not is_used:
                     unused_weights.append(a__  )

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




def 					lowerCamelCase__					(    a__	:	Optional[Any]						,   a__	:	List[str]  )		->						int:
       UpperCamelCase_											=							path.split("""."""  )
       return reduce(a__						,   a__						,   a__  )




def 					lowerCamelCase__					(    a__	:	Union[str, Any]						,   a__	:	int						,   a__	:	Optional[int]						,   a__	:	int  )		->						Any:
       UpperCamelCase_											=							access_by_string(a__						,   a__  )
       UpperCamelCase_											=							hf_weight.data

       if fsq_value.shape != hf_value.shape:
              raise ValueError(f'''{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'''  )
       UpperCamelCase_											=							fsq_value
       logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''  )




def 					lowerCamelCase__					(    a__	:	Dict						,   a__	:	str						,   a__	:	str						,   a__	:	str  )		->						int:
       UpperCamelCase_											=							full_name.split("""conv_layers."""  )[-1]
       UpperCamelCase_											=							name.split("""."""  )
       UpperCamelCase_											=							int(items[0]  )
       UpperCamelCase_											=							int(items[1]  )

       UpperCamelCase_											=							name.split("""."""  )[-1]
       if type_id == 0:
              UpperCamelCase_											=							"""conv"""
       elif type_id == 2:
              UpperCamelCase_											=							"""layer_norm"""
       else:
              unused_weights.append(a__  )
              return

       set_weights(a__						,   a__						,   a__						,   f'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''  )




def 					lowerCamelCase__					(    a__	:	Tuple						,   a__	:	Any						,   a__	:	Tuple						,   a__	:	Dict  )		->						Any:
       UpperCamelCase_											=							full_name.split("""pos_conv."""  )[-1]
       UpperCamelCase_											=							name.split("""."""  )
       UpperCamelCase_											=							int(items[0]  )
       UpperCamelCase_											=							int(items[1]  )

       UpperCamelCase_											=							name.split("""."""  )[-1]
       if type_id != 0:
              unused_weights.append(a__  )
              return
       else:
              UpperCamelCase_											=							"""conv"""

       set_weights(a__						,   a__						,   a__						,   f'''layers.{layer_id}.{layer_type}.{weight_type}'''  )




@torch.no_grad()
def 					lowerCamelCase__					(    a__	:	List[Any]						,   a__	:	List[Any]						,   a__	:	Optional[int]=None						,   a__	:	List[str]=None						,   a__	:	Optional[int]=True  )		->						str:
       if config_path is not None:
              UpperCamelCase_											=							DataaVecAudioConfig.from_pretrained(a__  )
       else:
              UpperCamelCase_											=							DataaVecAudioConfig()

       if not is_finetuned:
              # Modify final_proj layer name
              UpperCamelCase_											=							DataaVecAudioModel(a__  )
              UpperCamelCase_											=							os.path.dirname(a__  )

              UpperCamelCase_											=							torch.load(a__  )
              UpperCamelCase_											=							state_dict["""model"""].pop("""final_proj.0.weight"""  )
              UpperCamelCase_											=							state_dict["""model"""].pop("""final_proj.0.bias"""  )
              UpperCamelCase_											=							os.path.join(a__						,   """converted.pt"""  )
              torch.save(a__						,   a__  )
       else:
              UpperCamelCase_											=							DataaVecAudioForCTC(a__  )
              UpperCamelCase_											=							checkpoint_path

       def load_dataavec(a__	:	Optional[int]  ):
              UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_											=							fairseq.checkpoint_utils.load_model_ensemble_and_task([path]  )
              return model[0].eval()

       UpperCamelCase_											=							load_dataavec(a__  )

       recursively_load_weights(a__						,   a__						,   not is_finetuned  )

       UpperCamelCase_											=							WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""  )

       UpperCamelCase_											=							load_dataset("""patrickvonplaten/librispeech_asr_dummy"""						,   """clean"""						,   split="""validation"""  )
       UpperCamelCase_											=							[x["""array"""] for x in ds[:4]["""audio"""]]

       UpperCamelCase_											=							processor(a__						,   return_tensors="""pt"""						,   padding=a__  )

       UpperCamelCase_											=							inputs.input_values
       UpperCamelCase_											=							inputs.attention_mask
       #    input_values = inputs.input_values[:, :-1]
       #    attention_mask = inputs.attention_mask[:, :-1]

       hf_wavavec.eval()
       model.eval()
       if is_finetuned:
              UpperCamelCase_											=							model(source=a__						,   padding_mask=(1 - attention_mask)						,   mask=a__						,   features_only=a__  )[
                  """encoder_out"""
              ].transpose(0						,   1  )
              UpperCamelCase_											=							hf_wavavec(a__						,   attention_mask=a__  )["""logits"""]

              UpperCamelCase_											=							torch.argmax(a__						,   dim=-1  )
              UpperCamelCase_											=							processor.batch_decode(a__  )

              print(f'''Expected Output: {ds[:4]["text"]}, Pred: {output_string}'''  )
       else:
              UpperCamelCase_											=							model(source=a__						,   padding_mask=(1 - attention_mask)						,   mask=a__						,   features_only=a__  )[
                  """layer_results"""
              ][-1][0].transpose(0						,   1  )
              UpperCamelCase_											=							hf_wavavec(a__						,   attention_mask=a__  )["""last_hidden_state"""]

       print(our_output.shape						,   their_output.shape  )
       UpperCamelCase_											=							torch.max(torch.abs(our_output - their_output  )  ).item()
       print(f'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
       UpperCamelCase_											=							torch.allclose(a__						,   a__						,   atol=1e-3  )
       print("""Do both models output the same tensors?"""						,   """🔥""" if success else """💩"""  )
       if not success:
              raise Exception("""Something went wRoNg"""  )

       hf_wavavec.save_pretrained(a__  )

       if is_finetuned:
              processor.save_pretrained(a__  )
       else:
              processor.feature_extractor.save_pretrained(a__  )


if __name__ == "__main__":
      _A							=       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'''
      )
      _A							=       parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )



