




import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: BigBirdConfig
      lowerCAmelCase__			: jnp.dtype    =					jnp.floataa
      lowerCAmelCase__			: bool    =					True







      def       snake_case	(     self					: List[str]				):
          super().setup()
          __UpperCamelCase  		=						nn.Dense(5 ,       dtype=self.dtype				)







      def __call__(     self					: Optional[int] ,       *snake_case					: List[str] ,       **snake_case					: Any				):
          __UpperCamelCase  		=						super().__call__(*snake_case ,       **snake_case				)
          __UpperCamelCase  		=						self.cls(outputs[2]				)
          return outputs[:2] + (cls_out,)







class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Tuple    =					FlaxBigBirdForNaturalQuestionsModule







def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	Any:

    """simple docstring"""


    def cross_entropy(lowercase_					,     lowercase_					,     lowercase_=None      ):
        __UpperCamelCase  		=						logits.shape[-1]
        __UpperCamelCase  		=						(labels[..., None] == jnp.arange(lowercase_      )[None]).astype('''f4'''      )
        __UpperCamelCase  		=						jax.nn.log_softmax(lowercase_					,     axis=-1      )
        __UpperCamelCase  		=						-jnp.sum(labels * logits					,     axis=-1      )
        if reduction is not None:
            __UpperCamelCase  		=						reduction(lowercase_      )
        return loss

    __UpperCamelCase  		=						partial(lowercase_					,     reduction=jnp.mean      )
    __UpperCamelCase  		=						cross_entropy(lowercase_					,     lowercase_      )
    __UpperCamelCase  		=						cross_entropy(lowercase_					,     lowercase_      )
    __UpperCamelCase  		=						cross_entropy(lowercase_					,     lowercase_      )
    return (start_loss + end_loss + pooled_loss) / 3

@dataclass
class  _lowerCamelCase						:

      """simple docstring"""

      lowerCAmelCase__			: str    =					"google/bigbird-roberta-base"
      lowerCAmelCase__			: int    =					30_00
      lowerCAmelCase__			: int    =					1_05_00

      lowerCAmelCase__			: int    =					1_28
      lowerCAmelCase__			: int    =					3

      lowerCAmelCase__			: int    =					1
      lowerCAmelCase__			: int    =					5

      # tx_args
      lowerCAmelCase__			: float    =					3E-5
      lowerCAmelCase__			: float    =					0.0
      lowerCAmelCase__			: int    =					2_00_00
      lowerCAmelCase__			: float    =					0.0_095

      lowerCAmelCase__			: str    =					"bigbird-roberta-natural-questions"
      lowerCAmelCase__			: str    =					"training-expt"
      lowerCAmelCase__			: str    =					"data/nq-training.jsonl"
      lowerCAmelCase__			: str    =					"data/nq-validation.jsonl"







      def       snake_case	(     self					: List[Any]				):
          os.makedirs(self.base_dir ,       exist_ok=snake_case				)
          __UpperCamelCase  		=						os.path.join(self.base_dir ,       self.save_dir				)
          __UpperCamelCase  		=						self.batch_size_per_device * jax.device_count()







@dataclass
class  _lowerCamelCase						:

      """simple docstring"""

      lowerCAmelCase__			: int
      lowerCAmelCase__			: int    =					40_96  # no dynamic padding on TPUs







      def __call__(     self					: Any ,       snake_case					: int				):
          __UpperCamelCase  		=						self.collate_fn(snake_case				)
          __UpperCamelCase  		=						jax.tree_util.tree_map(snake_case ,       snake_case				)
          return batch







      def       snake_case	(     self					: List[str] ,       snake_case					: Optional[Any]				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.fetch_inputs(features['''input_ids''']				)
          __UpperCamelCase  		=						{
              '''input_ids''': jnp.array(snake_case ,       dtype=jnp.intaa				),
              '''attention_mask''': jnp.array(snake_case ,       dtype=jnp.intaa				),
              '''start_labels''': jnp.array(features['''start_token'''] ,       dtype=jnp.intaa				),
              '''end_labels''': jnp.array(features['''end_token'''] ,       dtype=jnp.intaa				),
              '''pooled_labels''': jnp.array(features['''category'''] ,       dtype=jnp.intaa				),
          }
          return batch







      def       snake_case	(     self					: Dict ,       snake_case					: list				):
          __UpperCamelCase  		=						[self._fetch_inputs(snake_case				) for ids in input_ids]
          return zip(*snake_case				)







      def       snake_case	(     self					: List[str] ,       snake_case					: list				):
          __UpperCamelCase  		=						[1 for _ in range(len(snake_case				)				)]
          while len(snake_case				) < self.max_length:
              input_ids.append(self.pad_id				)
              attention_mask.append(0				)
          return input_ids, attention_mask







def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=None      )       ->	List[str]:

    """simple docstring"""


    if seed is not None:
        __UpperCamelCase  		=						dataset.shuffle(seed=lowercase_      )
    for i in range(len(lowercase_      ) // batch_size      ):
        __UpperCamelCase  		=						dataset[i * batch_size : (i + 1) * batch_size]
        yield dict(lowercase_      )




@partial(jax.pmap					,     axis_name='''batch'''      )
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     **lowercase_      )       ->	Optional[int]:

    """simple docstring"""


    def loss_fn(lowercase_      ):
        __UpperCamelCase  		=						model_inputs.pop('''start_labels'''      )
        __UpperCamelCase  		=						model_inputs.pop('''end_labels'''      )
        __UpperCamelCase  		=						model_inputs.pop('''pooled_labels'''      )

        __UpperCamelCase  		=						state.apply_fn(**lowercase_					,     params=lowercase_					,     dropout_rng=lowercase_					,     train=lowercase_      )
        __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						outputs

        return state.loss_fn(
            lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     )

    __UpperCamelCase		,      __UpperCamelCase  		=						jax.random.split(lowercase_      )
    __UpperCamelCase  		=						jax.value_and_grad(lowercase_      )
    __UpperCamelCase		,      __UpperCamelCase  		=						grad_fn(state.params      )
    __UpperCamelCase  		=						jax.lax.pmean({'''loss''': loss}					,     axis_name='''batch'''      )
    __UpperCamelCase  		=						jax.lax.pmean(lowercase_					,     '''batch'''      )

    __UpperCamelCase  		=						state.apply_gradients(grads=lowercase_      )
    return state, metrics, new_drp_rng




@partial(jax.pmap					,     axis_name='''batch'''      )
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     **lowercase_      )       ->	Tuple:

    """simple docstring"""


    __UpperCamelCase  		=						model_inputs.pop('''start_labels'''      )
    __UpperCamelCase  		=						model_inputs.pop('''end_labels'''      )
    __UpperCamelCase  		=						model_inputs.pop('''pooled_labels'''      )

    __UpperCamelCase  		=						state.apply_fn(**lowercase_					,     params=state.params					,     train=lowercase_      )
    __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						outputs

    __UpperCamelCase  		=						state.loss_fn(lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )
    __UpperCamelCase  		=						jax.lax.pmean({'''loss''': loss}					,     axis_name='''batch'''      )
    return metrics

class  _lowerCamelCase						(			train_state.TrainState   ):

      """simple docstring"""

      lowerCAmelCase__			: Callable    =					struct.field(pytree_node=_SCREAMING_SNAKE_CASE   )







@dataclass
class  _lowerCamelCase						:

      """simple docstring"""

      lowerCAmelCase__			: Args
      lowerCAmelCase__			: Callable
      lowerCAmelCase__			: Callable
      lowerCAmelCase__			: Callable
      lowerCAmelCase__			: Callable
      lowerCAmelCase__			: wandb
      lowerCAmelCase__			: Callable    =					None







      def       snake_case	(     self					: Dict ,       snake_case					: List[str] ,       snake_case					: List[Any] ,       snake_case					: List[str] ,       snake_case					: Dict=None				):
          __UpperCamelCase  		=						model.params
          __UpperCamelCase  		=						TrainState.create(
              apply_fn=model.__call__ ,       params=snake_case ,       tx=snake_case ,       loss_fn=snake_case ,       )
          if ckpt_dir is not None:
              __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						restore_checkpoint(snake_case ,       snake_case				)
              __UpperCamelCase  		=						{
                  '''lr''': args.lr,
                  '''init_lr''': args.init_lr,
                  '''warmup_steps''': args.warmup_steps,
                  '''num_train_steps''': num_train_steps,
                  '''weight_decay''': args.weight_decay,
              }
              __UpperCamelCase		,      __UpperCamelCase  		=						build_tx(**snake_case				)
              __UpperCamelCase  		=						train_state.TrainState(
                  step=snake_case ,       apply_fn=model.__call__ ,       params=snake_case ,       tx=snake_case ,       opt_state=snake_case ,       )
              __UpperCamelCase  		=						args
              __UpperCamelCase  		=						data_collator
              __UpperCamelCase  		=						lr
              __UpperCamelCase  		=						params
          __UpperCamelCase  		=						jax_utils.replicate(snake_case				)
          return state







      def       snake_case	(     self					: List[Any] ,       snake_case					: List[str] ,       snake_case					: Dict ,       snake_case					: Any				):
          __UpperCamelCase  		=						self.args
          __UpperCamelCase  		=						len(snake_case				) // args.batch_size

          __UpperCamelCase  		=						jax.random.PRNGKey(0				)
          __UpperCamelCase  		=						jax.random.split(snake_case ,       jax.device_count()				)
          for epoch in range(args.max_epochs				):
              __UpperCamelCase  		=						jnp.array(0 ,       dtype=jnp.floataa				)
              __UpperCamelCase  		=						get_batched_dataset(snake_case ,       args.batch_size ,       seed=snake_case				)
              __UpperCamelCase  		=						0
              for batch in tqdm(snake_case ,       total=snake_case ,       desc=F"Running EPOCH-{epoch}"				):
                  __UpperCamelCase  		=						self.data_collator(snake_case				)
                  __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						self.train_step_fn(snake_case ,       snake_case ,       **snake_case				)
                  running_loss += jax_utils.unreplicate(metrics['''loss''']				)
                  i += 1
                  if i % args.logging_steps == 0:
                      __UpperCamelCase  		=						jax_utils.unreplicate(state.step				)
                      __UpperCamelCase  		=						running_loss.item() / i
                      __UpperCamelCase  		=						self.scheduler_fn(state_step - 1				)

                      __UpperCamelCase  		=						self.evaluate(snake_case ,       snake_case				)
                      __UpperCamelCase  		=						{
                          '''step''': state_step.item(),
                          '''eval_loss''': eval_loss.item(),
                          '''tr_loss''': tr_loss,
                          '''lr''': lr.item(),
                      }
                      tqdm.write(str(snake_case				)				)
                      self.logger.log(snake_case ,       commit=snake_case				)

                  if i % args.save_steps == 0:
                      self.save_checkpoint(args.save_dir + F"-e{epoch}-s{i}" ,       state=snake_case				)







      def       snake_case	(     self					: Dict ,       snake_case					: Tuple ,       snake_case					: List[Any]				):
          __UpperCamelCase  		=						get_batched_dataset(snake_case ,       self.args.batch_size				)
          __UpperCamelCase  		=						len(snake_case				) // self.args.batch_size
          __UpperCamelCase  		=						jnp.array(0 ,       dtype=jnp.floataa				)
          __UpperCamelCase  		=						0
          for batch in tqdm(snake_case ,       total=snake_case ,       desc='''Evaluating ... '''				):
              __UpperCamelCase  		=						self.data_collator(snake_case				)
              __UpperCamelCase  		=						self.val_step_fn(snake_case ,       **snake_case				)
              running_loss += jax_utils.unreplicate(metrics['''loss''']				)
              i += 1
          return running_loss / i







      def       snake_case	(     self					: Optional[Any] ,       snake_case					: str ,       snake_case					: Any				):
          __UpperCamelCase  		=						jax_utils.unreplicate(snake_case				)
          print(F"SAVING CHECKPOINT IN {save_dir}" ,       end=''' ... '''				)
          self.model_save_fn(snake_case ,       params=state.params				)
          with open(os.path.join(snake_case ,       '''opt_state.msgpack'''				) ,       '''wb'''				) as f:
              f.write(to_bytes(state.opt_state				)				)
          joblib.dump(self.args ,       os.path.join(snake_case ,       '''args.joblib'''				)				)
          joblib.dump(self.data_collator ,       os.path.join(snake_case ,       '''data_collator.joblib'''				)				)
          with open(os.path.join(snake_case ,       '''training_state.json'''				) ,       '''w'''				) as f:
              json.dump({'''step''': state.step.item()} ,       snake_case				)
          print('''DONE'''				)







def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Tuple:

    """simple docstring"""


    print(F"RESTORING CHECKPOINT FROM {save_dir}"					,     end=''' ... '''      )
    with open(os.path.join(lowercase_					,     '''flax_model.msgpack'''      )					,     '''rb'''      ) as f:
        __UpperCamelCase  		=						from_bytes(state.params					,     f.read()      )

    with open(os.path.join(lowercase_					,     '''opt_state.msgpack'''      )					,     '''rb'''      ) as f:
        __UpperCamelCase  		=						from_bytes(state.opt_state					,     f.read()      )

    __UpperCamelCase  		=						joblib.load(os.path.join(lowercase_					,     '''args.joblib'''      )      )
    __UpperCamelCase  		=						joblib.load(os.path.join(lowercase_					,     '''data_collator.joblib'''      )      )

    with open(os.path.join(lowercase_					,     '''training_state.json'''      )					,     '''r'''      ) as f:
        __UpperCamelCase  		=						json.load(lowercase_      )
    __UpperCamelCase  		=						training_state['''step''']

    print('''DONE'''      )
    return params, opt_state, step, args, data_collator




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase  		=						num_train_steps - warmup_steps
    __UpperCamelCase  		=						optax.linear_schedule(init_value=lowercase_					,     end_value=lowercase_					,     transition_steps=lowercase_      )
    __UpperCamelCase  		=						optax.linear_schedule(init_value=lowercase_					,     end_value=1E-7					,     transition_steps=lowercase_      )
    __UpperCamelCase  		=						optax.join_schedules(schedules=[warmup_fn, decay_fn]					,     boundaries=[warmup_steps]      )
    return lr




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	List[str]:

    """simple docstring"""


    def weight_decay_mask(lowercase_      ):
        __UpperCamelCase  		=						traverse_util.flatten_dict(lowercase_      )
        __UpperCamelCase  		=						{k: (v[-1] != '''bias''' and v[-2:] != ('''LayerNorm''', '''scale''')) for k, v in params.items()}
        return traverse_util.unflatten_dict(lowercase_      )

    __UpperCamelCase  		=						scheduler_fn(lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )

    __UpperCamelCase  		=						optax.adamw(learning_rate=lowercase_					,     weight_decay=lowercase_					,     mask=lowercase_      )
    return tx, lr












import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

a_   		=     {
    "post_extract_proj": "feature_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.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	List[str]:

    """simple docstring"""


    for attribute in key.split('''.'''      ):
        __UpperCamelCase  		=						getattr(lowercase_					,     lowercase_      )

    if weight_type is not None:
        __UpperCamelCase  		=						getattr(lowercase_					,     lowercase_      ).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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	List[str]:

    """simple docstring"""


    __UpperCamelCase  		=						[]
    __UpperCamelCase  		=						fairseq_model.state_dict()

    __UpperCamelCase  		=						hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        __UpperCamelCase  		=						False
        if "conv_layers" in name:
            load_conv_layer(
                lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     hf_model.config.feat_extract_norm == '''group'''					,     )
            __UpperCamelCase  		=						True
        else:
            for key, mapped_key in MAPPING.items():
                __UpperCamelCase  		=						'''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') 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(lowercase_      )[0].split('''.'''      )[-2]
                        __UpperCamelCase  		=						mapped_key.replace('''*'''					,     lowercase_      )
                    if "weight_g" in name:
                        __UpperCamelCase  		=						'''weight_g'''
                    elif "weight_v" in name:
                        __UpperCamelCase  		=						'''weight_v'''
                    elif "weight" in name:
                        __UpperCamelCase  		=						'''weight'''
                    elif "bias" in name:
                        __UpperCamelCase  		=						'''bias'''
                    else:
                        __UpperCamelCase  		=						None
                    set_recursively(lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )
                continue
        if not is_used:
            unused_weights.append(lowercase_      )

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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	Tuple:

    """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(lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	int:

    """simple docstring"""


    __UpperCamelCase  		=						SEWConfig()
    if is_finetuned:
        __UpperCamelCase  		=						model.wav_encoder.wav_model.cfg
    else:
        __UpperCamelCase  		=						model.cfg

    __UpperCamelCase  		=						fs_config.conv_bias
    __UpperCamelCase  		=						eval(fs_config.conv_feature_layers      )
    __UpperCamelCase  		=						[x[0] for x in conv_layers]
    __UpperCamelCase  		=						[x[1] for x in conv_layers]
    __UpperCamelCase  		=						[x[2] for x in conv_layers]
    __UpperCamelCase  		=						'''gelu'''
    __UpperCamelCase  		=						'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
    __UpperCamelCase  		=						0.0
    __UpperCamelCase  		=						fs_config.activation_fn.name
    __UpperCamelCase  		=						fs_config.encoder_embed_dim
    __UpperCamelCase  		=						0.02
    __UpperCamelCase  		=						fs_config.encoder_ffn_embed_dim
    __UpperCamelCase  		=						1E-5
    __UpperCamelCase  		=						fs_config.encoder_layerdrop
    __UpperCamelCase  		=						fs_config.encoder_attention_heads
    __UpperCamelCase  		=						fs_config.conv_pos_groups
    __UpperCamelCase  		=						fs_config.conv_pos
    __UpperCamelCase  		=						len(lowercase_      )
    __UpperCamelCase  		=						fs_config.encoder_layers
    __UpperCamelCase  		=						fs_config.squeeze_factor

    # take care of any params that are overridden by the Wav2VecCtc model
    if is_finetuned:
        __UpperCamelCase  		=						model.cfg
        __UpperCamelCase  		=						fs_config.final_dropout
        __UpperCamelCase  		=						fs_config.layerdrop
    __UpperCamelCase  		=						fs_config.activation_dropout
    __UpperCamelCase  		=						fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
    __UpperCamelCase  		=						fs_config.attention_dropout
    __UpperCamelCase  		=						fs_config.dropout_input
    __UpperCamelCase  		=						fs_config.dropout
    __UpperCamelCase  		=						fs_config.mask_channel_length
    __UpperCamelCase  		=						fs_config.mask_channel_prob
    __UpperCamelCase  		=						fs_config.mask_length
    __UpperCamelCase  		=						fs_config.mask_prob

    __UpperCamelCase  		=						'''Wav2Vec2FeatureExtractor'''
    __UpperCamelCase  		=						'''Wav2Vec2CTCTokenizer'''

    return config




@torch.no_grad()
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=None					,     lowercase_=None					,     lowercase_=True      )       ->	Any:

    """simple docstring"""


    if is_finetuned:
        __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]					,     arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''      )[:-1]      )}      )
    else:
        __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      )

    if config_path is not None:
        __UpperCamelCase  		=						SEWConfig.from_pretrained(lowercase_      )
    else:
        __UpperCamelCase  		=						convert_config(model[0]					,     lowercase_      )
    __UpperCamelCase  		=						model[0].eval()

    __UpperCamelCase  		=						True if config.feat_extract_norm == '''layer''' else False
    __UpperCamelCase  		=						WavaVecaFeatureExtractor(
        feature_size=1					,     sampling_rate=1_60_00					,     padding_value=0					,     do_normalize=lowercase_					,     return_attention_mask=lowercase_					,     )

    if is_finetuned:
        if dict_path:
            __UpperCamelCase  		=						Dictionary.load(lowercase_      )

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            __UpperCamelCase  		=						target_dict.pad_index
            __UpperCamelCase  		=						target_dict.bos_index
            __UpperCamelCase  		=						target_dict.pad_index
            __UpperCamelCase  		=						target_dict.bos_index
            __UpperCamelCase  		=						target_dict.eos_index
            __UpperCamelCase  		=						len(target_dict.symbols      )
            __UpperCamelCase  		=						os.path.join(lowercase_					,     '''vocab.json'''      )
            if not os.path.isdir(lowercase_      ):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowercase_      )      )
                return
            os.makedirs(lowercase_					,     exist_ok=lowercase_      )
            with open(lowercase_					,     '''w'''					,     encoding='''utf-8'''      ) as vocab_handle:
                json.dump(target_dict.indices					,     lowercase_      )
            __UpperCamelCase  		=						WavaVecaCTCTokenizer(
                lowercase_					,     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=lowercase_					,     )
            __UpperCamelCase  		=						WavaVecaProcessor(feature_extractor=lowercase_					,     tokenizer=lowercase_      )
            processor.save_pretrained(lowercase_      )

        __UpperCamelCase  		=						SEWForCTC(lowercase_      )
    else:
        __UpperCamelCase  		=						SEWModel(lowercase_      )
        feature_extractor.save_pretrained(lowercase_      )

    recursively_load_weights(lowercase_					,     lowercase_					,     lowercase_      )

    hf_model.save_pretrained(lowercase_      )


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(
     "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
 )
 a_   		=     parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )






