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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    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',
    'attention.self.query_proj': 'encoder.encoder.layer.*.attention.self.query_proj',
    'attention.self.key_proj': 'encoder.encoder.layer.*.attention.self.key_proj',
    'attention.self.value_proj': 'encoder.encoder.layer.*.attention.self.value_proj',
    'attention.output.dense': 'encoder.encoder.layer.*.attention.output.dense',
    'attention.output.LayerNorm': 'encoder.encoder.layer.*.attention.output.LayerNorm',
    'intermediate.dense': 'encoder.encoder.layer.*.intermediate.dense',
    'output.dense': 'encoder.encoder.layer.*.output.dense',
    'output.LayerNorm': 'encoder.encoder.layer.*.output.LayerNorm',
    'encoder.encoder.rel_embeddings': 'encoder.encoder.rel_embeddings',
    'encoder.encoder.LayerNorm': 'encoder.encoder.LayerNorm',
    '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      __lowercase  (		lowerCamelCase      :  str						,						lowerCamelCase      :  Union[str, Any]						,						lowerCamelCase      :  Optional[int]						,						lowerCamelCase      :  Any						,						lowerCamelCase      :  int			):
 for attribute in key.split('.'			):
  UpperCamelCase_      :		Optional[Any]				=      getattr(UpperCamelCase__						,						UpperCamelCase__			)

 if weight_type is not None:
  UpperCamelCase_      :		Union[str, Any]				=      getattr(UpperCamelCase__						,						UpperCamelCase__			).shape
 else:
  UpperCamelCase_      :		Union[str, Any]				=      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_      :		Dict				=      value
 elif weight_type == "weight_g":
  UpperCamelCase_      :		Optional[int]				=      value
 elif weight_type == "weight_v":
  UpperCamelCase_      :		List[Any]				=      value
 elif weight_type == "bias":
  UpperCamelCase_      :		Union[str, Any]				=      value
 else:
  UpperCamelCase_      :		Optional[int]				=      value

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





def      __lowercase  (		lowerCamelCase      :  str						,						lowerCamelCase      :  Optional[Any]						,						lowerCamelCase      :  int			):
 UpperCamelCase_      :		Dict				=      []
 UpperCamelCase_      :		Optional[int]				=      fairseq_model.state_dict()

 UpperCamelCase_      :		Any				=      hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  UpperCamelCase_      :		str				=      False
  if "conv_layers" in name:
   load_conv_layer(
       UpperCamelCase__						,						UpperCamelCase__						,						UpperCamelCase__						,						UpperCamelCase__						,						hf_model.config.feat_extract_norm == 'group'						,						)
   UpperCamelCase_      :		Any				=      True
  else:
   for key, mapped_key in MAPPING.items():
    UpperCamelCase_      :		str				=      '''sew_d.''' + 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_      :		Dict				=      True
     if "*" in mapped_key:
      UpperCamelCase_      :		List[str]				=      name.split(UpperCamelCase__			)[0].split('.'			)[-2]
      if not layer_index.isnumeric():
       continue
      UpperCamelCase_      :		str				=      mapped_key.replace('*'						,						UpperCamelCase__			)
     if "weight_g" in name:
      UpperCamelCase_      :		int				=      '''weight_g'''
     elif "weight_v" in name:
      UpperCamelCase_      :		Optional[int]				=      '''weight_v'''
     elif "weight" in name:
      UpperCamelCase_      :		int				=      '''weight'''
     elif "bias" in name:
      UpperCamelCase_      :		int				=      '''bias'''
     else:
      UpperCamelCase_      :		Dict				=      None
     set_recursively(UpperCamelCase__						,						UpperCamelCase__						,						UpperCamelCase__						,						UpperCamelCase__						,						UpperCamelCase__			)
    continue
  if not is_used:
   unused_weights.append(UpperCamelCase__			)

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





def      __lowercase  (		lowerCamelCase      :  Tuple						,						lowerCamelCase      :  Optional[Any]						,						lowerCamelCase      :  Tuple						,						lowerCamelCase      :  Any						,						lowerCamelCase      :  str			):
 UpperCamelCase_      :		List[Any]				=      full_name.split('conv_layers.'			)[-1]
 UpperCamelCase_      :		Any				=      name.split('.'			)
 UpperCamelCase_      :		Union[str, Any]				=      int(items[0]			)
 UpperCamelCase_      :		int				=      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_      :		Tuple				=      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_      :		Union[str, Any]				=      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_      :		Union[str, Any]				=      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_      :		int				=      value
   logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."			)
 else:
  unused_weights.append(UpperCamelCase__			)





def      __lowercase  (		lowerCamelCase      :  List[Any]						,						lowerCamelCase      :  Any			):
 UpperCamelCase_      :		Any				=      SEWDConfig()
 if is_finetuned:
  UpperCamelCase_      :		str				=      model.wav_encoder.wav_model.cfg
 else:
  UpperCamelCase_      :		Optional[Any]				=      model.cfg

 UpperCamelCase_      :		Optional[Any]				=      fs_config.conv_bias
 UpperCamelCase_      :		Optional[int]				=      eval(fs_config.conv_feature_layers			)
 UpperCamelCase_      :		Dict				=      [x[0] for x in conv_layers]
 UpperCamelCase_      :		int				=      [x[1] for x in conv_layers]
 UpperCamelCase_      :		Optional[int]				=      [x[2] for x in conv_layers]
 UpperCamelCase_      :		int				=      '''gelu'''
 UpperCamelCase_      :		int				=      '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
 UpperCamelCase_      :		Tuple				=      0.0
 UpperCamelCase_      :		List[Any]				=      fs_config.activation_fn.name
 UpperCamelCase_      :		Optional[Any]				=      fs_config.encoder_embed_dim
 UpperCamelCase_      :		Optional[int]				=      0.0_2
 UpperCamelCase_      :		List[Any]				=      fs_config.encoder_ffn_embed_dim
 UpperCamelCase_      :		Dict				=      1e-5
 UpperCamelCase_      :		Union[str, Any]				=      fs_config.encoder_layerdrop
 UpperCamelCase_      :		Tuple				=      fs_config.encoder_attention_heads
 UpperCamelCase_      :		Optional[Any]				=      fs_config.conv_pos_groups
 UpperCamelCase_      :		Any				=      fs_config.conv_pos
 UpperCamelCase_      :		Optional[Any]				=      len(UpperCamelCase__			)
 UpperCamelCase_      :		Any				=      fs_config.encoder_layers
 UpperCamelCase_      :		Dict				=      fs_config.squeeze_factor
 # DeBERTa-specific parameters:
 UpperCamelCase_      :		int				=      fs_config.max_position_embeddings
 UpperCamelCase_      :		Any				=      fs_config.position_buckets
 UpperCamelCase_      :		Optional[int]				=      fs_config.share_att_key
 UpperCamelCase_      :		Dict				=      fs_config.relative_attention
 UpperCamelCase_      :		List[Any]				=      fs_config.position_biased_input
 UpperCamelCase_      :		str				=      tuple(fs_config.pos_att_type.split('|'			)			)
 UpperCamelCase_      :		List[Any]				=      fs_config.norm_rel_ebd

 # take care of any params that are overridden by the Wav2VecCtc model
 if is_finetuned:
  UpperCamelCase_      :		Any				=      model.cfg
  UpperCamelCase_      :		Union[str, Any]				=      fs_config.final_dropout
  UpperCamelCase_      :		Union[str, Any]				=      fs_config.layerdrop
 UpperCamelCase_      :		str				=      fs_config.activation_dropout
 UpperCamelCase_      :		List[Any]				=      fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
 UpperCamelCase_      :		List[Any]				=      fs_config.attention_dropout
 UpperCamelCase_      :		List[Any]				=      fs_config.dropout_input
 UpperCamelCase_      :		str				=      fs_config.dropout
 UpperCamelCase_      :		List[Any]				=      fs_config.mask_channel_length
 UpperCamelCase_      :		Tuple				=      fs_config.mask_channel_prob
 UpperCamelCase_      :		List[str]				=      fs_config.mask_length
 UpperCamelCase_      :		Union[str, Any]				=      fs_config.mask_prob

 UpperCamelCase_      :		int				=      '''Wav2Vec2FeatureExtractor'''
 UpperCamelCase_      :		str				=      '''Wav2Vec2CTCTokenizer'''

 return config





@torch.no_grad()
def      __lowercase  (		lowerCamelCase      :  str						,						lowerCamelCase      :  int						,						lowerCamelCase      :  Tuple=None						,						lowerCamelCase      :  Any=None						,						lowerCamelCase      :  Union[str, Any]=True			):

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

 if config_path is not None:
  UpperCamelCase_      :		List[str]				=      SEWDConfig.from_pretrained(UpperCamelCase__			)
 else:
  UpperCamelCase_      :		Optional[Any]				=      convert_config(model[0]						,						UpperCamelCase__			)
 UpperCamelCase_      :		Dict				=      model[0].eval()

 UpperCamelCase_      :		List[str]				=      True if config.feat_extract_norm == '''layer''' else False
 UpperCamelCase_      :		Optional[int]				=      WavaVecaFeatureExtractor(
     feature_size=1						,						sampling_rate=16000						,						padding_value=0						,						do_normalize=UpperCamelCase__						,						return_attention_mask=UpperCamelCase__						,						)

 if is_finetuned:
  if dict_path:
   UpperCamelCase_      :		List[str]				=      Dictionary.load(UpperCamelCase__			)

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

  UpperCamelCase_      :		Tuple				=      SEWDForCTC(UpperCamelCase__			)
 else:
  UpperCamelCase_      :		Any				=      SEWDModel(UpperCamelCase__			)
  feature_extractor.save_pretrained(UpperCamelCase__			)

 recursively_load_weights(UpperCamelCase__						,						UpperCamelCase__						,						UpperCamelCase__			)

 hf_model.save_pretrained(UpperCamelCase__			)


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
   )




from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

a_					      = {
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}





def      __lowercase  (		lowerCamelCase      :  dict						,						lowerCamelCase      :  str			):
 UpperCamelCase_      :		Tuple				=      {start}
 UpperCamelCase_      :		Optional[Any]				=      [start]
 UpperCamelCase_      :		Queue				=      Queue()
 queue.put(lowerCamelCase			)
 while not queue.empty():
  UpperCamelCase_      :		Union[str, Any]				=      queue.get()
  for w in graph[v]:
   if w not in explored:
    explored.add(lowerCamelCase			)
    result.append(lowerCamelCase			)
    queue.put(lowerCamelCase			)
 return result





def      __lowercase  (		lowerCamelCase      :  dict						,						lowerCamelCase      :  str			):
 UpperCamelCase_      :		Dict				=      {start}
 UpperCamelCase_      :		Optional[int]				=      [start]
 UpperCamelCase_      :		Union[str, Any]				=      deque([start]			)
 while queue:
  UpperCamelCase_      :		Optional[int]				=      queue.popleft()
  for child in graph[v]:
   if child not in visited:
    visited.add(lowerCamelCase			)
    result.append(lowerCamelCase			)
    queue.append(lowerCamelCase			)
 return result





def      __lowercase  (		lowerCamelCase      :  str			):
 UpperCamelCase_      :		str				=      F"from __main__ import G, {name}"
 UpperCamelCase_      :		int				=      10000
 UpperCamelCase_      :		Optional[Any]				=      timeit(F"{name}(G, 'A')"						,						setup=lowerCamelCase						,						number=lowerCamelCase			)
 print(F"{name:<35} finished {number} runs in {res:.5f} seconds"			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   benchmark_function('breadth_first_search')
   benchmark_function('breadth_first_search_with_deque')
   # breadth_first_search                finished 10000 runs in 0.20999 seconds
   # breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds



