


'''simple docstring'''

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_  :				Dict						= logging.get_logger(__name__)

a_  :				Tuple						= {
    """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 __snake_case					(      UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						Optional[Any]		):
 for attribute in key.split("."		):
  lowerCamelCase_      =  getattr(UpperCAmelCase_					,				UpperCAmelCase_		)

 if weight_type is not None:
  lowerCamelCase_      =  getattr(UpperCAmelCase_					,				UpperCAmelCase_		).shape
 else:
  lowerCamelCase_      =  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":
  lowerCamelCase_      =  value
 elif weight_type == "weight_g":
  lowerCamelCase_      =  value
 elif weight_type == "weight_v":
  lowerCamelCase_      =  value
 elif weight_type == "bias":
  lowerCamelCase_      =  value
 else:
  lowerCamelCase_      =  value

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





def __snake_case					(      UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						str		):
 lowerCamelCase_      =  []
 lowerCamelCase_      =  fairseq_model.state_dict()

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

 for name, value in fairseq_dict.items():
  lowerCamelCase_      =  False
  if "conv_layers" in name:
   load_conv_layer(
       UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				hf_model.config.feat_extract_norm == "group"					,				)
   lowerCamelCase_      =  True
  else:
   for key, mapped_key in MAPPING.items():
    lowerCamelCase_      =  "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]:
     lowerCamelCase_      =  True
     if "*" in mapped_key:
      lowerCamelCase_      =  name.split(UpperCAmelCase_		)[0].split("."		)[-2]
      lowerCamelCase_      =  mapped_key.replace("*"					,				UpperCAmelCase_		)
     if "weight_g" in name:
      lowerCamelCase_      =  "weight_g"
     elif "weight_v" in name:
      lowerCamelCase_      =  "weight_v"
     elif "weight" in name:
      lowerCamelCase_      =  "weight"
     elif "bias" in name:
      lowerCamelCase_      =  "bias"
     else:
      lowerCamelCase_      =  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 __snake_case					(      UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						List[Any]					,				UpperCAmelCase_						:						List[Any]					,				UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						List[Any]		):
 lowerCamelCase_      =  full_name.split("conv_layers."		)[-1]
 lowerCamelCase_      =  name.split("."		)
 lowerCamelCase_      =  int(items[0]		)
 lowerCamelCase_      =  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.'''
   )
   lowerCamelCase_      =  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.'''
   )
   lowerCamelCase_      =  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."
   )
   lowerCamelCase_      =  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.'''
   )
   lowerCamelCase_      =  value
   logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
 else:
  unused_weights.append(UpperCAmelCase_		)





def __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Any		):
 lowerCamelCase_      =  SEWConfig()
 if is_finetuned:
  lowerCamelCase_      =  model.wav_encoder.wav_model.cfg
 else:
  lowerCamelCase_      =  model.cfg

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

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

 lowerCamelCase_      =  "Wav2Vec2FeatureExtractor"
 lowerCamelCase_      =  "Wav2Vec2CTCTokenizer"

 return config





@torch.no_grad()
def __snake_case					(      UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						List[str]=None					,				UpperCAmelCase_						:						List[Any]=None					,				UpperCAmelCase_						:						int=True		):

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

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

 lowerCamelCase_      =  True if config.feat_extract_norm == "layer" else False
 lowerCamelCase_      =  WavaVecaFeatureExtractor(
     feature_size=1					,				sampling_rate=16000					,				padding_value=0					,				do_normalize=UpperCAmelCase_					,				return_attention_mask=UpperCAmelCase_					,				)

 if is_finetuned:
  if dict_path:
   lowerCamelCase_      =  Dictionary.load(UpperCAmelCase_		)

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   lowerCamelCase_      =  target_dict.pad_index
   lowerCamelCase_      =  target_dict.bos_index
   lowerCamelCase_      =  target_dict.pad_index
   lowerCamelCase_      =  target_dict.bos_index
   lowerCamelCase_      =  target_dict.eos_index
   lowerCamelCase_      =  len(target_dict.symbols		)
   lowerCamelCase_      =  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_		)
   lowerCamelCase_      =  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_					,				)
   lowerCamelCase_      =  WavaVecaProcessor(feature_extractor=UpperCAmelCase_					,				tokenizer=UpperCAmelCase_		)
   processor.save_pretrained(UpperCAmelCase_		)

  lowerCamelCase_      =  SEWForCTC(UpperCAmelCase_		)
 else:
  lowerCamelCase_      =  SEWModel(UpperCAmelCase_		)
  feature_extractor.save_pretrained(UpperCAmelCase_		)

 recursively_load_weights(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		)

 hf_model.save_pretrained(UpperCAmelCase_		)


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







'''simple docstring'''

from __future__ import annotations

def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       list[list[int]]:






       """simple docstring"""







       lowercase__      			= []
       create_all_state(1  , A  , A  , []  , A       )
       return result

def _SCREAMING_SNAKE_CASE      (A  , A  , A  , A  , A  , )       ->       None:






       """simple docstring"""







       if level == 0:
              total_list.append(current_list[:]       )
              return

       for i in range(A  , total_number - level + 2       ):
              current_list.append(A       )
              create_all_state(i + 1  , A  , level - 1  , A  , A       )
              current_list.pop()

def _SCREAMING_SNAKE_CASE      (A       )       ->       None:






       """simple docstring"""







       for i in total_list:
              print(*A       )


if __name__ == "__main__":
 lowerCamelCase							:   Tuple  						=	4
 lowerCamelCase							:   Union[str, Any]  						=	2
 lowerCamelCase							:   Dict  						=	generate_all_combinations(n, k)
 print_all_state(total_list)




