"""simple docstring"""







from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

_SCREAMING_SNAKE_CASE	:		Tuple   =   {
    name: (getattr(transformers, name), getattr(transformers, name + '''Fast''')) for name in SLOW_TO_FAST_CONVERTERS
}

_SCREAMING_SNAKE_CASE	:		Any   =   datasets.load_dataset('''xnli''', split='''test+validation''')

_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   0
_SCREAMING_SNAKE_CASE	:		int   =   0
_SCREAMING_SNAKE_CASE	:		List[Any]   =   0
_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   0



def       A__    (	lowercase_     :	Optional[int]      , lowercase_     :	Optional[Any]      , lowercase_     :	List[Any]      , lowercase_     :	Dict						)  ->							List[Any]:
   if spm_diff == list(reversed(lowercase_						)						):
      # AAA -> AA+A vs A+AA case.
      return True
   elif len(lowercase_						) == len(lowercase_						) and fast.decode(lowercase_						) == fast.decode(lowercase_						):
      # Second order OK
      # Barrich -> Barr + ich vs Bar + rich
      return True
   __lowerCamelCase    		=					slow.encode(slow.decode(lowercase_						)						)
   __lowerCamelCase    		=					fast.encode(fast.decode(lowercase_						)						)
   if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
      # Type 3 error.
      # Snehagatha ->
      #       Sne, h, aga, th, a
      #       Sne, ha, gat, ha
      # Encoding the wrong with sp does not even recover what spm gave us
      # It fits tokenizer however...
      return True
   return False



def       A__    (	lowercase_     :	Dict      , lowercase_     :	str      , lowercase_     :	Union[str, Any]						)  ->							Dict:
   __lowerCamelCase    		=					fast.encode_plus(lowercase_						)[0]
   __lowerCamelCase    		=					enc.offsets
   __lowerCamelCase		,__lowerCamelCase    		=					offsets[idx], offsets[idx - 1]
   if curr is not None and line[curr[0] : curr[1]] == "\u200f":
      return True
   if prev is not None and line[prev[0] : prev[1]] == "\u200f":
      return True



def       A__    (	lowercase_     :	Union[str, Any]      , lowercase_     :	Optional[Any]      , lowercase_     :	Union[str, Any]      , lowercase_     :	Union[str, Any]      , lowercase_     :	Any						)  ->							Any:
   # Encoding can be the same with same result AAA -> A + AA vs AA + A
   # We can check that we use at least exactly the same number of tokens.
   for i, (spm_id, tok_id) in enumerate(zip(lowercase_      , lowercase_						)						):
      if spm_id != tok_id:
         break
   __lowerCamelCase    		=					i
   for i, (spm_id, tok_id) in enumerate(zip(reversed(lowercase_						)      , reversed(lowercase_						)						)						):
      if spm_id != tok_id:
         break
   __lowerCamelCase    		=					len(lowercase_						) - i

   __lowerCamelCase    		=					spm_ids[first:last]
   __lowerCamelCase    		=					tok_ids[first:last]

   if check_diff(lowercase_      , lowercase_      , lowercase_      , lowercase_						):
      return True

   if check_LTR_mark(lowercase_      , lowercase_      , lowercase_						):
      return True

   if last - first > 5:
      # We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
      __lowerCamelCase    		=					Counter(spm_ids[first:last]						)
      __lowerCamelCase    		=					Counter(tok_ids[first:last]						)

      __lowerCamelCase    		=					{spm_ for (spm_, si) in spms.items() if toks.get(spm_      , 0						) == si}
      __lowerCamelCase    		=					3
      for i in range(last - first - min_width						):
         if all(spm_ids[first + i + j] in removable_tokens for j in range(lowercase_						)						):
            __lowerCamelCase    		=					[
                k
                for k in range(last - first - min_width						)
                if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
            ]
            for j in possible_matches:
               if check_diff(spm_ids[first : first + i]      , tok_ids[first : first + j]      , lowercase_      , lowercase_						) and check_details(
                   lowercase_      , spm_ids[first + i : last]      , tok_ids[first + j : last]      , lowercase_      , lowercase_      , ):
                  return True

   print(F'''Spm: {[fast.decode([spm_ids[i]]						) for i in range(lowercase_      , lowercase_						)]}'''						)
   try:
      print(F'''Tok: {[fast.decode([tok_ids[i]]						) for i in range(lowercase_      , lowercase_						)]}'''						)
   except Exception:
      pass

   __lowerCamelCase    		=					fast.decode(spm_ids[:first]						)
   __lowerCamelCase    		=					fast.decode(spm_ids[last:]						)
   __lowerCamelCase    		=					fast.decode(spm_ids[first:last]						)
   print()
   print(lowercase_						)
   return False



def       A__    (	lowercase_     :	str      , lowercase_     :	Dict      , lowercase_     :	Union[str, Any]						)  ->							Any:
   global perfect
   global imperfect
   global wrong
   global total

   __lowerCamelCase    		=					slow.encode(lowercase_						)
   __lowerCamelCase    		=					fast.encode(lowercase_						)

   __lowerCamelCase    		=					False
   total += 1

   if slow_ids != fast_ids:
      if check_details(lowercase_      , lowercase_      , lowercase_      , lowercase_      , lowercase_						):
         __lowerCamelCase    		=					True
         imperfect += 1
      else:
         wrong += 1
   else:
      perfect += 1

   if total % 10000 == 0:
      print(F'''({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})'''						)

   if skip_assert:
      return

   assert (
       slow_ids == fast_ids
   ), F'''line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(lowercase_						)}\n{fast.tokenize(lowercase_						)}'''



def       A__    (	lowercase_     :	Union[str, Any]      , lowercase_     :	Tuple						)  ->							Tuple:
   global batch_total
   for i in range(len(lowercase_						)						):
      # premise, all languages
      for text in dataset[i]["premise"].values():
         test_string(lowercase_      , lowercase_      , lowercase_						)

      # hypothesis, all languages
      for text in dataset[i]["hypothesis"]["translation"]:
         test_string(lowercase_      , lowercase_      , lowercase_						)


if __name__ == "__main__":
     for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
          _SCREAMING_SNAKE_CASE	:		List[str]   =   list(slow_class.max_model_input_sizes.keys())
          for checkpoint in checkpoint_names:
               _SCREAMING_SNAKE_CASE	:		Optional[Any]   =   0
               _SCREAMING_SNAKE_CASE	:		Union[str, Any]   =   0
               _SCREAMING_SNAKE_CASE	:		Optional[int]   =   0
               _SCREAMING_SNAKE_CASE	:		List[str]   =   0

               print(F"========================== Checking {name}: {checkpoint} ==========================")
               _SCREAMING_SNAKE_CASE	:		Union[str, Any]   =   slow_class.from_pretrained(checkpoint, force_download=True)
               _SCREAMING_SNAKE_CASE	:		Dict   =   fast_class.from_pretrained(checkpoint, force_download=True)
               test_tokenizer(slow, fast)
               print(F"Accuracy {perfect * 1_0_0 / total:.2f}")








'''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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowerCAmelCase_      =   {
    "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   lowerCAmelCase(  a__    :  List[Any]      , a__    :  List[str]      , a__    :  Optional[int]      , a__    :  Optional[Any]      , a__    :  List[Any]      ):







       '''simple docstring'''






       for attribute in key.split("."      ):
              lowerCamelCase__				   =      getattr(a__      , a__      )

       if weight_type is not None:
              lowerCamelCase__				   =      getattr(a__      , a__      ).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   lowerCAmelCase(  a__    :  Any      , a__    :  List[str]      , a__    :  str      ):







       '''simple docstring'''






       lowerCamelCase__				   =      []
       lowerCamelCase__				   =      fairseq_model.state_dict()

       lowerCamelCase__				   =      hf_model.sew_d.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(
                         a__      , a__      , a__      , a__      , hf_model.config.feat_extract_norm == "group"      , )
                     lowerCamelCase__				   =      True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowerCamelCase__				   =      "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]:
                                   lowerCamelCase__				   =      True
                                   if "*" in mapped_key:
                                          lowerCamelCase__				   =      name.split(a__      )[0].split("."      )[-2]
                                          if not layer_index.isnumeric():
                                                 continue
                                          lowerCamelCase__				   =      mapped_key.replace("*"      , a__      )
                                   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(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__    :  Dict      , a__    :  List[Any]      , a__    :  Union[str, Any]      , a__    :  List[str]      ):







       '''simple docstring'''






       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(a__      )







def   lowerCAmelCase(  a__    :  Optional[int]      , a__    :  Tuple      ):







       '''simple docstring'''






       lowerCamelCase__				   =      SEWDConfig()
       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__				   =      0.0_2
       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(a__      )
       lowerCamelCase__				   =      fs_config.encoder_layers
       lowerCamelCase__				   =      fs_config.squeeze_factor
       # DeBERTa-specific parameters:
       lowerCamelCase__				   =      fs_config.max_position_embeddings
       lowerCamelCase__				   =      fs_config.position_buckets
       lowerCamelCase__				   =      fs_config.share_att_key
       lowerCamelCase__				   =      fs_config.relative_attention
       lowerCamelCase__				   =      fs_config.position_biased_input
       lowerCamelCase__				   =      tuple(fs_config.pos_att_type.split("|"      )      )
       lowerCamelCase__				   =      fs_config.norm_rel_ebd

       # 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   lowerCAmelCase(  a__    :  List[Any]      , a__    :  int      , a__    :  Any=None      , a__    :  Any=None      , a__    :  Any=True      ):







       '''simple docstring'''






       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__				   =      SEWDConfig.from_pretrained(a__      )
       else:
              lowerCamelCase__				   =      convert_config(model[0]      , a__      )
       lowerCamelCase__				   =      model[0].eval()

       lowerCamelCase__				   =      True if config.feat_extract_norm == "layer" else False
       lowerCamelCase__				   =      WavaVecaFeatureExtractor(
           feature_size=1      , sampling_rate=1_6000      , padding_value=0      , do_normalize=a__      , return_attention_mask=a__      , )

       if is_finetuned:
              if dict_path:
                     lowerCamelCase__				   =      Dictionary.load(a__      )

                     # 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(a__      , "vocab.json"      )
                     if not os.path.isdir(a__      ):
                            logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(a__      )      )
                            return
                     os.makedirs(a__      , exist_ok=a__      )
                     with open(a__      , "w"      , encoding="utf-8"      ) as vocab_handle:
                            json.dump(target_dict.indices      , a__      )
                     lowerCamelCase__				   =      WavaVecaCTCTokenizer(
                         a__      , 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=a__      , )
                     lowerCamelCase__				   =      WavaVecaProcessor(feature_extractor=a__      , tokenizer=a__      )
                     processor.save_pretrained(a__      )

              lowerCamelCase__				   =      SEWDForCTC(a__      )
       else:
              lowerCamelCase__				   =      SEWDModel(a__      )
              feature_extractor.save_pretrained(a__      )

       recursively_load_weights(a__      , a__      , a__      )

       hf_model.save_pretrained(a__      )


if __name__ == "__main__":
    lowerCAmelCase_      =   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"
    )
    lowerCAmelCase_      =   parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )

