





import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class     snake_case_		(   a,		unittest.TestCase							):






 '''simple docstring'''





 __UpperCamelCase       =      CTRLTokenizer
 __UpperCamelCase       =      False
 __UpperCamelCase       =      False

 def  __UpperCAmelCase						(  self		)	->       Any:
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    UpperCAmelCase__						=["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
    UpperCAmelCase__						=dict(zip(A_,		range(len(A_		)		)		)		)
    UpperCAmelCase__						=["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
    UpperCAmelCase__						={"unk_token": "<unk>"}

    UpperCAmelCase__						=os.path.join(self.tmpdirname,		VOCAB_FILES_NAMES["vocab_file"]		)
    UpperCAmelCase__						=os.path.join(self.tmpdirname,		VOCAB_FILES_NAMES["merges_file"]		)
    with open(self.vocab_file,		"w",		encoding="utf-8"		) as fp:
       fp.write(json.dumps(A_		) + "\n"		)
    with open(self.merges_file,		"w",		encoding="utf-8"		) as fp:
       fp.write("\n".join(A_		)		)

 def  __UpperCAmelCase						(  self,		**A_		)	->       int:
    kwargs.update(self.special_tokens_map		)
    return CTRLTokenizer.from_pretrained(self.tmpdirname,		**A_		)

 def  __UpperCAmelCase						(  self,		A_		)	->       Optional[int]:
    UpperCAmelCase__						="adapt react readapt apt"
    UpperCAmelCase__						="adapt react readapt apt"
    return input_text, output_text






 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=CTRLTokenizer(self.vocab_file,		self.merges_file,		**self.special_tokens_map		)
    UpperCAmelCase__						="adapt react readapt apt"
    UpperCAmelCase__						="adapt re@@ a@@ c@@ t re@@ adapt apt".split()
    UpperCAmelCase__						=tokenizer.tokenize(A_		)
    self.assertListEqual(A_,		A_		)

    UpperCAmelCase__						=tokens + [tokenizer.unk_token]

    UpperCAmelCase__						=[0, 1, 2, 4, 5, 1, 0, 3, 6]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(A_		),		A_		)










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()
UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    '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 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A     ):




   '''simple docstring'''

   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

   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 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''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(
             A				,							A				,							A				,							A				,							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(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 "weight" in name:
                  UpperCAmelCase__						="weight"
               elif "bias" in name:
                  UpperCAmelCase__						="bias"
               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 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A     ):




   '''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(A     )






def 				_UpperCAmelCase			(    A				,							A     ):




   '''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(A     )
   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 				_UpperCAmelCase			(    A				,							A				,							A=None				,							A=None				,							A=True     ):




   '''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(A     )
   else:
      UpperCAmelCase__						=convert_config(model[0]				,							A     )
   UpperCAmelCase__						=model[0].eval()

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

   if is_finetuned:
      if dict_path:
         UpperCAmelCase__						=Dictionary.load(A     )

         # 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(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     )
         UpperCAmelCase__						=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				,							)
         UpperCAmelCase__						=WavaVecaProcessor(feature_extractor=A				,							tokenizer=A     )
         processor.save_pretrained(A     )

      UpperCAmelCase__						=SEWForCTC(A     )
   else:
      UpperCAmelCase__						=SEWModel(A     )
      feature_extractor.save_pretrained(A     )

   recursively_load_weights(A				,							A				,							A     )

   hf_model.save_pretrained(A     )


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



