






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_		:		List[str]		     =			logging.get_logger(__name__)

A_		:		Optional[int]		     =			{
    '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__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Union[str, Any]:
  for attribute in key.split('.'  ):
    UpperCamelCase_: Dict   				=    getattr(UpperCAmelCase__     ,   UpperCAmelCase__  )

  if weight_type is not None:
    UpperCamelCase_: Tuple   				=    getattr(UpperCAmelCase__     ,   UpperCAmelCase__  ).shape
  else:
    UpperCamelCase_: str   				=    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_: List[str]   				=    value
  elif weight_type == "weight_g":
    UpperCamelCase_: List[str]   				=    value
  elif weight_type == "weight_v":
    UpperCamelCase_: Tuple   				=    value
  elif weight_type == "bias":
    UpperCamelCase_: Dict   				=    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   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    List[str]:
  UpperCamelCase_: Optional[Any]   				=    []
  UpperCamelCase_: List[str]   				=    fairseq_model.state_dict()

  UpperCamelCase_: str   				=    hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    UpperCamelCase_: List[Any]   				=    False
    if "conv_layers" in name:
      load_conv_layer(
          UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   hf_model.config.feat_extract_norm == 'group'     ,   )
      UpperCamelCase_: str   				=    True
    else:
      for key, mapped_key in MAPPING.items():
        UpperCamelCase_: List[Any]   				=    '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_: str   				=    True
          if "*" in mapped_key:
            UpperCamelCase_: Union[str, Any]   				=    name.split(UpperCAmelCase__  )[0].split('.'  )[-2]
            UpperCamelCase_: str   				=    mapped_key.replace('*'     ,   UpperCAmelCase__  )
          if "weight_g" in name:
            UpperCamelCase_: Optional[int]   				=    'weight_g'
          elif "weight_v" in name:
            UpperCamelCase_: List[Any]   				=    'weight_v'
          elif "weight" in name:
            UpperCamelCase_: Optional[Any]   				=    'weight'
          elif "bias" in name:
            UpperCamelCase_: Union[str, Any]   				=    'bias'
          else:
            UpperCamelCase_: int   				=    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__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  UpperCamelCase_: Optional[int]   				=    full_name.split('conv_layers.'  )[-1]
  UpperCamelCase_: Any   				=    name.split('.'  )
  UpperCamelCase_: Dict   				=    int(items[0]  )
  UpperCamelCase_: Optional[Any]   				=    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_: List[Any]   				=    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_: List[str]   				=    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_: int   				=    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_: Optional[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   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Tuple:
  UpperCamelCase_: Any   				=    SEWConfig()
  if is_finetuned:
    UpperCamelCase_: List[Any]   				=    model.wav_encoder.wav_model.cfg
  else:
    UpperCamelCase_: Union[str, Any]   				=    model.cfg

  UpperCamelCase_: int   				=    fs_config.conv_bias
  UpperCamelCase_: Union[str, Any]   				=    eval(fs_config.conv_feature_layers  )
  UpperCamelCase_: Union[str, Any]   				=    [x[0] for x in conv_layers]
  UpperCamelCase_: Tuple   				=    [x[1] for x in conv_layers]
  UpperCamelCase_: Tuple   				=    [x[2] for x in conv_layers]
  UpperCamelCase_: Optional[int]   				=    'gelu'
  UpperCamelCase_: Dict   				=    'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
  UpperCamelCase_: Optional[Any]   				=    0.0
  UpperCamelCase_: Union[str, Any]   				=    fs_config.activation_fn.name
  UpperCamelCase_: List[Any]   				=    fs_config.encoder_embed_dim
  UpperCamelCase_: Optional[int]   				=    0.02
  UpperCamelCase_: List[str]   				=    fs_config.encoder_ffn_embed_dim
  UpperCamelCase_: List[Any]   				=    1E-5
  UpperCamelCase_: Optional[Any]   				=    fs_config.encoder_layerdrop
  UpperCamelCase_: Tuple   				=    fs_config.encoder_attention_heads
  UpperCamelCase_: Dict   				=    fs_config.conv_pos_groups
  UpperCamelCase_: Dict   				=    fs_config.conv_pos
  UpperCamelCase_: Dict   				=    len(UpperCAmelCase__  )
  UpperCamelCase_: Optional[int]   				=    fs_config.encoder_layers
  UpperCamelCase_: Tuple   				=    fs_config.squeeze_factor

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

  UpperCamelCase_: Dict   				=    'Wav2Vec2FeatureExtractor'
  UpperCamelCase_: int   				=    'Wav2Vec2CTCTokenizer'

  return config

@torch.no_grad()
def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__=None     ,   UpperCAmelCase__=None     ,   UpperCAmelCase__=True  )	->    Tuple:

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

  if config_path is not None:
    UpperCamelCase_: int   				=    SEWConfig.from_pretrained(UpperCAmelCase__  )
  else:
    UpperCamelCase_: str   				=    convert_config(model[0]     ,   UpperCAmelCase__  )
  UpperCamelCase_: str   				=    model[0].eval()

  UpperCamelCase_: List[str]   				=    True if config.feat_extract_norm == 'layer' else False
  UpperCamelCase_: str   				=    WavaVecaFeatureExtractor(
      feature_size=1     ,   sampling_rate=1_6_0_0_0     ,   padding_value=0     ,   do_normalize=UpperCAmelCase__     ,   return_attention_mask=UpperCAmelCase__     ,   )

  if is_finetuned:
    if dict_path:
      UpperCamelCase_: Union[str, Any]   				=    Dictionary.load(UpperCAmelCase__  )

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      UpperCamelCase_: int   				=    target_dict.pad_index
      UpperCamelCase_: int   				=    target_dict.bos_index
      UpperCamelCase_: Dict   				=    target_dict.pad_index
      UpperCamelCase_: int   				=    target_dict.bos_index
      UpperCamelCase_: Union[str, Any]   				=    target_dict.eos_index
      UpperCamelCase_: Optional[int]   				=    len(target_dict.symbols  )
      UpperCamelCase_: Optional[int]   				=    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_: Optional[int]   				=    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   				=    SEWForCTC(UpperCAmelCase__  )
  else:
    UpperCamelCase_: Union[str, Any]   				=    SEWModel(UpperCAmelCase__  )
    feature_extractor.save_pretrained(UpperCAmelCase__  )

  recursively_load_weights(UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )

  hf_model.save_pretrained(UpperCAmelCase__  )


if __name__ == "__main__":
   A_		:		Dict		     =			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_		:		str		     =			parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )







import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class     _lowerCAmelCase(	UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		Optional[Any]									=CLIPTokenizer
       a    :		List[Any]									=CLIPTokenizerFast
       a    :		str									=True
       a    :		List[Any]									={}
       a    :		str									=False





       def       _a   ( self							):
         super().setUp()

         # fmt: off
         UpperCamelCase_: str   				=    ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
         # fmt: on
         UpperCamelCase_: Any   				=    dict(zip(_lowerCamelCase  ,      range(len(_lowerCamelCase							)							)							)							)
         UpperCamelCase_: Optional[int]   				=    ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
         UpperCamelCase_: Any   				=    {'unk_token': '<unk>'}

         UpperCamelCase_: List[Any]   				=    os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES['vocab_file']							)
         UpperCamelCase_: Optional[int]   				=    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(_lowerCamelCase							) + '\n'							)
         with open(self.merges_file  ,      'w'  ,      encoding='utf-8'							) as fp:
           fp.write('\n'.join(_lowerCamelCase							)							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         kwargs.update(self.special_tokens_map							)
         return CLIPTokenizer.from_pretrained(self.tmpdirname  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         kwargs.update(self.special_tokens_map							)
         return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase							):
         UpperCamelCase_: Union[str, Any]   				=    'lower newer'
         UpperCamelCase_: Tuple   				=    'lower newer'
         return input_text, output_text





       def       _a   ( self							):
         UpperCamelCase_: Dict   				=    CLIPTokenizer(self.vocab_file  ,      self.merges_file  ,      **self.special_tokens_map							)
         UpperCamelCase_: Optional[int]   				=    'lower newer'
         UpperCamelCase_: Any   				=    ['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
         UpperCamelCase_: Union[str, Any]   				=    tokenizer.tokenize(_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)

         UpperCamelCase_: str   				=    tokens + [tokenizer.unk_token]
         UpperCamelCase_: Optional[Any]   				=    [1_0, 2, 1_6, 9, 3, 2, 1_6, 2_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase							)  ,      _lowerCamelCase							)





       @require_ftfy
       def       _a   ( self							):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
             UpperCamelCase_: Optional[int]   				=    self.tokenizer_class.from_pretrained(_lowerCamelCase  ,      **_lowerCamelCase							)
             UpperCamelCase_: List[str]   				=    self.rust_tokenizer_class.from_pretrained(_lowerCamelCase  ,      **_lowerCamelCase							)

             UpperCamelCase_: Optional[int]   				=    'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
             UpperCamelCase_: Tuple   				=    tokenizer_s.tokenize(_lowerCamelCase							)
             UpperCamelCase_: Any   				=    tokenizer_r.tokenize(_lowerCamelCase							)

             self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)

             # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
             # with Tilde) encoded in 2 different ways
             UpperCamelCase_: List[str]   				=    'xa\u0303y' + ' ' + 'x\xe3y'
             UpperCamelCase_: List[Any]   				=    tokenizer_s.tokenize(_lowerCamelCase							)
             UpperCamelCase_: int   				=    tokenizer_r.tokenize(_lowerCamelCase							)

             self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)

             # Test that the tokenization is identical on unicode of space type
             UpperCamelCase_: Dict   				=    [
                 '\u0009',  # (horizontal tab, '\t')
                 '\u000B',  # (vertical tab)
                 '\u000C',  # (form feed)
                 '\u0020',  # (space, ' ')
                 '\u200E',  # (left-to-right mark):w
                 '\u200F',  # (right-to-left mark)
             ]
             for unicode_seq in spaces_unicodes:
               UpperCamelCase_: int   				=    tokenizer_s.tokenize(_lowerCamelCase							)
               UpperCamelCase_: Dict   				=    tokenizer_r.tokenize(_lowerCamelCase							)

               self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)

             # Test that the tokenization is identical on unicode of line break type
             UpperCamelCase_: List[str]   				=    [
                 '\u000A',  # (line feed, '\n')
                 '\r\n',  # (carriage return and line feed, '\r\n')
                 '\u000D',  # (carriage return, '\r')
                 '\r',  # (carriage return, '\r')
                 '\u000D',  # (carriage return, '\r')
                 '\u2028',  # (line separator)
                 '\u2029',  # (paragraph separator)
                 # "\u0085", # (next line)
             ]

             # The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
             # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
             # space (and thus into an empty list).

             for unicode_seq in line_break_unicodes:
               UpperCamelCase_: Optional[Any]   				=    tokenizer_s.tokenize(_lowerCamelCase							)
               UpperCamelCase_: str   				=    tokenizer_r.tokenize(_lowerCamelCase							)

               self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)





       def       _a   ( self							):
         # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
             UpperCamelCase_: str   				=    'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
             UpperCamelCase_: str   				=    f'''{text_of_1_token} {text_of_1_token}'''

             UpperCamelCase_: Optional[Any]   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      )
             UpperCamelCase_: int   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (0, len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (len(_lowerCamelCase							) + 1, len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )

             UpperCamelCase_: List[str]   				=    f''' {text}'''

             UpperCamelCase_: str   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      )
             UpperCamelCase_: Dict   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (1, 1 + len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (1 + len(_lowerCamelCase							) + 1, 1 + len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )





       def       _a   ( self							):
         # Test related to the breaking change introduced in transformers v4.17.0
         # We need to check that an error in raised when the user try to load a previous version of the tokenizer.
         with self.assertRaises(_lowerCamelCase							) as context:
           self.rust_tokenizer_class.from_pretrained('robot-test/old-clip-tokenizer'							)

         self.assertTrue(
             context.exception.args[0].startswith(
                 'The `backend_tokenizer` provided does not match the expected format.'							)							)





       @require_ftfy
       def       _a   ( self							):
         super().test_tokenization_python_rust_equals()





       def       _a   ( self							):
         # CLIP always lower cases letters
         pass