

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()
lowerCAmelCase__       = logging.get_logger(__name__)

lowerCAmelCase__       = {
    "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      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A   ,       __A			):
  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      _lowerCAmelCase(	__A   ,       __A   ,       __A			):
  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      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A   ,       __A			):
  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      _lowerCAmelCase(	__A   ,       __A			):
  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      _lowerCAmelCase(	__A   ,       __A   ,       __A=None   ,       __A=None   ,       __A=True			):

  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__":
      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
      )






import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCAmelCase__       = "src/diffusers"

# Matches is_xxx_available()
lowerCAmelCase__       = re.compile(r"is\_([a-z_]*)_available\(\)")
# Matches from xxx import bla
lowerCAmelCase__       = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")


lowerCAmelCase__       = "\n{0} = None\n"

lowerCAmelCase__       = "\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n"


lowerCAmelCase__       = "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  _re_backend.findall(__A			)
  if len(__A			) == 0:
    return None

  return "_and_".join(__A			)
def      _lowerCAmelCase(	):
  with open(os.path.join(__A   ,       "__init__.py"			)   ,       "r"   ,       encoding="utf-8"   ,       newline="\n"			) as f:
    UpperCAmelCase					      =  f.readlines()

  # Get to the point we do the actual imports for type checking
  UpperCAmelCase					      =  0
  UpperCAmelCase					      =  {}
  # Go through the end of the file
  while line_index < len(__A			):
    # If the line contains is_backend_available, we grab all objects associated with the `else` block
    UpperCAmelCase					      =  find_backend(lines[line_index]			)
    if backend is not None:
      while not lines[line_index].startswith("else:"			):
        line_index += 1
      line_index += 1
      UpperCAmelCase					      =  []
      # Until we unindent, add backend objects to the list
      while line_index < len(__A			) and len(lines[line_index]			) > 1:
        UpperCAmelCase					      =  lines[line_index]
        UpperCAmelCase					      =  _re_single_line_import.search(__A			)
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(", "			)			)
        elif line.startswith(" " * 8			):
          objects.append(line[8:-2]			)
        line_index += 1

      if len(__A			) > 0:
        UpperCAmelCase					      =  objects
    else:
      line_index += 1

  return backend_specific_objects
def      _lowerCAmelCase(	__A   ,       __A			):
  if name.isupper():
    return DUMMY_CONSTANT.format(__A			)
  elif name.islower():
    return DUMMY_FUNCTION.format(__A   ,       __A			)
  else:
    return DUMMY_CLASS.format(__A   ,       __A			)
def      _lowerCAmelCase(	__A=None			):
  if backend_specific_objects is None:
    UpperCAmelCase					      =  read_init()
  # For special correspondence backend to module name as used in the function requires_modulename
  UpperCAmelCase					      =  {}

  for backend, objects in backend_specific_objects.items():
    UpperCAmelCase					      =  "[" + ", ".join(F"\"{b}\"" for b in backend.split("_and_"			)			) + "]"
    UpperCAmelCase					      =  "# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
    dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
    dummy_file += "\n".join([create_dummy_object(__A   ,       __A			) for o in objects]			)
    UpperCAmelCase					      =  dummy_file

  return dummy_files
def      _lowerCAmelCase(	__A=False			):
  UpperCAmelCase					      =  create_dummy_files()
  # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
  UpperCAmelCase					      =  {"torch": "pt"}

  # Locate actual dummy modules and read their content.
  UpperCAmelCase					      =  os.path.join(__A   ,       "utils"			)
  UpperCAmelCase					      =  {
      backend: os.path.join(__A   ,       F"dummy_{short_names.get(__A   ,       __A			)}_objects.py"			)
      for backend in dummy_files.keys()
  }

  UpperCAmelCase					      =  {}
  for backend, file_path in dummy_file_paths.items():
    if os.path.isfile(__A			):
      with open(__A   ,       "r"   ,       encoding="utf-8"   ,       newline="\n"			) as f:
        UpperCAmelCase					      =  f.read()
    else:
      UpperCAmelCase					      =  ""

  for backend in dummy_files.keys():
    if dummy_files[backend] != actual_dummies[backend]:
      if overwrite:
        print(
            F"Updating diffusers.utils.dummy_{short_names.get(__A   ,       __A			)}_objects.py as the main "
            "__init__ has new objects."			)
        with open(dummy_file_paths[backend]   ,       "w"   ,       encoding="utf-8"   ,       newline="\n"			) as f:
          f.write(dummy_files[backend]			)
      else:
        raise ValueError(
            "The main __init__ has objects that are not present in "
            F"diffusers.utils.dummy_{short_names.get(__A   ,       __A			)}_objects.py. Run `make fix-copies` "
            "to fix this."			)


if __name__ == "__main__":
      lowerCAmelCase__       = argparse.ArgumentParser()
      parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
      lowerCAmelCase__       = parser.parse_args()

      check_dummies(args.fix_and_overwrite)



