




"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

UpperCamelCase								=					{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.linear_k': 'encoder.layers.*.self_attn.linear_k',
    'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
    'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
    'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
    'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
    'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
    'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
    'self_attn.rotary_emb': 'encoder.embed_positions',
    'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
    'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
    'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
    'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
    'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
    'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
    'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
    'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
    'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
    'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
    'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
    'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
UpperCamelCase								=					[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]



def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: str      ):
     """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

     if hf_shape != value.shape:
          raise ValueError(
              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
     elif weight_type == "running_mean":
          lowerCAmelCase__          =			value
     elif weight_type == "running_var":
          lowerCAmelCase__          =			value
     elif weight_type == "num_batches_tracked":
          lowerCAmelCase__          =			value
     elif weight_type == "inv_freq":
          lowerCAmelCase__          =			value
     else:
          lowerCAmelCase__          =			value

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



def 							_A       (						lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: Any      ):
     """simple docstring"""

     lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			fairseq_model.state_dict()

     lowerCAmelCase__          =			hf_model.wavaveca_conformer.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__          =			"wav2vec2_conformer." + mapped_key if mapped_key not in TOP_LEVEL_KEYS 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]
                              lowerCAmelCase__          =			mapped_key.replace("*"       ,    a__      )
                         if "pos_bias_u" in name:
                              lowerCAmelCase__          =			None
                         elif "pos_bias_v" in name:
                              lowerCAmelCase__          =			None
                         elif "weight_g" in name:
                              lowerCAmelCase__          =			"weight_g"
                         elif "weight_v" in name:
                              lowerCAmelCase__          =			"weight_v"
                         elif "bias" in name:
                              lowerCAmelCase__          =			"bias"
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              lowerCAmelCase__          =			"weight"
                         elif "running_mean" in name:
                              lowerCAmelCase__          =			"running_mean"
                         elif "inv_freq" in name:
                              lowerCAmelCase__          =			"inv_freq"
                         elif "running_var" in name:
                              lowerCAmelCase__          =			"running_var"
                         elif "num_batches_tracked" in name:
                              lowerCAmelCase__          =			"num_batches_tracked"
                         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 							_A       (						lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: List[Any]      ):
     """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:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        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:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        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:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        F'{full_name} has size {value.shape}, but'
                        F' {feature_extractor.conv_layers[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:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        F'{full_name} has size {value.shape}, but'
                        F' {feature_extractor.conv_layers[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__      )



@torch.no_grad()
def 							_A       (						lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: Optional[Any]=None       ,    lowerCAmelCase_					: List[Any]=None       ,    lowerCAmelCase_					: Optional[Any]=True      ):
     """simple docstring"""

     if config_path is not None:
          lowerCAmelCase__          =			WavaVecaConformerConfig.from_pretrained(a__       ,    hidden_act="swish"      )
     else:
          lowerCAmelCase__          =			WavaVecaConformerConfig()

     if "rope" in checkpoint_path:
          lowerCAmelCase__          =			"rotary"

     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.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__      )
               lowerCAmelCase__          =			target_dict.indices

               # fairseq has the <pad> and <s> switched
               lowerCAmelCase__          =			0
               lowerCAmelCase__          =			1
               with open(a__       ,    "w"       ,    encoding="utf-8"      ) as vocab_handle:
                    json.dump(a__       ,    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__          =			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__       ,    )
               lowerCAmelCase__          =			WavaVecaProcessor(feature_extractor=a__       ,    tokenizer=a__      )
               processor.save_pretrained(a__      )

          lowerCAmelCase__          =			WavaVecaConformerForCTC(a__      )
     else:
          lowerCAmelCase__          =			WavaVecaConformerForPreTraining(a__      )

     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__          =			argparse.Namespace(task="audio_pretraining"      )
          lowerCAmelCase__          =			fairseq.tasks.setup_task(a__      )

          lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,    task=a__      )

     lowerCAmelCase__          =			model[0].eval()

     recursively_load_weights(a__       ,    a__       ,    not is_finetuned      )

     hf_wavavec.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(
         '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
     )
     UpperCamelCase								=					parser.parse_args()
     convert_wavaveca_conformer_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )






import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


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


# This is to make sure the diffusers module imported is the one in the repo.
UpperCamelCase								=					importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(DIFFUSERS_PATH, '__init__.py'),
    submodule_search_locations=[DIFFUSERS_PATH],
)
UpperCamelCase								=					spec.loader.load_module()



def 							_A       (						lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Union[str, Any]      ):
     """simple docstring"""

     return line.startswith(lowerCAmelCase_      ) or len(lowerCAmelCase_      ) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$"       ,    lowerCAmelCase_      ) is not None



def 							_A       (						lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

     lowerCAmelCase__          =			object_name.split("."      )
     lowerCAmelCase__          =			0

     # First let's find the module where our object lives.
     lowerCAmelCase__          =			parts[i]
     while i < len(lowerCAmelCase_      ) and not os.path.isfile(os.path.join(lowerCAmelCase_       ,    F'{module}.py'      )      ):
          i += 1
          if i < len(lowerCAmelCase_      ):
               lowerCAmelCase__          =			os.path.join(lowerCAmelCase_       ,    parts[i]      )
     if i >= len(lowerCAmelCase_      ):
          raise ValueError(F'`object_name` should begin with the name of a module of diffusers but got {object_name}.'      )

     with open(os.path.join(lowerCAmelCase_       ,    F'{module}.py'      )       ,    "r"       ,    encoding="utf-8"       ,    newline="\n"      ) as f:
          lowerCAmelCase__          =			f.readlines()

     # Now let's find the class / func in the code!
     lowerCAmelCase__          =			""
     lowerCAmelCase__          =			0
     for name in parts[i + 1 :]:
          while (
              line_index < len(lowerCAmelCase_      ) and re.search(rF'^{indent}(class|def)\s+{name}(\(|\:)'       ,    lines[line_index]      ) is None
          ):
               line_index += 1
          indent += "    "
          line_index += 1

     if line_index >= len(lowerCAmelCase_      ):
          raise ValueError(F' {object_name} does not match any function or class in {module}.'      )

     # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
     lowerCAmelCase__          =			line_index
     while line_index < len(lowerCAmelCase_      ) and _should_continue(lines[line_index]       ,    lowerCAmelCase_      ):
          line_index += 1
     # Clean up empty lines at the end (if any).
     while len(lines[line_index - 1]      ) <= 1:
          line_index -= 1

     lowerCAmelCase__          =			lines[start_index:line_index]
     return "".join(lowerCAmelCase_      )


UpperCamelCase								=					re.compile(R'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)')
UpperCamelCase								=					re.compile(R'^\s*(\S+)->(\S+)(\s+.*|$)')
UpperCamelCase								=					re.compile(R'<FILL\s+[^>]*>')



def 							_A       (						lowerCAmelCase_					: Dict      ):
     """simple docstring"""

     lowerCAmelCase__          =			code.split("\n"      )
     lowerCAmelCase__          =			0
     while idx < len(lowerCAmelCase_      ) and len(lines[idx]      ) == 0:
          idx += 1
     if idx < len(lowerCAmelCase_      ):
          return re.search(r"^(\s*)\S"       ,    lines[idx]      ).groups()[0]
     return ""



def 							_A       (						lowerCAmelCase_					: int      ):
     """simple docstring"""

     lowerCAmelCase__          =			len(get_indent(lowerCAmelCase_      )      ) > 0
     if has_indent:
          lowerCAmelCase__          =			F'class Bla:\n{code}'
     lowerCAmelCase__          =			black.Mode(target_versions={black.TargetVersion.PYaa}       ,    line_length=119       ,    preview=lowerCAmelCase_      )
     lowerCAmelCase__          =			black.format_str(lowerCAmelCase_       ,    mode=lowerCAmelCase_      )
     lowerCAmelCase__     ,		lowerCAmelCase__          =			style_docstrings_in_code(lowerCAmelCase_      )
     return result[len("class Bla:\n"      ) :] if has_indent else result



def 							_A       (						lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: List[Any]=False      ):
     """simple docstring"""

     with open(lowerCAmelCase_       ,    "r"       ,    encoding="utf-8"       ,    newline="\n"      ) as f:
          lowerCAmelCase__          =			f.readlines()
     lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			0
     # Not a for loop cause `lines` is going to change (if `overwrite=True`).
     while line_index < len(lowerCAmelCase_      ):
          lowerCAmelCase__          =			_re_copy_warning.search(lines[line_index]      )
          if search is None:
               line_index += 1
               continue

          # There is some copied code here, let's retrieve the original.
          lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			search.groups()
          lowerCAmelCase__          =			find_code_in_diffusers(lowerCAmelCase_      )
          lowerCAmelCase__          =			get_indent(lowerCAmelCase_      )

          lowerCAmelCase__          =			line_index + 1 if indent == theoretical_indent else line_index + 2
          lowerCAmelCase__          =			theoretical_indent
          lowerCAmelCase__          =			start_index

          # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
          lowerCAmelCase__          =			True
          while line_index < len(lowerCAmelCase_      ) and should_continue:
               line_index += 1
               if line_index >= len(lowerCAmelCase_      ):
                    break
               lowerCAmelCase__          =			lines[line_index]
               lowerCAmelCase__          =			_should_continue(lowerCAmelCase_       ,    lowerCAmelCase_      ) and re.search(F'^{indent}# End copy'       ,    lowerCAmelCase_      ) is None
          # Clean up empty lines at the end (if any).
          while len(lines[line_index - 1]      ) <= 1:
               line_index -= 1

          lowerCAmelCase__          =			lines[start_index:line_index]
          lowerCAmelCase__          =			"".join(lowerCAmelCase_      )

          # Remove any nested `Copied from` comments to avoid circular copies
          lowerCAmelCase__          =			[line for line in theoretical_code.split("\n"      ) if _re_copy_warning.search(lowerCAmelCase_      ) is None]
          lowerCAmelCase__          =			"\n".join(lowerCAmelCase_      )

          # Before comparing, use the `replace_pattern` on the original code.
          if len(lowerCAmelCase_      ) > 0:
               lowerCAmelCase__          =			replace_pattern.replace("with"       ,    ""      ).split(","      )
               lowerCAmelCase__          =			[_re_replace_pattern.search(lowerCAmelCase_      ) for p in patterns]
               for pattern in patterns:
                    if pattern is None:
                         continue
                    lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			pattern.groups()
                    lowerCAmelCase__          =			re.sub(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )
                    if option.strip() == "all-casing":
                         lowerCAmelCase__          =			re.sub(obja.lower()       ,    obja.lower()       ,    lowerCAmelCase_      )
                         lowerCAmelCase__          =			re.sub(obja.upper()       ,    obja.upper()       ,    lowerCAmelCase_      )

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
               lowerCAmelCase__          =			blackify(lines[start_index - 1] + theoretical_code      )
               lowerCAmelCase__          =			theoretical_code[len(lines[start_index - 1]      ) :]

          # Test for a diff and act accordingly.
          if observed_code != theoretical_code:
               diffs.append([object_name, start_index]      )
               if overwrite:
                    lowerCAmelCase__          =			lines[:start_index] + [theoretical_code] + lines[line_index:]
                    lowerCAmelCase__          =			start_index + 1

     if overwrite and len(lowerCAmelCase_      ) > 0:
          # Warn the user a file has been modified.
          print(F'Detected changes, rewriting {filename}.'      )
          with open(lowerCAmelCase_       ,    "w"       ,    encoding="utf-8"       ,    newline="\n"      ) as f:
               f.writelines(lowerCAmelCase_      )
     return diffs



def 							_A       (						lowerCAmelCase_					: bool = False      ):
     """simple docstring"""

     lowerCAmelCase__          =			glob.glob(os.path.join(lowerCAmelCase_       ,    "**/*.py"      )       ,    recursive=lowerCAmelCase_      )
     lowerCAmelCase__          =			[]
     for filename in all_files:
          lowerCAmelCase__          =			is_copy_consistent(lowerCAmelCase_       ,    lowerCAmelCase_      )
          diffs += [F'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
     if not overwrite and len(lowerCAmelCase_      ) > 0:
          lowerCAmelCase__          =			"\n".join(lowerCAmelCase_      )
          raise Exception(
              "Found the following copy inconsistencies:\n"
              + diff
              + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."      )


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

     check_copies(args.fix_and_overwrite)
