"""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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__lowerCAmelCase    :       Optional[int]					     =logging.get_logger(__name__)

__lowerCAmelCase    :       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 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :Optional[Any]					,		lowerCAmelCase__     :List[Any]		) ->    Any:
      '''simple docstring'''


      for attribute in key.split("""."""		):
            lowercase														=	getattr(lowerCAmelCase__					,		lowerCAmelCase__		)

      if weight_type is not None:
            lowercase														=	getattr(lowerCAmelCase__					,		lowerCAmelCase__		).shape
      else:
            lowercase														=	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":
            lowercase														=	value
      elif weight_type == "weight_g":
            lowercase														=	value
      elif weight_type == "weight_v":
            lowercase														=	value
      elif weight_type == "bias":
            lowercase														=	value
      else:
            lowercase														=	value

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


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :List[Any]		) ->    List[Any]:
      '''simple docstring'''


      lowercase														=	[]
      lowercase														=	fairseq_model.state_dict()

      lowercase														=	hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            lowercase														=	False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__					,		hf_model.config.feat_extract_norm == """group"""					,		)
                  lowercase														=	True
            else:
                  for key, mapped_key in MAPPING.items():
                        lowercase														=	"""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]:
                              lowercase														=	True
                              if "*" in mapped_key:
                                    lowercase														=	name.split(lowerCAmelCase__		)[0].split("""."""		)[-2]
                                    lowercase														=	mapped_key.replace("""*"""					,		lowerCAmelCase__		)
                              if "weight_g" in name:
                                    lowercase														=	"""weight_g"""
                              elif "weight_v" in name:
                                    lowercase														=	"""weight_v"""
                              elif "weight" in name:
                                    lowercase														=	"""weight"""
                              elif "bias" in name:
                                    lowercase														=	"""bias"""
                              else:
                                    lowercase														=	None
                              set_recursively(lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__		)
                        continue
            if not is_used:
                  unused_weights.append(lowerCAmelCase__		)

      logger.warning(f'Unused weights: {unused_weights}'		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :List[Any]					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :Optional[int]					,		lowerCAmelCase__     :str		) ->    Union[str, Any]:
      '''simple docstring'''


      lowercase														=	full_name.split("""conv_layers."""		)[-1]
      lowercase														=	name.split("""."""		)
      lowercase														=	int(items[0]		)
      lowercase														=	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.'
                  )
                  lowercase														=	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.'
                  )
                  lowercase														=	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."
                  )
                  lowercase														=	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.'
                  )
                  lowercase														=	value
                  logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
      else:
            unused_weights.append(lowerCAmelCase__		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Optional[Any]		) ->    Union[str, Any]:
      '''simple docstring'''


      lowercase														=	SEWConfig()
      if is_finetuned:
            lowercase														=	model.wav_encoder.wav_model.cfg
      else:
            lowercase														=	model.cfg

      lowercase														=	fs_config.conv_bias
      lowercase														=	eval(fs_config.conv_feature_layers		)
      lowercase														=	[x[0] for x in conv_layers]
      lowercase														=	[x[1] for x in conv_layers]
      lowercase														=	[x[2] for x in conv_layers]
      lowercase														=	"""gelu"""
      lowercase														=	"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
      lowercase														=	0.0
      lowercase														=	fs_config.activation_fn.name
      lowercase														=	fs_config.encoder_embed_dim
      lowercase														=	0.02
      lowercase														=	fs_config.encoder_ffn_embed_dim
      lowercase														=	1e-5
      lowercase														=	fs_config.encoder_layerdrop
      lowercase														=	fs_config.encoder_attention_heads
      lowercase														=	fs_config.conv_pos_groups
      lowercase														=	fs_config.conv_pos
      lowercase														=	len(lowerCAmelCase__		)
      lowercase														=	fs_config.encoder_layers
      lowercase														=	fs_config.squeeze_factor

      # take care of any params that are overridden by the Wav2VecCtc model
      if is_finetuned:
            lowercase														=	model.cfg
            lowercase														=	fs_config.final_dropout
            lowercase														=	fs_config.layerdrop
      lowercase														=	fs_config.activation_dropout
      lowercase														=	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
      lowercase														=	fs_config.attention_dropout
      lowercase														=	fs_config.dropout_input
      lowercase														=	fs_config.dropout
      lowercase														=	fs_config.mask_channel_length
      lowercase														=	fs_config.mask_channel_prob
      lowercase														=	fs_config.mask_length
      lowercase														=	fs_config.mask_prob

      lowercase														=	"""Wav2Vec2FeatureExtractor"""
      lowercase														=	"""Wav2Vec2CTCTokenizer"""

      return config


@torch.no_grad()
def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :List[Any]					,		lowerCAmelCase__     :Union[str, Any]=None					,		lowerCAmelCase__     :int=None					,		lowerCAmelCase__     :Union[str, Any]=True		) ->    List[Any]:
      '''simple docstring'''


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

      if config_path is not None:
            lowercase														=	SEWConfig.from_pretrained(lowerCAmelCase__		)
      else:
            lowercase														=	convert_config(model[0]					,		lowerCAmelCase__		)
      lowercase														=	model[0].eval()

      lowercase														=	True if config.feat_extract_norm == """layer""" else False
      lowercase														=	WavaVecaFeatureExtractor(
          feature_size=1					,		sampling_rate=1_6_0_0_0					,		padding_value=0					,		do_normalize=lowerCAmelCase__					,		return_attention_mask=lowerCAmelCase__					,		)

      if is_finetuned:
            if dict_path:
                  lowercase														=	Dictionary.load(lowerCAmelCase__		)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  lowercase														=	target_dict.pad_index
                  lowercase														=	target_dict.bos_index
                  lowercase														=	target_dict.pad_index
                  lowercase														=	target_dict.bos_index
                  lowercase														=	target_dict.eos_index
                  lowercase														=	len(target_dict.symbols		)
                  lowercase														=	os.path.join(lowerCAmelCase__					,		"""vocab.json"""		)
                  if not os.path.isdir(lowerCAmelCase__		):
                        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowerCAmelCase__		)		)
                        return
                  os.makedirs(lowerCAmelCase__					,		exist_ok=lowerCAmelCase__		)
                  with open(lowerCAmelCase__					,		"""w"""					,		encoding="""utf-8"""		) as vocab_handle:
                        json.dump(target_dict.indices					,		lowerCAmelCase__		)
                  lowercase														=	WavaVecaCTCTokenizer(
                      lowerCAmelCase__					,		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=lowerCAmelCase__					,		)
                  lowercase														=	WavaVecaProcessor(feature_extractor=lowerCAmelCase__					,		tokenizer=lowerCAmelCase__		)
                  processor.save_pretrained(lowerCAmelCase__		)

            lowercase														=	SEWForCTC(lowerCAmelCase__		)
      else:
            lowercase														=	SEWModel(lowerCAmelCase__		)
            feature_extractor.save_pretrained(lowerCAmelCase__		)

      recursively_load_weights(lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__		)

      hf_model.save_pretrained(lowerCAmelCase__		)


if __name__ == "__main__":
       __lowerCAmelCase    :       str					     =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    :       List[Any]					     =parser.parse_args()
       convert_sew_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
       )




"""simple docstring"""






import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


__lowerCAmelCase    :       Dict					     =version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int					,		lowerCAmelCase__     :tuple					,		lowerCAmelCase__     :Path					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :List[str]=False					,		) ->    Union[str, Any]:
      '''simple docstring'''


      output_path.parent.mkdir(parents=lowerCAmelCase__					,		exist_ok=lowerCAmelCase__		)
      # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
      # so we check the torch version for backwards compatibility
      if is_torch_less_than_1_11:
            export(
                lowerCAmelCase__					,		lowerCAmelCase__					,		f=output_path.as_posix()					,		input_names=lowerCAmelCase__					,		output_names=lowerCAmelCase__					,		dynamic_axes=lowerCAmelCase__					,		do_constant_folding=lowerCAmelCase__					,		use_external_data_format=lowerCAmelCase__					,		enable_onnx_checker=lowerCAmelCase__					,		opset_version=lowerCAmelCase__					,		)
      else:
            export(
                lowerCAmelCase__					,		lowerCAmelCase__					,		f=output_path.as_posix()					,		input_names=lowerCAmelCase__					,		output_names=lowerCAmelCase__					,		dynamic_axes=lowerCAmelCase__					,		do_constant_folding=lowerCAmelCase__					,		opset_version=lowerCAmelCase__					,		)


@torch.no_grad()
def 			UpperCAmelCase__       (				lowerCAmelCase__     :str					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :bool = False		) ->    str:
      '''simple docstring'''


      lowercase														=	torch.floataa if fpaa else torch.floataa
      if fpaa and torch.cuda.is_available():
            lowercase														=	"""cuda"""
      elif fpaa and not torch.cuda.is_available():
            raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""		)
      else:
            lowercase														=	"""cpu"""
      lowercase														=	Path(lowerCAmelCase__		)

      # VAE DECODER
      lowercase														=	AutoencoderKL.from_pretrained(model_path + """/vae"""		)
      lowercase														=	vae_decoder.config.latent_channels
      # forward only through the decoder part
      lowercase														=	vae_decoder.decode
      onnx_export(
          lowerCAmelCase__					,		model_args=(
              torch.randn(1					,		lowerCAmelCase__					,		2_5					,		2_5		).to(device=lowerCAmelCase__					,		dtype=lowerCAmelCase__		),
              False,
          )					,		output_path=output_path / """vae_decoder""" / """model.onnx"""					,		ordered_input_names=["""latent_sample""", """return_dict"""]					,		output_names=["""sample"""]					,		dynamic_axes={
              """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
          }					,		opset=lowerCAmelCase__					,		)
      del vae_decoder


if __name__ == "__main__":
       __lowerCAmelCase    :       Tuple					     =argparse.ArgumentParser()

       parser.add_argument(
           """--model_path""",
           type=str,
           required=True,
           help="""Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).""",
       )

       parser.add_argument("""--output_path""", type=str, required=True, help="""Path to the output model.""")
       parser.add_argument(
           """--opset""",
           default=1_4,
           type=int,
           help="""The version of the ONNX operator set to use.""",
       )
       parser.add_argument("""--fp16""", action="""store_true""", default=False, help="""Export the models in `float16` mode""")

       __lowerCAmelCase    :       Dict					     =parser.parse_args()
       print(args.output_path)
       convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
       print("""SD: Done: ONNX""")



