






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

a						:			Dict     	=					{
    "post_extract_proj": "feature_projection.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.layer_norm": "encoder.layer_norm",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "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",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
a						:			Union[str, Any]     	=					[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]






def 							lowerCamelCase__ (       __lowerCamelCase     :	int						):
  __UpperCAmelCase				:      Dict	 =    {}
  with open(__lowerCamelCase    ,						"""r"""						) as file:
    for line_number, line in enumerate(__lowerCamelCase						):
      __UpperCAmelCase				:      str	 =    line.strip()
      if line:
        __UpperCAmelCase				:      Union[str, Any]	 =    line.split()
        __UpperCAmelCase				:      List[str]	 =    line_number
        __UpperCAmelCase				:      Optional[Any]	 =    words[0]
        __UpperCAmelCase				:      Any	 =    value
  return result






def 							lowerCamelCase__ (       __lowerCamelCase     :	Any    ,						__lowerCamelCase     :	int    ,						__lowerCamelCase     :	List[Any]    ,						__lowerCamelCase     :	int    ,						__lowerCamelCase     :	str						):





  for attribute in key.split("""."""						):
    __UpperCAmelCase				:      Dict	 =    getattr(__lowerCamelCase    ,						__lowerCamelCase						)

  __UpperCAmelCase				:      Optional[Any]	 =    None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(__lowerCamelCase						):
      __UpperCAmelCase				:      List[Any]	 =    PARAM_MAPPING[full_name.split("""."""						)[-1]]
      __UpperCAmelCase				:      List[str]	 =    "param"

  if weight_type is not None and weight_type != "param":
    __UpperCAmelCase				:      Dict	 =    getattr(__lowerCamelCase    ,						__lowerCamelCase						).shape
  elif weight_type is not None and weight_type == "param":
    __UpperCAmelCase				:      Optional[Any]	 =    hf_pointer
    for attribute in hf_param_name.split("""."""						):
      __UpperCAmelCase				:      int	 =    getattr(__lowerCamelCase    ,						__lowerCamelCase						)
    __UpperCAmelCase				:      str	 =    shape_pointer.shape

    # let's reduce dimension
    __UpperCAmelCase				:      List[Any]	 =    value[0]
  else:
    __UpperCAmelCase				:      int	 =    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":
    __UpperCAmelCase				:      Optional[int]	 =    value
  elif weight_type == "weight_g":
    __UpperCAmelCase				:      Any	 =    value
  elif weight_type == "weight_v":
    __UpperCAmelCase				:      Tuple	 =    value
  elif weight_type == "bias":
    __UpperCAmelCase				:      Tuple	 =    value
  elif weight_type == "param":
    for attribute in hf_param_name.split("""."""						):
      __UpperCAmelCase				:      Dict	 =    getattr(__lowerCamelCase    ,						__lowerCamelCase						)
    __UpperCAmelCase				:      List[str]	 =    value
  else:
    __UpperCAmelCase				:      List[Any]	 =    value

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






def 							lowerCamelCase__ (       __lowerCamelCase     :	Optional[int]    ,						__lowerCamelCase     :	Optional[Any]    ,						__lowerCamelCase     :	Tuple    ,						__lowerCamelCase     :	Tuple    ,						__lowerCamelCase     :	Optional[Any]						):
  __UpperCAmelCase				:      Optional[int]	 =    None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(__lowerCamelCase						):
      __UpperCAmelCase				:      Optional[Any]	 =    PARAM_MAPPING[full_name.split("""."""						)[-1]]
      __UpperCAmelCase				:      str	 =    "param"

  if weight_type is not None and weight_type != "param":
    __UpperCAmelCase				:      Union[str, Any]	 =    ".".join([key, weight_type]						)
  elif weight_type is not None and weight_type == "param":
    __UpperCAmelCase				:      List[str]	 =    ".".join([key, hf_param_name]						)
  else:
    __UpperCAmelCase				:      Union[str, Any]	 =    key

  __UpperCAmelCase				:      Optional[Any]	 =    value if "lm_head" in full_key else value[0]


a						:			List[Any]     	=					{
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}






def 							lowerCamelCase__ (       __lowerCamelCase     :	Optional[Any]    ,						__lowerCamelCase     :	int    ,						__lowerCamelCase     :	int=None    ,						__lowerCamelCase     :	int=None						):
  __UpperCAmelCase				:      Optional[Any]	 =    False
  for key, mapped_key in MAPPING.items():
    __UpperCAmelCase				:      int	 =    "wav2vec2." + 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]:
      __UpperCAmelCase				:      List[Any]	 =    True
      if "*" in mapped_key:
        __UpperCAmelCase				:      Any	 =    name.split(__lowerCamelCase						)[0].split("""."""						)[-2]
        __UpperCAmelCase				:      Any	 =    mapped_key.replace("""*"""    ,						__lowerCamelCase						)
      if "weight_g" in name:
        __UpperCAmelCase				:      Dict	 =    "weight_g"
      elif "weight_v" in name:
        __UpperCAmelCase				:      Union[str, Any]	 =    "weight_v"
      elif "bias" in name:
        __UpperCAmelCase				:      List[Any]	 =    "bias"
      elif "weight" in name:
        # TODO: don't match quantizer.weight_proj
        __UpperCAmelCase				:      List[Any]	 =    "weight"
      else:
        __UpperCAmelCase				:      Any	 =    None
      if hf_dict is not None:
        rename_dict(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase						)
      else:
        set_recursively(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase						)
      return is_used
  return is_used






def 							lowerCamelCase__ (       __lowerCamelCase     :	Optional[int]    ,						__lowerCamelCase     :	Optional[Any]    ,						__lowerCamelCase     :	Union[str, Any]						):
  __UpperCAmelCase				:      Tuple	 =    []
  __UpperCAmelCase				:      Any	 =    fairseq_model.state_dict()

  __UpperCAmelCase				:      str	 =    hf_model.wavaveca.feature_extractor

  for name, value in fairseq_dict.items():
    __UpperCAmelCase				:      List[str]	 =    False
    if "conv_layers" in name:
      load_conv_layer(
          __lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						hf_model.config.feat_extract_norm == """group"""    ,						)
      __UpperCAmelCase				:      Optional[int]	 =    True
    else:
      __UpperCAmelCase				:      Union[str, Any]	 =    load_wavaveca_layer(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase						)
    if not is_used:
      unused_weights.append(__lowerCamelCase						)

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






def 							lowerCamelCase__ (       __lowerCamelCase     :	Optional[int]    ,						__lowerCamelCase     :	str    ,						__lowerCamelCase     :	Tuple    ,						__lowerCamelCase     :	str    ,						__lowerCamelCase     :	Optional[Any]						):
  __UpperCAmelCase				:      List[str]	 =    full_name.split("""conv_layers."""						)[-1]
  __UpperCAmelCase				:      Union[str, Any]	 =    name.split("""."""						)
  __UpperCAmelCase				:      Union[str, Any]	 =    int(items[0]						)
  __UpperCAmelCase				:      Optional[int]	 =    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."""						)
      __UpperCAmelCase				:      List[Any]	 =    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."""						)
      __UpperCAmelCase				:      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:
      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."""						)
      __UpperCAmelCase				:      Optional[int]	 =    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."""						)
      __UpperCAmelCase				:      Tuple	 =    value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
  else:
    unused_weights.append(__lowerCamelCase						)






@torch.no_grad()
def 							lowerCamelCase__ (       __lowerCamelCase     :	str    ,						__lowerCamelCase     :	Optional[Any]    ,						__lowerCamelCase     :	Optional[Any]=None    ,						__lowerCamelCase     :	Optional[int]=None    ,						__lowerCamelCase     :	Optional[int]=True    ,						__lowerCamelCase     :	Optional[Any]=False						):





  if config_path is not None:
    __UpperCAmelCase				:      int	 =    WavaVecaConfig.from_pretrained(__lowerCamelCase						)
  else:
    __UpperCAmelCase				:      Any	 =    WavaVecaConfig()

  if is_seq_class:
    __UpperCAmelCase				:      Tuple	 =    read_txt_into_dict(__lowerCamelCase						)
    __UpperCAmelCase				:      Union[str, Any]	 =    idalabel
    __UpperCAmelCase				:      int	 =    WavaVecaForSequenceClassification(__lowerCamelCase						)
    __UpperCAmelCase				:      Optional[int]	 =    WavaVecaFeatureExtractor(
        feature_size=1    ,						sampling_rate=16000    ,						padding_value=0    ,						do_normalize=__lowerCamelCase    ,						return_attention_mask=__lowerCamelCase    ,						)
    feature_extractor.save_pretrained(__lowerCamelCase						)

  elif is_finetuned:
    if dict_path:
      __UpperCAmelCase				:      Any	 =    Dictionary.load(__lowerCamelCase						)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      __UpperCAmelCase				:      Optional[int]	 =    target_dict.pad_index
      __UpperCAmelCase				:      Union[str, Any]	 =    target_dict.bos_index
      __UpperCAmelCase				:      Tuple	 =    target_dict.eos_index
      __UpperCAmelCase				:      int	 =    len(target_dict.symbols						)
      __UpperCAmelCase				:      Tuple	 =    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						)
      __UpperCAmelCase				:      Union[str, Any]	 =    target_dict.indices

      # fairseq has the <pad> and <s> switched
      __UpperCAmelCase				:      Union[str, Any]	 =    0
      __UpperCAmelCase				:      List[str]	 =    1
      with open(__lowerCamelCase    ,						"""w"""    ,						encoding="""utf-8"""						) as vocab_handle:
        json.dump(__lowerCamelCase    ,						__lowerCamelCase						)
      __UpperCAmelCase				:      Optional[int]	 =    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    ,						)
      __UpperCAmelCase				:      str	 =    True if config.feat_extract_norm == "layer" else False
      __UpperCAmelCase				:      List[Any]	 =    WavaVecaFeatureExtractor(
          feature_size=1    ,						sampling_rate=16000    ,						padding_value=0    ,						do_normalize=__lowerCamelCase    ,						return_attention_mask=__lowerCamelCase    ,						)
      __UpperCAmelCase				:      int	 =    WavaVecaProcessor(feature_extractor=__lowerCamelCase    ,						tokenizer=__lowerCamelCase						)
      processor.save_pretrained(__lowerCamelCase						)

    __UpperCAmelCase				:      Tuple	 =    WavaVecaForCTC(__lowerCamelCase						)
  else:
    __UpperCAmelCase				:      Any	 =    WavaVecaForPreTraining(__lowerCamelCase						)

  if is_finetuned or is_seq_class:
    __UpperCAmelCase				:      List[Any]	 =    fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]    ,						arg_overrides={"""data""": """/""".join(dict_path.split("""/"""						)[:-1]						)}						)
  else:
    __UpperCAmelCase				:      Optional[Any]	 =    argparse.Namespace(task="""audio_pretraining"""						)
    __UpperCAmelCase				:      Optional[Any]	 =    fairseq.tasks.setup_task(__lowerCamelCase						)

    __UpperCAmelCase				:      int	 =    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    ,						task=__lowerCamelCase						)

  __UpperCAmelCase				:      Optional[int]	 =    model[0].eval()

  recursively_load_weights(__lowerCamelCase    ,						__lowerCamelCase    ,						not is_finetuned						)

  hf_wavavec.save_pretrained(__lowerCamelCase						)


if __name__ == "__main__":
       a						:			List[Any]     	=					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"
       )
       parser.add_argument(
           "--is_seq_class",
           action="store_true",
           help="Whether the model to convert is a fine-tuned sequence classification model or not",
       )
       a						:			int     	=					parser.parse_args()

       a						:			Any     	=					not args.not_finetuned and not args.is_seq_class
       convert_wavaveca_checkpoint(
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.config_path,
           args.dict_path,
           is_finetuned,
           args.is_seq_class,
       )






'''simple docstring'''


import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)

os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     if hor == 128:
          __A	:       int      =							("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
          __A	:       Union[str, Any]      =							(32, 128, 256)
          __A	:       str      =							("UpResnetBlock1D", "UpResnetBlock1D")

     elif hor == 32:
          __A	:       List[Any]      =							("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
          __A	:       Optional[Any]      =							(32, 64, 128, 256)
          __A	:       Dict      =							("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
     __A	:       Union[str, Any]      =							torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"							)
     __A	:       List[str]      =							model.state_dict()
     __A	:       Any      =							{
         "down_block_types": down_block_types,
         "block_out_channels": block_out_channels,
         "up_block_types": up_block_types,
         "layers_per_block": 1,
         "use_timestep_embedding": True,
         "out_block_type": "OutConv1DBlock",
         "norm_num_groups": 8,
         "downsample_each_block": False,
         "in_channels": 14,
         "out_channels": 14,
         "extra_in_channels": 0,
         "time_embedding_type": "positional",
         "flip_sin_to_cos": False,
         "freq_shift": 1,
         "sample_size": 6_5536,
         "mid_block_type": "MidResTemporalBlock1D",
         "act_fn": "mish",
     }
     __A	:       Union[str, Any]      =							UNetaDModel(**SCREAMING_SNAKE_CASE							)
     print(f"length of state dict: {len(state_dict.keys()							)}"							)
     print(f"length of value function dict: {len(hf_value_function.state_dict().keys()							)}"							)
     __A	:       Dict      =							dict(zip(model.state_dict().keys()       ,					hf_value_function.state_dict().keys()							)							)
     for k, v in mapping.items():
          __A	:       Optional[int]      =							state_dict.pop(SCREAMING_SNAKE_CASE							)
     hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE							)

     torch.save(hf_value_function.state_dict()       ,					f"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"							)
     with open(f"hub/hopper-medium-v2/unet/hor{hor}/config.json"       ,					"w"							) as f:
          json.dump(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Optional[Any]      =							{
         "in_channels": 14,
         "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
         "up_block_types": (),
         "out_block_type": "ValueFunction",
         "mid_block_type": "ValueFunctionMidBlock1D",
         "block_out_channels": (32, 64, 128, 256),
         "layers_per_block": 1,
         "downsample_each_block": True,
         "sample_size": 6_5536,
         "out_channels": 14,
         "extra_in_channels": 0,
         "time_embedding_type": "positional",
         "use_timestep_embedding": True,
         "flip_sin_to_cos": False,
         "freq_shift": 1,
         "norm_num_groups": 8,
         "act_fn": "mish",
     }

     __A	:       Optional[int]      =							torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"							)
     __A	:       Dict      =							model
     __A	:       Union[str, Any]      =							UNetaDModel(**SCREAMING_SNAKE_CASE							)
     print(f"length of state dict: {len(state_dict.keys()							)}"							)
     print(f"length of value function dict: {len(hf_value_function.state_dict().keys()							)}"							)

     __A	:       int      =							dict(zip(state_dict.keys()       ,					hf_value_function.state_dict().keys()							)							)
     for k, v in mapping.items():
          __A	:       str      =							state_dict.pop(SCREAMING_SNAKE_CASE							)

     hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE							)

     torch.save(hf_value_function.state_dict()       ,					"hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"							)
     with open("hub/hopper-medium-v2/value_function/config.json"       ,					"w"							) as f:
          json.dump(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
      unet(32)
      # unet(128)
      value_function()

