





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    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.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''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}







def 					lowerCAmelCase__(		lowercase				:			Union[str, Any]      ,   lowercase				:			Any      ,   lowercase				:			Any      ,   lowercase				:			Any      ,   lowercase				:			Union[str, Any] )       ->			List[str]:







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

  if weight_type is not None:
    __snake_case      : Any						=				getattr(lowercase      ,   lowercase ).shape
  else:
    __snake_case      : Union[str, Any]						=				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":
    __snake_case      : str						=				value
  elif weight_type == "weight_g":
    __snake_case      : List[str]						=				value
  elif weight_type == "weight_v":
    __snake_case      : str						=				value
  elif weight_type == "bias":
    __snake_case      : Union[str, Any]						=				value
  else:
    __snake_case      : str						=				value

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







def 					lowerCAmelCase__(		lowercase				:			Any      ,   lowercase				:			Optional[Any]      ,   lowercase				:			Optional[int] )       ->			int:
  __snake_case      : List[Any]						=				[]
  __snake_case      : int						=				fairseq_model.state_dict()

  __snake_case      : List[Any]						=				hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    __snake_case      : List[Any]						=				False
    if "conv_layers" in name:
      load_conv_layer(
          lowercase      ,   lowercase      ,   lowercase      ,   lowercase      ,   hf_model.config.feat_extract_norm == "group"      ,   )
      __snake_case      : List[str]						=				True
    else:
      for key, mapped_key in MAPPING.items():
        __snake_case      : Dict						=				'''hubert.''' + 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] and not is_finetuned):
          __snake_case      : Optional[Any]						=				True
          if "*" in mapped_key:
            __snake_case      : List[str]						=				name.split(lowercase )[0].split("." )[-2]
            __snake_case      : Optional[int]						=				mapped_key.replace("*"      ,   lowercase )
          if "weight_g" in name:
            __snake_case      : int						=				'''weight_g'''
          elif "weight_v" in name:
            __snake_case      : List[Any]						=				'''weight_v'''
          elif "weight" in name:
            __snake_case      : Dict						=				'''weight'''
          elif "bias" in name:
            __snake_case      : Dict						=				'''bias'''
          else:
            __snake_case      : Dict						=				None
          set_recursively(lowercase      ,   lowercase      ,   lowercase      ,   lowercase      ,   lowercase )
        continue
    if not is_used:
      unused_weights.append(lowercase )

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







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







@torch.no_grad()
def 					lowerCAmelCase__(		lowercase				:			Union[str, Any]      ,   lowercase				:			Dict      ,   lowercase				:			Dict=None      ,   lowercase				:			Tuple=None      ,   lowercase				:			Optional[Any]=True )       ->			Optional[int]:







  if config_path is not None:
    __snake_case      : Tuple						=				HubertConfig.from_pretrained(lowercase )
  else:
    __snake_case      : Optional[int]						=				HubertConfig()

  if is_finetuned:
    if dict_path:
      __snake_case      : Optional[int]						=				Dictionary.load(lowercase )

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      __snake_case      : Optional[int]						=				target_dict.pad_index
      __snake_case      : Dict						=				target_dict.bos_index
      __snake_case      : str						=				target_dict.eos_index
      __snake_case      : Dict						=				len(target_dict.symbols )
      __snake_case      : List[Any]						=				os.path.join(lowercase      ,   "vocab.json" )
      if not os.path.isdir(lowercase ):
        logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(lowercase ) )
        return
      os.makedirs(lowercase      ,   exist_ok=lowercase )
      with open(lowercase      ,   "w"      ,   encoding="utf-8" ) as vocab_handle:
        json.dump(target_dict.indices      ,   lowercase )
      __snake_case      : Optional[int]						=				WavaVecaCTCTokenizer(
          lowercase      ,   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=lowercase      ,   )
      __snake_case      : Union[str, Any]						=				True if config.feat_extract_norm == '''layer''' else False
      __snake_case      : Any						=				WavaVecaFeatureExtractor(
          feature_size=1      ,   sampling_rate=1_6000      ,   padding_value=0      ,   do_normalize=lowercase      ,   return_attention_mask=lowercase      ,   )
      __snake_case      : Any						=				WavaVecaProcessor(feature_extractor=lowercase      ,   tokenizer=lowercase )
      processor.save_pretrained(lowercase )

    __snake_case      : List[str]						=				HubertForCTC(lowercase )
  else:
    __snake_case      : str						=				HubertModel(lowercase )

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

  __snake_case      : Dict						=				model[0].eval()

  recursively_load_weights(lowercase      ,   lowercase      ,   lowercase )

  hf_wavavec.save_pretrained(lowercase )


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_hubert_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )


from __future__ import annotations

from PIL import Image

# Define glider example
__lowercase							 =					[
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
__lowercase							 =					[[0, 1, 0], [0, 1, 0], [0, 1, 0]]



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :int			  =		[]
    for i in range(len(SCREAMING_SNAKE_CASE	)	):
        __UpperCamelCase     :Dict			  =		[]
        for j in range(len(cells[i]	)	):
            # Get the number of live neighbours
            __UpperCamelCase     :List[str]			  =		0
            if i > 0 and j > 0:
                neighbour_count += cells[i - 1][j - 1]
            if i > 0:
                neighbour_count += cells[i - 1][j]
            if i > 0 and j < len(cells[i]	) - 1:
                neighbour_count += cells[i - 1][j + 1]
            if j > 0:
                neighbour_count += cells[i][j - 1]
            if j < len(cells[i]	) - 1:
                neighbour_count += cells[i][j + 1]
            if i < len(SCREAMING_SNAKE_CASE	) - 1 and j > 0:
                neighbour_count += cells[i + 1][j - 1]
            if i < len(SCREAMING_SNAKE_CASE	) - 1:
                neighbour_count += cells[i + 1][j]
            if i < len(SCREAMING_SNAKE_CASE	) - 1 and j < len(cells[i]	) - 1:
                neighbour_count += cells[i + 1][j + 1]

            # Rules of the game of life (excerpt from Wikipedia):
            # 1. Any live cell with two or three live neighbours survives.
            # 2. Any dead cell with three live neighbours becomes a live cell.
            # 3. All other live cells die in the next generation.
            #    Similarly, all other dead cells stay dead.
            __UpperCamelCase     :List[str]			  =		cells[i][j] == 1
            if (
                (alive and 2 <= neighbour_count <= 3)
                or not alive
                and neighbour_count == 3
            ):
                next_generation_row.append(1	)
            else:
                next_generation_row.append(0	)

        next_generation.append(SCREAMING_SNAKE_CASE	)
    return next_generation



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		[]
    for _ in range(SCREAMING_SNAKE_CASE	):
        # Create output image
        __UpperCamelCase     :Dict			  =		Image.new('''RGB'''  ,			(len(cells[0]	), len(SCREAMING_SNAKE_CASE	))	)
        __UpperCamelCase     :Any			  =		img.load()

        # Save cells to image
        for x in range(len(SCREAMING_SNAKE_CASE	)	):
            for y in range(len(cells[0]	)	):
                __UpperCamelCase     :Optional[Any]			  =		255 - cells[y][x] * 255
                __UpperCamelCase     :int			  =		(colour, colour, colour)

        # Save image
        images.append(SCREAMING_SNAKE_CASE	)
        __UpperCamelCase     :Optional[int]			  =		new_generation(SCREAMING_SNAKE_CASE	)
    return images


if __name__ == "__main__":
    __lowercase							 =					generate_images(GLIDER, 16)
    images[0].save('''out.gif''', save_all=True, append_images=images[1:])





