
"""simple docstring"""






import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




@skip_mps
class 			_SCREAMING_SNAKE_CASE					(			A__	, unittest.TestCase							):
			UpperCAmelCase_  :Dict				 =						VideoToVideoSDPipeline
			UpperCAmelCase_  :List[Any]				 =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"video"}							) - {"image", "width", "height"}
			UpperCAmelCase_  :int				 =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"video"}							) - {"image"}
			UpperCAmelCase_  :int				 =						PipelineTesterMixin.required_optional_params - {"latents"}
			UpperCAmelCase_  :Union[str, Any]				 =						False

			# No `output_type`.
			UpperCAmelCase_  :Optional[Any]				 =						frozenset(
			    [
			        "num_inference_steps",
			        "generator",
			        "latents",
			        "return_dict",
			        "callback",
			        "callback_steps",
			    ]							)






			def  __lowerCAmelCase (					self       )			->     Tuple:
						torch.manual_seed(0       )
						lowerCAmelCase_    :Any			     =  UNetaDConditionModel(
						    block_out_channels=(32, 64, 64, 64)       ,   layers_per_block=2       ,   sample_size=32       ,   in_channels=4       ,   out_channels=4       ,   down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D""")       ,   up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""")       ,   cross_attention_dim=32       ,   attention_head_dim=4       ,   )
						lowerCAmelCase_    :int			     =  DDIMScheduler(
						    beta_start=0.0_0_0_8_5       ,   beta_end=0.0_1_2       ,   beta_schedule="""scaled_linear"""       ,   clip_sample=__A       ,   set_alpha_to_one=__A       ,   )
						torch.manual_seed(0       )
						lowerCAmelCase_    :int			     =  AutoencoderKL(
						    block_out_channels=[32, 64]       ,   in_channels=3       ,   out_channels=3       ,   down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]       ,   up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]       ,   latent_channels=4       ,   sample_size=128       ,   )
						torch.manual_seed(0       )
						lowerCAmelCase_    :Optional[int]			     =  CLIPTextConfig(
						    bos_token_id=0       ,   eos_token_id=2       ,   hidden_size=32       ,   intermediate_size=37       ,   layer_norm_eps=1E-05       ,   num_attention_heads=4       ,   num_hidden_layers=5       ,   pad_token_id=1       ,   vocab_size=1000       ,   hidden_act="""gelu"""       ,   projection_dim=512       ,   )
						lowerCAmelCase_    :Any			     =  CLIPTextModel(__A       )
						lowerCAmelCase_    :Optional[int]			     =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

						lowerCAmelCase_    :List[str]			     =  {
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """vae""": vae,
						    """text_encoder""": text_encoder,
						    """tokenizer""": tokenizer,
						}
						return components






			def  __lowerCAmelCase (					self       ,   __A       ,   __A=0       )			->     Dict:
						# 3 frames
						lowerCAmelCase_    :str			     =  floats_tensor((1, 3, 3, 32, 32)       ,   rng=random.Random(__A       )       ).to(__A       )

						if str(__A       ).startswith("""mps"""       ):
									lowerCAmelCase_    :List[str]			     =  torch.manual_seed(__A       )
						else:
									lowerCAmelCase_    :Optional[Any]			     =  torch.Generator(device=__A       ).manual_seed(__A       )
						lowerCAmelCase_    :List[Any]			     =  {
						    """prompt""": """A painting of a squirrel eating a burger""",
						    """video""": video,
						    """generator""": generator,
						    """num_inference_steps""": 2,
						    """guidance_scale""": 6.0,
						    """output_type""": """pt""",
						}
						return inputs






			def  __lowerCAmelCase (					self       )			->     Optional[Any]:
						lowerCAmelCase_    :Optional[int]			     =  """cpu"""  # ensure determinism for the device-dependent torch.Generator
						lowerCAmelCase_    :Optional[Any]			     =  self.get_dummy_components()
						lowerCAmelCase_    :Optional[int]			     =  VideoToVideoSDPipeline(**__A       )
						lowerCAmelCase_    :List[Any]			     =  sd_pipe.to(__A       )
						sd_pipe.set_progress_bar_config(disable=__A       )

						lowerCAmelCase_    :int			     =  self.get_dummy_inputs(__A       )
						lowerCAmelCase_    :List[str]			     =  """np"""
						lowerCAmelCase_    :List[str]			     =  sd_pipe(**__A       ).frames
						lowerCAmelCase_    :Any			     =  frames[0][-3:, -3:, -1]

						assert frames[0].shape == (32, 32, 3)
						lowerCAmelCase_    :Union[str, Any]			     =  np.array([106, 117, 113, 174, 137, 112, 148, 151, 131]       )

						assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2






			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()       ,   reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,   )
			def  __lowerCAmelCase (					self       )			->     Tuple:
						self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__A       ,   expected_max_diff=5E-3       )






			@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""       )
			def  __lowerCAmelCase (					self       )			->     Union[str, Any]:
						pass






			@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""       )
			def  __lowerCAmelCase (					self       )			->     List[str]:
						pass






			@unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline."""       )
			def  __lowerCAmelCase (					self       )			->     Any:
						pass






			def  __lowerCAmelCase (					self       )			->     str:
						return super().test_progress_bar()




@slow
@skip_mps
class 			_SCREAMING_SNAKE_CASE					(			unittest.TestCase							):






			def  __lowerCAmelCase (					self       )			->     Dict:
						lowerCAmelCase_    :str			     =  VideoToVideoSDPipeline.from_pretrained("""cerspense/zeroscope_v2_XL"""       ,   torch_dtype=torch.floataa       )
						pipe.enable_model_cpu_offload()

						# 10 frames
						lowerCAmelCase_    :int			     =  torch.Generator(device="""cpu"""       ).manual_seed(0       )
						lowerCAmelCase_    :Union[str, Any]			     =  torch.randn((1, 10, 3, 1024, 576)       ,   generator=__A       )
						lowerCAmelCase_    :Optional[int]			     =  video.to("""cuda"""       )

						lowerCAmelCase_    :Tuple			     =  """Spiderman is surfing"""

						lowerCAmelCase_    :Union[str, Any]			     =  pipe(__A       ,   video=__A       ,   generator=__A       ,   num_inference_steps=3       ,   output_type="""pt"""       ).frames

						lowerCAmelCase_    :str			     =  np.array([-1.0_4_5_8_9_8_4, -1.1_2_7_9_2_9_7, -0.9_6_6_3_0_8_6, -0.9_1_5_0_3_9_0_6, -0.7_5_0_9_7_6_5_6]       )
						assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array       ).sum() < 1E-2







"""simple docstring"""
import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
lowercase__					       =	logging.get_logger('transformers.models.encodec')

lowercase__					       =	{
    'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
    'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
    'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
    'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
lowercase__					       =	{
    'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
    'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
    'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
    'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
    'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
    'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
    'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
    'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
    'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
    'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
    'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
    'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
    'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
    'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
    'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
    'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
    'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
    'encoder.model.13.lstm': 'encoder.layers.13.lstm',
    'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
lowercase__					       =	{
    'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
    'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
    'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
    'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
    'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
    'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
    'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
    'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
    'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
    'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
    'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
    'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
    'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
    'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
    'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
    'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
    'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
    'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
lowercase__					       =	{
    'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
    'decoder.model.1.lstm': 'decoder.layers.1.lstm',
    'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
    'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
    'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
    'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
    'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
    'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
    'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
    'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
    'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
    'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
    'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
    'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
    'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
    'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
    'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
    'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
    'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
lowercase__					       =	{
    'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
    'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
    'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
    'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
    'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
    'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
    'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
    'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
    'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
    'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
    'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
    'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
    'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
    'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
    'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
    'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
    'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
    'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
lowercase__					       =	{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
lowercase__					       =	{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
lowercase__					       =	[]
lowercase__					       =	[]







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->int:
  for attribute in key.split('.'   ):
    a__:     str										=	getattr(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

  if weight_type is not None:
    a__:     List[str]										=	getattr(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ).shape
  else:
    a__:     Optional[Any]										=	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":
    a__:     str										=	value
  elif weight_type == "weight_g":
    a__:     int										=	value
  elif weight_type == "weight_v":
    a__:     Tuple										=	value
  elif weight_type == "bias":
    a__:     Dict										=	value
  elif weight_type == "running_mean":
    a__:     Any										=	value
  elif weight_type == "running_var":
    a__:     Tuple										=	value
  elif weight_type == "num_batches_tracked":
    a__:     List[str]										=	value
  elif weight_type == "weight_ih_l0":
    a__:     List[Any]										=	value
  elif weight_type == "weight_hh_l0":
    a__:     List[Any]										=	value
  elif weight_type == "bias_ih_l0":
    a__:     List[Any]										=	value
  elif weight_type == "bias_hh_l0":
    a__:     List[Any]										=	value
  elif weight_type == "weight_ih_l1":
    a__:     int										=	value
  elif weight_type == "weight_hh_l1":
    a__:     str										=	value
  elif weight_type == "bias_ih_l1":
    a__:     Union[str, Any]										=	value
  elif weight_type == "bias_hh_l1":
    a__:     Any										=	value
  else:
    a__:     Union[str, Any]										=	value

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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Dict:
  for key in ignore_keys:
    if key.endswith('.*'   ):
      if name.startswith(key[:-1]   ):
        return True
    elif ".*." in key:
      a__							,    a__:     Optional[Any]										=	key.split('.*.'   )
      if prefix in name and suffix in name:
        return True
    elif key in name:
      return True
  return False







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->List[str]:
  a__:     List[Any]										=	[]

  if model_name == "encodec_24khz" or "encodec_32khz":
    a__:     Optional[int]										=	MAPPING_24K
  elif model_name == "encodec_48khz":
    a__:     List[Any]										=	MAPPING_48K
  else:
    raise ValueError(F'Unsupported model: {model_name}'   )

  for name, value in orig_dict.items():
    if should_ignore(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
      logger.info(F'{name} was ignored'   )
      continue

    a__:     int										=	False
    for key, mapped_key in MAPPING.items():
      if "*" in key:
        a__							,    a__:     str										=	key.split('.*.'   )
        if prefix in name and suffix in name:
          a__:     List[str]										=	suffix

      if key in name:
        # HACK otherwise .embed gets initialized with .embed_avg too
        if key.endswith('embed'   ) and name.endswith('embed_avg'   ):
          continue

        a__:     List[str]										=	True
        if "*" in mapped_key:
          a__:     List[str]										=	name.split(_SCREAMING_SNAKE_CASE   )[0].split('.'   )[-2]
          a__:     str										=	mapped_key.replace('*'		,     _SCREAMING_SNAKE_CASE   )
        if "weight_g" in name:
          a__:     int										=	'weight_g'
        elif "weight_v" in name:
          a__:     Dict										=	'weight_v'
        elif "weight_ih_l0" in name:
          a__:     int										=	'weight_ih_l0'
        elif "weight_hh_l0" in name:
          a__:     Union[str, Any]										=	'weight_hh_l0'
        elif "bias_ih_l0" in name:
          a__:     Optional[Any]										=	'bias_ih_l0'
        elif "bias_hh_l0" in name:
          a__:     Optional[int]										=	'bias_hh_l0'
        elif "weight_ih_l1" in name:
          a__:     Dict										=	'weight_ih_l1'
        elif "weight_hh_l1" in name:
          a__:     Optional[Any]										=	'weight_hh_l1'
        elif "bias_ih_l1" in name:
          a__:     List[str]										=	'bias_ih_l1'
        elif "bias_hh_l1" in name:
          a__:     Optional[Any]										=	'bias_hh_l1'
        elif "bias" in name:
          a__:     List[str]										=	'bias'
        elif "weight" in name:
          a__:     Any										=	'weight'
        elif "running_mean" in name:
          a__:     Dict										=	'running_mean'
        elif "running_var" in name:
          a__:     Dict										=	'running_var'
        elif "num_batches_tracked" in name:
          a__:     Dict										=	'num_batches_tracked'
        else:
          a__:     List[str]										=	None
        set_recursively(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      continue
    if not is_used:
      unused_weights.append(_SCREAMING_SNAKE_CASE   )

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







@torch.no_grad()
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None		,     _SCREAMING_SNAKE_CASE=None		,     )	->int:
  if config_path is not None:
    a__:     Dict										=	EncodecConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )
  else:
    a__:     Tuple										=	EncodecConfig()

  if model_name == "encodec_24khz":
    pass  # config is already correct
  elif model_name == "encodec_32khz":
    a__:     Any										=	[8, 5, 4, 4]
    a__:     List[str]										=	[2.2]
    a__:     List[Any]										=	64
    a__:     Dict										=	32000
    a__:     Union[str, Any]										=	2048
    a__:     Union[str, Any]										=	False
    a__:     Any										=	False
    a__:     Optional[Any]										=	False
  elif model_name == "encodec_48khz":
    a__:     Optional[int]										=	[8, 5, 4, 2]
    a__:     Union[str, Any]										=	[3.0, 6.0, 12.0, 24.0]
    a__:     List[str]										=	48000
    a__:     Tuple										=	2
    a__:     Optional[Any]										=	False
    a__:     Optional[int]										=	'time_group_norm'
    a__:     Union[str, Any]										=	True
    a__:     Dict										=	1.0
    a__:     str										=	0.01
  else:
    raise ValueError(F'Unknown model name: {model_name}'   )

  a__:     Optional[int]										=	EncodecModel(_SCREAMING_SNAKE_CASE   )

  a__:     List[str]										=	EncodecFeatureExtractor(
      feature_size=config.audio_channels		,     sampling_rate=config.sampling_rate		,     chunk_length_s=config.chunk_length_s		,     overlap=config.overlap		,     )
  feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE   )

  a__:     int										=	torch.load(_SCREAMING_SNAKE_CASE   )
  if "best_state" in original_checkpoint:
    # we might have a training state saved, in which case discard the yaml results and just retain the weights
    a__:     str										=	original_checkpoint['best_state']
  recursively_load_weights(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  model.save_pretrained(_SCREAMING_SNAKE_CASE   )

  if repo_id:
    print('Pushing to the hub...'   )
    feature_extractor.push_to_hub(_SCREAMING_SNAKE_CASE   )
    model.push_to_hub(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
 lowercase__					       =	argparse.ArgumentParser()
 parser.add_argument(
     '--model',
     default='encodec_24khz',
     type=str,
     help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
 )
 parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
 parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
 parser.add_argument(
     '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
 )
 parser.add_argument(
     '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
 )

 lowercase__					       =	parser.parse_args()
 convert_checkpoint(
     args.model,
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )


