


'''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()
__A :  Any    							=				logging.get_logger("transformers.models.encodec")

__A :  Tuple    							=				{
    "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",
}
__A :  Optional[int]    							=				{
    "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",
}
__A :  Dict    							=				{
    "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",
}
__A :  Optional[int]    							=				{
    "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",
}
__A :  List[Any]    							=				{
    "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",
}
__A :  List[Any]    							=				{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__A :  str    							=				{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__A :  List[str]    							=				[]
__A :  Any    							=				[]







def     UpperCamelCase_      (   A__ :       Tuple							,   A__ :       int							,   A__ :       Dict							,   A__ :       str							,   A__ :       str ):
		'''simple docstring'''





		for attribute in key.split(""".""" ):
				lowerCAmelCase_      :				Tuple      =       getattr(A__							,   A__ )

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

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







def     UpperCamelCase_      (   A__ :       Optional[Any]							,   A__ :       Dict ):
		'''simple docstring'''





		for key in ignore_keys:
				if key.endswith(""".*""" ):
						if name.startswith(key[:-1] ):
								return True
				elif ".*." in key:
						lowerCAmelCase_      :				int      =       key.split(""".*.""" )
						if prefix in name and suffix in name:
								return True
				elif key in name:
						return True
		return False







def     UpperCamelCase_      (   A__ :       Any							,   A__ :       Union[str, Any]							,   A__ :       int ):
		'''simple docstring'''





		lowerCAmelCase_      :				str      =       []

		if model_name == "encodec_24khz" or "encodec_32khz":
				lowerCAmelCase_      :				List[str]      =       MAPPING_24K
		elif model_name == "encodec_48khz":
				lowerCAmelCase_      :				int      =       MAPPING_48K
		else:
				raise ValueError(f'Unsupported model: {model_name}' )

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

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

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

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

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







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Tuple							,   A__ :       List[Any]							,   A__ :       Any							,   A__ :       Union[str, Any]=None							,   A__ :       str=None							,   ):
		'''simple docstring'''





		if config_path is not None:
				lowerCAmelCase_      :				int      =       EncodecConfig.from_pretrained(A__ )
		else:
				lowerCAmelCase_      :				List[str]      =       EncodecConfig()

		if model_name == "encodec_24khz":
				pass  # config is already correct
		elif model_name == "encodec_32khz":
				lowerCAmelCase_      :				str      =       [8, 5, 4, 4]
				lowerCAmelCase_      :				str      =       [2.2]
				lowerCAmelCase_      :				Optional[Any]      =       64
				lowerCAmelCase_      :				str      =       3_20_00
				lowerCAmelCase_      :				Optional[Any]      =       20_48
				lowerCAmelCase_      :				Union[str, Any]      =       False
				lowerCAmelCase_      :				Tuple      =       False
				lowerCAmelCase_      :				Any      =       False
		elif model_name == "encodec_48khz":
				lowerCAmelCase_      :				Dict      =       [8, 5, 4, 2]
				lowerCAmelCase_      :				Tuple      =       [3.0, 6.0, 12.0, 24.0]
				lowerCAmelCase_      :				Optional[int]      =       4_80_00
				lowerCAmelCase_      :				Any      =       2
				lowerCAmelCase_      :				Optional[Any]      =       False
				lowerCAmelCase_      :				List[str]      =       """time_group_norm"""
				lowerCAmelCase_      :				Tuple      =       True
				lowerCAmelCase_      :				int      =       1.0
				lowerCAmelCase_      :				Tuple      =       0.01
		else:
				raise ValueError(f'Unknown model name: {model_name}' )

		lowerCAmelCase_      :				List[Any]      =       EncodecModel(A__ )

		lowerCAmelCase_      :				int      =       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(A__ )

		lowerCAmelCase_      :				Optional[int]      =       torch.load(A__ )
		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
				lowerCAmelCase_      :				Optional[Any]      =       original_checkpoint["""best_state"""]
		recursively_load_weights(A__							,   A__							,   A__ )
		model.save_pretrained(A__ )

		if repo_id:
				print("""Pushing to the hub...""" )
				feature_extractor.push_to_hub(A__ )
				model.push_to_hub(A__ )


if __name__ == "__main__":
		__A :  str    							=				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."
		)

		__A :  str    							=				parser.parse_args()
		convert_checkpoint(
		    args.model,
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.push_to_hub,
		)







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__A :  List[str]    							=				{
    "configuration_owlvit": [
        "OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "OwlViTConfig",
        "OwlViTOnnxConfig",
        "OwlViTTextConfig",
        "OwlViTVisionConfig",
    ],
    "processing_owlvit": ["OwlViTProcessor"],
}


try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  Optional[int]    							=				["OwlViTFeatureExtractor"]
  __A :  str    							=				["OwlViTImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  Tuple    							=				[
      "OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "OwlViTModel",
      "OwlViTPreTrainedModel",
      "OwlViTTextModel",
      "OwlViTVisionModel",
      "OwlViTForObjectDetection",
  ]

if TYPE_CHECKING:
  from .configuration_owlvit import (
      OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      OwlViTConfig,
      OwlViTOnnxConfig,
      OwlViTTextConfig,
      OwlViTVisionConfig,
  )
  from .processing_owlvit import OwlViTProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_owlvit import OwlViTFeatureExtractor
    from .image_processing_owlvit import OwlViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_owlvit import (
        OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OwlViTForObjectDetection,
        OwlViTModel,
        OwlViTPreTrainedModel,
        OwlViTTextModel,
        OwlViTVisionModel,
    )

else:
  import sys

  __A :  List[Any]    							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



