



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

_lowerCamelCase	:       int      	=	{
    "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",
}
_lowerCamelCase	:       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",
}
_lowerCamelCase	:       Optional[Any]      	=	{
    "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",
}
_lowerCamelCase	:       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",
}
_lowerCamelCase	:       Union[str, 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",
}
_lowerCamelCase	:       Optional[int]      	=	{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
_lowerCamelCase	:       Optional[Any]      	=	{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
_lowerCamelCase	:       Optional[int]      	=	[]
_lowerCamelCase	:       Union[str, Any]      	=	[]







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    ,						A__    )       ->	List[str]:






   """simple docstring"""
   for attribute in key.split('.'    ):
      UpperCamelCase					=  getattr(A__    ,						A__    )

   if weight_type is not None:
      UpperCamelCase					=  getattr(A__    ,						A__    ).shape
   else:
      UpperCamelCase					=  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					=  value
   elif weight_type == "weight_g":
      UpperCamelCase					=  value
   elif weight_type == "weight_v":
      UpperCamelCase					=  value
   elif weight_type == "bias":
      UpperCamelCase					=  value
   elif weight_type == "running_mean":
      UpperCamelCase					=  value
   elif weight_type == "running_var":
      UpperCamelCase					=  value
   elif weight_type == "num_batches_tracked":
      UpperCamelCase					=  value
   elif weight_type == "weight_ih_l0":
      UpperCamelCase					=  value
   elif weight_type == "weight_hh_l0":
      UpperCamelCase					=  value
   elif weight_type == "bias_ih_l0":
      UpperCamelCase					=  value
   elif weight_type == "bias_hh_l0":
      UpperCamelCase					=  value
   elif weight_type == "weight_ih_l1":
      UpperCamelCase					=  value
   elif weight_type == "weight_hh_l1":
      UpperCamelCase					=  value
   elif weight_type == "bias_ih_l1":
      UpperCamelCase					=  value
   elif weight_type == "bias_hh_l1":
      UpperCamelCase					=  value
   else:
      UpperCamelCase					=  value

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







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	List[Any]:






   """simple docstring"""
   for key in ignore_keys:
      if key.endswith('.*'    ):
         if name.startswith(key[:-1]    ):
            return True
      elif ".*." in key:
         UpperCamelCase				,      UpperCamelCase					=  key.split('.*.'    )
         if prefix in name and suffix in name:
            return True
      elif key in name:
         return True
   return False







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	int:






   """simple docstring"""
   UpperCamelCase					=  []

   if model_name == "encodec_24khz" or "encodec_32khz":
      UpperCamelCase					=  MAPPING_24K
   elif model_name == "encodec_48khz":
      UpperCamelCase					=  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

      UpperCamelCase					=  False
      for key, mapped_key in MAPPING.items():
         if "*" in key:
            UpperCamelCase				,      UpperCamelCase					=  key.split('.*.'    )
            if prefix in name and suffix in name:
               UpperCamelCase					=  suffix

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

            UpperCamelCase					=  True
            if "*" in mapped_key:
               UpperCamelCase					=  name.split(A__    )[0].split('.'    )[-2]
               UpperCamelCase					=  mapped_key.replace('*'    ,						A__    )
            if "weight_g" in name:
               UpperCamelCase					=  'weight_g'
            elif "weight_v" in name:
               UpperCamelCase					=  'weight_v'
            elif "weight_ih_l0" in name:
               UpperCamelCase					=  'weight_ih_l0'
            elif "weight_hh_l0" in name:
               UpperCamelCase					=  'weight_hh_l0'
            elif "bias_ih_l0" in name:
               UpperCamelCase					=  'bias_ih_l0'
            elif "bias_hh_l0" in name:
               UpperCamelCase					=  'bias_hh_l0'
            elif "weight_ih_l1" in name:
               UpperCamelCase					=  'weight_ih_l1'
            elif "weight_hh_l1" in name:
               UpperCamelCase					=  'weight_hh_l1'
            elif "bias_ih_l1" in name:
               UpperCamelCase					=  'bias_ih_l1'
            elif "bias_hh_l1" in name:
               UpperCamelCase					=  'bias_hh_l1'
            elif "bias" in name:
               UpperCamelCase					=  'bias'
            elif "weight" in name:
               UpperCamelCase					=  'weight'
            elif "running_mean" in name:
               UpperCamelCase					=  'running_mean'
            elif "running_var" in name:
               UpperCamelCase					=  'running_var'
            elif "num_batches_tracked" in name:
               UpperCamelCase					=  'num_batches_tracked'
            else:
               UpperCamelCase					=  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 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__=None    ,						A__=None    ,						)       ->	Optional[int]:






   """simple docstring"""
   if config_path is not None:
      UpperCamelCase					=  EncodecConfig.from_pretrained(A__    )
   else:
      UpperCamelCase					=  EncodecConfig()

   if model_name == "encodec_24khz":
      pass  # config is already correct
   elif model_name == "encodec_32khz":
      UpperCamelCase					=  [8, 5, 4, 4]
      UpperCamelCase					=  [2.2]
      UpperCamelCase					=  64
      UpperCamelCase					=  32_000
      UpperCamelCase					=  2_048
      UpperCamelCase					=  False
      UpperCamelCase					=  False
      UpperCamelCase					=  False
   elif model_name == "encodec_48khz":
      UpperCamelCase					=  [8, 5, 4, 2]
      UpperCamelCase					=  [3.0, 6.0, 12.0, 24.0]
      UpperCamelCase					=  48_000
      UpperCamelCase					=  2
      UpperCamelCase					=  False
      UpperCamelCase					=  'time_group_norm'
      UpperCamelCase					=  True
      UpperCamelCase					=  1.0
      UpperCamelCase					=  0.01
   else:
      raise ValueError(F"""Unknown model name: {model_name}"""    )

   UpperCamelCase					=  EncodecModel(A__    )

   UpperCamelCase					=  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__    )

   UpperCamelCase					=  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
      UpperCamelCase					=  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__":
   _lowerCamelCase	:       List[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."
   )

   _lowerCamelCase	:       Dict      	=	parser.parse_args()
   convert_checkpoint(
       args.model,
       args.checkpoint_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.push_to_hub,
   )








import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import MobileNetVaImageProcessor







class 				__lowerCAmelCase       (    unittest.TestCase):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__=7		,      lowerCAmelCase__=3		,      lowerCAmelCase__=1_8		,      lowerCAmelCase__=3_0		,      lowerCAmelCase__=4_0_0		,      lowerCAmelCase__=True		,      lowerCAmelCase__=None		,      lowerCAmelCase__=True		,      lowerCAmelCase__=None		,      )   ->						Optional[int]:



        '''simple docstring'''




        a__	:					str										=size if size is not None else {"shortest_edge": 2_0}
        a__	:					Union[str, Any]										=crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
        a__	:					Tuple										=parent
        a__	:					Optional[int]										=batch_size
        a__	:					Any										=num_channels
        a__	:					List[str]										=image_size
        a__	:					Dict										=min_resolution
        a__	:					List[Any]										=max_resolution
        a__	:					Dict										=do_resize
        a__	:					Union[str, Any]										=size
        a__	:					str										=do_center_crop
        a__	:					List[str]										=crop_size







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }







@require_torch
@require_vision
class 				__lowerCAmelCase       (    UpperCamelCase__						,				unittest.TestCase):
      _lowercase				:					Optional[Any]						=			MobileNetVaImageProcessor if is_vision_available() else None







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Optional[int]										=MobileNetVaImageProcessingTester(self							)







      @property
      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        return self.image_processor_tester.prepare_image_processor_dict()







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					List[str]										=self.image_processing_class(**self.image_processor_dict							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_resize"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "size"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_center_crop"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "crop_size"							)							)







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					Any										=self.image_processing_class.from_dict(self.image_processor_dict							)
        self.assertEqual(image_processor.size		,      {"shortest_edge": 2_0}							)
        self.assertEqual(image_processor.crop_size		,      {"height": 1_8, "width": 1_8}							)

        a__	:					Dict										=self.image_processing_class.from_dict(self.image_processor_dict		,      size=4_2		,      crop_size=8_4							)
        self.assertEqual(image_processor.size		,      {"shortest_edge": 4_2}							)
        self.assertEqual(image_processor.crop_size		,      {"height": 8_4, "width": 8_4}							)







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        pass







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					Dict										=self.image_processing_class(**self.image_processor_dict							)
        # create random PIL images
        a__	:					Optional[Any]										=prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,      Image.Image							)

        # Test not batched input
        a__	:					List[Any]										=image_processing(image_inputs[0]		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )

        # Test batched
        a__	:					Dict										=image_processing(lowerCAmelCase__		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )







      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        a__	:					str										=self.image_processing_class(**self.image_processor_dict							)
        # create random numpy tensors
        a__	:					str										=prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase__		,      numpify=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,      np.ndarray							)

        # Test not batched input
        a__	:					List[str]										=image_processing(image_inputs[0]		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )

        # Test batched
        a__	:					Union[str, Any]										=image_processing(lowerCAmelCase__		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					Any										=self.image_processing_class(**self.image_processor_dict							)
        # create random PyTorch tensors
        a__	:					int										=prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase__		,      torchify=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,      torch.Tensor							)

        # Test not batched input
        a__	:					Optional[Any]										=image_processing(image_inputs[0]		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )

        # Test batched
        a__	:					str										=image_processing(lowerCAmelCase__		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )






