


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   :					List[Any]	=      logging.get_logger("transformers.models.encodec")

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

def    lowercase_	(	_A    :					Union[str, Any]					,      _A    :					Union[str, Any]					,      _A    :					Optional[Any]					,      _A    :					Optional[int]					,      _A    :					int		)     ->   Optional[Any]:

      """simple docstring"""




      for attribute in key.split("."		):
            lowerCamelCase__	:       Optional[Any]					=						getattr(_A					,      _A		)

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

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

def    lowercase_	(	_A    :					Tuple					,      _A    :					Any		)     ->   Optional[int]:

      """simple docstring"""




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

def    lowercase_	(	_A    :					List[Any]					,      _A    :					Optional[int]					,      _A    :					Optional[Any]		)     ->   List[str]:

      """simple docstring"""




      lowerCamelCase__	:       Dict					=						[]

      if model_name == "encodec_24khz" or "encodec_32khz":
            lowerCamelCase__	:       str					=						MAPPING_24K
      elif model_name == "encodec_48khz":
            lowerCamelCase__	:       Any					=						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__	:       int					=						False
            for key, mapped_key in MAPPING.items():
                  if "*" in key:
                        lowerCamelCase__	:       Any					=						key.split(".*."		)
                        if prefix in name and suffix in name:
                              lowerCamelCase__	:       Optional[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__	:       Optional[Any]					=						True
                        if "*" in mapped_key:
                              lowerCamelCase__	:       Optional[Any]					=						name.split(_A		)[0].split("."		)[-2]
                              lowerCamelCase__	:       str					=						mapped_key.replace("*"					,      _A		)
                        if "weight_g" in name:
                              lowerCamelCase__	:       Any					=						"weight_g"
                        elif "weight_v" in name:
                              lowerCamelCase__	:       List[Any]					=						"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__	:       Optional[int]					=						"bias_hh_l0"
                        elif "weight_ih_l1" in name:
                              lowerCamelCase__	:       Dict					=						"weight_ih_l1"
                        elif "weight_hh_l1" in name:
                              lowerCamelCase__	:       Optional[Any]					=						"weight_hh_l1"
                        elif "bias_ih_l1" in name:
                              lowerCamelCase__	:       List[str]					=						"bias_ih_l1"
                        elif "bias_hh_l1" in name:
                              lowerCamelCase__	:       List[Any]					=						"bias_hh_l1"
                        elif "bias" in name:
                              lowerCamelCase__	:       str					=						"bias"
                        elif "weight" in name:
                              lowerCamelCase__	:       List[Any]					=						"weight"
                        elif "running_mean" in name:
                              lowerCamelCase__	:       Any					=						"running_mean"
                        elif "running_var" in name:
                              lowerCamelCase__	:       List[Any]					=						"running_var"
                        elif "num_batches_tracked" in name:
                              lowerCamelCase__	:       str					=						"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    lowercase_	(	_A    :					Tuple					,      _A    :					Union[str, Any]					,      _A    :					List[str]					,      _A    :					List[str]=None					,      _A    :					Optional[int]=None					,      )     ->   Tuple:

      """simple docstring"""




      if config_path is not None:
            lowerCamelCase__	:       str					=						EncodecConfig.from_pretrained(_A		)
      else:
            lowerCamelCase__	:       Optional[int]					=						EncodecConfig()

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

      lowerCamelCase__	:       Dict					=						EncodecModel(_A		)

      lowerCamelCase__	:       Dict					=						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__	:       Dict					=						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__	:       List[str]					=						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   :					Dict	=      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   :					List[Any]	=      parser.parse_args()
    convert_checkpoint(
        args.model,
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       @slow
       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						TFCamembertModel.from_pretrained("jplu/tf-camembert-base" )

             lowerCamelCase__	:       str					=						tf.convert_to_tensor(
                 [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]      ,   dtype=tf.intaa      ,   )  # J'aime le camembert !"

             lowerCamelCase__	:       Any					=						model(__lowerCamelCase )["last_hidden_state"]
             lowerCamelCase__	:       List[str]					=						tf.TensorShape((1, 10, 768) )
             self.assertEqual(output.shape      ,   __lowerCamelCase )
             # compare the actual values for a slice.
             lowerCamelCase__	:       str					=						tf.convert_to_tensor(
                 [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]      ,   dtype=tf.floataa      ,   )
             # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
             # camembert.eval()
             # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

             self.assertTrue(np.allclose(output[:, :3, :3].numpy()      ,   expected_slice.numpy()      ,   atol=1E-4 ) )


