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

a					:	Any							=		{
    '''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					:	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''',
}
a					:	List[Any]							=		{
    '''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					:	Dict							=		{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
a					:	int							=		{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
a					:	str							=		[]
a					:	List[Any]							=		[]


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      _A      )    ->  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  _UpperCamelCase	(						_A					,      _A      )    ->  Optional[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  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  List[str]:





    """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  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A=None					,      _A=None					,      )    ->  Tuple:





    """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          =   6_4
        _UpperCAmelCase          =   3_2_0_0_0
        _UpperCAmelCase          =   2_0_4_8
        _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          =   4_8_0_0_0
        _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__":
 a					:	Tuple							=		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					:	int							=		parser.parse_args()
 convert_checkpoint(
     args.model,
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )

"""simple docstring"""



import unittest

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

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 		a_						( unittest.TestCase	):







    @property
    def      _snake_case					(			self			:       Dict     )		->int:






        '''simple docstring'''






        torch.manual_seed(0     )
        _UpperCAmelCase          =   UNetaDModel(
            block_out_channels=(32, 64)     ,					layers_per_block=2     ,					sample_size=32     ,					in_channels=3     ,					out_channels=3     ,					down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")     ,					up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")     ,					)
        return model







    @property
    def      _snake_case					(			self			:       Optional[Any]     )		->str:






        '''simple docstring'''






        torch.manual_seed(0     )
        _UpperCAmelCase          =   VQModel(
            block_out_channels=[32, 64]     ,					in_channels=3     ,					out_channels=3     ,					down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]     ,					up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]     ,					latent_channels=3     ,					)
        return model







    @property
    def      _snake_case					(			self			:       List[Any]     )		->Optional[int]:






        '''simple docstring'''






        torch.manual_seed(0     )
        _UpperCAmelCase          =   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=10_00     ,					)
        return CLIPTextModel(__UpperCamelCase     )







    def      _snake_case					(			self			:       List[str]     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.dummy_uncond_unet
        _UpperCAmelCase          =   DDIMScheduler()
        _UpperCAmelCase          =   self.dummy_vq_model

        _UpperCAmelCase          =   LDMPipeline(unet=__UpperCamelCase     ,					vqvae=__UpperCamelCase     ,					scheduler=__UpperCamelCase     )
        ldm.to(__UpperCamelCase     )
        ldm.set_progress_bar_config(disable=__UpperCamelCase     )

        _UpperCAmelCase          =   torch.manual_seed(0     )
        _UpperCAmelCase          =   ldm(generator=__UpperCamelCase     ,					num_inference_steps=2     ,					output_type="""numpy"""     ).images

        _UpperCAmelCase          =   torch.manual_seed(0     )
        _UpperCAmelCase          =   ldm(generator=__UpperCamelCase     ,					num_inference_steps=2     ,					output_type="""numpy"""     ,					return_dict=__UpperCamelCase     )[0]

        _UpperCAmelCase          =   image[0, -3:, -3:, -1]
        _UpperCAmelCase          =   image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        _UpperCAmelCase          =   np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2]     )
        _UpperCAmelCase          =   1e-2 if torch_device != """mps""" else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < tolerance
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < tolerance







@slow
@require_torch
class 		a_						( unittest.TestCase	):







    def      _snake_case					(			self			:       List[Any]     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256"""     )
        ldm.to(__UpperCamelCase     )
        ldm.set_progress_bar_config(disable=__UpperCamelCase     )

        _UpperCAmelCase          =   torch.manual_seed(0     )
        _UpperCAmelCase          =   ldm(generator=__UpperCamelCase     ,					num_inference_steps=5     ,					output_type="""numpy"""     ).images

        _UpperCAmelCase          =   image[0, -3:, -3:, -1]

        assert image.shape == (1, 2_56, 2_56, 3)
        _UpperCAmelCase          =   np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7]     )
        _UpperCAmelCase          =   1e-2 if torch_device != """mps""" else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < tolerance