


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

_A		       =						{
    '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		       =						{
    '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		       =						{
    '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		       =						{
    '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		       =						{
    '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		       =						{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
_A		       =						{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
_A		       =						[]
_A		       =						[]

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					List[str]			,     SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					List[str]			,     SCREAMING_SNAKE_CASE__			:					List[str]				):
     for attribute in key.split('.'				):
          __UpperCamelCase    							=getattr(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)

     if weight_type is not None:
          __UpperCamelCase    							=getattr(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				).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				(			SCREAMING_SNAKE_CASE__			:					Optional[Any]			,     SCREAMING_SNAKE_CASE__			:					Tuple				):
     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				(			SCREAMING_SNAKE_CASE__			:					Optional[int]			,     SCREAMING_SNAKE_CASE__			:					Optional[Any]			,     SCREAMING_SNAKE_CASE__			:					Optional[int]				):
     __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(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				):
               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(SCREAMING_SNAKE_CASE__				)[0].split('.'				)[-2]
                         __UpperCamelCase    							=mapped_key.replace('*'			,     SCREAMING_SNAKE_CASE__				)
                    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(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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Any			,     SCREAMING_SNAKE_CASE__			:					Dict			,     SCREAMING_SNAKE_CASE__			:					Dict			,     SCREAMING_SNAKE_CASE__			:					Optional[int]=None			,     SCREAMING_SNAKE_CASE__			:					int=None			,     ):
     if config_path is not None:
          __UpperCamelCase    							=EncodecConfig.from_pretrained(SCREAMING_SNAKE_CASE__				)
     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    							=3_20_00
          __UpperCamelCase    							=20_48
          __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_80_00
          __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(SCREAMING_SNAKE_CASE__				)

     __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(SCREAMING_SNAKE_CASE__				)

     __UpperCamelCase    							=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
          __UpperCamelCase    							=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__":
   _A		       =						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		       =						parser.parse_args()
   convert_checkpoint(
       args.model,
       args.checkpoint_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.push_to_hub,
   )









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "yolos"






    def __init__(			self					,   A_=768					,   A_=12					,   A_=12					,   A_=3072					,   A_="gelu"					,   A_=0.0					,   A_=0.0					,   A_=0.02					,   A_=1E-12					,   A_=[512, 864]					,   A_=16					,   A_=3					,   A_=True					,   A_=100					,   A_=True					,   A_=False					,   A_=1					,   A_=5					,   A_=2					,   A_=5					,   A_=2					,   A_=0.1					,   **A_					,   )  ->       Any:
         super().__init__(**A_    )

         __UpperCamelCase    							=hidden_size
         __UpperCamelCase    							=num_hidden_layers
         __UpperCamelCase    							=num_attention_heads
         __UpperCamelCase    							=intermediate_size
         __UpperCamelCase    							=hidden_act
         __UpperCamelCase    							=hidden_dropout_prob
         __UpperCamelCase    							=attention_probs_dropout_prob
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=layer_norm_eps
         __UpperCamelCase    							=image_size
         __UpperCamelCase    							=patch_size
         __UpperCamelCase    							=num_channels
         __UpperCamelCase    							=qkv_bias
         __UpperCamelCase    							=num_detection_tokens
         __UpperCamelCase    							=use_mid_position_embeddings
         __UpperCamelCase    							=auxiliary_loss
         # Hungarian matcher
         __UpperCamelCase    							=class_cost
         __UpperCamelCase    							=bbox_cost
         __UpperCamelCase    							=giou_cost
         # Loss coefficients
         __UpperCamelCase    							=bbox_loss_coefficient
         __UpperCamelCase    							=giou_loss_coefficient
         __UpperCamelCase    							=eos_coefficient






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		str      =  version.parse("1.11"		)






    @property
    def _a					(			self    )  ->       Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ]    )






    @property
    def _a					(			self    )  ->       float:
         return 1E-4






    @property
    def _a					(			self    )  ->       int:
         return 12





