
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_						     =					logging.get_logger(__name__)


lowerCamelCase_						     =					['''model.decoder.embed_positions.weights''']






def 						__magic_name__			(   __a      :	List[Any]						):



     '''simple docstring'''




     if "emb" in name:
          UpperCamelCase__				=    name.replace("""emb"""	,		"""model.decoder.embed_tokens"""						)
     if "transformer" in name:
          UpperCamelCase__				=    name.replace("""transformer"""	,		"""model.decoder"""						)
     if "cross_attention" in name:
          UpperCamelCase__				=    name.replace("""cross_attention"""	,		"""encoder_attn"""						)
     if "linear1" in name:
          UpperCamelCase__				=    name.replace("""linear1"""	,		"""fc1"""						)
     if "linear2" in name:
          UpperCamelCase__				=    name.replace("""linear2"""	,		"""fc2"""						)
     if "norm1" in name:
          UpperCamelCase__				=    name.replace("""norm1"""	,		"""self_attn_layer_norm"""						)
     if "norm_cross" in name:
          UpperCamelCase__				=    name.replace("""norm_cross"""	,		"""encoder_attn_layer_norm"""						)
     if "norm2" in name:
          UpperCamelCase__				=    name.replace("""norm2"""	,		"""final_layer_norm"""						)
     if "out_norm" in name:
          UpperCamelCase__				=    name.replace("""out_norm"""	,		"""model.decoder.layer_norm"""						)
     if "linears" in name:
          UpperCamelCase__				=    name.replace("""linears"""	,		"""lm_heads"""						)
     if "condition_provider.conditioners.description.output_proj" in name:
          UpperCamelCase__				=    name.replace("""condition_provider.conditioners.description.output_proj"""	,		"""enc_to_dec_proj"""						)
     return name






def 						__magic_name__			(   __a      :	OrderedDict	,		__a      :	int						):



     '''simple docstring'''




     UpperCamelCase__				=    list(state_dict.keys()						)
     UpperCamelCase__				=    {}
     for key in keys:
          UpperCamelCase__				=    state_dict.pop(__a						)
          UpperCamelCase__				=    rename_keys(__a						)
          if "in_proj_weight" in key:
               # split fused qkv proj
               UpperCamelCase__				=    val[:hidden_size, :]
               UpperCamelCase__				=    val[hidden_size : 2 * hidden_size, :]
               UpperCamelCase__				=    val[-hidden_size:, :]
          elif "enc_to_dec_proj" in key:
               UpperCamelCase__				=    val
          else:
               UpperCamelCase__				=    val
     return state_dict, enc_dec_proj_state_dict






def 						__magic_name__			(   __a      :	str						):



     '''simple docstring'''




     if checkpoint == "small":
          # default config values
          UpperCamelCase__				=    1_024
          UpperCamelCase__				=    24
          UpperCamelCase__				=    16
     elif checkpoint == "medium":
          UpperCamelCase__				=    1_536
          UpperCamelCase__				=    48
          UpperCamelCase__				=    24
     elif checkpoint == "large":
          UpperCamelCase__				=    2_048
          UpperCamelCase__				=    48
          UpperCamelCase__				=    32
     else:
          raise ValueError(f"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."						)
     UpperCamelCase__				=    MusicgenDecoderConfig(
         hidden_size=__a	,		ffn_dim=hidden_size * 4	,		num_hidden_layers=__a	,		num_attention_heads=__a	,		)
     return config






@torch.no_grad()
def 						__magic_name__			(   __a      :	Union[str, Any]	,		__a      :	List[str]=None	,		__a      :	List[str]=None	,		__a      :	List[str]="cpu"						):



     '''simple docstring'''




     UpperCamelCase__				=    MusicGen.get_pretrained(__a	,		device=__a						)
     UpperCamelCase__				=    decoder_config_from_checkpoint(__a						)

     UpperCamelCase__				=    fairseq_model.lm.state_dict()
     UpperCamelCase__		,	UpperCamelCase__				=    rename_state_dict(
         __a	,		hidden_size=decoder_config.hidden_size						)

     UpperCamelCase__				=    TaEncoderModel.from_pretrained("""t5-base"""						)
     UpperCamelCase__				=    EncodecModel.from_pretrained("""facebook/encodec_32khz"""						)
     UpperCamelCase__				=    MusicgenForCausalLM(__a						).eval()

     # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
     UpperCamelCase__		,	UpperCamelCase__				=    decoder.load_state_dict(__a	,		strict=__a						)

     for key in missing_keys.copy():
          if key.startswith(("""text_encoder""", """audio_encoder""")						) or key in EXPECTED_MISSING_KEYS:
               missing_keys.remove(__a						)

     if len(__a						) > 0:
          raise ValueError(f"Missing key(s) in state_dict: {missing_keys}"						)

     if len(__a						) > 0:
          raise ValueError(f"Unexpected key(s) in state_dict: {unexpected_keys}"						)

     # init the composite model
     UpperCamelCase__				=    MusicgenForConditionalGeneration(text_encoder=__a	,		audio_encoder=__a	,		decoder=__a						)

     # load the pre-trained enc-dec projection (from the decoder state dict)
     model.enc_to_dec_proj.load_state_dict(__a						)

     # check we can do a forward pass
     UpperCamelCase__				=    torch.arange(0	,		8	,		dtype=torch.long						).reshape(2	,		-1						)
     UpperCamelCase__				=    input_ids.reshape(2 * 4	,		-1						)

     with torch.no_grad():
          UpperCamelCase__				=    model(input_ids=__a	,		decoder_input_ids=__a						).logits

     if logits.shape != (8, 1, 2_048):
          raise ValueError("""Incorrect shape for logits"""						)

     # now construct the processor
     UpperCamelCase__				=    AutoTokenizer.from_pretrained("""t5-base"""						)
     UpperCamelCase__				=    AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz"""	,		padding_side="""left"""						)

     UpperCamelCase__				=    MusicgenProcessor(feature_extractor=__a	,		tokenizer=__a						)

     # set the appropriate bos/pad token ids
     UpperCamelCase__				=    2_048
     UpperCamelCase__				=    2_048

     # set other default generation config params
     UpperCamelCase__				=    int(30 * audio_encoder.config.frame_rate						)
     UpperCamelCase__				=    True
     UpperCamelCase__				=    3.0

     if pytorch_dump_folder is not None:
          Path(__a						).mkdir(exist_ok=__a						)
          logger.info(f"Saving model {checkpoint} to {pytorch_dump_folder}"						)
          model.save_pretrained(__a						)
          processor.save_pretrained(__a						)

     if repo_id:
          logger.info(f"Pushing model {checkpoint} to {repo_id}"						)
          model.push_to_hub(__a						)
          processor.push_to_hub(__a						)


if __name__ == "__main__":
    lowerCamelCase_						     =					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--checkpoint''',
        default='''small''',
        type=str,
        help='''Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder''',
        required=True,
        default=None,
        type=str,
        help='''Path to the output PyTorch model directory.''',
    )
    parser.add_argument(
        '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
    )
    parser.add_argument(
        '''--device''', default='''cpu''', type=str, help='''Torch device to run the conversion, either cpu or cuda.'''
    )

    lowerCamelCase_						     =					parser.parse_args()
    convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)







import argparse
from collections import defaultdict

import yaml


lowerCamelCase_						     =					'''docs/source/en/_toctree.yml'''






def 						__magic_name__			(   __a      :	Union[str, Any]						):



     '''simple docstring'''




     UpperCamelCase__				=    defaultdict(__a						)
     for doc in model_doc:
          counts[doc["local"]] += 1
     UpperCamelCase__				=    [key for key, value in counts.items() if value > 1]

     UpperCamelCase__				=    []
     for duplicate_key in duplicates:
          UpperCamelCase__				=    list({doc["""title"""] for doc in model_doc if doc["""local"""] == duplicate_key}						)
          if len(__a						) > 1:
               raise ValueError(
                   f"{duplicate_key} is present several times in the documentation table of content at "
                   """`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
                   """others."""						)
          # Only add this once
          new_doc.append({"""local""": duplicate_key, """title""": titles[0]}						)

     # Add none duplicate-keys
     new_doc.extend([doc for doc in model_doc if counts[doc["""local"""]] == 1]						)

     # Sort
     return sorted(__a	,		key=lambda __a						: s["title"].lower()						)






def 						__magic_name__			(   __a      :	int=False						):



     '''simple docstring'''




     with open(__a	,		encoding="""utf-8"""						) as f:
          UpperCamelCase__				=    yaml.safe_load(f.read()						)

     # Get to the API doc
     UpperCamelCase__				=    0
     while content[api_idx]["title"] != "API":
          api_idx += 1
     UpperCamelCase__				=    content[api_idx]["""sections"""]

     # Then to the model doc
     UpperCamelCase__				=    0
     while api_doc[model_idx]["title"] != "Models":
          model_idx += 1

     UpperCamelCase__				=    api_doc[model_idx]["""sections"""]

     UpperCamelCase__				=    [(idx, section) for idx, section in enumerate(__a						) if """sections""" in section]
     UpperCamelCase__				=    False
     for idx, modality_doc in modalities_docs:
          UpperCamelCase__				=    modality_doc["""sections"""]
          UpperCamelCase__				=    clean_model_doc_toc(__a						)

          if old_modality_doc != new_modality_doc:
               UpperCamelCase__				=    True
               if overwrite:
                    UpperCamelCase__				=    new_modality_doc

     if diff:
          if overwrite:
               UpperCamelCase__				=    model_doc
               UpperCamelCase__				=    api_doc
               with open(__a	,		"""w"""	,		encoding="""utf-8"""						) as f:
                    f.write(yaml.dump(__a	,		allow_unicode=__a						)						)
          else:
               raise ValueError(
                   """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""						)


if __name__ == "__main__":
    lowerCamelCase_						     =					argparse.ArgumentParser()
    parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
    lowerCamelCase_						     =					parser.parse_args()

    check_model_doc(args.fix_and_overwrite)





