"""simple docstring"""


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()
UpperCAmelCase__     						=  logging.get_logger(__name__)


UpperCAmelCase__     						=  ["""model.decoder.embed_positions.weights"""]






def 			__UpperCAmelCase    (	lowercase						):







  """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 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					list(state_dict.keys()						)
  _UpperCAmelCase         =					{}
  for key in keys:
    _UpperCAmelCase         =					state_dict.pop(lowercase						)
    _UpperCAmelCase         =					rename_keys(lowercase						)
    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 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  if checkpoint == "small":
    # default config values
    _UpperCAmelCase         =					10_24
    _UpperCAmelCase         =					24
    _UpperCAmelCase         =					16
  elif checkpoint == "medium":
    _UpperCAmelCase         =					15_36
    _UpperCAmelCase         =					48
    _UpperCAmelCase         =					24
  elif checkpoint == "large":
    _UpperCAmelCase         =					20_48
    _UpperCAmelCase         =					48
    _UpperCAmelCase         =					32
  else:
    raise ValueError(f'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'''						)
  _UpperCAmelCase         =					MusicgenDecoderConfig(
      hidden_size=lowercase      ,ffn_dim=hidden_size * 4      ,num_hidden_layers=lowercase      ,num_attention_heads=lowercase      ,)
  return config






@torch.no_grad()
def 			__UpperCAmelCase    (	lowercase      ,lowercase=None      ,lowercase=None      ,lowercase="cpu"						):







  """simple docstring"""
  _UpperCAmelCase         =					MusicGen.get_pretrained(lowercase      ,device=lowercase						)
  _UpperCAmelCase         =					decoder_config_from_checkpoint(lowercase						)

  _UpperCAmelCase         =					fairseq_model.lm.state_dict()
  _UpperCAmelCase       , _UpperCAmelCase         =					rename_state_dict(
      lowercase      ,hidden_size=decoder_config.hidden_size						)

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

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

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

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

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

  # init the composite model
  _UpperCAmelCase         =					MusicgenForConditionalGeneration(text_encoder=lowercase      ,audio_encoder=lowercase      ,decoder=lowercase						)

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

  # 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=lowercase      ,decoder_input_ids=lowercase						).logits

  if logits.shape != (8, 1, 20_48):
    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=lowercase      ,tokenizer=lowercase						)

  # set the appropriate bos/pad token ids
  _UpperCAmelCase         =					20_48
  _UpperCAmelCase         =					20_48

  # 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(lowercase						).mkdir(exist_ok=lowercase						)
    logger.info(f'''Saving model {checkpoint} to {pytorch_dump_folder}'''						)
    model.save_pretrained(lowercase						)
    processor.save_pretrained(lowercase						)

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


if __name__ == "__main__":
    UpperCAmelCase__     						=  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."""
    )

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

"""simple docstring"""


from __future__ import annotations

from collections import Counter
from random import random


class 				a :


    def __init__(    self		:     Union[str, Any]    ):
      _UpperCAmelCase         =					{}


    def    lowerCAmelCase_     (    self		:     Optional[int]      , __lowerCAmelCase		:     str    ):
      _UpperCAmelCase         =					{}


    def    lowerCAmelCase_     (    self		:     str      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     float    ):
      if nodea not in self.connections:
        self.add_node(__lowerCAmelCase    )
      if nodea not in self.connections:
        self.add_node(__lowerCAmelCase    )
      _UpperCAmelCase         =					probability


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      return list(self.connections    )


    def    lowerCAmelCase_     (    self		:     List[Any]      , __lowerCAmelCase		:     str    ):
      _UpperCAmelCase         =					0
      _UpperCAmelCase         =					random()

      for dest in self.connections[node]:
        current_probability += self.connections[node][dest]
        if current_probability > random_value:
          return dest
      return ""






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					MarkovChainGraphUndirectedUnweighted()

  for nodea, nodea, probability in transitions:
    graph.add_transition_probability(lowercase      ,lowercase      ,lowercase						)

  _UpperCAmelCase         =					Counter(graph.get_nodes()						)
  _UpperCAmelCase         =					start

  for _ in range(lowercase						):
    _UpperCAmelCase         =					graph.transition(lowercase						)
    visited[node] += 1

  return visited


if __name__ == "__main__":
    import doctest

    doctest.testmod()
