





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()
lowercase_          =		logging.get_logger(__name__)


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




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




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




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       MusicgenDecoderConfig:
  if checkpoint == "small":
    # default config values
    lowercase__           =  1024
    lowercase__           =  24
    lowercase__           =  16
  elif checkpoint == "medium":
    lowercase__           =  1536
    lowercase__           =  48
    lowercase__           =  24
  elif checkpoint == "large":
    lowercase__           =  2048
    lowercase__           =  48
    lowercase__           =  32
  else:
    raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""		)
  lowercase__           =  MusicgenDecoderConfig(
      hidden_size=_SCREAMING_SNAKE_CASE		,						ffn_dim=hidden_size * 4		,						num_hidden_layers=_SCREAMING_SNAKE_CASE		,						num_attention_heads=_SCREAMING_SNAKE_CASE		,						)
  return config




@torch.no_grad()
def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=None		,						_SCREAMING_SNAKE_CASE=None		,						_SCREAMING_SNAKE_CASE="cpu"		)    ->       Optional[int]:
  lowercase__           =  MusicGen.get_pretrained(_SCREAMING_SNAKE_CASE		,						device=_SCREAMING_SNAKE_CASE		)
  lowercase__           =  decoder_config_from_checkpoint(_SCREAMING_SNAKE_CASE		)

  lowercase__           =  fairseq_model.lm.state_dict()
  lowercase__      ,   lowercase__           =  rename_state_dict(
      _SCREAMING_SNAKE_CASE		,						hidden_size=decoder_config.hidden_size		)

  lowercase__           =  TaEncoderModel.from_pretrained('t5-base'		)
  lowercase__           =  EncodecModel.from_pretrained('facebook/encodec_32khz'		)
  lowercase__           =  MusicgenForCausalLM(_SCREAMING_SNAKE_CASE		).eval()

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

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

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

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

  # init the composite model
  lowercase__           =  MusicgenForConditionalGeneration(text_encoder=_SCREAMING_SNAKE_CASE		,						audio_encoder=_SCREAMING_SNAKE_CASE		,						decoder=_SCREAMING_SNAKE_CASE		)

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

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

  with torch.no_grad():
    lowercase__           =  model(input_ids=_SCREAMING_SNAKE_CASE		,						decoder_input_ids=_SCREAMING_SNAKE_CASE		).logits

  if logits.shape != (8, 1, 2048):
    raise ValueError('Incorrect shape for logits'		)

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

  lowercase__           =  MusicgenProcessor(feature_extractor=_SCREAMING_SNAKE_CASE		,						tokenizer=_SCREAMING_SNAKE_CASE		)

  # set the appropriate bos/pad token ids
  lowercase__           =  2048
  lowercase__           =  2048

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

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

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


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

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









import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


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




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Any:
  lowercase__           =  MobileNetVaConfig(layer_norm_eps=0.0_0_1		)

  if "_quant" in model_name:
    raise ValueError('Quantized models are not supported.'		)

  lowercase__           =  re.match(R'^mobilenet_v1_([^_]*)_([^_]*)$'		,						_SCREAMING_SNAKE_CASE		)
  if matches:
    lowercase__           =  float(matches[1]		)
    lowercase__           =  int(matches[2]		)

  # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
  # the usual 1000. The first class (index 0) is "background".
  lowercase__           =  1001
  lowercase__           =  'imagenet-1k-id2label.json'
  lowercase__           =  'huggingface/label-files'
  lowercase__           =  json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						repo_type='dataset'		)		,						'r'		)		)
  lowercase__           =  {int(_SCREAMING_SNAKE_CASE		) + 1: v for k, v in idalabel.items()}
  lowercase__           =  'background'
  lowercase__           =  idalabel
  lowercase__           =  {v: k for k, v in idalabel.items()}

  return config




def       __UpperCamelCase  ()    ->       int:
  lowercase__           =  'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowercase__           =  Image.open(requests.get(_SCREAMING_SNAKE_CASE		,						stream=_SCREAMING_SNAKE_CASE		).raw		)
  return im




@torch.no_grad()
def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=False		)    ->       Union[str, Any]:
  lowercase__           =  get_mobilenet_va_config(_SCREAMING_SNAKE_CASE		)

  # Load 🤗 model
  lowercase__           =  MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE		).eval()

  # Load weights from TensorFlow checkpoint
  load_tf_weights_in_mobilenet_va(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)

  # Check outputs on an image, prepared by MobileNetV1ImageProcessor
  lowercase__           =  MobileNetVaImageProcessor(
      crop_size={'width': config.image_size, 'height': config.image_size}		,						size={'shortest_edge': config.image_size + 32}		,						)
  lowercase__           =  image_processor(images=prepare_img()		,						return_tensors='pt'		)
  lowercase__           =  model(**_SCREAMING_SNAKE_CASE		)
  lowercase__           =  outputs.logits

  assert logits.shape == (1, 1001)

  if model_name == "mobilenet_v1_1.0_224":
    lowercase__           =  torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]		)
  elif model_name == "mobilenet_v1_0.75_192":
    lowercase__           =  torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]		)
  else:
    lowercase__           =  None

  if expected_logits is not None:
    assert torch.allclose(logits[0, :3]		,						_SCREAMING_SNAKE_CASE		,						atol=1E-4		)

  Path(_SCREAMING_SNAKE_CASE		).mkdir(exist_ok=_SCREAMING_SNAKE_CASE		)
  print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""		)
  model.save_pretrained(_SCREAMING_SNAKE_CASE		)
  print(F"""Saving image processor to {pytorch_dump_folder_path}"""		)
  image_processor.save_pretrained(_SCREAMING_SNAKE_CASE		)

  if push_to_hub:
    print('Pushing to the hub...'		)
    lowercase__           =  'google/' + model_name
    image_processor.push_to_hub(_SCREAMING_SNAKE_CASE		)
    model.push_to_hub(_SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
      lowercase_          =		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default="""mobilenet_v1_1.0_224""",
          type=str,
          help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.""",
      )
      parser.add_argument(
          """--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt file)."""
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
      )
      parser.add_argument(
          """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
      )

      lowercase_          =		parser.parse_args()
      convert_movilevit_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )


