'''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()
_A			:      Optional[int]					      =				logging.get_logger(__name__)


_A			:      List[Any]					      =				['''model.decoder.embed_positions.weights''']







def      UpperCamelCase_						(					snake_case_       :       Dict      )					->     Optional[int]:






 '''simple docstring'''

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







def      UpperCamelCase_						(					snake_case_       :       Any						,		snake_case_       :       Tuple      )					->     List[Any]:






 '''simple docstring'''

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







def      UpperCamelCase_						(					snake_case_       :       str      )					->     Optional[Any]:






 '''simple docstring'''

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







@torch.no_grad()
def      UpperCamelCase_						(					snake_case_       :       List[Any]						,		snake_case_       :       List[Any]=None						,		snake_case_       :       Optional[Any]=None						,		snake_case_       :       Union[str, Any]="cpu"      )					->     Optional[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						MusicGen.get_pretrained(lowerCAmelCase_						,		device=lowerCAmelCase_      )
 __lowerCAmelCase											=						decoder_config_from_checkpoint(lowerCAmelCase_      )

 __lowerCAmelCase											=						fairseq_model.lm.state_dict()
 __lowerCAmelCase							,       __lowerCAmelCase											=						rename_state_dict(
     lowerCAmelCase_						,		hidden_size=decoder_config.hidden_size      )

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

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

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

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

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

 # init the composite model
 __lowerCAmelCase											=						MusicgenForConditionalGeneration(text_encoder=lowerCAmelCase_						,		audio_encoder=lowerCAmelCase_						,		decoder=lowerCAmelCase_      )

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

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

 with torch.no_grad():
  __lowerCAmelCase											=						model(input_ids=lowerCAmelCase_						,		decoder_input_ids=lowerCAmelCase_      ).logits

 if logits.shape != (8, 1, 20_48):
  raise ValueError("""Incorrect shape for logits"""      )

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

 __lowerCAmelCase											=						MusicgenProcessor(feature_extractor=lowerCAmelCase_						,		tokenizer=lowerCAmelCase_      )

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

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

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

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


if __name__ == "__main__":
 _A			:      Union[str, Any]					      =				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.'''
 )

 _A			:      int					      =				parser.parse_args()
 convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)










"""simple docstring"""







import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


a__				:							Tuple						=     False




class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	pass




@slow
@require_torch_gpu
class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				int   )       ->	int:
						__SCREAMING_SNAKE_CASE						   =				VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
						pipe.to(UpperCAmelCase__   )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )
						__SCREAMING_SNAKE_CASE						   =				torch.manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				pipe(
						    image=UpperCAmelCase__			,			generator=UpperCAmelCase__			,			guidance_scale=7.5			,			num_inference_steps=5_0			,			output_type="numpy"			,			).images

						__SCREAMING_SNAKE_CASE						   =				image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						__SCREAMING_SNAKE_CASE						   =				np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]   )

						assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
