

"""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()
lowerCamelCase__ :     Optional[Any]							  =     logging.get_logger(__name__)


lowerCamelCase__ :     List[str]							  =     ['''model.decoder.embed_positions.weights''']
def 					UpperCamelCase    (	_lowerCAmelCase   :  Optional[Any]       ) ->	Tuple:
	if "emb" in name:
		_UpperCAmelCase			:							Tuple	  =    name.replace("""emb""",     """model.decoder.embed_tokens"""       )
	if "transformer" in name:
		_UpperCAmelCase			:							Optional[Any]	  =    name.replace("""transformer""",     """model.decoder"""       )
	if "cross_attention" in name:
		_UpperCAmelCase			:							int	  =    name.replace("""cross_attention""",     """encoder_attn"""       )
	if "linear1" in name:
		_UpperCAmelCase			:							Optional[int]	  =    name.replace("""linear1""",     """fc1"""       )
	if "linear2" in name:
		_UpperCAmelCase			:							Optional[int]	  =    name.replace("""linear2""",     """fc2"""       )
	if "norm1" in name:
		_UpperCAmelCase			:							List[Any]	  =    name.replace("""norm1""",     """self_attn_layer_norm"""       )
	if "norm_cross" in name:
		_UpperCAmelCase			:							Tuple	  =    name.replace("""norm_cross""",     """encoder_attn_layer_norm"""       )
	if "norm2" in name:
		_UpperCAmelCase			:							Dict	  =    name.replace("""norm2""",     """final_layer_norm"""       )
	if "out_norm" in name:
		_UpperCAmelCase			:							List[str]	  =    name.replace("""out_norm""",     """model.decoder.layer_norm"""       )
	if "linears" in name:
		_UpperCAmelCase			:							List[str]	  =    name.replace("""linears""",     """lm_heads"""       )
	if "condition_provider.conditioners.description.output_proj" in name:
		_UpperCAmelCase			:							str	  =    name.replace("""condition_provider.conditioners.description.output_proj""",     """enc_to_dec_proj"""       )
	return name
def 					UpperCamelCase    (	_lowerCAmelCase   :  Tuple,     _lowerCAmelCase   :  List[Any]       ) ->	List[Any]:
	_UpperCAmelCase			:							Dict	  =    list(state_dict.keys()       )
	_UpperCAmelCase			:							List[Any]	  =    {}
	for key in keys:
		_UpperCAmelCase			:							str	  =    state_dict.pop(_lowerCAmelCase       )
		_UpperCAmelCase			:							List[Any]	  =    rename_keys(_lowerCAmelCase       )
		if "in_proj_weight" in key:
			# split fused qkv proj
			_UpperCAmelCase			:							int	  =    val[:hidden_size, :]
			_UpperCAmelCase			:							Union[str, Any]	  =    val[hidden_size : 2 * hidden_size, :]
			_UpperCAmelCase			:							Any	  =    val[-hidden_size:, :]
		elif "enc_to_dec_proj" in key:
			_UpperCAmelCase			:							Any	  =    val
		else:
			_UpperCAmelCase			:							Optional[Any]	  =    val
	return state_dict, enc_dec_proj_state_dict
def 					UpperCamelCase    (	_lowerCAmelCase   :  Dict       ) ->	Union[str, Any]:
	if checkpoint == "small":
		# default config values
		_UpperCAmelCase			:							Tuple	  =    1024
		_UpperCAmelCase			:							List[str]	  =    24
		_UpperCAmelCase			:							Any	  =    16
	elif checkpoint == "medium":
		_UpperCAmelCase			:							int	  =    1536
		_UpperCAmelCase			:							int	  =    48
		_UpperCAmelCase			:							List[str]	  =    24
	elif checkpoint == "large":
		_UpperCAmelCase			:							Tuple	  =    2048
		_UpperCAmelCase			:							int	  =    48
		_UpperCAmelCase			:							List[str]	  =    32
	else:
		raise ValueError(f'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'''       )
	_UpperCAmelCase			:							Dict	  =    MusicgenDecoderConfig(
	    hidden_size=_lowerCAmelCase,     ffn_dim=hidden_size * 4,     num_hidden_layers=_lowerCAmelCase,     num_attention_heads=_lowerCAmelCase,     )
	return config
@torch.no_grad()
def 					UpperCamelCase    (	_lowerCAmelCase   :  Union[str, Any],     _lowerCAmelCase   :  List[str]=None,     _lowerCAmelCase   :  Union[str, Any]=None,     _lowerCAmelCase   :  List[Any]="cpu"       ) ->	Union[str, Any]:
	_UpperCAmelCase			:							List[Any]	  =    MusicGen.get_pretrained(_lowerCAmelCase,     device=_lowerCAmelCase       )
	_UpperCAmelCase			:							Any	  =    decoder_config_from_checkpoint(_lowerCAmelCase       )

	_UpperCAmelCase			:							List[str]	  =    fairseq_model.lm.state_dict()
	_UpperCAmelCase  ,      _UpperCAmelCase			:							Union[str, Any]	  =    rename_state_dict(
	    _lowerCAmelCase,     hidden_size=decoder_config.hidden_size       )

	_UpperCAmelCase			:							Tuple	  =    TaEncoderModel.from_pretrained("""t5-base"""       )
	_UpperCAmelCase			:							str	  =    EncodecModel.from_pretrained("""facebook/encodec_32khz"""       )
	_UpperCAmelCase			:							Any	  =    MusicgenForCausalLM(_lowerCAmelCase       ).eval()

	# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
	_UpperCAmelCase  ,      _UpperCAmelCase			:							Dict	  =    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
	_UpperCAmelCase			:							str	  =    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
	_UpperCAmelCase			:							List[Any]	  =    torch.arange(0,     8,     dtype=torch.long       ).reshape(2,     -1       )
	_UpperCAmelCase			:							List[str]	  =    input_ids.reshape(2 * 4,     -1       )

	with torch.no_grad():
		_UpperCAmelCase			:							List[Any]	  =    model(input_ids=_lowerCAmelCase,     decoder_input_ids=_lowerCAmelCase       ).logits

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

	# now construct the processor
	_UpperCAmelCase			:							List[str]	  =    AutoTokenizer.from_pretrained("""t5-base"""       )
	_UpperCAmelCase			:							str	  =    AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz""",     padding_side="""left"""       )

	_UpperCAmelCase			:							Any	  =    MusicgenProcessor(feature_extractor=_lowerCAmelCase,     tokenizer=_lowerCAmelCase       )

	# set the appropriate bos/pad token ids
	_UpperCAmelCase			:							Optional[Any]	  =    2048
	_UpperCAmelCase			:							str	  =    2048

	# set other default generation config params
	_UpperCAmelCase			:							int	  =    int(30 * audio_encoder.config.frame_rate       )
	_UpperCAmelCase			:							List[Any]	  =    True
	_UpperCAmelCase			:							int	  =    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__":
						lowerCamelCase__ :     List[str]							  =     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__ :     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

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import MobileViTImageProcessor


class 				a (						unittest.TestCase      ):


    def __init__(    self		:     Dict      , __lowerCAmelCase		:     Tuple      , __lowerCAmelCase		:     Optional[Any]=7      , __lowerCAmelCase		:     Optional[Any]=3      , __lowerCAmelCase		:     Optional[Any]=18      , __lowerCAmelCase		:     str=30      , __lowerCAmelCase		:     List[str]=400      , __lowerCAmelCase		:     Union[str, Any]=True      , __lowerCAmelCase		:     str=None      , __lowerCAmelCase		:     Optional[int]=True      , __lowerCAmelCase		:     int=None      , __lowerCAmelCase		:     List[str]=True      , ):
      _UpperCAmelCase         =					size if size is not None else {"""shortest_edge""": 20}
      _UpperCAmelCase         =					crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
      _UpperCAmelCase         =					parent
      _UpperCAmelCase         =					batch_size
      _UpperCAmelCase         =					num_channels
      _UpperCAmelCase         =					image_size
      _UpperCAmelCase         =					min_resolution
      _UpperCAmelCase         =					max_resolution
      _UpperCAmelCase         =					do_resize
      _UpperCAmelCase         =					size
      _UpperCAmelCase         =					do_center_crop
      _UpperCAmelCase         =					crop_size
      _UpperCAmelCase         =					do_flip_channel_order


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_flip_channel_order": self.do_flip_channel_order,
      }



@require_torch
@require_vision
class 				a (						lowerCAmelCase_		,							unittest.TestCase      ):
    _snake_case   :     Optional[int]   							=							MobileViTImageProcessor if is_vision_available() else None


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      _UpperCAmelCase         =					MobileViTImageProcessingTester(self    )


    @property
    def    lowerCAmelCase_     (    self		:     Tuple    ):
      return self.image_processor_tester.prepare_image_processor_dict()


    def    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_resize"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """size"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_center_crop"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """center_crop"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_flip_channel_order"""    )    )


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      _UpperCAmelCase         =					self.image_processing_class.from_dict(self.image_processor_dict    )
      self.assertEqual(image_processor.size      , {"""shortest_edge""": 20}    )
      self.assertEqual(image_processor.crop_size      , {"""height""": 18, """width""": 18}    )

      _UpperCAmelCase         =					self.image_processing_class.from_dict(self.image_processor_dict      , size=42      , crop_size=84    )
      self.assertEqual(image_processor.size      , {"""shortest_edge""": 42}    )
      self.assertEqual(image_processor.crop_size      , {"""height""": 84, """width""": 84}    )


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      pass


    def    lowerCAmelCase_     (    self		:     Dict    ):
      # Initialize image_processing
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      # create random PIL images
      _UpperCAmelCase         =					prepare_image_inputs(self.image_processor_tester      , equal_resolution=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , Image.Image    )

      # Test not batched input
      _UpperCAmelCase         =					image_processing(image_inputs[0]      , return_tensors="""pt"""    ).pixel_values
      self.assertEqual(
          encoded_images.shape      , (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )

      # Test batched
      _UpperCAmelCase         =					image_processing(__lowerCAmelCase      , return_tensors="""pt"""    ).pixel_values
      self.assertEqual(
          encoded_images.shape      , (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )


    def    lowerCAmelCase_     (    self		:     str    ):
      # Initialize image_processing
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      # create random numpy tensors
      _UpperCAmelCase         =					prepare_image_inputs(self.image_processor_tester      , equal_resolution=__lowerCAmelCase      , numpify=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , np.ndarray    )

      # Test not batched input
      _UpperCAmelCase         =					image_processing(image_inputs[0]      , return_tensors="""pt"""    ).pixel_values
      self.assertEqual(
          encoded_images.shape      , (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )

      # Test batched
      _UpperCAmelCase         =					image_processing(__lowerCAmelCase      , return_tensors="""pt"""    ).pixel_values
      self.assertEqual(
          encoded_images.shape      , (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      # Initialize image_processing
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      # create random PyTorch tensors
      _UpperCAmelCase         =					prepare_image_inputs(self.image_processor_tester      , equal_resolution=__lowerCAmelCase      , torchify=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , torch.Tensor    )

      # Test not batched input
      _UpperCAmelCase         =					image_processing(image_inputs[0]      , return_tensors="""pt"""    ).pixel_values
      self.assertEqual(
          encoded_images.shape      , (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )

      # Test batched
      _UpperCAmelCase         =					image_processing(__lowerCAmelCase      , return_tensors="""pt"""    ).pixel_values
      self.assertEqual(
          encoded_images.shape      , (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )
