






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       :	int             = logging.get_logger(__name__)


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





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





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





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





@torch.no_grad()
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_=None ,     lowerCamelCase_=None ,     lowerCamelCase_="cpu"):
					a__          =  MusicGen.get_pretrained(lowerCamelCase__ ,     device=lowerCamelCase__)
					a__          =  decoder_config_from_checkpoint(lowerCamelCase__)

					a__          =  fairseq_model.lm.state_dict()
					a__       ,a__          =  rename_state_dict(
					    lowerCamelCase__ ,     hidden_size=decoder_config.hidden_size)

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

					# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
					a__       ,a__          =  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
					a__          =  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
					a__          =  torch.arange(0 ,     8 ,     dtype=torch.long).reshape(2 ,     -1)
					a__          =  input_ids.reshape(2 * 4 ,     -1)

					with torch.no_grad():
										a__          =  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
					a__          =  AutoTokenizer.from_pretrained('''t5-base''')
					a__          =  AutoFeatureExtractor.from_pretrained('''facebook/encodec_32khz''' ,     padding_side='''left''')

					a__          =  MusicgenProcessor(feature_extractor=lowerCamelCase__ ,     tokenizer=lowerCamelCase__)

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

					# set other default generation config params
					a__          =  int(30 * audio_encoder.config.frame_rate)
					a__          =  True
					a__          =  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       :	Dict             = parser.parse_args()
	convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__a       :	Tuple             = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Tuple             = ['DeiTFeatureExtractor']
	__a       :	Optional[Any]             = ['DeiTImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Optional[int]             = [
	    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'DeiTForImageClassification',
	    'DeiTForImageClassificationWithTeacher',
	    'DeiTForMaskedImageModeling',
	    'DeiTModel',
	    'DeiTPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Dict             = [
	    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFDeiTForImageClassification',
	    'TFDeiTForImageClassificationWithTeacher',
	    'TFDeiTForMaskedImageModeling',
	    'TFDeiTModel',
	    'TFDeiTPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_deit import DeiTFeatureExtractor
		from .image_processing_deit import DeiTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deit import (
		    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DeiTForImageClassification,
		    DeiTForImageClassificationWithTeacher,
		    DeiTForMaskedImageModeling,
		    DeiTModel,
		    DeiTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_deit import (
		    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDeiTForImageClassification,
		    TFDeiTForImageClassificationWithTeacher,
		    TFDeiTForMaskedImageModeling,
		    TFDeiTModel,
		    TFDeiTPreTrainedModel,
		)


else:
	import sys

	__a       :	List[Any]             = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


