


'''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__    	=     logging.get_logger(__name__)


lowerCamelCase__    	=     ['model.decoder.embed_positions.weights']






def  __lowerCAmelCase (__lowerCAmelCase   ):
		if "emb" in name:
				_UpperCAmelCase		:       List[str]								=      name.replace("emb"      , "model.decoder.embed_tokens"   )
		if "transformer" in name:
				_UpperCAmelCase		:       List[str]								=      name.replace("transformer"      , "model.decoder"   )
		if "cross_attention" in name:
				_UpperCAmelCase		:       Union[str, Any]								=      name.replace("cross_attention"      , "encoder_attn"   )
		if "linear1" in name:
				_UpperCAmelCase		:       Tuple								=      name.replace("linear1"      , "fc1"   )
		if "linear2" in name:
				_UpperCAmelCase		:       Optional[Any]								=      name.replace("linear2"      , "fc2"   )
		if "norm1" in name:
				_UpperCAmelCase		:       List[Any]								=      name.replace("norm1"      , "self_attn_layer_norm"   )
		if "norm_cross" in name:
				_UpperCAmelCase		:       int								=      name.replace("norm_cross"      , "encoder_attn_layer_norm"   )
		if "norm2" in name:
				_UpperCAmelCase		:       Union[str, Any]								=      name.replace("norm2"      , "final_layer_norm"   )
		if "out_norm" in name:
				_UpperCAmelCase		:       Optional[int]								=      name.replace("out_norm"      , "model.decoder.layer_norm"   )
		if "linears" in name:
				_UpperCAmelCase		:       Any								=      name.replace("linears"      , "lm_heads"   )
		if "condition_provider.conditioners.description.output_proj" in name:
				_UpperCAmelCase		:       Any								=      name.replace("condition_provider.conditioners.description.output_proj"      , "enc_to_dec_proj"   )
		return name






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
		_UpperCAmelCase		:       str								=      list(state_dict.keys()   )
		_UpperCAmelCase		:       Tuple								=      {}
		for key in keys:
				_UpperCAmelCase		:       List[Any]								=      state_dict.pop(__lowerCAmelCase   )
				_UpperCAmelCase		:       Tuple								=      rename_keys(__lowerCAmelCase   )
				if "in_proj_weight" in key:
						# split fused qkv proj
						_UpperCAmelCase		:       Any								=      val[:hidden_size, :]
						_UpperCAmelCase		:       Union[str, Any]								=      val[hidden_size : 2 * hidden_size, :]
						_UpperCAmelCase		:       Optional[int]								=      val[-hidden_size:, :]
				elif "enc_to_dec_proj" in key:
						_UpperCAmelCase		:       Optional[Any]								=      val
				else:
						_UpperCAmelCase		:       Dict								=      val
		return state_dict, enc_dec_proj_state_dict






def  __lowerCAmelCase (__lowerCAmelCase   ):
		if checkpoint == "small":
				# default config values
				_UpperCAmelCase		:       Any								=      1_024
				_UpperCAmelCase		:       Tuple								=      24
				_UpperCAmelCase		:       Union[str, Any]								=      16
		elif checkpoint == "medium":
				_UpperCAmelCase		:       List[Any]								=      1_536
				_UpperCAmelCase		:       str								=      48
				_UpperCAmelCase		:       str								=      24
		elif checkpoint == "large":
				_UpperCAmelCase		:       Dict								=      2_048
				_UpperCAmelCase		:       Optional[int]								=      48
				_UpperCAmelCase		:       Any								=      32
		else:
				raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""   )
		_UpperCAmelCase		:       Optional[int]								=      MusicgenDecoderConfig(
		    hidden_size=__lowerCAmelCase      , ffn_dim=hidden_size * 4      , num_hidden_layers=__lowerCAmelCase      , num_attention_heads=__lowerCAmelCase      , )
		return config






@torch.no_grad()
def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase=None      , __lowerCAmelCase=None      , __lowerCAmelCase="cpu"   ):
		_UpperCAmelCase		:       str								=      MusicGen.get_pretrained(__lowerCAmelCase      , device=__lowerCAmelCase   )
		_UpperCAmelCase		:       Union[str, Any]								=      decoder_config_from_checkpoint(__lowerCAmelCase   )

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

		_UpperCAmelCase		:       Tuple								=      TaEncoderModel.from_pretrained("t5-base"   )
		_UpperCAmelCase		:       int								=      EncodecModel.from_pretrained("facebook/encodec_32khz"   )
		_UpperCAmelCase		:       Tuple								=      MusicgenForCausalLM(__lowerCAmelCase   ).eval()

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

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

		if logits.shape != (8, 1, 2_048):
				raise ValueError("Incorrect shape for logits"   )

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

		_UpperCAmelCase		:       Optional[int]								=      MusicgenProcessor(feature_extractor=__lowerCAmelCase      , tokenizer=__lowerCAmelCase   )

		# set the appropriate bos/pad token ids
		_UpperCAmelCase		:       Dict								=      2_048
		_UpperCAmelCase		:       List[str]								=      2_048

		# set other default generation config params
		_UpperCAmelCase		:       Dict								=      int(30 * audio_encoder.config.frame_rate   )
		_UpperCAmelCase		:       Any								=      True
		_UpperCAmelCase		:       Dict								=      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__    	=     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__    	=     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_torch_available


lowerCamelCase__    	=     {
    'configuration_instructblip': [
        'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InstructBlipConfig',
        'InstructBlipQFormerConfig',
        'InstructBlipVisionConfig',
    ],
    'processing_instructblip': ['InstructBlipProcessor'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__    	=     [
						    'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'InstructBlipQFormerModel',
						    'InstructBlipPreTrainedModel',
						    'InstructBlipForConditionalGeneration',
						    'InstructBlipVisionModel',
						]

if TYPE_CHECKING:
						from .configuration_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    InstructBlipConfig,
						    InstructBlipQFormerConfig,
						    InstructBlipVisionConfig,
						)
						from .processing_instructblip import InstructBlipProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_instructblip import (
												    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InstructBlipForConditionalGeneration,
												    InstructBlipPreTrainedModel,
												    InstructBlipQFormerModel,
												    InstructBlipVisionModel,
												)

else:
						import sys

						lowerCamelCase__    	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



