
'''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()
_lowercase          =						logging.get_logger(__name__)


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


def A (__lowerCamelCase :Optional[int]						):
						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 A (__lowerCamelCase :OrderedDict					,      __lowerCamelCase :int						):
						_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 A (__lowerCamelCase :str						):
						if checkpoint == "small":
												# default config values
												_lowerCAmelCase    		=					1024
												_lowerCAmelCase    		=					24
												_lowerCAmelCase    		=					16
						elif checkpoint == "medium":
												_lowerCAmelCase    		=					1536
												_lowerCAmelCase    		=					48
												_lowerCAmelCase    		=					24
						elif checkpoint == "large":
												_lowerCAmelCase    		=					2048
												_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 A (__lowerCamelCase :str					,      __lowerCamelCase :Union[str, Any]=None					,      __lowerCamelCase :List[str]=None					,      __lowerCamelCase :Optional[int]="cpu"						):
						_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, 2048):
												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    		=					2048
						_lowerCAmelCase    		=					2048

						# 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__":
						_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)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase   :    Tuple     =		{
    "configuration_blenderbot_small": [
        "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotSmallConfig",
        "BlenderbotSmallOnnxConfig",
    ],
    "tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    List[Any]     =		["BlenderbotSmallTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    List[str]     =		[
		    "BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "BlenderbotSmallForCausalLM",
		    "BlenderbotSmallForConditionalGeneration",
		    "BlenderbotSmallModel",
		    "BlenderbotSmallPreTrainedModel",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    Any     =		[
		    "TFBlenderbotSmallForConditionalGeneration",
		    "TFBlenderbotSmallModel",
		    "TFBlenderbotSmallPreTrainedModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    List[Any]     =		[
		    "FlaxBlenderbotSmallForConditionalGeneration",
		    "FlaxBlenderbotSmallModel",
		    "FlaxBlenderbotSmallPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_blenderbot_small import (
		    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlenderbotSmallConfig,
		    BlenderbotSmallOnnxConfig,
		)
		from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blenderbot_small import (
				    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlenderbotSmallForCausalLM,
				    BlenderbotSmallForConditionalGeneration,
				    BlenderbotSmallModel,
				    BlenderbotSmallPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blenderbot_small import (
				    TFBlenderbotSmallForConditionalGeneration,
				    TFBlenderbotSmallModel,
				    TFBlenderbotSmallPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_blenderbot_small import (
				    FlaxBlenderbotSmallForConditionalGeneration,
				    FlaxBlenderbotSmallModel,
				    FlaxBlenderbotSmallPreTrainedModel,
				)

else:
		import sys

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

