


"""simple docstring"""
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


__SCREAMING_SNAKE_CASE							:    int		=    logging.get_logger(__name__)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[int]:
							snake_case_           =		b.T
							snake_case_           =		np.sum(np.square(_SCREAMING_SNAKE_CASE				)				, axis=1				)
							snake_case_           =		np.sum(np.square(_SCREAMING_SNAKE_CASE				)				, axis=0				)
							snake_case_           =		np.matmul(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
							snake_case_           =		aa[:, None] - 2 * ab + ba[None, :]
							return d







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				str:
							snake_case_           =		x.reshape(-1				, 3				)
							snake_case_           =		squared_euclidean_distance(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
							return np.argmin(_SCREAMING_SNAKE_CASE				, axis=1				)





class      __A     (snake_case__):





							'''simple docstring'''

							__lowercase:		str							      =    ["""pixel_values"""]



							def __init__(				self							:      Tuple		,       UpperCAmelCase_							:      Optional[Union[List[List[int]], np.ndarray]] = None		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      Dict[str, int] = None		,       UpperCAmelCase_							:      PILImageResampling = PILImageResampling.BILINEAR		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      bool = True		,       **UpperCAmelCase_							:      Optional[int]		,       )					->None:



														"""simple docstring"""



														super().__init__(**UpperCAmelCase_       )
														snake_case_           =		size if size is not None else {"""height""": 256, """width""": 256}
														snake_case_           =		get_size_dict(UpperCAmelCase_       )
														snake_case_           =		np.array(UpperCAmelCase_       ) if clusters is not None else None
														snake_case_           =		do_resize
														snake_case_           =		size
														snake_case_           =		resample
														snake_case_           =		do_normalize
														snake_case_           =		do_color_quantize



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      np.ndarray		,       UpperCAmelCase_							:      Dict[str, int]		,       UpperCAmelCase_							:      PILImageResampling = PILImageResampling.BILINEAR		,       UpperCAmelCase_							:      Optional[Union[str, ChannelDimension]] = None		,       **UpperCAmelCase_							:      Tuple		,       )					->np.ndarray:



														"""simple docstring"""



														snake_case_           =		get_size_dict(UpperCAmelCase_       )
														if "height" not in size or "width" not in size:
																					raise ValueError(F"""Size dictionary must contain both height and width keys. Got {size.keys()}"""       )
														return resize(
														    UpperCAmelCase_		,       size=(size["""height"""], size["""width"""])		,       resample=UpperCAmelCase_		,       data_format=UpperCAmelCase_		,       **UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      np.ndarray		,       UpperCAmelCase_							:      Optional[Union[str, ChannelDimension]] = None		,       )					->np.ndarray:



														"""simple docstring"""



														snake_case_           =		rescale(image=UpperCAmelCase_		,       scale=1 / 127.5		,       data_format=UpperCAmelCase_       )
														snake_case_           =		image - 1
														return image



							def 		lowerCAmelCase       (				self							:      List[str]		,       UpperCAmelCase_							:      ImageInput		,       UpperCAmelCase_							:      bool = None		,       UpperCAmelCase_							:      Dict[str, int] = None		,       UpperCAmelCase_							:      PILImageResampling = None		,       UpperCAmelCase_							:      bool = None		,       UpperCAmelCase_							:      Optional[bool] = None		,       UpperCAmelCase_							:      Optional[Union[List[List[int]], np.ndarray]] = None		,       UpperCAmelCase_							:      Optional[Union[str, TensorType]] = None		,       UpperCAmelCase_							:      Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST		,       **UpperCAmelCase_							:      Any		,       )					->PIL.Image.Image:



														"""simple docstring"""



														snake_case_           =		do_resize if do_resize is not None else self.do_resize
														snake_case_           =		size if size is not None else self.size
														snake_case_           =		get_size_dict(UpperCAmelCase_       )
														snake_case_           =		resample if resample is not None else self.resample
														snake_case_           =		do_normalize if do_normalize is not None else self.do_normalize
														snake_case_           =		do_color_quantize if do_color_quantize is not None else self.do_color_quantize
														snake_case_           =		clusters if clusters is not None else self.clusters
														snake_case_           =		np.array(UpperCAmelCase_       )

														snake_case_           =		make_list_of_images(UpperCAmelCase_       )

														if not valid_images(UpperCAmelCase_       ):
																					raise ValueError(
																					    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																					    """torch.Tensor, tf.Tensor or jax.ndarray."""       )

														if do_resize and size is None or resample is None:
																					raise ValueError("""Size and resample must be specified if do_resize is True."""       )

														if do_color_quantize and clusters is None:
																					raise ValueError("""Clusters must be specified if do_color_quantize is True."""       )

														# All transformations expect numpy arrays.
														snake_case_           =		[to_numpy_array(UpperCAmelCase_       ) for image in images]

														if do_resize:
																					snake_case_           =		[self.resize(image=UpperCAmelCase_		,       size=UpperCAmelCase_		,       resample=UpperCAmelCase_       ) for image in images]

														if do_normalize:
																					snake_case_           =		[self.normalize(image=UpperCAmelCase_       ) for image in images]

														if do_color_quantize:
																					snake_case_           =		[to_channel_dimension_format(UpperCAmelCase_		,       ChannelDimension.LAST       ) for image in images]
																					# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
																					snake_case_           =		np.array(UpperCAmelCase_       )
																					snake_case_           =		color_quantize(UpperCAmelCase_		,       UpperCAmelCase_       ).reshape(images.shape[:-1]       )

																					# flatten to (batch_size, height*width)
																					snake_case_           =		images.shape[0]
																					snake_case_           =		images.reshape(UpperCAmelCase_		,       -1       )

																					# We need to convert back to a list of images to keep consistent behaviour across processors.
																					snake_case_           =		list(UpperCAmelCase_       )
														else:
																					snake_case_           =		[to_channel_dimension_format(UpperCAmelCase_		,       UpperCAmelCase_       ) for image in images]

														snake_case_           =		{"""input_ids""": images}
														return BatchFeature(data=UpperCAmelCase_		,       tensor_type=UpperCAmelCase_       )







"""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()
__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE							:    int		=    ['model.decoder.embed_positions.weights']







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







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







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				MusicgenDecoderConfig:
							if checkpoint == "small":
														# default config values
														snake_case_           =		1_024
														snake_case_           =		24
														snake_case_           =		16
							elif checkpoint == "medium":
														snake_case_           =		1_536
														snake_case_           =		48
														snake_case_           =		24
							elif checkpoint == "large":
														snake_case_           =		2_048
														snake_case_           =		48
														snake_case_           =		32
							else:
														raise ValueError(f"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""				)
							snake_case_           =		MusicgenDecoderConfig(
							    hidden_size=_SCREAMING_SNAKE_CASE				, ffn_dim=hidden_size * 4				, num_hidden_layers=_SCREAMING_SNAKE_CASE				, num_attention_heads=_SCREAMING_SNAKE_CASE				, )
							return config







@torch.no_grad()
def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE=None				, _SCREAMING_SNAKE_CASE=None				, _SCREAMING_SNAKE_CASE="cpu"				)    ->				Tuple:
							snake_case_           =		MusicGen.get_pretrained(_SCREAMING_SNAKE_CASE				, device=_SCREAMING_SNAKE_CASE				)
							snake_case_           =		decoder_config_from_checkpoint(_SCREAMING_SNAKE_CASE				)

							snake_case_           =		fairseq_model.lm.state_dict()
							snake_case_       ,						snake_case_           =		rename_state_dict(
							    _SCREAMING_SNAKE_CASE				, hidden_size=decoder_config.hidden_size				)

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

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

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

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

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

							# init the composite model
							snake_case_           =		MusicgenForConditionalGeneration(text_encoder=_SCREAMING_SNAKE_CASE				, audio_encoder=_SCREAMING_SNAKE_CASE				, decoder=_SCREAMING_SNAKE_CASE				)

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

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

							with torch.no_grad():
														snake_case_           =		model(input_ids=_SCREAMING_SNAKE_CASE				, decoder_input_ids=_SCREAMING_SNAKE_CASE				).logits

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

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

							snake_case_           =		MusicgenProcessor(feature_extractor=_SCREAMING_SNAKE_CASE				, tokenizer=_SCREAMING_SNAKE_CASE				)

							# set the appropriate bos/pad token ids
							snake_case_           =		2_048
							snake_case_           =		2_048

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

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

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:    Tuple		=    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.'
				)

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



