





'''simple docstring'''


import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

lowerCAmelCase_   :   List[Any]       =					collections.namedtuple("_Datasets", ["train", "validation", "test"])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
lowerCAmelCase_   :   Dict       =					"https://storage.googleapis.com/cvdf-datasets/mnist/"







def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    List[str]:
			a__     = numpy.dtype(numpy.uintaa     ).newbyteorder(">"     )
			return numpy.frombuffer(bytestream.read(4     )					,  dtype=__lowerCamelCase     )[0]







@deprecated(__lowerCamelCase					,  "Please use tf.data to implement this functionality."     )
def    _lowerCamelCase  (__lowerCamelCase  :     List[str]     )   ->    List[str]:
			print("Extracting"					,  f.name     )
			with gzip.GzipFile(fileobj=__lowerCamelCase     ) as bytestream:
						a__     = _readaa(__lowerCamelCase     )
						if magic != 2051:
									raise ValueError(
									    "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)     )
						a__     = _readaa(__lowerCamelCase     )
						a__     = _readaa(__lowerCamelCase     )
						a__     = _readaa(__lowerCamelCase     )
						a__     = bytestream.read(rows * cols * num_images     )
						a__     = numpy.frombuffer(__lowerCamelCase					,  dtype=numpy.uinta     )
						a__     = data.reshape(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  1     )
						return data







@deprecated(__lowerCamelCase					,  "Please use tf.one_hot on tensors."     )
def    _lowerCamelCase  (__lowerCamelCase  :     Optional[Any]					,  __lowerCamelCase  :     Optional[int]     )   ->    Dict:
			a__     = labels_dense.shape[0]
			a__     = numpy.arange(__lowerCamelCase     ) * num_classes
			a__     = numpy.zeros((num_labels, num_classes)     )
			a__     = 1
			return labels_one_hot







@deprecated(__lowerCamelCase					,  "Please use tf.data to implement this functionality."     )
def    _lowerCamelCase  (__lowerCamelCase  :     List[str]					,  __lowerCamelCase  :     Tuple=False					,  __lowerCamelCase  :     Dict=10     )   ->    Any:
			print("Extracting"					,  f.name     )
			with gzip.GzipFile(fileobj=__lowerCamelCase     ) as bytestream:
						a__     = _readaa(__lowerCamelCase     )
						if magic != 2049:
									raise ValueError(
									    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)     )
						a__     = _readaa(__lowerCamelCase     )
						a__     = bytestream.read(__lowerCamelCase     )
						a__     = numpy.frombuffer(__lowerCamelCase					,  dtype=numpy.uinta     )
						if one_hot:
									return _dense_to_one_hot(__lowerCamelCase					,  __lowerCamelCase     )
						return labels






class       UpperCamelCase__  :







	@deprecated(
	    lowerCamelCase , "Please use alternatives such as official/mnist/_DataSet.py"
	    " from tensorflow/models." , )
	def __init__(				self			:   List[str] , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   Any , lowerCamelCase			:   str=False , lowerCamelCase			:   Tuple=False , lowerCamelCase			:   Optional[int]=dtypes.floataa , lowerCamelCase			:   List[str]=True , lowerCamelCase			:   Dict=None , ):




				'''simple docstring'''
				a__   ,				a__     = random_seed.get_seed(lowerCamelCase		)
				# If op level seed is not set, use whatever graph level seed is returned
				numpy.random.seed(seeda if seed is None else seeda		)
				a__     = dtypes.as_dtype(lowerCamelCase		).base_dtype
				if dtype not in (dtypes.uinta, dtypes.floataa):
							raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype		)
				if fake_data:
							a__     = 1_0_0_0_0
							a__     = one_hot
				else:
							assert (
							    images.shape[0] == labels.shape[0]
							), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
							a__     = images.shape[0]

							# Convert shape from [num examples, rows, columns, depth]
							# to [num examples, rows*columns] (assuming depth == 1)
							if reshape:
										assert images.shape[3] == 1
										a__     = images.reshape(
										    images.shape[0] , images.shape[1] * images.shape[2]		)
							if dtype == dtypes.floataa:
										# Convert from [0, 255] -> [0.0, 1.0].
										a__     = images.astype(numpy.floataa		)
										a__     = numpy.multiply(lowerCamelCase , 1.0 / 255.0		)
				a__     = images
				a__     = labels
				a__     = 0
				a__     = 0







	@property
	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				return self._images







	@property
	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				return self._labels







	@property
	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				return self._num_examples







	@property
	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				return self._epochs_completed







	def 						__a   (				self			:   List[str] , lowerCamelCase			:   str , lowerCamelCase			:   Any=False , lowerCamelCase			:   List[Any]=True		):




				'''simple docstring'''
				if fake_data:
							a__     = [1] * 7_8_4
							a__     = [1] + [0] * 9 if self.one_hot else 0
							return (
							    [fake_image for _ in range(lowerCamelCase		)],
							    [fake_label for _ in range(lowerCamelCase		)],
							)
				a__     = self._index_in_epoch
				# Shuffle for the first epoch
				if self._epochs_completed == 0 and start == 0 and shuffle:
							a__     = numpy.arange(self._num_examples		)
							numpy.random.shuffle(lowerCamelCase		)
							a__     = self.images[perma]
							a__     = self.labels[perma]
				# Go to the next epoch
				if start + batch_size > self._num_examples:
							# Finished epoch
							self._epochs_completed += 1
							# Get the rest examples in this epoch
							a__     = self._num_examples - start
							a__     = self._images[start : self._num_examples]
							a__     = self._labels[start : self._num_examples]
							# Shuffle the data
							if shuffle:
										a__     = numpy.arange(self._num_examples		)
										numpy.random.shuffle(lowerCamelCase		)
										a__     = self.images[perm]
										a__     = self.labels[perm]
							# Start next epoch
							a__     = 0
							a__     = batch_size - rest_num_examples
							a__     = self._index_in_epoch
							a__     = self._images[start:end]
							a__     = self._labels[start:end]
							return (
							    numpy.concatenate((images_rest_part, images_new_part) , axis=0		),
							    numpy.concatenate((labels_rest_part, labels_new_part) , axis=0		),
							)
				else:
							self._index_in_epoch += batch_size
							a__     = self._index_in_epoch
							return self._images[start:end], self._labels[start:end]







@deprecated(__lowerCamelCase					,  "Please write your own downloading logic."     )
def    _lowerCamelCase  (__lowerCamelCase  :     Optional[int]					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     List[Any]     )   ->    Tuple:
			if not gfile.Exists(__lowerCamelCase     ):
						gfile.MakeDirs(__lowerCamelCase     )
			a__     = os.path.join(__lowerCamelCase					,  __lowerCamelCase     )
			if not gfile.Exists(__lowerCamelCase     ):
						urllib.request.urlretrieve(__lowerCamelCase					,  __lowerCamelCase     )  # noqa: S310
						with gfile.GFile(__lowerCamelCase     ) as f:
									a__     = f.size()
						print("Successfully downloaded"					,  __lowerCamelCase					,  __lowerCamelCase					,  "bytes."     )
			return filepath







@deprecated(
    __lowerCamelCase					,  "Please use alternatives such as:" " tensorflow_datasets.load('mnist')"     )
def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]					,  __lowerCamelCase  :     Optional[Any]=False					,  __lowerCamelCase  :     Dict=False					,  __lowerCamelCase  :     Union[str, Any]=dtypes.floataa					,  __lowerCamelCase  :     Union[str, Any]=True					,  __lowerCamelCase  :     int=5000					,  __lowerCamelCase  :     List[str]=None					,  __lowerCamelCase  :     Any=DEFAULT_SOURCE_URL					,  )   ->    int:
			if fake_data:

						def fake():
									return _DataSet(
									    []					,  []					,  fake_data=__lowerCamelCase					,  one_hot=__lowerCamelCase					,  dtype=__lowerCamelCase					,  seed=__lowerCamelCase     )

						a__     = fake()
						a__     = fake()
						a__     = fake()
						return _Datasets(train=__lowerCamelCase					,  validation=__lowerCamelCase					,  test=__lowerCamelCase     )

			if not source_url:  # empty string check
						a__     = DEFAULT_SOURCE_URL

			a__     = "train-images-idx3-ubyte.gz"
			a__     = "train-labels-idx1-ubyte.gz"
			a__     = "t10k-images-idx3-ubyte.gz"
			a__     = "t10k-labels-idx1-ubyte.gz"

			a__     = _maybe_download(
			    __lowerCamelCase					,  __lowerCamelCase					,  source_url + train_images_file     )
			with gfile.Open(__lowerCamelCase					,  "rb"     ) as f:
						a__     = _extract_images(__lowerCamelCase     )

			a__     = _maybe_download(
			    __lowerCamelCase					,  __lowerCamelCase					,  source_url + train_labels_file     )
			with gfile.Open(__lowerCamelCase					,  "rb"     ) as f:
						a__     = _extract_labels(__lowerCamelCase					,  one_hot=__lowerCamelCase     )

			a__     = _maybe_download(
			    __lowerCamelCase					,  __lowerCamelCase					,  source_url + test_images_file     )
			with gfile.Open(__lowerCamelCase					,  "rb"     ) as f:
						a__     = _extract_images(__lowerCamelCase     )

			a__     = _maybe_download(
			    __lowerCamelCase					,  __lowerCamelCase					,  source_url + test_labels_file     )
			with gfile.Open(__lowerCamelCase					,  "rb"     ) as f:
						a__     = _extract_labels(__lowerCamelCase					,  one_hot=__lowerCamelCase     )

			if not 0 <= validation_size <= len(__lowerCamelCase     ):
						a__     = (
						    "Validation size should be between 0 and "
						    f'''{len(__lowerCamelCase     )}. Received: {validation_size}.'''
						)
						raise ValueError(__lowerCamelCase     )

			a__     = train_images[:validation_size]
			a__     = train_labels[:validation_size]
			a__     = train_images[validation_size:]
			a__     = train_labels[validation_size:]

			a__     = {"dtype": dtype, "reshape": reshape, "seed": seed}

			a__     = _DataSet(__lowerCamelCase					,  __lowerCamelCase					,  **__lowerCamelCase     )
			a__     = _DataSet(__lowerCamelCase					,  __lowerCamelCase					,  **__lowerCamelCase     )
			a__     = _DataSet(__lowerCamelCase					,  __lowerCamelCase					,  **__lowerCamelCase     )

			return _Datasets(train=__lowerCamelCase					,  validation=__lowerCamelCase					,  test=__lowerCamelCase     )










'''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_   :   List[str]       =					logging.get_logger(__name__)


lowerCAmelCase_   :   Optional[int]       =					["model.decoder.embed_positions.weights"]







def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    Optional[int]:
			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    _lowerCamelCase  (__lowerCamelCase  :     OrderedDict					,  __lowerCamelCase  :     int     )   ->    Tuple[Dict, Dict]:
			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    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    MusicgenDecoderConfig:
			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    _lowerCamelCase  (__lowerCamelCase  :     Union[str, Any]					,  __lowerCamelCase  :     List[str]=None					,  __lowerCamelCase  :     Dict=None					,  __lowerCamelCase  :     List[Any]="cpu"     )   ->    str:
			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__":
		lowerCAmelCase_   :   List[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."
		)

		lowerCAmelCase_   :   str       =					parser.parse_args()
		convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)



