"""simple docstring"""






import glob
import os
import random
from string import ascii_lowercase, digits

import cva
lowerCAmelCase__						:						Optional[int]		  =			''
lowerCAmelCase__						:						List[Any]		  =			''
lowerCAmelCase__						:						List[str]		  =			''
lowerCAmelCase__						:						List[Any]		  =			1  # (0 is vertical, 1 is horizontal)



def 						a_		(      ):
				UpperCAmelCase__   ,			UpperCAmelCase__    =							get_dataset(lowerCamelCase      ,    lowerCamelCase )
				print('Processing...' )
				UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							update_image_and_anno(lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase )

				for index, image in enumerate(lowerCamelCase ):
								# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
								UpperCAmelCase__    =							random_chars(3_2 )
								UpperCAmelCase__    =							paths[index].split(os.sep )[-1].rsplit('.'      ,    1 )[0]
								UpperCAmelCase__    =							f'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
								cva.imwrite(f'''/{file_root}.jpg'''      ,    lowerCamelCase      ,    [cva.IMWRITE_JPEG_QUALITY, 8_5] )
								print(f'''Success {index+1}/{len(lowerCamelCase )} with {file_name}''' )
								UpperCAmelCase__    =							[]
								for anno in new_annos[index]:
												UpperCAmelCase__    =							f'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
												annos_list.append(lowerCamelCase )
								with open(f'''/{file_root}.txt'''      ,    'w' ) as outfile:
												outfile.write('\n'.join(line for line in annos_list ) )



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							[]
				UpperCAmelCase__    =							[]
				for label_file in glob.glob(os.path.join(lowerCamelCase      ,    '*.txt' ) ):
								UpperCAmelCase__    =							label_file.split(os.sep )[-1].rsplit('.'      ,    1 )[0]
								with open(lowerCamelCase ) as in_file:
												UpperCAmelCase__    =							in_file.readlines()
								UpperCAmelCase__    =							os.path.join(lowerCamelCase      ,    f'''{label_name}.jpg''' )

								UpperCAmelCase__    =							[]
								for obj_list in obj_lists:
												UpperCAmelCase__    =							obj_list.rstrip('\n' ).split(' ' )
												boxes.append(
												    [
												        int(obj[0] ),
												        float(obj[1] ),
												        float(obj[2] ),
												        float(obj[3] ),
												        float(obj[4] ),
												    ] )
								if not boxes:
												continue
								img_paths.append(lowerCamelCase )
								labels.append(lowerCamelCase )
				return img_paths, labels



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase = 1 ):
				UpperCAmelCase__    =							[]
				UpperCAmelCase__    =							[]
				UpperCAmelCase__    =							[]
				for idx in range(len(lowerCamelCase ) ):
								UpperCAmelCase__    =							[]
								UpperCAmelCase__    =							img_list[idx]
								path_list.append(lowerCamelCase )
								UpperCAmelCase__    =							anno_list[idx]
								UpperCAmelCase__    =							cva.imread(lowerCamelCase )
								if flip_type == 1:
												UpperCAmelCase__    =							cva.flip(lowerCamelCase      ,    lowerCamelCase )
												for bbox in img_annos:
																UpperCAmelCase__    =							1 - bbox[1]
																new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
								elif flip_type == 0:
												UpperCAmelCase__    =							cva.flip(lowerCamelCase      ,    lowerCamelCase )
												for bbox in img_annos:
																UpperCAmelCase__    =							1 - bbox[2]
																new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
								new_annos_lists.append(lowerCamelCase )
								new_imgs_list.append(lowerCamelCase )
				return new_imgs_list, new_annos_lists, path_list



def 						a_		(      lowerCamelCase = 3_2 ):
				assert number_char > 1, "The number of character should greater than 1"
				UpperCAmelCase__    =							ascii_lowercase + digits
				return "".join(random.choice(lowerCamelCase ) for _ in range(lowerCamelCase ) )


if __name__ == "__main__":
						main()
						print('DONE ✅')




"""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__						:						Optional[int]		  =			logging.get_logger(__name__)


lowerCAmelCase__						:						Optional[Any]		  =			['model.decoder.embed_positions.weights']



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



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



def 						a_		(      lowerCamelCase ):
				if checkpoint == "small":
								# default config values
								UpperCAmelCase__    =							1_0_2_4
								UpperCAmelCase__    =							2_4
								UpperCAmelCase__    =							1_6
				elif checkpoint == "medium":
								UpperCAmelCase__    =							1_5_3_6
								UpperCAmelCase__    =							4_8
								UpperCAmelCase__    =							2_4
				elif checkpoint == "large":
								UpperCAmelCase__    =							2_0_4_8
								UpperCAmelCase__    =							4_8
								UpperCAmelCase__    =							3_2
				else:
								raise ValueError(f'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.''' )
				UpperCAmelCase__    =							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      ,    lowerCamelCase=None      ,    lowerCamelCase=None      ,    lowerCamelCase="cpu" ):
				UpperCAmelCase__    =							MusicGen.get_pretrained(lowerCamelCase      ,    device=lowerCamelCase )
				UpperCAmelCase__    =							decoder_config_from_checkpoint(lowerCamelCase )

				UpperCAmelCase__    =							fairseq_model.lm.state_dict()
				UpperCAmelCase__   ,			UpperCAmelCase__    =							rename_state_dict(
				    lowerCamelCase      ,    hidden_size=decoder_config.hidden_size )

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

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

				with torch.no_grad():
								UpperCAmelCase__    =							model(input_ids=lowerCamelCase      ,    decoder_input_ids=lowerCamelCase ).logits

				if logits.shape != (8, 1, 2_0_4_8):
								raise ValueError('Incorrect shape for logits' )

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

				UpperCAmelCase__    =							MusicgenProcessor(feature_extractor=lowerCamelCase      ,    tokenizer=lowerCamelCase )

				# set the appropriate bos/pad token ids
				UpperCAmelCase__    =							2_0_4_8
				UpperCAmelCase__    =							2_0_4_8

				# set other default generation config params
				UpperCAmelCase__    =							int(3_0 * audio_encoder.config.frame_rate )
				UpperCAmelCase__    =							True
				UpperCAmelCase__    =							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__						:						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.'
						)

						lowerCAmelCase__						:						List[str]		  =			parser.parse_args()
						convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)



