import os
import zipfile

import pytest

from datasets.utils.extract import (
    BzipaExtractor,
    Extractor,
    GzipExtractor,
    LzaExtractor,
    SevenZipExtractor,
    TarExtractor,
    XzExtractor,
    ZipExtractor,
    ZstdExtractor,
)

from .utils import require_lza, require_pyazr, require_zstandard



@pytest.mark.parametrize(
    'compression_format, is_archive'      ,    [
        ('7z', True),
        ('bz2', False),
        ('gzip', False),
        ('lz4', False),
        ('tar', True),
        ('xz', False),
        ('zip', True),
        ('zstd', False),
    ]      ,    )
def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    ):
				UpperCAmelCase__    =							{
				    '7z': (seven_zip_file, SevenZipExtractor),
				    'bz2': (bza_file, BzipaExtractor),
				    'gzip': (gz_file, GzipExtractor),
				    'lz4': (lza_file, LzaExtractor),
				    'tar': (tar_file, TarExtractor),
				    'xz': (xz_file, XzExtractor),
				    'zip': (zip_file, ZipExtractor),
				    'zstd': (zstd_file, ZstdExtractor),
				}
				UpperCAmelCase__   ,			UpperCAmelCase__    =							input_paths_and_base_extractors[compression_format]
				if input_path is None:
								UpperCAmelCase__    =							f'''for \'{compression_format}\' compression_format, '''
								if compression_format == "7z":
												reason += require_pyazr.kwargs["reason"]
								elif compression_format == "lz4":
												reason += require_lza.kwargs["reason"]
								elif compression_format == "zstd":
												reason += require_zstandard.kwargs["reason"]
								pytest.skip(_A )
				assert base_extractor.is_extractable(_A )
				UpperCAmelCase__    =							tmp_path / ('extracted' if is_archive else 'extracted.txt')
				base_extractor.extract(_A      ,    _A )
				if is_archive:
								assert output_path.is_dir()
								for file_path in output_path.iterdir():
												assert file_path.name == text_file.name
												UpperCAmelCase__    =							file_path.read_text(encoding='utf-8' )
				else:
								UpperCAmelCase__    =							output_path.read_text(encoding='utf-8' )
				UpperCAmelCase__    =							text_file.read_text(encoding='utf-8' )
				assert extracted_file_content == expected_file_content



@pytest.mark.parametrize(
    'compression_format, is_archive'      ,    [
        ('7z', True),
        ('bz2', False),
        ('gzip', False),
        ('lz4', False),
        ('tar', True),
        ('xz', False),
        ('zip', True),
        ('zstd', False),
    ]      ,    )
def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    ):
				UpperCAmelCase__    =							{
				    '7z': seven_zip_file,
				    'bz2': bza_file,
				    'gzip': gz_file,
				    'lz4': lza_file,
				    'tar': tar_file,
				    'xz': xz_file,
				    'zip': zip_file,
				    'zstd': zstd_file,
				}
				UpperCAmelCase__    =							input_paths[compression_format]
				if input_path is None:
								UpperCAmelCase__    =							f'''for \'{compression_format}\' compression_format, '''
								if compression_format == "7z":
												reason += require_pyazr.kwargs["reason"]
								elif compression_format == "lz4":
												reason += require_lza.kwargs["reason"]
								elif compression_format == "zstd":
												reason += require_zstandard.kwargs["reason"]
								pytest.skip(_A )
				UpperCAmelCase__    =							Extractor.infer_extractor_format(_A )
				assert extractor_format is not None
				UpperCAmelCase__    =							tmp_path / ('extracted' if is_archive else 'extracted.txt')
				Extractor.extract(_A      ,    _A      ,    _A )
				if is_archive:
								assert output_path.is_dir()
								for file_path in output_path.iterdir():
												assert file_path.name == text_file.name
												UpperCAmelCase__    =							file_path.read_text(encoding='utf-8' )
				else:
								UpperCAmelCase__    =							output_path.read_text(encoding='utf-8' )
				UpperCAmelCase__    =							text_file.read_text(encoding='utf-8' )
				assert extracted_file_content == expected_file_content



@pytest.fixture
def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				import tarfile

				UpperCAmelCase__    =							tmp_path / 'data_dot_dot'
				directory.mkdir()
				UpperCAmelCase__    =							directory / 'tar_file_with_dot_dot.tar'
				with tarfile.TarFile(_A      ,    'w' ) as f:
								f.add(_A      ,    arcname=os.path.join('..'      ,    text_file.name ) )
				return path



@pytest.fixture
def 						a_		(      lowerCamelCase ):
				import tarfile

				UpperCAmelCase__    =							tmp_path / 'data_sym_link'
				directory.mkdir()
				UpperCAmelCase__    =							directory / 'tar_file_with_sym_link.tar'
				os.symlink('..'      ,    directory / 'subdir'      ,    target_is_directory=_A )
				with tarfile.TarFile(_A      ,    'w' ) as f:
								f.add(str(directory / 'subdir' )      ,    arcname='subdir' )  # str required by os.readlink on Windows and Python < 3.8
				return path



@pytest.mark.parametrize(
    'insecure_tar_file, error_log'      ,    [('tar_file_with_dot_dot', 'illegal path'), ('tar_file_with_sym_link', 'Symlink')]      ,    )
def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							{
				    'tar_file_with_dot_dot': tar_file_with_dot_dot,
				    'tar_file_with_sym_link': tar_file_with_sym_link,
				}
				UpperCAmelCase__    =							insecure_tar_files[insecure_tar_file]
				UpperCAmelCase__    =							tmp_path / 'extracted'
				TarExtractor.extract(_A      ,    _A )
				assert caplog.text
				for record in caplog.records:
								assert record.levelname == "ERROR"
								assert error_log in record.msg







def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							tmpdir / 'not_a_zip_file'
				# From: https://github.com/python/cpython/pull/5053
				UpperCAmelCase__    =							(
				    b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'
				    b'\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'
				    b'DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'
				    b'\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'
				)
				with not_a_zip_file.open('wb' ) as f:
								f.write(_A )
				assert zipfile.is_zipfile(str(_A ) )  # is a false positive for `zipfile`
				assert not ZipExtractor.is_extractable(_A )  # but we're right




"""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)



