


"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil





class      __A     (SCREAMING_SNAKE_CASE__):





							'''simple docstring'''

							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->int:



														"""simple docstring"""



														snake_case_           =		pa.array(TypedSequence([1, 2, 3]       )       )
														self.assertEqual(arr.type		,       pa.intaa()       )



							def 		lowerCAmelCase       (				self							:      Any       )					->Optional[Any]:



														"""simple docstring"""



														with self.assertRaises(_lowercase       ):
																					snake_case_           =		pa.array(TypedSequence([1, 2, 3]       )		,       type=pa.intaa()       )



							def 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->str:



														"""simple docstring"""



														with self.assertRaises(_lowercase       ):
																					snake_case_           =		pa.array(TypedSequence([1, 2, 3]		,       try_type=Value("""bool"""       )		,       type=Value("""int64"""       )       )       )



							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		pa.array(TypedSequence([1, 2, 3]		,       type=Value("""int32"""       )       )       )
														self.assertEqual(arr.type		,       pa.intaa()       )



							def 		lowerCAmelCase       (				self							:      str       )					->Dict:



														"""simple docstring"""



														with self.assertRaises((TypeError, pa.lib.ArrowInvalid)       ):
																					snake_case_           =		pa.array(TypedSequence(["""foo""", """bar"""]		,       type=Value("""int64"""       )       )       )



							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		pa.array(TypedSequence([1, 2, 3]		,       try_type=Value("""int32"""       )       )       )
														self.assertEqual(arr.type		,       pa.intaa()       )



							def 		lowerCAmelCase       (				self							:      Any       )					->Dict:



														"""simple docstring"""



														snake_case_           =		pa.array(TypedSequence(["""foo""", """bar"""]		,       try_type=Value("""int64"""       )       )       )
														self.assertEqual(arr.type		,       pa.string()       )



							def 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->int:



														"""simple docstring"""



														snake_case_           =		pa.array(TypedSequence([[[1, 2, 3]]]		,       type=ArrayaD((1, 3)		,       """int64"""       )       )       )
														self.assertEqual(arr.type		,       ArrayaDExtensionType((1, 3)		,       """int64"""       )       )



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->List[Any]:



														"""simple docstring"""



														with self.assertRaises((TypeError, pa.lib.ArrowInvalid)       ):
																					snake_case_           =		pa.array(TypedSequence(["""foo""", """bar"""]		,       type=ArrayaD((1, 3)		,       """int64"""       )       )       )



							def 		lowerCAmelCase       (				self							:      int       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_           =		pa.array(TypedSequence([[[1, 2, 3]]]		,       try_type=ArrayaD((1, 3)		,       """int64"""       )       )       )
														self.assertEqual(arr.type		,       ArrayaDExtensionType((1, 3)		,       """int64"""       )       )



							def 		lowerCAmelCase       (				self							:      List[str]       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		pa.array(TypedSequence(["""foo""", """bar"""]		,       try_type=ArrayaD((1, 3)		,       """int64"""       )       )       )
														self.assertEqual(arr.type		,       pa.string()       )



							@require_pil
							def 		lowerCAmelCase       (				self							:      Any       )					->List[str]:



														"""simple docstring"""



														import PIL.Image

														snake_case_           =		PIL.Image.fromarray(np.arange(10		,       dtype=np.uinta       ).reshape(2		,       5       )       )
														with patch(
														    """datasets.arrow_writer.cast_to_python_objects"""		,       side_effect=_lowercase       ) as mock_cast_to_python_objects:
																					snake_case_           =		pa.array(TypedSequence([{"""path""": None, """bytes""": B"""image_bytes"""}, pil_image]		,       type=Image()       )       )
																					snake_case_           =		mock_cast_to_python_objects.call_args_list[-1]
																					self.assertIn("""optimize_list_casting"""		,       _lowercase       )
																					self.assertFalse(kwargs["""optimize_list_casting"""]       )







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Tuple:
							snake_case_           =		pa.BufferReader(__UpperCamelCase				) if isinstance(__UpperCamelCase				, pa.Buffer				) else pa.memory_map(__UpperCamelCase				)
							snake_case_           =		pa.ipc.open_stream(__UpperCamelCase				)
							snake_case_           =		f.read_all()
							assert len(pa_table.to_batches()				) == expected_num_chunks
							assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
							del pa_table







@pytest.mark.parametrize("""writer_batch_size"""				, [None, 1, 10]				)
@pytest.mark.parametrize(
    """fields"""				, [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]				)
def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Dict:
							snake_case_           =		pa.BufferOutputStream()
							snake_case_           =		pa.schema(__UpperCamelCase				) if fields else None
							with ArrowWriter(stream=__UpperCamelCase				, schema=__UpperCamelCase				, writer_batch_size=__UpperCamelCase				) as writer:
														writer.write({"""col_1""": """foo""", """col_2""": 1}				)
														writer.write({"""col_1""": """bar""", """col_2""": 2}				)
														snake_case_           =		writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							if not fields:
														snake_case_           =		{"""col_1""": pa.string(), """col_2""": pa.intaa()}
							assert writer._schema == pa.schema(__UpperCamelCase				, metadata=writer._schema.metadata				)
							_check_output(output.getvalue()				, expected_num_chunks=num_examples if writer_batch_size == 1 else 1				)







def 						_a				(      )    ->				List[str]:
							snake_case_           =		pa.BufferOutputStream()
							snake_case_           =		Features({"""labels""": ClassLabel(names=["""neg""", """pos"""]				)}				)
							with ArrowWriter(stream=__UpperCamelCase				, features=__UpperCamelCase				) as writer:
														writer.write({"""labels""": 0}				)
														writer.write({"""labels""": 1}				)
														snake_case_           =		writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							assert writer._schema == features.arrow_schema
							assert writer._schema.metadata == features.arrow_schema.metadata
							snake_case_           =		pa.BufferReader(output.getvalue()				)
							snake_case_           =		pa.ipc.open_stream(__UpperCamelCase				)
							snake_case_           =		f.read_all()
							snake_case_           =		pa_table.schema
							assert pa_table.num_rows == 2
							assert schema == features.arrow_schema
							assert schema.metadata == features.arrow_schema.metadata
							assert features == Features.from_arrow_schema(__UpperCamelCase				)







@pytest.mark.parametrize("""writer_batch_size"""				, [None, 1, 10]				)
def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				List[Any]:
							snake_case_           =		pa.BufferOutputStream()
							with ArrowWriter(
							    stream=__UpperCamelCase				, writer_batch_size=__UpperCamelCase				, hash_salt="""split_name"""				, check_duplicates=__UpperCamelCase				, ) as writer:
														with pytest.raises(__UpperCamelCase				):
																					writer.write({"""col_1""": """foo""", """col_2""": 1}				, key=[1, 2]				)
																					snake_case_           =		writer.finalize()







@pytest.mark.parametrize("""writer_batch_size"""				, [None, 2, 10]				)
def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				int:
							snake_case_           =		pa.BufferOutputStream()
							with ArrowWriter(
							    stream=__UpperCamelCase				, writer_batch_size=__UpperCamelCase				, hash_salt="""split_name"""				, check_duplicates=__UpperCamelCase				, ) as writer:
														with pytest.raises(__UpperCamelCase				):
																					writer.write({"""col_1""": """foo""", """col_2""": 1}				, key=10				)
																					writer.write({"""col_1""": """bar""", """col_2""": 2}				, key=10				)
																					snake_case_           =		writer.finalize()







@pytest.mark.parametrize("""writer_batch_size"""				, [None, 2, 10]				)
def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				str:
							snake_case_           =		pa.BufferOutputStream()
							with ArrowWriter(
							    stream=__UpperCamelCase				, writer_batch_size=__UpperCamelCase				, hash_salt="""split_name"""				, check_duplicates=__UpperCamelCase				, ) as writer:
														writer.write({"""col_1""": """foo""", """col_2""": 1}				, key=1				)
														writer.write({"""col_1""": """bar""", """col_2""": 2}				, key=2				)
														snake_case_           =		writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							_check_output(output.getvalue()				, expected_num_chunks=num_examples if writer_batch_size == 1 else 1				)







@pytest.mark.parametrize("""writer_batch_size"""				, [None, 1, 10]				)
@pytest.mark.parametrize(
    """fields"""				, [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]				)
def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[int]:
							snake_case_           =		pa.BufferOutputStream()
							snake_case_           =		pa.schema(__UpperCamelCase				) if fields else None
							with ArrowWriter(stream=__UpperCamelCase				, schema=__UpperCamelCase				, writer_batch_size=__UpperCamelCase				) as writer:
														writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}				)
														writer.write_batch({"""col_1""": [], """col_2""": []}				)
														snake_case_           =		writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							if not fields:
														snake_case_           =		{"""col_1""": pa.string(), """col_2""": pa.intaa()}
							assert writer._schema == pa.schema(__UpperCamelCase				, metadata=writer._schema.metadata				)
							_check_output(output.getvalue()				, expected_num_chunks=num_examples if writer_batch_size == 1 else 1				)







@pytest.mark.parametrize("""writer_batch_size"""				, [None, 1, 10]				)
@pytest.mark.parametrize(
    """fields"""				, [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]				)
def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Tuple:
							snake_case_           =		pa.BufferOutputStream()
							snake_case_           =		pa.schema(__UpperCamelCase				) if fields else None
							with ArrowWriter(stream=__UpperCamelCase				, schema=__UpperCamelCase				, writer_batch_size=__UpperCamelCase				) as writer:
														writer.write_table(pa.Table.from_pydict({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}				)				)
														snake_case_           =		writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							if not fields:
														snake_case_           =		{"""col_1""": pa.string(), """col_2""": pa.intaa()}
							assert writer._schema == pa.schema(__UpperCamelCase				, metadata=writer._schema.metadata				)
							_check_output(output.getvalue()				, expected_num_chunks=num_examples if writer_batch_size == 1 else 1				)







@pytest.mark.parametrize("""writer_batch_size"""				, [None, 1, 10]				)
@pytest.mark.parametrize(
    """fields"""				, [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]				)
def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				List[Any]:
							snake_case_           =		pa.BufferOutputStream()
							snake_case_           =		pa.schema(__UpperCamelCase				) if fields else None
							with ArrowWriter(stream=__UpperCamelCase				, schema=__UpperCamelCase				, writer_batch_size=__UpperCamelCase				) as writer:
														writer.write_row(pa.Table.from_pydict({"""col_1""": ["""foo"""], """col_2""": [1]}				)				)
														writer.write_row(pa.Table.from_pydict({"""col_1""": ["""bar"""], """col_2""": [2]}				)				)
														snake_case_           =		writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							if not fields:
														snake_case_           =		{"""col_1""": pa.string(), """col_2""": pa.intaa()}
							assert writer._schema == pa.schema(__UpperCamelCase				, metadata=writer._schema.metadata				)
							_check_output(output.getvalue()				, expected_num_chunks=num_examples if writer_batch_size == 1 else 1				)







def 						_a				(      )    ->				Dict:






							with tempfile.TemporaryDirectory() as tmp_dir:
														snake_case_           =		{"""col_1""": pa.string(), """col_2""": pa.intaa()}
														snake_case_           =		os.path.join(__UpperCamelCase				, """test.arrow"""				)
														with ArrowWriter(path=__UpperCamelCase				, schema=pa.schema(__UpperCamelCase				)				) as writer:
																					writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}				)
																					snake_case_           =		writer.finalize()
														assert num_examples == 2
														assert num_bytes > 0
														assert writer._schema == pa.schema(__UpperCamelCase				, metadata=writer._schema.metadata				)
														_check_output(__UpperCamelCase				, 1				)







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				Optional[Any]:






							if pa.types.is_list(__UpperCamelCase				):
														return get_base_dtype(arr_type.value_type				)
							else:
														return arr_type







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[int]:






							if isinstance(lst[0]				, __UpperCamelCase				):
														change_first_primitive_element_in_list(lst[0]				, __UpperCamelCase				)
							else:
														snake_case_           =		value







@pytest.mark.parametrize("""optimized_int_type, expected_dtype"""				, [(None, pa.intaa()), (Value("""int32"""				), pa.intaa())]				)
@pytest.mark.parametrize("""sequence"""				, [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]				)
def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				int:
							snake_case_           =		pa.array(TypedSequence(__UpperCamelCase				, optimized_int_type=__UpperCamelCase				)				)
							assert get_base_dtype(arr.type				) == expected_dtype







@pytest.mark.parametrize(
    """col, expected_dtype"""				, [
        ("""attention_mask""", pa.inta()),
        ("""special_tokens_mask""", pa.inta()),
        ("""token_type_ids""", pa.inta()),
        ("""input_ids""", pa.intaa()),
        ("""other""", pa.intaa()),
    ]				, )
@pytest.mark.parametrize("""sequence"""				, [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]				)
def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				int:
							snake_case_           =		pa.array(OptimizedTypedSequence(__UpperCamelCase				, col=__UpperCamelCase				)				)
							assert get_base_dtype(arr.type				) == expected_dtype

							# not in range
							if col != "other":
														# avoids errors due to in-place modifications
														snake_case_           =		copy.deepcopy(__UpperCamelCase				)
														snake_case_           =		np.iinfo(expected_dtype.to_pandas_dtype()				).max + 1
														change_first_primitive_element_in_list(__UpperCamelCase				, __UpperCamelCase				)
														snake_case_           =		pa.array(OptimizedTypedSequence(__UpperCamelCase				, col=__UpperCamelCase				)				)
														assert get_base_dtype(arr.type				) == pa.intaa()







@pytest.mark.parametrize("""raise_exception"""				, [False, True]				)
def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[Any]:
							snake_case_           =		str(tmp_path / """dataset-train.arrow"""				)
							try:
														with ArrowWriter(path=__UpperCamelCase				) as writer:
																					if raise_exception:
																												raise pa.lib.ArrowInvalid()
																					else:
																												writer.stream.close()
							except pa.lib.ArrowInvalid:
														pass
							finally:
														assert writer.stream.closed







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				Optional[int]:
							snake_case_           =		"""mock://dataset-train.arrow"""
							with ArrowWriter(path=__UpperCamelCase				, storage_options=mockfs.storage_options				) as writer:
														assert isinstance(writer._fs				, type(__UpperCamelCase				)				)
														assert writer._fs.storage_options == mockfs.storage_options
														writer.write({"""col_1""": """foo""", """col_2""": 1}				)
														writer.write({"""col_1""": """bar""", """col_2""": 2}				)
														snake_case_           =		writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							assert mockfs.exists(__UpperCamelCase				)







def 						_a				(      )    ->				Optional[Any]:
							snake_case_           =		pa.BufferOutputStream()
							with ParquetWriter(stream=__UpperCamelCase				) as writer:
														writer.write({"""col_1""": """foo""", """col_2""": 1}				)
														writer.write({"""col_1""": """bar""", """col_2""": 2}				)
														snake_case_           =		writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							snake_case_           =		pa.BufferReader(output.getvalue()				)
							snake_case_           =		pq.read_table(__UpperCamelCase				)
							assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}







@require_pil
@pytest.mark.parametrize("""embed_local_files"""				, [False, True]				)
def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				int:
							import PIL.Image

							snake_case_           =		str(tmp_path / """test_image_rgb.jpg"""				)
							PIL.Image.fromarray(np.zeros((5, 5)				, dtype=np.uinta				)				).save(__UpperCamelCase				, format="""png"""				)
							snake_case_           =		pa.BufferOutputStream()
							with ParquetWriter(
							    stream=__UpperCamelCase				, features=Features({"""image""": Image()}				)				, embed_local_files=__UpperCamelCase				) as writer:
														writer.write({"""image""": image_path}				)
														writer.finalize()
							snake_case_           =		pa.BufferReader(output.getvalue()				)
							snake_case_           =		pq.read_table(__UpperCamelCase				)
							snake_case_           =		pa_table.to_pydict()
							if embed_local_files:
														assert isinstance(out["""image"""][0]["""path"""]				, __UpperCamelCase				)
														with open(__UpperCamelCase				, """rb"""				) as f:
																					assert out["image"][0]["bytes"] == f.read()
							else:
														assert out["image"][0]["path"] == image_path
														assert out["image"][0]["bytes"] is None







def 						_a				(      )    ->				Union[str, Any]:
							snake_case_           =		pa.schema([pa.field("""col_1"""				, pa.string()				, nullable=__UpperCamelCase				)]				)
							snake_case_           =		pa.BufferOutputStream()
							with ArrowWriter(stream=__UpperCamelCase				) as writer:
														writer._build_writer(inferred_schema=__UpperCamelCase				)
														assert writer._schema == pa.schema([pa.field("""col_1"""				, pa.string()				)]				)







"""simple docstring"""
import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE							:    str		=    logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE							:    Optional[int]		=    'https://openaipublic.azureedge.net/jukebox/models/'
__SCREAMING_SNAKE_CASE							:    List[Any]		=    {
    'jukebox-1b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '1b_lyrics/prior_level_2.pth.tar',
    ],
    'jukebox-5b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '5b_lyrics/prior_level_2.pth.tar',
    ],
}







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				int:
							if key.endswith(""".model.1.bias"""				) and len(key.split("""."""				)				) > 10:
														snake_case_           =		key.replace(""".model.1.bias"""				, """.conv1d_1.bias"""				)
							elif key.endswith(""".model.1.weight"""				) and len(key.split("""."""				)				) > 10:
														snake_case_           =		key.replace(""".model.1.weight"""				, """.conv1d_1.weight"""				)
							elif key.endswith(""".model.3.bias"""				) and len(key.split("""."""				)				) > 10:
														snake_case_           =		key.replace(""".model.3.bias"""				, """.conv1d_2.bias"""				)
							elif key.endswith(""".model.3.weight"""				) and len(key.split("""."""				)				) > 10:
														snake_case_           =		key.replace(""".model.3.weight"""				, """.conv1d_2.weight"""				)

							if "conditioner_blocks.0." in key:
														snake_case_           =		key.replace("""conditioner_blocks.0"""				, """conditioner_blocks"""				)

							if "prime_prior" in key:
														snake_case_           =		key.replace("""prime_prior"""				, """encoder"""				)

							if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
														snake_case_           =		key.replace(""".emb."""				, """."""				)

							if key.endswith("""k"""				):  # replace vqvae.X.k with vqvae.X.codebook
														return key.replace(""".k"""				, """.codebook"""				)
							if "y_emb." in key:
														return key.replace("""y_emb."""				, """metadata_embedding."""				)

							if "x_emb.emb." in key:
														snake_case_           =		key.replace("""0.x_emb.emb"""				, """embed_tokens"""				)

							if "prime_state_ln" in key:
														return key.replace("""prime_state_ln"""				, """encoder.final_layer_norm"""				)
							if ".ln" in key:
														return key.replace(""".ln"""				, """.layer_norm"""				)
							if "_ln" in key:
														return key.replace("""_ln"""				, """_layer_norm"""				)

							if "prime_state_proj" in key:
														return key.replace("""prime_state_proj"""				, """encoder.proj_in"""				)
							if "prime_x_out" in key:
														return key.replace("""prime_x_out"""				, """encoder.lm_head"""				)
							if "prior.x_out" in key:
														return key.replace("""x_out"""				, """fc_proj_out"""				)
							if "x_emb" in key:
														return key.replace("""x_emb"""				, """embed_tokens"""				)

							return key







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Union[str, Any]:
							snake_case_           =		{}
							import re

							snake_case_           =		re.compile(r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""				)
							snake_case_           =		re.compile(
							    r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""				)
							snake_case_           =		re.compile(r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""				)

							snake_case_           =		re.compile(r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""				)
							snake_case_           =		re.compile(
							    r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""				)
							snake_case_           =		re.compile(r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""				)

							snake_case_           =		re.compile(r"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"""				)
							snake_case_           =		re.compile(
							    r"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""				)
							snake_case_           =		re.compile(r"""conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)"""				)

							for original_key, value in state_dict.items():
														# rename vqvae.encoder keys
														if re_encoder_block_conv_in.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_encoder_block_conv_in.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		int(groups[2]				) * 2 + int(groups[3]				)
																					snake_case_           =		f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
																					snake_case_           =		re_encoder_block_conv_in.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_encoder_block_resnet.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_encoder_block_resnet.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		int(groups[2]				) * 2 + int(groups[3]				)
																					snake_case_           =		{"""1""": 1, """3""": 2}[groups[-2]]
																					snake_case_           =		f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
																					snake_case_           =		f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
																					snake_case_           =		prefix + resnet_block
																					snake_case_           =		re_encoder_block_resnet.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_encoder_block_proj_out.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_encoder_block_proj_out.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
																					snake_case_           =		re_encoder_block_proj_out.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														# rename vqvae.decoder keys
														elif re_decoder_block_conv_out.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_decoder_block_conv_out.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		int(groups[2]				) * 2 + int(groups[3]				) - 2
																					snake_case_           =		f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
																					snake_case_           =		re_decoder_block_conv_out.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_decoder_block_resnet.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_decoder_block_resnet.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		int(groups[2]				) * 2 + int(groups[3]				) - 2
																					snake_case_           =		{"""1""": 1, """3""": 2}[groups[-2]]
																					snake_case_           =		f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
																					snake_case_           =		f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
																					snake_case_           =		prefix + resnet_block
																					snake_case_           =		re_decoder_block_resnet.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_decoder_block_proj_in.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_decoder_block_proj_in.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
																					snake_case_           =		re_decoder_block_proj_in.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														# rename prior cond.model to upsampler.upsample_block and resnet
														elif re_prior_cond_conv_out.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_prior_cond_conv_out.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		int(groups[1]				) * 2 + int(groups[2]				) - 2
																					snake_case_           =		f"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
																					snake_case_           =		re_prior_cond_conv_out.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_prior_cond_resnet.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_prior_cond_resnet.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		int(groups[1]				) * 2 + int(groups[2]				) - 2
																					snake_case_           =		{"""1""": 1, """3""": 2}[groups[-2]]
																					snake_case_           =		f"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
																					snake_case_           =		f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
																					snake_case_           =		prefix + resnet_block
																					snake_case_           =		re_prior_cond_resnet.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_prior_cond_proj_in.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_prior_cond_proj_in.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
																					snake_case_           =		re_prior_cond_proj_in.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														# keep original key
														else:
																					snake_case_           =		original_key

														snake_case_           =		replace_key(_SCREAMING_SNAKE_CASE				)

														if f"""{key_prefix}.{key}""" not in model_state_dict or key is None:
																					print(f"""failed converting {original_key} to {key}, does not match"""				)

														# handle missmatched shape
														elif value.shape != model_state_dict[f"""{key_prefix}.{key}"""].shape:
																					snake_case_           =		model_state_dict[f"""{key_prefix}.{key}"""]
																					print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""				)
																					snake_case_           =		original_key

														snake_case_           =		original_key
														snake_case_           =		value

							return new_dict







@torch.no_grad()
def 						_a				(      _SCREAMING_SNAKE_CASE=None				, _SCREAMING_SNAKE_CASE=None				)    ->				Optional[int]:
							for file in MODEL_MAPPING[model_name]:
														if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split("/"				)[-1]}"""				):
																					snake_case_           =		requests.get(f"""{PREFIX}{file}"""				, allow_redirects=_SCREAMING_SNAKE_CASE				)
																					os.makedirs(f"""{pytorch_dump_folder_path}/"""				, exist_ok=_SCREAMING_SNAKE_CASE				)
																					open(f"""{pytorch_dump_folder_path}/{file.split("/"				)[-1]}"""				, """wb"""				).write(r.content				)

							snake_case_           =		MODEL_MAPPING[model_name.split("""/"""				)[-1]]

							snake_case_           =		JukeboxConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		JukeboxModel(_SCREAMING_SNAKE_CASE				)

							snake_case_           =		[]
							snake_case_           =		{}
							for i, dict_name in enumerate(_SCREAMING_SNAKE_CASE				):
														snake_case_           =		torch.load(f"""{pytorch_dump_folder_path}/{dict_name.split("/"				)[-1]}"""				)["""model"""]

														snake_case_           =		{}
														for k in old_dic.keys():
																					if k.endswith(""".b"""				):
																												snake_case_           =		old_dic[k]
																					elif k.endswith(""".w"""				):
																												snake_case_           =		old_dic[k]
																					elif "level_2" not in dict_name and "cond.model." in k:
																												snake_case_           =		old_dic[k]
																					else:
																												snake_case_           =		old_dic[k]

														snake_case_           =		"""vqvae""" if i == 0 else f"""priors.{3 - i}"""
														snake_case_           =		fix_jukebox_keys(_SCREAMING_SNAKE_CASE				, model.state_dict()				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
														weight_dict.append(_SCREAMING_SNAKE_CASE				)

							snake_case_           =		weight_dict.pop(0				)
							model.vqvae.load_state_dict(_SCREAMING_SNAKE_CASE				)
							for i in range(len(_SCREAMING_SNAKE_CASE				)				):
														model.priors[i].load_state_dict(weight_dict[2 - i]				)

							Path(_SCREAMING_SNAKE_CASE				).mkdir(exist_ok=_SCREAMING_SNAKE_CASE				)
							with open(f"""{pytorch_dump_folder_path}/mapping.json"""				, """w"""				) as txtfile:
														json.dump(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

							print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""				)
							model.save_pretrained(_SCREAMING_SNAKE_CASE				)

							return weight_dict


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default='jukebox-5b-lyrics',
				    type=str,
				    help='Name of the model you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path',
				    default='jukebox-5b-lyrics-converted',
				    type=str,
				    help='Path to the output PyTorch model directory.',
				)
				__SCREAMING_SNAKE_CASE							:    str		=    parser.parse_args()
				convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



