



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 SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    def       _lowerCAmelCase (	self		):
        A			:	Dict										=       pa.array(TypedSequence([1, 2, 3]		)		)
        self.assertEqual(arr.type,		pa.intaa()		)






    def       _lowerCAmelCase (	self		):
        with self.assertRaises(lowerCamelCase__		):
            A			:	Any										=       pa.array(TypedSequence([1, 2, 3]		),		type=pa.intaa()		)






    def       _lowerCAmelCase (	self		):
        with self.assertRaises(lowerCamelCase__		):
            A			:	Optional[Any]										=       pa.array(TypedSequence([1, 2, 3],		try_type=Value("""bool"""		),		type=Value("""int64"""		)		)		)






    def       _lowerCAmelCase (	self		):
        A			:	Union[str, Any]										=       pa.array(TypedSequence([1, 2, 3],		type=Value("""int32"""		)		)		)
        self.assertEqual(arr.type,		pa.intaa()		)






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






    def       _lowerCAmelCase (	self		):
        A			:	Dict										=       pa.array(TypedSequence([1, 2, 3],		try_type=Value("""int32"""		)		)		)
        self.assertEqual(arr.type,		pa.intaa()		)






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       pa.array(TypedSequence(["""foo""", """bar"""],		try_type=Value("""int64"""		)		)		)
        self.assertEqual(arr.type,		pa.string()		)






    def       _lowerCAmelCase (	self		):
        A			:	Optional[int]										=       pa.array(TypedSequence([[[1, 2, 3]]],		type=ArrayaD((1, 3),		"""int64"""		)		)		)
        self.assertEqual(arr.type,		ArrayaDExtensionType((1, 3),		"""int64"""		)		)






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






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       pa.array(TypedSequence([[[1, 2, 3]]],		try_type=ArrayaD((1, 3),		"""int64"""		)		)		)
        self.assertEqual(arr.type,		ArrayaDExtensionType((1, 3),		"""int64"""		)		)






    def       _lowerCAmelCase (	self		):
        A			:	str										=       pa.array(TypedSequence(["""foo""", """bar"""],		try_type=ArrayaD((1, 3),		"""int64"""		)		)		)
        self.assertEqual(arr.type,		pa.string()		)






    @require_pil
    def       _lowerCAmelCase (	self		):
        import PIL.Image

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




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  Tuple:



    """simple docstring"""






    A			:	Optional[Any]										=       pa.BufferReader(_lowerCAmelCase		) if isinstance(_lowerCAmelCase				,					pa.Buffer		) else pa.memory_map(_lowerCAmelCase		)
    A			:	Any										=       pa.ipc.open_stream(_lowerCAmelCase		)
    A			:	pa.Table										=       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      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  Any:



    """simple docstring"""






    A			:	Optional[Any]										=       pa.BufferOutputStream()
    A			:	int										=       pa.schema(_lowerCAmelCase		) if fields else None
    with ArrowWriter(stream=_lowerCAmelCase				,					schema=_lowerCAmelCase				,					writer_batch_size=_lowerCAmelCase		) as writer:
        writer.write({"""col_1""": """foo""", """col_2""": 1}		)
        writer.write({"""col_1""": """bar""", """col_2""": 2}		)
        A					,							A			:	List[Any]										=       writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        A			:	List[str]										=       {"""col_1""": pa.string(), """col_2""": pa.intaa()}
    assert writer._schema == pa.schema(_lowerCAmelCase				,					metadata=writer._schema.metadata		)
    _check_output(output.getvalue()				,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1		)




def      __UpperCamelCase     (  )	->  List[str]:



    """simple docstring"""






    A			:	Dict										=       pa.BufferOutputStream()
    A			:	int										=       Features({"""labels""": ClassLabel(names=["""neg""", """pos"""]		)}		)
    with ArrowWriter(stream=_lowerCAmelCase				,					features=_lowerCAmelCase		) as writer:
        writer.write({"""labels""": 0}		)
        writer.write({"""labels""": 1}		)
        A					,							A			:	Any										=       writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    assert writer._schema == features.arrow_schema
    assert writer._schema.metadata == features.arrow_schema.metadata
    A			:	Optional[Any]										=       pa.BufferReader(output.getvalue()		)
    A			:	Optional[int]										=       pa.ipc.open_stream(_lowerCAmelCase		)
    A			:	pa.Table										=       f.read_all()
    A			:	Optional[int]										=       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(_lowerCAmelCase		)




@pytest.mark.parametrize("""writer_batch_size"""				,					[None, 1, 10]		)
def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Optional[int]:



    """simple docstring"""






    A			:	Dict										=       pa.BufferOutputStream()
    with ArrowWriter(
        stream=_lowerCAmelCase				,					writer_batch_size=_lowerCAmelCase				,					hash_salt="""split_name"""				,					check_duplicates=_lowerCAmelCase				,					) as writer:
        with pytest.raises(_lowerCAmelCase		):
            writer.write({"""col_1""": """foo""", """col_2""": 1}				,					key=[1, 2]		)
            A					,							A			:	int										=       writer.finalize()




@pytest.mark.parametrize("""writer_batch_size"""				,					[None, 2, 10]		)
def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Dict:



    """simple docstring"""






    A			:	List[Any]										=       pa.BufferOutputStream()
    with ArrowWriter(
        stream=_lowerCAmelCase				,					writer_batch_size=_lowerCAmelCase				,					hash_salt="""split_name"""				,					check_duplicates=_lowerCAmelCase				,					) as writer:
        with pytest.raises(_lowerCAmelCase		):
            writer.write({"""col_1""": """foo""", """col_2""": 1}				,					key=10		)
            writer.write({"""col_1""": """bar""", """col_2""": 2}				,					key=10		)
            A					,							A			:	int										=       writer.finalize()




@pytest.mark.parametrize("""writer_batch_size"""				,					[None, 2, 10]		)
def      __UpperCamelCase     (  _lowerCAmelCase		)	->  str:



    """simple docstring"""






    A			:	Any										=       pa.BufferOutputStream()
    with ArrowWriter(
        stream=_lowerCAmelCase				,					writer_batch_size=_lowerCAmelCase				,					hash_salt="""split_name"""				,					check_duplicates=_lowerCAmelCase				,					) as writer:
        writer.write({"""col_1""": """foo""", """col_2""": 1}				,					key=1		)
        writer.write({"""col_1""": """bar""", """col_2""": 2}				,					key=2		)
        A					,							A			:	Dict										=       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      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  Any:



    """simple docstring"""






    A			:	Tuple										=       pa.BufferOutputStream()
    A			:	List[Any]										=       pa.schema(_lowerCAmelCase		) if fields else None
    with ArrowWriter(stream=_lowerCAmelCase				,					schema=_lowerCAmelCase				,					writer_batch_size=_lowerCAmelCase		) as writer:
        writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}		)
        writer.write_batch({"""col_1""": [], """col_2""": []}		)
        A					,							A			:	Any										=       writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        A			:	List[Any]										=       {"""col_1""": pa.string(), """col_2""": pa.intaa()}
    assert writer._schema == pa.schema(_lowerCAmelCase				,					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      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  Any:



    """simple docstring"""






    A			:	str										=       pa.BufferOutputStream()
    A			:	str										=       pa.schema(_lowerCAmelCase		) if fields else None
    with ArrowWriter(stream=_lowerCAmelCase				,					schema=_lowerCAmelCase				,					writer_batch_size=_lowerCAmelCase		) as writer:
        writer.write_table(pa.Table.from_pydict({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}		)		)
        A					,							A			:	List[str]										=       writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        A			:	str										=       {"""col_1""": pa.string(), """col_2""": pa.intaa()}
    assert writer._schema == pa.schema(_lowerCAmelCase				,					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      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  Tuple:



    """simple docstring"""






    A			:	Optional[Any]										=       pa.BufferOutputStream()
    A			:	Optional[int]										=       pa.schema(_lowerCAmelCase		) if fields else None
    with ArrowWriter(stream=_lowerCAmelCase				,					schema=_lowerCAmelCase				,					writer_batch_size=_lowerCAmelCase		) 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]}		)		)
        A					,							A			:	List[Any]										=       writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        A			:	List[Any]										=       {"""col_1""": pa.string(), """col_2""": pa.intaa()}
    assert writer._schema == pa.schema(_lowerCAmelCase				,					metadata=writer._schema.metadata		)
    _check_output(output.getvalue()				,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1		)




def      __UpperCamelCase     (  )	->  Optional[int]:



    """simple docstring"""






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




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Any:



    """simple docstring"""






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




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  int:



    """simple docstring"""






    if isinstance(lst[0]				,					_lowerCAmelCase		):
        change_first_primitive_element_in_list(lst[0]				,					_lowerCAmelCase		)
    else:
        A			:	List[str]										=       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      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  Optional[int]:



    """simple docstring"""






    A			:	Dict										=       pa.array(TypedSequence(_lowerCAmelCase				,					optimized_int_type=_lowerCAmelCase		)		)
    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      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  List[Any]:



    """simple docstring"""






    A			:	List[Any]										=       pa.array(OptimizedTypedSequence(_lowerCAmelCase				,					col=_lowerCAmelCase		)		)
    assert get_base_dtype(arr.type		) == expected_dtype

    # not in range
    if col != "other":
        # avoids errors due to in-place modifications
        A			:	Dict										=       copy.deepcopy(_lowerCAmelCase		)
        A			:	Union[str, Any]										=       np.iinfo(expected_dtype.to_pandas_dtype()		).max + 1
        change_first_primitive_element_in_list(_lowerCAmelCase				,					_lowerCAmelCase		)
        A			:	Optional[Any]										=       pa.array(OptimizedTypedSequence(_lowerCAmelCase				,					col=_lowerCAmelCase		)		)
        assert get_base_dtype(arr.type		) == pa.intaa()




@pytest.mark.parametrize("""raise_exception"""				,					[False, True]		)
def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  List[str]:



    """simple docstring"""






    A			:	Tuple										=       str(tmp_path / """dataset-train.arrow"""		)
    try:
        with ArrowWriter(path=_lowerCAmelCase		) as writer:
            if raise_exception:
                raise pa.lib.ArrowInvalid()
            else:
                writer.stream.close()
    except pa.lib.ArrowInvalid:
        pass
    finally:
        assert writer.stream.closed




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  List[str]:



    """simple docstring"""






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




def      __UpperCamelCase     (  )	->  List[str]:



    """simple docstring"""






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




@require_pil
@pytest.mark.parametrize("""embed_local_files"""				,					[False, True]		)
def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  List[str]:



    """simple docstring"""






    import PIL.Image

    A			:	Union[str, Any]										=       str(tmp_path / """test_image_rgb.jpg"""		)
    PIL.Image.fromarray(np.zeros((5, 5)				,					dtype=np.uinta		)		).save(_lowerCAmelCase				,					format="""png"""		)
    A			:	Tuple										=       pa.BufferOutputStream()
    with ParquetWriter(
        stream=_lowerCAmelCase				,					features=Features({"""image""": Image()}		)				,					embed_local_files=_lowerCAmelCase		) as writer:
        writer.write({"""image""": image_path}		)
        writer.finalize()
    A			:	str										=       pa.BufferReader(output.getvalue()		)
    A			:	pa.Table										=       pq.read_table(_lowerCAmelCase		)
    A			:	List[Any]										=       pa_table.to_pydict()
    if embed_local_files:
        assert isinstance(out["""image"""][0]["""path"""]				,					_lowerCAmelCase		)
        with open(_lowerCAmelCase				,					"""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      __UpperCamelCase     (  )	->  Tuple:



    """simple docstring"""






    A			:	Tuple										=       pa.schema([pa.field("""col_1"""				,					pa.string()				,					nullable=_lowerCAmelCase		)]		)
    A			:	Any										=       pa.BufferOutputStream()
    with ArrowWriter(stream=_lowerCAmelCase		) as writer:
        writer._build_writer(inferred_schema=_lowerCAmelCase		)
        assert writer._schema == pa.schema([pa.field("""col_1"""				,					pa.string()		)]		)







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


SCREAMING_SNAKE_CASE_:Union[str, Any]					=						"""https://openaipublic.azureedge.net/jukebox/models/"""
SCREAMING_SNAKE_CASE_:Optional[int]					=						{
    """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      __UpperCamelCase     (  _lowerCAmelCase		)	->  Dict:



    """simple docstring"""






    if key.endswith(""".model.1.bias"""		) and len(key.split("""."""		)		) > 10:
        A			:	Optional[int]										=       key.replace(""".model.1.bias"""				,					""".conv1d_1.bias"""		)
    elif key.endswith(""".model.1.weight"""		) and len(key.split("""."""		)		) > 10:
        A			:	Any										=       key.replace(""".model.1.weight"""				,					""".conv1d_1.weight"""		)
    elif key.endswith(""".model.3.bias"""		) and len(key.split("""."""		)		) > 10:
        A			:	str										=       key.replace(""".model.3.bias"""				,					""".conv1d_2.bias"""		)
    elif key.endswith(""".model.3.weight"""		) and len(key.split("""."""		)		) > 10:
        A			:	Optional[Any]										=       key.replace(""".model.3.weight"""				,					""".conv1d_2.weight"""		)

    if "conditioner_blocks.0." in key:
        A			:	List[str]										=       key.replace("""conditioner_blocks.0"""				,					"""conditioner_blocks"""		)

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

    if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
        A			:	List[str]										=       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:
        A			:	Optional[int]										=       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      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  Tuple:



    """simple docstring"""






    A			:	List[str]										=       {}
    import re

    A			:	Any										=       re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""		)
    A			:	str										=       re.compile(
        R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""		)
    A			:	Union[str, Any]										=       re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""		)

    A			:	List[Any]										=       re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""		)
    A			:	Optional[Any]										=       re.compile(
        R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""		)
    A			:	List[str]										=       re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""		)

    A			:	Optional[Any]										=       re.compile(R"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"""		)
    A			:	Tuple										=       re.compile(
        R"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""		)
    A			:	List[Any]										=       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(_lowerCAmelCase		):
            A			:	Optional[Any]										=       re_encoder_block_conv_in.match(_lowerCAmelCase		)
            A			:	Tuple										=       regex_match.groups()
            A			:	str										=       int(groups[2]		) * 2 + int(groups[3]		)
            A			:	int										=       f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
            A			:	Any										=       re_encoder_block_conv_in.sub(_lowerCAmelCase				,					_lowerCAmelCase		)

        elif re_encoder_block_resnet.fullmatch(_lowerCAmelCase		):
            A			:	Optional[int]										=       re_encoder_block_resnet.match(_lowerCAmelCase		)
            A			:	str										=       regex_match.groups()
            A			:	Optional[int]										=       int(groups[2]		) * 2 + int(groups[3]		)
            A			:	Any										=       {"""1""": 1, """3""": 2}[groups[-2]]
            A			:	int										=       f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'''
            A			:	List[str]										=       f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
            A			:	str										=       prefix + resnet_block
            A			:	str										=       re_encoder_block_resnet.sub(_lowerCAmelCase				,					_lowerCAmelCase		)

        elif re_encoder_block_proj_out.fullmatch(_lowerCAmelCase		):
            A			:	List[str]										=       re_encoder_block_proj_out.match(_lowerCAmelCase		)
            A			:	List[Any]										=       regex_match.groups()
            A			:	List[Any]										=       f'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
            A			:	Optional[int]										=       re_encoder_block_proj_out.sub(_lowerCAmelCase				,					_lowerCAmelCase		)

        # rename vqvae.decoder keys
        elif re_decoder_block_conv_out.fullmatch(_lowerCAmelCase		):
            A			:	Union[str, Any]										=       re_decoder_block_conv_out.match(_lowerCAmelCase		)
            A			:	Dict										=       regex_match.groups()
            A			:	Union[str, Any]										=       int(groups[2]		) * 2 + int(groups[3]		) - 2
            A			:	Optional[int]										=       f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'''
            A			:	int										=       re_decoder_block_conv_out.sub(_lowerCAmelCase				,					_lowerCAmelCase		)

        elif re_decoder_block_resnet.fullmatch(_lowerCAmelCase		):
            A			:	Optional[int]										=       re_decoder_block_resnet.match(_lowerCAmelCase		)
            A			:	List[Any]										=       regex_match.groups()
            A			:	List[str]										=       int(groups[2]		) * 2 + int(groups[3]		) - 2
            A			:	str										=       {"""1""": 1, """3""": 2}[groups[-2]]
            A			:	Optional[int]										=       f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'''
            A			:	Union[str, Any]										=       f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
            A			:	Tuple										=       prefix + resnet_block
            A			:	Tuple										=       re_decoder_block_resnet.sub(_lowerCAmelCase				,					_lowerCAmelCase		)

        elif re_decoder_block_proj_in.fullmatch(_lowerCAmelCase		):
            A			:	Optional[Any]										=       re_decoder_block_proj_in.match(_lowerCAmelCase		)
            A			:	Any										=       regex_match.groups()
            A			:	Optional[Any]										=       f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
            A			:	Dict										=       re_decoder_block_proj_in.sub(_lowerCAmelCase				,					_lowerCAmelCase		)

        # rename prior cond.model to upsampler.upsample_block and resnet
        elif re_prior_cond_conv_out.fullmatch(_lowerCAmelCase		):
            A			:	Optional[int]										=       re_prior_cond_conv_out.match(_lowerCAmelCase		)
            A			:	List[Any]										=       regex_match.groups()
            A			:	List[Any]										=       int(groups[1]		) * 2 + int(groups[2]		) - 2
            A			:	Tuple										=       f'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
            A			:	List[str]										=       re_prior_cond_conv_out.sub(_lowerCAmelCase				,					_lowerCAmelCase		)

        elif re_prior_cond_resnet.fullmatch(_lowerCAmelCase		):
            A			:	Any										=       re_prior_cond_resnet.match(_lowerCAmelCase		)
            A			:	Any										=       regex_match.groups()
            A			:	Tuple										=       int(groups[1]		) * 2 + int(groups[2]		) - 2
            A			:	Optional[Any]										=       {"""1""": 1, """3""": 2}[groups[-2]]
            A			:	Tuple										=       f'''conditioner_blocks.upsampler.upsample_block.{block_index}.'''
            A			:	List[str]										=       f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
            A			:	Dict										=       prefix + resnet_block
            A			:	Union[str, Any]										=       re_prior_cond_resnet.sub(_lowerCAmelCase				,					_lowerCAmelCase		)

        elif re_prior_cond_proj_in.fullmatch(_lowerCAmelCase		):
            A			:	List[Any]										=       re_prior_cond_proj_in.match(_lowerCAmelCase		)
            A			:	Optional[int]										=       regex_match.groups()
            A			:	Tuple										=       f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
            A			:	Optional[int]										=       re_prior_cond_proj_in.sub(_lowerCAmelCase				,					_lowerCAmelCase		)

        # keep original key
        else:
            A			:	str										=       original_key

        A			:	List[str]										=       replace_key(_lowerCAmelCase		)

        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:
            A			:	str										=       model_state_dict[f'''{key_prefix}.{key}''']
            print(f'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'''		)
            A			:	Union[str, Any]										=       original_key

        A			:	Union[str, Any]										=       original_key
        A			:	List[str]										=       value

    return new_dict




@torch.no_grad()
def      __UpperCamelCase     (  _lowerCAmelCase=None				,					_lowerCAmelCase=None		)	->  Tuple:



    """simple docstring"""






    for file in MODEL_MAPPING[model_name]:
        if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split("/"		)[-1]}'''		):
            A			:	Optional[Any]										=       requests.get(f'''{PREFIX}{file}'''				,					allow_redirects=_lowerCAmelCase		)
            os.makedirs(f'''{pytorch_dump_folder_path}/'''				,					exist_ok=_lowerCAmelCase		)
            open(f'''{pytorch_dump_folder_path}/{file.split("/"		)[-1]}'''				,					"""wb"""		).write(r.content		)

    A			:	Optional[int]										=       MODEL_MAPPING[model_name.split("""/"""		)[-1]]

    A			:	List[Any]										=       JukeboxConfig.from_pretrained(_lowerCAmelCase		)
    A			:	Dict										=       JukeboxModel(_lowerCAmelCase		)

    A			:	str										=       []
    A			:	Optional[int]										=       {}
    for i, dict_name in enumerate(_lowerCAmelCase		):
        A			:	str										=       torch.load(f'''{pytorch_dump_folder_path}/{dict_name.split("/"		)[-1]}'''		)["""model"""]

        A			:	Optional[int]										=       {}
        for k in old_dic.keys():
            if k.endswith(""".b"""		):
                A			:	Dict										=       old_dic[k]
            elif k.endswith(""".w"""		):
                A			:	Optional[Any]										=       old_dic[k]
            elif "level_2" not in dict_name and "cond.model." in k:
                A			:	Union[str, Any]										=       old_dic[k]
            else:
                A			:	Optional[int]										=       old_dic[k]

        A			:	List[str]										=       """vqvae""" if i == 0 else f'''priors.{3 - i}'''
        A			:	List[Any]										=       fix_jukebox_keys(_lowerCAmelCase				,					model.state_dict()				,					_lowerCAmelCase				,					_lowerCAmelCase		)
        weight_dict.append(_lowerCAmelCase		)

    A			:	List[Any]										=       weight_dict.pop(0		)
    model.vqvae.load_state_dict(_lowerCAmelCase		)
    for i in range(len(_lowerCAmelCase		)		):
        model.priors[i].load_state_dict(weight_dict[2 - i]		)

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

    print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''		)
    model.save_pretrained(_lowerCAmelCase		)

    return weight_dict


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:List[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_:int					=						parser.parse_args()
     convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)


