


'''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        __snake_case	(       UpperCamelCase_):







				"""simple docstring"""



				def __lowercase       (       self   :  Any		)       ->  Tuple:
						lowerCAmelCase_      :				str      =       pa.array(TypedSequence([1, 2, 3]		)		)
						self.assertEqual(arr.type   ,						pa.intaa()		)

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


						with self.assertRaises(_a		):
								lowerCAmelCase_      :				Any      =       pa.array(TypedSequence([1, 2, 3]		)   ,						type=pa.intaa()		)

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


						with self.assertRaises(_a		):
								lowerCAmelCase_      :				Union[str, Any]      =       pa.array(TypedSequence([1, 2, 3]   ,						try_type=Value("""bool"""		)   ,						type=Value("""int64"""		)		)		)

				def __lowercase       (       self   :  List[Any]		)       ->  Dict:
						lowerCAmelCase_      :				Optional[Any]      =       pa.array(TypedSequence([1, 2, 3]   ,						type=Value("""int32"""		)		)		)
						self.assertEqual(arr.type   ,						pa.intaa()		)

				def __lowercase       (       self   :  List[str]		)       ->  Tuple:


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

				def __lowercase       (       self   :  str		)       ->  Optional[int]:
						lowerCAmelCase_      :				List[str]      =       pa.array(TypedSequence([1, 2, 3]   ,						try_type=Value("""int32"""		)		)		)
						self.assertEqual(arr.type   ,						pa.intaa()		)

				def __lowercase       (       self   :  Dict		)       ->  Optional[Any]:
						lowerCAmelCase_      :				Tuple      =       pa.array(TypedSequence(["""foo""", """bar"""]   ,						try_type=Value("""int64"""		)		)		)
						self.assertEqual(arr.type   ,						pa.string()		)

				def __lowercase       (       self   :  List[Any]		)       ->  Dict:
						lowerCAmelCase_      :				Optional[int]      =       pa.array(TypedSequence([[[1, 2, 3]]]   ,						type=ArrayaD((1, 3)   ,						"""int64"""		)		)		)
						self.assertEqual(arr.type   ,						ArrayaDExtensionType((1, 3)   ,						"""int64"""		)		)

				def __lowercase       (       self   :  List[Any]		)       ->  List[Any]:


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

				def __lowercase       (       self   :  Optional[Any]		)       ->  Tuple:
						lowerCAmelCase_      :				List[Any]      =       pa.array(TypedSequence([[[1, 2, 3]]]   ,						try_type=ArrayaD((1, 3)   ,						"""int64"""		)		)		)
						self.assertEqual(arr.type   ,						ArrayaDExtensionType((1, 3)   ,						"""int64"""		)		)

				def __lowercase       (       self   :  Optional[int]		)       ->  Any:
						lowerCAmelCase_      :				Optional[Any]      =       pa.array(TypedSequence(["""foo""", """bar"""]   ,						try_type=ArrayaD((1, 3)   ,						"""int64"""		)		)		)
						self.assertEqual(arr.type   ,						pa.string()		)





				@require_pil
				def __lowercase       (       self   :  int		)       ->  Any:
						import PIL.Image

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







def     UpperCamelCase_      (   A__ :       int							,   A__ :       Dict ):
		'''simple docstring'''





		lowerCAmelCase_      :				Tuple      =       pa.BufferReader(__lowerCAmelCase ) if isinstance(__lowerCAmelCase							,   pa.Buffer ) else pa.memory_map(__lowerCAmelCase )
		lowerCAmelCase_      :				Optional[int]      =       pa.ipc.open_stream(__lowerCAmelCase )
		lowerCAmelCase_      :				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_      (   A__ :       Any							,   A__ :       Any ):
		'''simple docstring'''





		lowerCAmelCase_      :				Tuple      =       pa.BufferOutputStream()
		lowerCAmelCase_      :				List[str]      =       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} )
				lowerCAmelCase_      :				str      =       writer.finalize()
		assert num_examples == 2
		assert num_bytes > 0
		if not fields:
				lowerCAmelCase_      :				int      =       {"""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_      (   ):
		'''simple docstring'''





		lowerCAmelCase_      :				Optional[Any]      =       pa.BufferOutputStream()
		lowerCAmelCase_      :				List[Any]      =       Features({"""labels""": ClassLabel(names=["""neg""", """pos"""] )} )
		with ArrowWriter(stream=__lowerCAmelCase							,   features=__lowerCAmelCase ) as writer:
				writer.write({"""labels""": 0} )
				writer.write({"""labels""": 1} )
				lowerCAmelCase_      :				int      =       writer.finalize()
		assert num_examples == 2
		assert num_bytes > 0
		assert writer._schema == features.arrow_schema
		assert writer._schema.metadata == features.arrow_schema.metadata
		lowerCAmelCase_      :				Tuple      =       pa.BufferReader(output.getvalue() )
		lowerCAmelCase_      :				str      =       pa.ipc.open_stream(__lowerCAmelCase )
		lowerCAmelCase_      :				pa.Table      =       f.read_all()
		lowerCAmelCase_      :				Any      =       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_      (   A__ :       Tuple ):
		'''simple docstring'''





		lowerCAmelCase_      :				Union[str, 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=[1, 2] )
						lowerCAmelCase_      :				str      =       writer.finalize()







@pytest.mark.parametrize("""writer_batch_size"""							,   [None, 2, 10] )
def     UpperCamelCase_      (   A__ :       List[str] ):
		'''simple docstring'''





		lowerCAmelCase_      :				Union[str, 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 )
						lowerCAmelCase_      :				Tuple      =       writer.finalize()







@pytest.mark.parametrize("""writer_batch_size"""							,   [None, 2, 10] )
def     UpperCamelCase_      (   A__ :       List[Any] ):
		'''simple docstring'''





		lowerCAmelCase_      :				int      =       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 )
				lowerCAmelCase_      :				Union[str, Any]      =       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_      (   A__ :       Dict							,   A__ :       Dict ):
		'''simple docstring'''





		lowerCAmelCase_      :				Any      =       pa.BufferOutputStream()
		lowerCAmelCase_      :				Optional[int]      =       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""": []} )
				lowerCAmelCase_      :				Optional[int]      =       writer.finalize()
		assert num_examples == 2
		assert num_bytes > 0
		if not fields:
				lowerCAmelCase_      :				Tuple      =       {"""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_      (   A__ :       Tuple							,   A__ :       str ):
		'''simple docstring'''





		lowerCAmelCase_      :				Dict      =       pa.BufferOutputStream()
		lowerCAmelCase_      :				List[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]} ) )
				lowerCAmelCase_      :				int      =       writer.finalize()
		assert num_examples == 2
		assert num_bytes > 0
		if not fields:
				lowerCAmelCase_      :				Dict      =       {"""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_      (   A__ :       Any							,   A__ :       str ):
		'''simple docstring'''





		lowerCAmelCase_      :				List[str]      =       pa.BufferOutputStream()
		lowerCAmelCase_      :				Optional[Any]      =       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]} ) )
				lowerCAmelCase_      :				int      =       writer.finalize()
		assert num_examples == 2
		assert num_bytes > 0
		if not fields:
				lowerCAmelCase_      :				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_      (   ):
		'''simple docstring'''





		with tempfile.TemporaryDirectory() as tmp_dir:
				lowerCAmelCase_      :				List[Any]      =       {"""col_1""": pa.string(), """col_2""": pa.intaa()}
				lowerCAmelCase_      :				Dict      =       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]} )
						lowerCAmelCase_      :				str      =       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_      (   A__ :       Tuple ):
		'''simple docstring'''





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







def     UpperCamelCase_      (   A__ :       Any							,   A__ :       Optional[int] ):
		'''simple docstring'''





		if isinstance(lst[0]							,   __lowerCAmelCase ):
				change_first_primitive_element_in_list(lst[0]							,   __lowerCAmelCase )
		else:
				lowerCAmelCase_      :				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_      (   A__ :       Union[str, Any]							,   A__ :       str							,   A__ :       Optional[int] ):
		'''simple docstring'''





		lowerCAmelCase_      :				Any      =       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_      (   A__ :       str							,   A__ :       Tuple							,   A__ :       Tuple ):
		'''simple docstring'''





		lowerCAmelCase_      :				List[str]      =       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
				lowerCAmelCase_      :				int      =       copy.deepcopy(__lowerCAmelCase )
				lowerCAmelCase_      :				Tuple      =       np.iinfo(expected_dtype.to_pandas_dtype() ).max + 1
				change_first_primitive_element_in_list(__lowerCAmelCase							,   __lowerCAmelCase )
				lowerCAmelCase_      :				Optional[int]      =       pa.array(OptimizedTypedSequence(__lowerCAmelCase							,   col=__lowerCAmelCase ) )
				assert get_base_dtype(arr.type ) == pa.intaa()







@pytest.mark.parametrize("""raise_exception"""							,   [False, True] )
def     UpperCamelCase_      (   A__ :       Any							,   A__ :       str ):
		'''simple docstring'''





		lowerCAmelCase_      :				Optional[Any]      =       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_      (   A__ :       Optional[int] ):
		'''simple docstring'''





		lowerCAmelCase_      :				int      =       """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} )
				lowerCAmelCase_      :				Union[str, Any]      =       writer.finalize()
		assert num_examples == 2
		assert num_bytes > 0
		assert mockfs.exists(__lowerCAmelCase )







def     UpperCamelCase_      (   ):
		'''simple docstring'''





		lowerCAmelCase_      :				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} )
				lowerCAmelCase_      :				int      =       writer.finalize()
		assert num_examples == 2
		assert num_bytes > 0
		lowerCAmelCase_      :				Union[str, Any]      =       pa.BufferReader(output.getvalue() )
		lowerCAmelCase_      :				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_      (   A__ :       Union[str, Any]							,   A__ :       int ):
		'''simple docstring'''





		import PIL.Image

		lowerCAmelCase_      :				List[str]      =       str(tmp_path / """test_image_rgb.jpg""" )
		PIL.Image.fromarray(np.zeros((5, 5)							,   dtype=np.uinta ) ).save(__lowerCAmelCase							,   format="""png""" )
		lowerCAmelCase_      :				str      =       pa.BufferOutputStream()
		with ParquetWriter(
		    stream=__lowerCAmelCase							,   features=Features({"""image""": Image()} )							,   embed_local_files=__lowerCAmelCase ) as writer:
				writer.write({"""image""": image_path} )
				writer.finalize()
		lowerCAmelCase_      :				str      =       pa.BufferReader(output.getvalue() )
		lowerCAmelCase_      :				pa.Table      =       pq.read_table(__lowerCAmelCase )
		lowerCAmelCase_      :				Tuple      =       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_      (   ):
		'''simple docstring'''





		lowerCAmelCase_      :				Any      =       pa.schema([pa.field("""col_1"""							,   pa.string()							,   nullable=__lowerCAmelCase )] )
		lowerCAmelCase_      :				List[str]      =       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() )] )







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


__A :  str    							=				"https://openaipublic.azureedge.net/jukebox/models/"
__A :  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     UpperCamelCase_      (   A__ :       int ):
  '''simple docstring'''





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

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

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

  if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
    lowerCAmelCase_      :				int      =       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:
    lowerCAmelCase_      :				List[Any]      =       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_      (   A__ :       Dict							,   A__ :       Optional[Any]							,   A__ :       Tuple							,   A__ :       Any ):
  '''simple docstring'''





  lowerCAmelCase_      :				Union[str, Any]      =       {}
  import re

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

  lowerCAmelCase_      :				int      =       re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)""" )
  lowerCAmelCase_      :				Optional[int]      =       re.compile(
      R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""" )
  lowerCAmelCase_      :				Union[str, Any]      =       re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)""" )

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

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

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

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

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

    elif re_decoder_block_proj_in.fullmatch(A__ ):
      lowerCAmelCase_      :				str      =       re_decoder_block_proj_in.match(A__ )
      lowerCAmelCase_      :				Optional[int]      =       regex_match.groups()
      lowerCAmelCase_      :				str      =       f'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
      lowerCAmelCase_      :				Union[str, Any]      =       re_decoder_block_proj_in.sub(A__							,   A__ )

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

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

    elif re_prior_cond_proj_in.fullmatch(A__ ):
      lowerCAmelCase_      :				List[str]      =       re_prior_cond_proj_in.match(A__ )
      lowerCAmelCase_      :				Optional[Any]      =       regex_match.groups()
      lowerCAmelCase_      :				Any      =       f'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
      lowerCAmelCase_      :				List[str]      =       re_prior_cond_proj_in.sub(A__							,   A__ )

    # keep original key
    else:
      lowerCAmelCase_      :				Optional[Any]      =       original_key

    lowerCAmelCase_      :				Optional[Any]      =       replace_key(A__ )

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

    lowerCAmelCase_      :				Dict      =       original_key
    lowerCAmelCase_      :				Optional[int]      =       value

  return new_dict







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Optional[Any]=None							,   A__ :       str=None ):
  '''simple docstring'''





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

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

  lowerCAmelCase_      :				List[str]      =       JukeboxConfig.from_pretrained(A__ )
  lowerCAmelCase_      :				Dict      =       JukeboxModel(A__ )

  lowerCAmelCase_      :				Optional[Any]      =       []
  lowerCAmelCase_      :				Dict      =       {}
  for i, dict_name in enumerate(A__ ):
    lowerCAmelCase_      :				List[Any]      =       torch.load(f'{pytorch_dump_folder_path}/{dict_name.split("/" )[-1]}' )["""model"""]

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

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

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

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

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

  return weight_dict


if __name__ == "__main__":
  __A :  str    							=				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.",
  )
  __A :  Optional[Any]    							=				parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



