


"""simple docstring"""
import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
				import textwrap

				from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
				import torch

				from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
				__SCREAMING_SNAKE_CASE							:    Any		=    False


__SCREAMING_SNAKE_CASE							:    List[str]		=    logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    'ybelkada/fonts'







def 						_a				(      )    ->				Union[str, Any]:
							if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
														raise ImportError(
														    f"""You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use """
														    """Pix2StructImageProcessor. Please upgrade torch."""				)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Tuple:
							requires_backends(_SCREAMING_SNAKE_CASE				, ["""torch"""]				)
							_check_torch_version()

							snake_case_           =		image_tensor.unsqueeze(0				)
							snake_case_           =		torch.nn.functional.unfold(_SCREAMING_SNAKE_CASE				, (patch_height, patch_width)				, stride=(patch_height, patch_width)				)
							snake_case_           =		patches.reshape(image_tensor.size(0				)				, image_tensor.size(1				)				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, -1				)
							snake_case_           =		patches.permute(0				, 4				, 2				, 3				, 1				).reshape(
							    image_tensor.size(2				) // patch_height				, image_tensor.size(3				) // patch_width				, image_tensor.size(1				) * patch_height * patch_width				, )
							return patches.unsqueeze(0				)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE = 36				, _SCREAMING_SNAKE_CASE = "black"				, _SCREAMING_SNAKE_CASE = "white"				, _SCREAMING_SNAKE_CASE = 5				, _SCREAMING_SNAKE_CASE = 5				, _SCREAMING_SNAKE_CASE = 5				, _SCREAMING_SNAKE_CASE = 5				, _SCREAMING_SNAKE_CASE = None				, _SCREAMING_SNAKE_CASE = None				, )    ->				Image.Image:
							requires_backends(_SCREAMING_SNAKE_CASE				, """vision"""				)
							# Add new lines so that each line is no more than 80 characters.

							snake_case_           =		textwrap.TextWrapper(width=80				)
							snake_case_           =		wrapper.wrap(text=_SCREAMING_SNAKE_CASE				)
							snake_case_           =		"""\n""".join(_SCREAMING_SNAKE_CASE				)

							if font_bytes is not None and font_path is None:
														snake_case_           =		io.BytesIO(_SCREAMING_SNAKE_CASE				)
							elif font_path is not None:
														snake_case_           =		font_path
							else:
														snake_case_           =		hf_hub_download(_SCREAMING_SNAKE_CASE				, """Arial.TTF"""				)
							snake_case_           =		ImageFont.truetype(_SCREAMING_SNAKE_CASE				, encoding="""UTF-8"""				, size=_SCREAMING_SNAKE_CASE				)

							# Use a temporary canvas to determine the width and height in pixels when
							# rendering the text.
							snake_case_           =		ImageDraw.Draw(Image.new("""RGB"""				, (1, 1)				, _SCREAMING_SNAKE_CASE				)				)
							snake_case_       ,						snake_case_       ,						snake_case_       ,						snake_case_           =		temp_draw.textbbox((0, 0)				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

							# Create the actual image with a bit of padding around the text.
							snake_case_           =		text_width + left_padding + right_padding
							snake_case_           =		text_height + top_padding + bottom_padding
							snake_case_           =		Image.new("""RGB"""				, (image_width, image_height)				, _SCREAMING_SNAKE_CASE				)
							snake_case_           =		ImageDraw.Draw(_SCREAMING_SNAKE_CASE				)
							draw.text(xy=(left_padding, top_padding)				, text=_SCREAMING_SNAKE_CASE				, fill=_SCREAMING_SNAKE_CASE				, font=_SCREAMING_SNAKE_CASE				)
							return image







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, **_SCREAMING_SNAKE_CASE				)    ->				Any:
							requires_backends(_SCREAMING_SNAKE_CASE				, """vision"""				)

							# Convert to PIL image if necessary
							snake_case_           =		to_pil_image(_SCREAMING_SNAKE_CASE				)

							snake_case_           =		render_text(_SCREAMING_SNAKE_CASE				, **_SCREAMING_SNAKE_CASE				)
							snake_case_           =		max(header_image.width				, image.width				)

							snake_case_           =		int(image.height * (new_width / image.width)				)
							snake_case_           =		int(header_image.height * (new_width / header_image.width)				)

							snake_case_           =		Image.new("""RGB"""				, (new_width, new_height + new_header_height)				, """white"""				)
							new_image.paste(header_image.resize((new_width, new_header_height)				)				, (0, 0)				)
							new_image.paste(image.resize((new_width, new_height)				)				, (0, new_header_height)				)

							# Convert back to the original framework if necessary
							snake_case_           =		to_numpy_array(_SCREAMING_SNAKE_CASE				)

							if infer_channel_dimension_format(_SCREAMING_SNAKE_CASE				) == ChannelDimension.LAST:
														snake_case_           =		to_channel_dimension_format(_SCREAMING_SNAKE_CASE				, ChannelDimension.LAST				)

							return new_image





class      __A     (snake_case__):





							'''simple docstring'''

							__lowercase:		Tuple							      =    ["""flattened_patches"""]



							def __init__(				self							:      Optional[Any]		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      Dict[str, int] = None		,       UpperCAmelCase_							:      int = 2_048		,       UpperCAmelCase_							:      bool = False		,       **UpperCAmelCase_							:      Any		,       )					->None:



														"""simple docstring"""



														super().__init__(**UpperCAmelCase_       )
														snake_case_           =		patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
														snake_case_           =		do_normalize
														snake_case_           =		do_convert_rgb
														snake_case_           =		max_patches
														snake_case_           =		is_vqa



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      np.ndarray		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      dict		,       **UpperCAmelCase_							:      List[Any]       )					->np.ndarray:



														"""simple docstring"""



														requires_backends(self.extract_flattened_patches		,       """torch"""       )
														_check_torch_version()

														# convert to torch
														snake_case_           =		to_channel_dimension_format(UpperCAmelCase_		,       ChannelDimension.FIRST       )
														snake_case_           =		torch.from_numpy(UpperCAmelCase_       )

														snake_case_       ,						snake_case_           =		patch_size["""height"""], patch_size["""width"""]
														snake_case_       ,						snake_case_           =		get_image_size(UpperCAmelCase_       )

														# maximize scale s.t.
														snake_case_           =		math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)       )
														snake_case_           =		max(min(math.floor(scale * image_height / patch_height       )		,       UpperCAmelCase_       )		,       1       )
														snake_case_           =		max(min(math.floor(scale * image_width / patch_width       )		,       UpperCAmelCase_       )		,       1       )
														snake_case_           =		max(num_feasible_rows * patch_height		,       1       )
														snake_case_           =		max(num_feasible_cols * patch_width		,       1       )

														snake_case_           =		torch.nn.functional.interpolate(
														    image.unsqueeze(0       )		,       size=(resized_height, resized_width)		,       mode="""bilinear"""		,       align_corners=UpperCAmelCase_		,       antialias=UpperCAmelCase_		,       ).squeeze(0       )

														# [1, rows, columns, patch_height * patch_width * image_channels]
														snake_case_           =		torch_extract_patches(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )

														snake_case_           =		patches.shape
														snake_case_           =		patches_shape[1]
														snake_case_           =		patches_shape[2]
														snake_case_           =		patches_shape[3]

														# [rows * columns, patch_height * patch_width * image_channels]
														snake_case_           =		patches.reshape([rows * columns, depth]       )

														# [rows * columns, 1]
														snake_case_           =		torch.arange(UpperCAmelCase_       ).reshape([rows, 1]       ).repeat(1		,       UpperCAmelCase_       ).reshape([rows * columns, 1]       )
														snake_case_           =		torch.arange(UpperCAmelCase_       ).reshape([1, columns]       ).repeat(UpperCAmelCase_		,       1       ).reshape([rows * columns, 1]       )

														# Offset by 1 so the ids do not contain zeros, which represent padding.
														row_ids += 1
														col_ids += 1

														# Prepare additional patch features.
														# [rows * columns, 1]
														snake_case_           =		row_ids.to(torch.floataa       )
														snake_case_           =		col_ids.to(torch.floataa       )

														# [rows * columns, 2 + patch_height * patch_width * image_channels]
														snake_case_           =		torch.cat([row_ids, col_ids, patches]		,       -1       )

														# [max_patches, 2 + patch_height * patch_width * image_channels]
														snake_case_           =		torch.nn.functional.pad(UpperCAmelCase_		,       [0, 0, 0, max_patches - (rows * columns)]       ).float()

														snake_case_           =		to_numpy_array(UpperCAmelCase_       )

														return result



							def 		lowerCAmelCase       (				self							:      Any		,       UpperCAmelCase_							:      np.ndarray		,       UpperCAmelCase_							:      Optional[Union[str, ChannelDimension]] = None		,       **UpperCAmelCase_							:      Union[str, Any]       )					->np.ndarray:



														"""simple docstring"""



														if image.dtype == np.uinta:
																					snake_case_           =		image.astype(np.floataa       )

														# take mean across the whole `image`
														snake_case_           =		np.mean(UpperCAmelCase_       )
														snake_case_           =		np.std(UpperCAmelCase_       )
														snake_case_           =		max(UpperCAmelCase_		,       1.0 / math.sqrt(np.prod(image.shape       )       )       )

														return normalize(UpperCAmelCase_		,       mean=UpperCAmelCase_		,       std=UpperCAmelCase_		,       **UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      ImageInput		,       UpperCAmelCase_							:      Optional[str] = None		,       UpperCAmelCase_							:      bool = None		,       UpperCAmelCase_							:      Optional[bool] = None		,       UpperCAmelCase_							:      Optional[int] = None		,       UpperCAmelCase_							:      Optional[Dict[str, int]] = None		,       UpperCAmelCase_							:      Optional[Union[str, TensorType]] = None		,       UpperCAmelCase_							:      ChannelDimension = ChannelDimension.FIRST		,       **UpperCAmelCase_							:      Union[str, Any]		,       )					->ImageInput:



														"""simple docstring"""



														snake_case_           =		do_normalize if do_normalize is not None else self.do_normalize
														snake_case_           =		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
														snake_case_           =		patch_size if patch_size is not None else self.patch_size
														snake_case_           =		max_patches if max_patches is not None else self.max_patches
														snake_case_           =		self.is_vqa

														if kwargs.get("""data_format"""		,       UpperCAmelCase_       ) is not None:
																					raise ValueError("""data_format is not an accepted input as the outputs are """       )

														snake_case_           =		make_list_of_images(UpperCAmelCase_       )

														if not valid_images(UpperCAmelCase_       ):
																					raise ValueError(
																					    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																					    """torch.Tensor, tf.Tensor or jax.ndarray."""       )

														# PIL RGBA images are converted to RGB
														if do_convert_rgb:
																					snake_case_           =		[convert_to_rgb(UpperCAmelCase_       ) for image in images]

														# All transformations expect numpy arrays.
														snake_case_           =		[to_numpy_array(UpperCAmelCase_       ) for image in images]

														if is_vqa:
																					if header_text is None:
																												raise ValueError("""A header text must be provided for VQA models."""       )
																					snake_case_           =		kwargs.pop("""font_bytes"""		,       UpperCAmelCase_       )
																					snake_case_           =		kwargs.pop("""font_path"""		,       UpperCAmelCase_       )

																					if isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ):
																												snake_case_           =		[header_text] * len(UpperCAmelCase_       )

																					snake_case_           =		[
																					    render_header(UpperCAmelCase_		,       header_text[i]		,       font_bytes=UpperCAmelCase_		,       font_path=UpperCAmelCase_       )
																					    for i, image in enumerate(UpperCAmelCase_       )
																					]

														if do_normalize:
																					snake_case_           =		[self.normalize(image=UpperCAmelCase_       ) for image in images]

														# convert to torch tensor and permute
														snake_case_           =		[
														    self.extract_flattened_patches(image=UpperCAmelCase_		,       max_patches=UpperCAmelCase_		,       patch_size=UpperCAmelCase_       )
														    for image in images
														]

														# create attention mask in numpy
														snake_case_           =		[(image.sum(axis=-1       ) != 0).astype(np.floataa       ) for image in images]

														snake_case_           =		BatchFeature(
														    data={"""flattened_patches""": images, """attention_mask""": attention_masks}		,       tensor_type=UpperCAmelCase_       )

														return encoded_outputs







"""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)



