


'''simple docstring'''






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import DetaImageProcessor




class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=7   ,       lowerCamelCase_=3   ,       lowerCamelCase_=30   ,       lowerCamelCase_=4_00   ,       lowerCamelCase_=True   ,       lowerCamelCase_=None   ,       lowerCamelCase_=True   ,       lowerCamelCase_=[0.5, 0.5, 0.5]   ,       lowerCamelCase_=[0.5, 0.5, 0.5]   ,       lowerCamelCase_=True   ,       lowerCamelCase_=1 / 2_55   ,       lowerCamelCase_=True   ,       )			->				Tuple:
														# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
														lowerCAmelCase__			  =      size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
														lowerCAmelCase__			  =      parent
														lowerCAmelCase__			  =      batch_size
														lowerCAmelCase__			  =      num_channels
														lowerCAmelCase__			  =      min_resolution
														lowerCAmelCase__			  =      max_resolution
														lowerCAmelCase__			  =      do_resize
														lowerCAmelCase__			  =      size
														lowerCAmelCase__			  =      do_normalize
														lowerCAmelCase__			  =      image_mean
														lowerCAmelCase__			  =      image_std
														lowerCAmelCase__			  =      do_rescale
														lowerCAmelCase__			  =      rescale_factor
														lowerCAmelCase__			  =      do_pad







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														return {
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_normalize": self.do_normalize,
														    "image_mean": self.image_mean,
														    "image_std": self.image_std,
														    "do_rescale": self.do_rescale,
														    "rescale_factor": self.rescale_factor,
														    "do_pad": self.do_pad,
														}







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=False  )			->				Any:
														if not batched:
																					lowerCAmelCase__			  =      image_inputs[0]
																					if isinstance(lowercase_   ,       Image.Image  ):
																												lowerCAmelCase__  ,   lowerCAmelCase__			  =      image.size
																					else:
																												lowerCAmelCase__  ,   lowerCAmelCase__			  =      image.shape[1], image.shape[2]
																					if w < h:
																												lowerCAmelCase__			  =      int(self.size['''shortest_edge'''] * h / w  )
																												lowerCAmelCase__			  =      self.size['''shortest_edge''']
																					elif w > h:
																												lowerCAmelCase__			  =      self.size['''shortest_edge''']
																												lowerCAmelCase__			  =      int(self.size['''shortest_edge'''] * w / h  )
																					else:
																												lowerCAmelCase__			  =      self.size['''shortest_edge''']
																												lowerCAmelCase__			  =      self.size['''shortest_edge''']

														else:
																					lowerCAmelCase__			  =      []
																					for image in image_inputs:
																												lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.get_expected_values([image]  )
																												expected_values.append((expected_height, expected_width)  )
																					lowerCAmelCase__			  =      max(lowercase_   ,       key=lambda lowerCamelCase_  : item[0]  )[0]
																					lowerCAmelCase__			  =      max(lowercase_   ,       key=lambda lowerCamelCase_  : item[1]  )[1]

														return expected_height, expected_width




@require_torch
@require_vision
class 						a__							(				UpperCamelCase__							,							unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  Union[str, Any]      		= DetaImageProcessor if is_vision_available() else None







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      DetaImageProcessingTester(self  )







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														return self.image_processor_tester.prepare_image_processor_dict()







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      self.image_processing_class(**self.image_processor_dict  )
														self.assertTrue(hasattr(lowercase_   ,       '''image_mean'''  )  )
														self.assertTrue(hasattr(lowercase_   ,       '''image_std'''  )  )
														self.assertTrue(hasattr(lowercase_   ,       '''do_normalize'''  )  )
														self.assertTrue(hasattr(lowercase_   ,       '''do_resize'''  )  )
														self.assertTrue(hasattr(lowercase_   ,       '''do_rescale'''  )  )
														self.assertTrue(hasattr(lowercase_   ,       '''do_pad'''  )  )
														self.assertTrue(hasattr(lowercase_   ,       '''size'''  )  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      self.image_processing_class.from_dict(self.image_processor_dict  )
														self.assertEqual(image_processor.size   ,       {'''shortest_edge''': 18, '''longest_edge''': 13_33}  )
														self.assertEqual(image_processor.do_pad   ,       lowercase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														pass







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														# Initialize image_processing
														lowerCAmelCase__			  =      self.image_processing_class(**self.image_processor_dict  )
														# create random PIL images
														lowerCAmelCase__			  =      prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=lowercase_  )
														for image in image_inputs:
																					self.assertIsInstance(lowercase_   ,       Image.Image  )

														# Test not batched input
														lowerCAmelCase__			  =      image_processing(image_inputs[0]   ,       return_tensors='''pt'''  ).pixel_values

														lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.image_processor_tester.get_expected_values(lowercase_  )

														self.assertEqual(
														    encoded_images.shape   ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,       )

														# Test batched
														lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.image_processor_tester.get_expected_values(lowercase_   ,       batched=lowercase_  )

														lowerCAmelCase__			  =      image_processing(lowercase_   ,       return_tensors='''pt'''  ).pixel_values
														self.assertEqual(
														    encoded_images.shape   ,       (
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        expected_height,
														        expected_width,
														    )   ,       )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														# Initialize image_processing
														lowerCAmelCase__			  =      self.image_processing_class(**self.image_processor_dict  )
														# create random numpy tensors
														lowerCAmelCase__			  =      prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=lowercase_   ,       numpify=lowercase_  )
														for image in image_inputs:
																					self.assertIsInstance(lowercase_   ,       np.ndarray  )

														# Test not batched input
														lowerCAmelCase__			  =      image_processing(image_inputs[0]   ,       return_tensors='''pt'''  ).pixel_values

														lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.image_processor_tester.get_expected_values(lowercase_  )

														self.assertEqual(
														    encoded_images.shape   ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,       )

														# Test batched
														lowerCAmelCase__			  =      image_processing(lowercase_   ,       return_tensors='''pt'''  ).pixel_values

														lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.image_processor_tester.get_expected_values(lowercase_   ,       batched=lowercase_  )

														self.assertEqual(
														    encoded_images.shape   ,       (
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        expected_height,
														        expected_width,
														    )   ,       )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														# Initialize image_processing
														lowerCAmelCase__			  =      self.image_processing_class(**self.image_processor_dict  )
														# create random PyTorch tensors
														lowerCAmelCase__			  =      prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=lowercase_   ,       torchify=lowercase_  )
														for image in image_inputs:
																					self.assertIsInstance(lowercase_   ,       torch.Tensor  )

														# Test not batched input
														lowerCAmelCase__			  =      image_processing(image_inputs[0]   ,       return_tensors='''pt'''  ).pixel_values

														lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.image_processor_tester.get_expected_values(lowercase_  )

														self.assertEqual(
														    encoded_images.shape   ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,       )

														# Test batched
														lowerCAmelCase__			  =      image_processing(lowercase_   ,       return_tensors='''pt'''  ).pixel_values

														lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.image_processor_tester.get_expected_values(lowercase_   ,       batched=lowercase_  )

														self.assertEqual(
														    encoded_images.shape   ,       (
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        expected_height,
														        expected_width,
														    )   ,       )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														# prepare image and target
														lowerCAmelCase__			  =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
														with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''   ,       '''r'''  ) as f:
																					lowerCAmelCase__			  =      json.loads(f.read()  )

														lowerCAmelCase__			  =      {'''image_id''': 3_97_69, '''annotations''': target}

														# encode them
														lowerCAmelCase__			  =      DetaImageProcessor()
														lowerCAmelCase__			  =      image_processing(images=lowercase_   ,       annotations=lowercase_   ,       return_tensors='''pt'''  )

														# verify pixel values
														lowerCAmelCase__			  =      torch.Size([1, 3, 8_00, 10_66]  )
														self.assertEqual(encoding['''pixel_values'''].shape   ,       lowercase_  )

														lowerCAmelCase__			  =      torch.tensor([0.2_796, 0.3_138, 0.3_481]  )
														self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,       lowercase_   ,       atol=1e-4  )  )

														# verify area
														lowerCAmelCase__			  =      torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]  )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,       lowercase_  )  )
														# verify boxes
														lowerCAmelCase__			  =      torch.Size([6, 4]  )
														self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,       lowercase_  )
														lowerCAmelCase__			  =      torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]  )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,       lowercase_   ,       atol=1e-3  )  )
														# verify image_id
														lowerCAmelCase__			  =      torch.tensor([3_97_69]  )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,       lowercase_  )  )
														# verify is_crowd
														lowerCAmelCase__			  =      torch.tensor([0, 0, 0, 0, 0, 0]  )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,       lowercase_  )  )
														# verify class_labels
														lowerCAmelCase__			  =      torch.tensor([75, 75, 63, 65, 17, 17]  )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,       lowercase_  )  )
														# verify orig_size
														lowerCAmelCase__			  =      torch.tensor([4_80, 6_40]  )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,       lowercase_  )  )
														# verify size
														lowerCAmelCase__			  =      torch.tensor([8_00, 10_66]  )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,       lowercase_  )  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														# prepare image, target and masks_path
														lowerCAmelCase__			  =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
														with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''   ,       '''r'''  ) as f:
																					lowerCAmelCase__			  =      json.loads(f.read()  )

														lowerCAmelCase__			  =      {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

														lowerCAmelCase__			  =      pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''  )

														# encode them
														lowerCAmelCase__			  =      DetaImageProcessor(format='''coco_panoptic'''  )
														lowerCAmelCase__			  =      image_processing(images=lowercase_   ,       annotations=lowercase_   ,       masks_path=lowercase_   ,       return_tensors='''pt'''  )

														# verify pixel values
														lowerCAmelCase__			  =      torch.Size([1, 3, 8_00, 10_66]  )
														self.assertEqual(encoding['''pixel_values'''].shape   ,       lowercase_  )

														lowerCAmelCase__			  =      torch.tensor([0.2_796, 0.3_138, 0.3_481]  )
														self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,       lowercase_   ,       atol=1e-4  )  )

														# verify area
														lowerCAmelCase__			  =      torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]  )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,       lowercase_  )  )
														# verify boxes
														lowerCAmelCase__			  =      torch.Size([6, 4]  )
														self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,       lowercase_  )
														lowerCAmelCase__			  =      torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]  )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,       lowercase_   ,       atol=1e-3  )  )
														# verify image_id
														lowerCAmelCase__			  =      torch.tensor([3_97_69]  )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,       lowercase_  )  )
														# verify is_crowd
														lowerCAmelCase__			  =      torch.tensor([0, 0, 0, 0, 0, 0]  )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,       lowercase_  )  )
														# verify class_labels
														lowerCAmelCase__			  =      torch.tensor([17, 17, 63, 75, 75, 93]  )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,       lowercase_  )  )
														# verify masks
														lowerCAmelCase__			  =      82_28_73
														self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()   ,       lowercase_  )
														# verify orig_size
														lowerCAmelCase__			  =      torch.tensor([4_80, 6_40]  )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,       lowercase_  )  )
														# verify size
														lowerCAmelCase__			  =      torch.tensor([8_00, 10_66]  )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,       lowercase_  )  )






'''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()
__lowerCAmelCase				:      Any						      =logging.get_logger(__name__)


__lowerCAmelCase				:      int						      ="https://openaipublic.azureedge.net/jukebox/models/"
__lowerCAmelCase				:      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							(				_lowerCamelCase   :   str			):
    if key.endswith(".model.1.bias"			) and len(key.split("."			)			) > 10:
        A__							=     key.replace(".model.1.bias"		,			".conv1d_1.bias"			)
    elif key.endswith(".model.1.weight"			) and len(key.split("."			)			) > 10:
        A__							=     key.replace(".model.1.weight"		,			".conv1d_1.weight"			)
    elif key.endswith(".model.3.bias"			) and len(key.split("."			)			) > 10:
        A__							=     key.replace(".model.3.bias"		,			".conv1d_2.bias"			)
    elif key.endswith(".model.3.weight"			) and len(key.split("."			)			) > 10:
        A__							=     key.replace(".model.3.weight"		,			".conv1d_2.weight"			)

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

    if "prime_prior" in key:
        A__							=     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__							=     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__							=     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   :   Optional[Any]		,			_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   Dict		,			_lowerCamelCase   :   str			):
    A__							=     {}
    import re

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

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

    A__							=     re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"			)
    A__							=     re.compile(
        r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"			)
    A__							=     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__							=     re_encoder_block_conv_in.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     int(groups[2]			) * 2 + int(groups[3]			)
            A__							=     F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
            A__							=     re_encoder_block_conv_in.sub(_lowerCamelCase		,			_lowerCamelCase			)

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

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

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

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

        elif re_decoder_block_proj_in.fullmatch(_lowerCamelCase			):
            A__							=     re_decoder_block_proj_in.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
            A__							=     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__							=     re_prior_cond_conv_out.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     int(groups[1]			) * 2 + int(groups[2]			) - 2
            A__							=     F"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
            A__							=     re_prior_cond_conv_out.sub(_lowerCamelCase		,			_lowerCamelCase			)

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

        elif re_prior_cond_proj_in.fullmatch(_lowerCamelCase			):
            A__							=     re_prior_cond_proj_in.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     F"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
            A__							=     re_prior_cond_proj_in.sub(_lowerCamelCase		,			_lowerCamelCase			)

        # keep original key
        else:
            A__							=     original_key

        A__							=     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__							=     model_state_dict[F"{key_prefix}.{key}"]
            print(F"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"			)
            A__							=     original_key

        A__							=     original_key
        A__							=     value

    return new_dict





@torch.no_grad()
def 		UpperCamelCase							(				_lowerCamelCase   :   str=None		,			_lowerCamelCase   :   Dict=None			):
    for file in MODEL_MAPPING[model_name]:
        if not os.path.isfile(F"{pytorch_dump_folder_path}/{file.split('/'			)[-1]}"			):
            A__							=     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__							=     MODEL_MAPPING[model_name.split("/"			)[-1]]

    A__							=     JukeboxConfig.from_pretrained(_lowerCamelCase			)
    A__							=     JukeboxModel(_lowerCamelCase			)

    A__							=     []
    A__							=     {}
    for i, dict_name in enumerate(_lowerCamelCase			):
        A__							=     torch.load(F"{pytorch_dump_folder_path}/{dict_name.split('/'			)[-1]}"			)["model"]

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

        A__							=     "vqvae" if i == 0 else F"priors.{3 - i}"
        A__							=     fix_jukebox_keys(_lowerCamelCase		,			model.state_dict()		,			_lowerCamelCase		,			_lowerCamelCase			)
        weight_dict.append(_lowerCamelCase			)

    A__							=     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__":
  __lowerCAmelCase				:      Dict						      =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.",
  )
  __lowerCAmelCase				:      int						      =parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



