




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()
__UpperCamelCase					:       Optional[int]    					=      logging.get_logger(__name__)


__UpperCamelCase					:       str    					=      "https://openaipublic.azureedge.net/jukebox/models/"
__UpperCamelCase					:       Tuple    					=      {
    "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		(	__lowerCamelCase    )      ->       List[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 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	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 		__A		(	__lowerCamelCase=None							,			__lowerCamelCase=None    )      ->       Tuple:
	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__":
		__UpperCamelCase					:       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.",
		)
		__UpperCamelCase					:       int    					=      parser.parse_args()
		convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


__UpperCamelCase					:       Optional[Any]    					=      False

@skip_mps
class 		__lowerCAmelCase	(	__magic_name__      ,       __magic_name__      ,       __magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  StableDiffusionAttendAndExcitePipeline
							UpperCamelCase__					     =  False
							UpperCamelCase__					     =  TEXT_TO_IMAGE_PARAMS
							UpperCamelCase__					     =  TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''} )
							UpperCamelCase__					     =  TEXT_TO_IMAGE_IMAGE_PARAMS
							UpperCamelCase__					     =  TEXT_TO_IMAGE_IMAGE_PARAMS
							@classmethod
							def       lowerCamelCase__							(						cls						:Union[str, Any]				):







								'''simple docstring'''







								super().setUpClass()
								torch.use_deterministic_algorithms(__magic_name__				)
							@classmethod
							def       lowerCamelCase__							(						cls						:Union[str, Any]				):







								'''simple docstring'''







								super().tearDownClass()
								torch.use_deterministic_algorithms(__magic_name__				)
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								torch.manual_seed(0				)
								a										=							UNetaDConditionModel(
								    block_out_channels=(32, 64)		,      layers_per_block=1		,      sample_size=32		,      in_channels=4		,      out_channels=4		,      down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")		,      up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")		,      cross_attention_dim=32		,      attention_head_dim=(2, 4)		,      use_linear_projection=__magic_name__		,      )
								a										=							DDIMScheduler(
								    beta_start=0.00085		,      beta_end=0.012		,      beta_schedule="""scaled_linear"""		,      clip_sample=__magic_name__		,      set_alpha_to_one=__magic_name__		,      )
								torch.manual_seed(0				)
								a										=							AutoencoderKL(
								    block_out_channels=[32, 64]		,      in_channels=3		,      out_channels=3		,      down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]		,      up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]		,      latent_channels=4		,      sample_size=128		,      )
								torch.manual_seed(0				)
								a										=							CLIPTextConfig(
								    bos_token_id=0		,      eos_token_id=2		,      hidden_size=32		,      intermediate_size=37		,      layer_norm_eps=1E-0_5		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=1000		,      hidden_act="""gelu"""		,      projection_dim=512		,      )
								a										=							CLIPTextModel(__magic_name__				)
								a										=							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

								a										=							{
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """vae""": vae,
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								    """safety_checker""": None,
								    """feature_extractor""": None,
								}

								return components
							def       lowerCamelCase__							(						self						:Dict		,      __magic_name__						:Optional[int]		,      __magic_name__						:Optional[int]=0				):







								'''simple docstring'''







								if str(__magic_name__				).startswith("""mps"""				):
									a										=							torch.manual_seed(__magic_name__				)
								else:
									a										=							torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)
								a										=							a										=							{
								    """prompt""": """a cat and a frog""",
								    """token_indices""": [2, 5],
								    """generator""": generator,
								    """num_inference_steps""": 1,
								    """guidance_scale""": 6.0,
								    """output_type""": """numpy""",
								    """max_iter_to_alter""": 2,
								    """thresholds""": {0: 0.7},
								}
								return inputs
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								a										=							"""cpu"""

								a										=							self.get_dummy_components()
								a										=							self.pipeline_class(**__magic_name__				)
								pipe.to(__magic_name__				)
								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							self.get_dummy_inputs(__magic_name__				)
								a										=							pipe(**__magic_name__				).images
								a										=							image[0, -3:, -3:, -1]

								self.assertEqual(image.shape		,      (1, 64, 64, 3)				)
								a										=							np.array(
								    [0.63905364, 0.62897307, 0.48599017, 0.5133624, 0.5550048, 0.45769516, 0.50326973, 0.5023139, 0.45384496]				)
								a										=							np.abs(image_slice.flatten() - expected_slice				).max()
								self.assertLessEqual(__magic_name__		,      1E-3				)
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								super().test_cpu_offload_forward_pass(expected_max_diff=5E-4				)
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								self._test_inference_batch_consistent(batch_sizes=[1, 2]				)
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								self._test_inference_batch_single_identical(batch_size=2		,      expected_max_diff=7E-4				)
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3				)
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4				)
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								super().test_save_load_local(expected_max_difference=5E-4				)





							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								super().test_save_load_optional_components(expected_max_difference=4E-4				)







@require_torch_gpu
@slow
class 		__lowerCAmelCase	(	unittest.TestCase ):
							@classmethod
							def       lowerCamelCase__							(						cls						:int				):







								'''simple docstring'''







								super().setUpClass()
								torch.use_deterministic_algorithms(__magic_name__				)
							@classmethod
							def       lowerCamelCase__							(						cls						:Any				):







								'''simple docstring'''







								super().tearDownClass()
								torch.use_deterministic_algorithms(__magic_name__				)
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()





							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							torch.manual_seed(51				)

								a										=							StableDiffusionAttendAndExcitePipeline.from_pretrained(
								    """CompVis/stable-diffusion-v1-4"""		,      safety_checker=__magic_name__		,      torch_dtype=torch.floataa				)
								pipe.to("""cuda"""				)

								a										=							"""a painting of an elephant with glasses"""
								a										=							[5, 7]

								a										=							pipe(
								    prompt=__magic_name__		,      token_indices=__magic_name__		,      guidance_scale=7.5		,      generator=__magic_name__		,      num_inference_steps=5		,      max_iter_to_alter=5		,      output_type="""numpy"""		,      ).images[0]

								a										=							load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy"""				)
								assert np.abs((expected_image - image).max()				) < 5E-1

