import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, 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 PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class  A_			(							a_      ,							a_      ,							unittest.TestCase			):
  _SCREAMING_SNAKE_CASE     					=      StableDiffusionSAGPipeline
  _SCREAMING_SNAKE_CASE     					=      TEXT_TO_IMAGE_PARAMS
  _SCREAMING_SNAKE_CASE     					=      TEXT_TO_IMAGE_BATCH_PARAMS
  _SCREAMING_SNAKE_CASE     					=      TEXT_TO_IMAGE_IMAGE_PARAMS
  _SCREAMING_SNAKE_CASE     					=      TEXT_TO_IMAGE_IMAGE_PARAMS
  _SCREAMING_SNAKE_CASE     					=      False

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   torch.manual_seed(0							)
   __a							=	UNetaDConditionModel(
       block_out_channels=(32, 64)  ,				layers_per_block=2  ,				sample_size=32  ,				in_channels=4  ,				out_channels=4  ,				down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")  ,				up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")  ,				cross_attention_dim=32  ,				)
   __a							=	DDIMScheduler(
       beta_start=0.0_00_85  ,				beta_end=0.0_12  ,				beta_schedule="scaled_linear"  ,				clip_sample=__SCREAMING_SNAKE_CASE  ,				set_alpha_to_one=__SCREAMING_SNAKE_CASE  ,				)
   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  ,				)
   torch.manual_seed(0							)
   __a							=	CLIPTextConfig(
       bos_token_id=0  ,				eos_token_id=2  ,				hidden_size=32  ,				intermediate_size=37  ,				layer_norm_eps=1E-05  ,				num_attention_heads=4  ,				num_hidden_layers=5  ,				pad_token_id=1  ,				vocab_size=10_00  ,				)
   __a							=	CLIPTextModel(__SCREAMING_SNAKE_CASE							)
   __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 		_UpperCAmelCase (		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=0							):
   if str(__SCREAMING_SNAKE_CASE							).startswith("mps"							):
    __a							=	torch.manual_seed(__SCREAMING_SNAKE_CASE							)
   else:
    __a							=	torch.Generator(device=__SCREAMING_SNAKE_CASE							).manual_seed(__SCREAMING_SNAKE_CASE							)
   __a							=	{
       "prompt": ".",
       "generator": generator,
       "num_inference_steps": 2,
       "guidance_scale": 1.0,
       "sag_scale": 1.0,
       "output_type": "numpy",
   }
   return inputs

  def 		_UpperCAmelCase (		self			:   Tuple							):
   super().test_inference_batch_single_identical(expected_max_diff=3E-3							)
@slow
@require_torch_gpu
class  A_			(							unittest.TestCase			):

  def 		_UpperCAmelCase (		self			:   Tuple							):
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"							)
   __a							=	sag_pipe.to(__SCREAMING_SNAKE_CASE							)
   sag_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

   __a							=	"."
   __a							=	torch.manual_seed(0							)
   __a							=	sag_pipe(
       [prompt]  ,				generator=__SCREAMING_SNAKE_CASE  ,				guidance_scale=7.5  ,				sag_scale=1.0  ,				num_inference_steps=20  ,				output_type="np"							)

   __a							=	output.images

   __a							=	image[0, -3:, -3:, -1]

   assert image.shape == (1, 5_12, 5_12, 3)
   __a							=	np.array([0.15_68, 0.17_38, 0.16_95, 0.16_93, 0.15_07, 0.17_05, 0.15_47, 0.17_51, 0.19_49]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 5E-2

  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"							)
   __a							=	sag_pipe.to(__SCREAMING_SNAKE_CASE							)
   sag_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

   __a							=	"."
   __a							=	torch.manual_seed(0							)
   __a							=	sag_pipe(
       [prompt]  ,				generator=__SCREAMING_SNAKE_CASE  ,				guidance_scale=7.5  ,				sag_scale=1.0  ,				num_inference_steps=20  ,				output_type="np"							)

   __a							=	output.images

   __a							=	image[0, -3:, -3:, -1]

   assert image.shape == (1, 5_12, 5_12, 3)
   __a							=	np.array([0.34_59, 0.28_76, 0.25_37, 0.30_02, 0.26_71, 0.21_60, 0.30_26, 0.22_62, 0.23_71]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 5E-2

  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"							)
   __a							=	sag_pipe.to(__SCREAMING_SNAKE_CASE							)
   sag_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

   __a							=	"."
   __a							=	torch.manual_seed(0							)
   __a							=	sag_pipe(
       [prompt]  ,				width=7_68  ,				height=5_12  ,				generator=__SCREAMING_SNAKE_CASE  ,				guidance_scale=7.5  ,				sag_scale=1.0  ,				num_inference_steps=20  ,				output_type="np"  ,				)

   __a							=	output.images

   assert image.shape == (1, 5_12, 7_68, 3)



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


SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		"""https://openaipublic.azureedge.net/jukebox/models/"""
SCREAMING_SNAKE_CASE			:						int				  =		{
    """jukebox-1b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """1b_lyrics/prior_level_2.pth.tar""",
    ],
    """jukebox-5b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """5b_lyrics/prior_level_2.pth.tar""",
    ],
}



def 	__A		(      _A       ):



 """simple docstring"""


 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		(      _A				,					_A				,					_A				,					_A       ):



 """simple docstring"""


 __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(_A       ):
   __a							=	re_encoder_block_conv_in.match(_A       )
   __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(_A				,					_A       )

  elif re_encoder_block_resnet.fullmatch(_A       ):
   __a							=	re_encoder_block_resnet.match(_A       )
   __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(_A				,					_A       )

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

  # rename vqvae.decoder keys
  elif re_decoder_block_conv_out.fullmatch(_A       ):
   __a							=	re_decoder_block_conv_out.match(_A       )
   __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(_A				,					_A       )

  elif re_decoder_block_resnet.fullmatch(_A       ):
   __a							=	re_decoder_block_resnet.match(_A       )
   __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(_A				,					_A       )

  elif re_decoder_block_proj_in.fullmatch(_A       ):
   __a							=	re_decoder_block_proj_in.match(_A       )
   __a							=	regex_match.groups()
   __a							=	f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
   __a							=	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       ):
   __a							=	re_prior_cond_conv_out.match(_A       )
   __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(_A				,					_A       )

  elif re_prior_cond_resnet.fullmatch(_A       ):
   __a							=	re_prior_cond_resnet.match(_A       )
   __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(_A				,					_A       )

  elif re_prior_cond_proj_in.fullmatch(_A       ):
   __a							=	re_prior_cond_proj_in.match(_A       )
   __a							=	regex_match.groups()
   __a							=	f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
   __a							=	re_prior_cond_proj_in.sub(_A				,					_A       )

  # keep original key
  else:
   __a							=	original_key

  __a							=	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:
   __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		(      _A=None				,					_A=None       ):



 """simple docstring"""


 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=_A       )
   os.makedirs(f"""{pytorch_dump_folder_path}/"""				,					exist_ok=_A       )
   open(f"""{pytorch_dump_folder_path}/{file.split("/"       )[-1]}"""				,					"wb"       ).write(r.content       )

 __a							=	MODEL_MAPPING[model_name.split("/"       )[-1]]

 __a							=	JukeboxConfig.from_pretrained(_A       )
 __a							=	JukeboxModel(_A       )

 __a							=	[]
 __a							=	{}
 for i, dict_name in enumerate(_A       ):
  __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(_A				,					model.state_dict()				,					_A				,					_A       )
  weight_dict.append(_A       )

 __a							=	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__":
  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			:						Optional[int]				  =		parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)


