





'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





def 		UpperCamelCase							(				_lowerCamelCase   :   Tuple			):
    if "cls_token" in name:
        A__							=     name.replace("cls_token"		,			"vit.embeddings.cls_token"			)
    if "mask_token" in name:
        A__							=     name.replace("mask_token"		,			"decoder.mask_token"			)
    if "decoder_pos_embed" in name:
        A__							=     name.replace("decoder_pos_embed"		,			"decoder.decoder_pos_embed"			)
    if "pos_embed" in name and "decoder" not in name:
        A__							=     name.replace("pos_embed"		,			"vit.embeddings.position_embeddings"			)
    if "patch_embed.proj" in name:
        A__							=     name.replace("patch_embed.proj"		,			"vit.embeddings.patch_embeddings.projection"			)
    if "patch_embed.norm" in name:
        A__							=     name.replace("patch_embed.norm"		,			"vit.embeddings.norm"			)
    if "decoder_blocks" in name:
        A__							=     name.replace("decoder_blocks"		,			"decoder.decoder_layers"			)
    if "blocks" in name:
        A__							=     name.replace("blocks"		,			"vit.encoder.layer"			)
    if "attn.proj" in name:
        A__							=     name.replace("attn.proj"		,			"attention.output.dense"			)
    if "attn" in name:
        A__							=     name.replace("attn"		,			"attention.self"			)
    if "norm1" in name:
        A__							=     name.replace("norm1"		,			"layernorm_before"			)
    if "norm2" in name:
        A__							=     name.replace("norm2"		,			"layernorm_after"			)
    if "mlp.fc1" in name:
        A__							=     name.replace("mlp.fc1"		,			"intermediate.dense"			)
    if "mlp.fc2" in name:
        A__							=     name.replace("mlp.fc2"		,			"output.dense"			)
    if "decoder_embed" in name:
        A__							=     name.replace("decoder_embed"		,			"decoder.decoder_embed"			)
    if "decoder_norm" in name:
        A__							=     name.replace("decoder_norm"		,			"decoder.decoder_norm"			)
    if "decoder_pred" in name:
        A__							=     name.replace("decoder_pred"		,			"decoder.decoder_pred"			)
    if "norm.weight" in name and "decoder" not in name:
        A__							=     name.replace("norm.weight"		,			"vit.layernorm.weight"			)
    if "norm.bias" in name and "decoder" not in name:
        A__							=     name.replace("norm.bias"		,			"vit.layernorm.bias"			)

    return name





def 		UpperCamelCase							(				_lowerCamelCase   :   Dict		,			_lowerCamelCase   :   int			):
    for key in orig_state_dict.copy().keys():
        A__							=     orig_state_dict.pop(_lowerCamelCase			)

        if "qkv" in key:
            A__							=     key.split("."			)
            A__							=     int(key_split[1]			)
            if "decoder_blocks" in key:
                A__							=     config.decoder_hidden_size
                A__							=     "decoder.decoder_layers."
                if "weight" in key:
                    A__							=     val[:dim, :]
                    A__							=     val[dim : dim * 2, :]
                    A__							=     val[-dim:, :]
                elif "bias" in key:
                    A__							=     val[:dim]
                    A__							=     val[dim : dim * 2]
                    A__							=     val[-dim:]
            else:
                A__							=     config.hidden_size
                A__							=     "vit.encoder.layer."
                if "weight" in key:
                    A__							=     val[:dim, :]
                    A__							=     val[dim : dim * 2, :]
                    A__							=     val[-dim:, :]
                elif "bias" in key:
                    A__							=     val[:dim]
                    A__							=     val[dim : dim * 2]
                    A__							=     val[-dim:]

        else:
            A__							=     val

    return orig_state_dict





def 		UpperCamelCase							(				_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   Optional[int]			):
    A__							=     ViTMAEConfig()
    if "large" in checkpoint_url:
        A__							=     10_24
        A__							=     40_96
        A__							=     24
        A__							=     16
    elif "huge" in checkpoint_url:
        A__							=     14
        A__							=     12_80
        A__							=     51_20
        A__							=     32
        A__							=     16

    A__							=     ViTMAEForPreTraining(_lowerCamelCase			)

    A__							=     torch.hub.load_state_dict_from_url(_lowerCamelCase		,			map_location="cpu"			)["model"]

    A__							=     ViTMAEImageProcessor(size=config.image_size			)

    A__							=     convert_state_dict(_lowerCamelCase		,			_lowerCamelCase			)

    model.load_state_dict(_lowerCamelCase			)
    model.eval()

    A__							=     "https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"

    A__							=     Image.open(requests.get(_lowerCamelCase		,			stream=_lowerCamelCase			).raw			)
    A__							=     ViTMAEImageProcessor(size=config.image_size			)
    A__							=     image_processor(images=_lowerCamelCase		,			return_tensors="pt"			)

    # forward pass
    torch.manual_seed(2			)
    A__							=     model(**_lowerCamelCase			)
    A__							=     outputs.logits

    if "large" in checkpoint_url:
        A__							=     torch.tensor(
            [[-0.7_3_0_9, -0.7_1_2_8, -1.0_1_6_9], [-1.0_1_6_1, -0.9_0_5_8, -1.1_8_7_8], [-1.0_4_7_8, -0.9_4_1_1, -1.1_9_1_1]]			)
    elif "huge" in checkpoint_url:
        A__							=     torch.tensor(
            [[-1.1_5_9_9, -0.9_1_9_9, -1.2_2_2_1], [-1.1_9_5_2, -0.9_2_6_9, -1.2_3_0_7], [-1.2_1_4_3, -0.9_3_3_7, -1.2_2_6_2]]			)
    else:
        A__							=     torch.tensor(
            [[-0.9_1_9_2, -0.8_4_8_1, -1.1_2_5_9], [-1.1_3_4_9, -1.0_0_3_4, -1.2_5_9_9], [-1.1_7_5_7, -1.0_4_2_9, -1.2_7_2_6]]			)

    # verify logits
    assert torch.allclose(logits[0, :3, :3]		,			_lowerCamelCase		,			atol=1e-4			)

    print(F"Saving model to {pytorch_dump_folder_path}"			)
    model.save_pretrained(_lowerCamelCase			)

    print(F"Saving image processor to {pytorch_dump_folder_path}"			)
    image_processor.save_pretrained(_lowerCamelCase			)


if __name__ == "__main__":
  __lowerCAmelCase				:      List[Any]						      =argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--checkpoint_url",
      default="https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth",
      type=str,
      help="URL of the checkpoint you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
  )

  __lowerCAmelCase				:      int						      =parser.parse_args()
  convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










'''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)



