

"""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()
__A						       =      logging.get_logger(__name__)


__A						       =      "https://openaipublic.azureedge.net/jukebox/models/"
__A						       =      {
    "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			)     ->     Optional[Any]:
		if key.endswith(".model.1.bias"			) and len(key.split("."			)			) > 1_0:
				__lowerCAmelCase:	Optional[int]   				=      key.replace(".model.1.bias" ,			".conv1d_1.bias"			)
		elif key.endswith(".model.1.weight"			) and len(key.split("."			)			) > 1_0:
				__lowerCAmelCase:	List[str]   				=      key.replace(".model.1.weight" ,			".conv1d_1.weight"			)
		elif key.endswith(".model.3.bias"			) and len(key.split("."			)			) > 1_0:
				__lowerCAmelCase:	Dict   				=      key.replace(".model.3.bias" ,			".conv1d_2.bias"			)
		elif key.endswith(".model.3.weight"			) and len(key.split("."			)			) > 1_0:
				__lowerCAmelCase:	List[Any]   				=      key.replace(".model.3.weight" ,			".conv1d_2.weight"			)

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

		if "prime_prior" in key:
				__lowerCAmelCase:	Tuple   				=      key.replace("prime_prior" ,			"encoder"			)

		if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
				__lowerCAmelCase:	Union[str, Any]   				=      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:
				__lowerCAmelCase:	str   				=      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			)     ->     int:
		__lowerCAmelCase:	Union[str, Any]   				=      {}
		import re

		__lowerCAmelCase:	List[Any]   				=      re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"			)
		__lowerCAmelCase:	List[Any]   				=      re.compile(
		    R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"			)
		__lowerCAmelCase:	List[str]   				=      re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"			)

		__lowerCAmelCase:	Tuple   				=      re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"			)
		__lowerCAmelCase:	Union[str, Any]   				=      re.compile(
		    R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"			)
		__lowerCAmelCase:	Dict   				=      re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"			)

		__lowerCAmelCase:	str   				=      re.compile(R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"			)
		__lowerCAmelCase:	Any   				=      re.compile(
		    R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"			)
		__lowerCAmelCase:	str   				=      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(snake_case__			):
						__lowerCAmelCase:	int   				=      re_encoder_block_conv_in.match(snake_case__			)
						__lowerCAmelCase:	Optional[Any]   				=      regex_match.groups()
						__lowerCAmelCase:	List[str]   				=      int(groups[2]			) * 2 + int(groups[3]			)
						__lowerCAmelCase:	Union[str, Any]   				=      F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
						__lowerCAmelCase:	List[str]   				=      re_encoder_block_conv_in.sub(snake_case__ ,			snake_case__			)

				elif re_encoder_block_resnet.fullmatch(snake_case__			):
						__lowerCAmelCase:	Union[str, Any]   				=      re_encoder_block_resnet.match(snake_case__			)
						__lowerCAmelCase:	Dict   				=      regex_match.groups()
						__lowerCAmelCase:	str   				=      int(groups[2]			) * 2 + int(groups[3]			)
						__lowerCAmelCase:	str   				=      {'1': 1, '3': 2}[groups[-2]]
						__lowerCAmelCase:	Optional[int]   				=      F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
						__lowerCAmelCase:	Tuple   				=      F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
						__lowerCAmelCase:	Optional[int]   				=      prefix + resnet_block
						__lowerCAmelCase:	List[Any]   				=      re_encoder_block_resnet.sub(snake_case__ ,			snake_case__			)

				elif re_encoder_block_proj_out.fullmatch(snake_case__			):
						__lowerCAmelCase:	str   				=      re_encoder_block_proj_out.match(snake_case__			)
						__lowerCAmelCase:	Tuple   				=      regex_match.groups()
						__lowerCAmelCase:	Dict   				=      F"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
						__lowerCAmelCase:	str   				=      re_encoder_block_proj_out.sub(snake_case__ ,			snake_case__			)

				# rename vqvae.decoder keys
				elif re_decoder_block_conv_out.fullmatch(snake_case__			):
						__lowerCAmelCase:	int   				=      re_decoder_block_conv_out.match(snake_case__			)
						__lowerCAmelCase:	Optional[int]   				=      regex_match.groups()
						__lowerCAmelCase:	List[str]   				=      int(groups[2]			) * 2 + int(groups[3]			) - 2
						__lowerCAmelCase:	Union[str, Any]   				=      F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
						__lowerCAmelCase:	Tuple   				=      re_decoder_block_conv_out.sub(snake_case__ ,			snake_case__			)

				elif re_decoder_block_resnet.fullmatch(snake_case__			):
						__lowerCAmelCase:	Any   				=      re_decoder_block_resnet.match(snake_case__			)
						__lowerCAmelCase:	List[str]   				=      regex_match.groups()
						__lowerCAmelCase:	Optional[int]   				=      int(groups[2]			) * 2 + int(groups[3]			) - 2
						__lowerCAmelCase:	Union[str, Any]   				=      {'1': 1, '3': 2}[groups[-2]]
						__lowerCAmelCase:	List[str]   				=      F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
						__lowerCAmelCase:	Optional[int]   				=      F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
						__lowerCAmelCase:	Optional[int]   				=      prefix + resnet_block
						__lowerCAmelCase:	Any   				=      re_decoder_block_resnet.sub(snake_case__ ,			snake_case__			)

				elif re_decoder_block_proj_in.fullmatch(snake_case__			):
						__lowerCAmelCase:	Optional[int]   				=      re_decoder_block_proj_in.match(snake_case__			)
						__lowerCAmelCase:	Optional[Any]   				=      regex_match.groups()
						__lowerCAmelCase:	Dict   				=      F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
						__lowerCAmelCase:	Any   				=      re_decoder_block_proj_in.sub(snake_case__ ,			snake_case__			)

				# rename prior cond.model to upsampler.upsample_block and resnet
				elif re_prior_cond_conv_out.fullmatch(snake_case__			):
						__lowerCAmelCase:	Union[str, Any]   				=      re_prior_cond_conv_out.match(snake_case__			)
						__lowerCAmelCase:	Tuple   				=      regex_match.groups()
						__lowerCAmelCase:	Union[str, Any]   				=      int(groups[1]			) * 2 + int(groups[2]			) - 2
						__lowerCAmelCase:	int   				=      F"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
						__lowerCAmelCase:	Union[str, Any]   				=      re_prior_cond_conv_out.sub(snake_case__ ,			snake_case__			)

				elif re_prior_cond_resnet.fullmatch(snake_case__			):
						__lowerCAmelCase:	Union[str, Any]   				=      re_prior_cond_resnet.match(snake_case__			)
						__lowerCAmelCase:	int   				=      regex_match.groups()
						__lowerCAmelCase:	List[str]   				=      int(groups[1]			) * 2 + int(groups[2]			) - 2
						__lowerCAmelCase:	Union[str, Any]   				=      {'1': 1, '3': 2}[groups[-2]]
						__lowerCAmelCase:	Tuple   				=      F"conditioner_blocks.upsampler.upsample_block.{block_index}."
						__lowerCAmelCase:	int   				=      F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
						__lowerCAmelCase:	Any   				=      prefix + resnet_block
						__lowerCAmelCase:	Union[str, Any]   				=      re_prior_cond_resnet.sub(snake_case__ ,			snake_case__			)

				elif re_prior_cond_proj_in.fullmatch(snake_case__			):
						__lowerCAmelCase:	List[Any]   				=      re_prior_cond_proj_in.match(snake_case__			)
						__lowerCAmelCase:	Any   				=      regex_match.groups()
						__lowerCAmelCase:	Dict   				=      F"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
						__lowerCAmelCase:	Dict   				=      re_prior_cond_proj_in.sub(snake_case__ ,			snake_case__			)

				# keep original key
				else:
						__lowerCAmelCase:	Tuple   				=      original_key

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

				__lowerCAmelCase:	Dict   				=      original_key
				__lowerCAmelCase:	Any   				=      value

		return new_dict






@torch.no_grad()
def    a__   (   __SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=None			)     ->     Dict:
		for file in MODEL_MAPPING[model_name]:
				if not os.path.isfile(F"{pytorch_dump_folder_path}/{file.split('/'			)[-1]}"			):
						__lowerCAmelCase:	Dict   				=      requests.get(F"{PREFIX}{file}" ,			allow_redirects=snake_case__			)
						os.makedirs(F"{pytorch_dump_folder_path}/" ,			exist_ok=snake_case__			)
						open(F"{pytorch_dump_folder_path}/{file.split('/'			)[-1]}" ,			"wb"			).write(r.content			)

		__lowerCAmelCase:	str   				=      MODEL_MAPPING[model_name.split("/"			)[-1]]

		__lowerCAmelCase:	int   				=      JukeboxConfig.from_pretrained(snake_case__			)
		__lowerCAmelCase:	Optional[Any]   				=      JukeboxModel(snake_case__			)

		__lowerCAmelCase:	Optional[Any]   				=      []
		__lowerCAmelCase:	List[Any]   				=      {}
		for i, dict_name in enumerate(snake_case__			):
				__lowerCAmelCase:	Tuple   				=      torch.load(F"{pytorch_dump_folder_path}/{dict_name.split('/'			)[-1]}"			)['model']

				__lowerCAmelCase:	Optional[int]   				=      {}
				for k in old_dic.keys():
						if k.endswith(".b"			):
								__lowerCAmelCase:	Any   				=      old_dic[k]
						elif k.endswith(".w"			):
								__lowerCAmelCase:	List[str]   				=      old_dic[k]
						elif "level_2" not in dict_name and "cond.model." in k:
								__lowerCAmelCase:	Any   				=      old_dic[k]
						else:
								__lowerCAmelCase:	str   				=      old_dic[k]

				__lowerCAmelCase:	Optional[int]   				=      'vqvae' if i == 0 else F"priors.{3 - i}"
				__lowerCAmelCase:	Optional[int]   				=      fix_jukebox_keys(snake_case__ ,			model.state_dict() ,			snake_case__ ,			snake_case__			)
				weight_dict.append(snake_case__			)

		__lowerCAmelCase:	List[str]   				=      weight_dict.pop(0			)
		model.vqvae.load_state_dict(snake_case__			)
		for i in range(len(snake_case__			)			):
				model.priors[i].load_state_dict(weight_dict[2 - i]			)

		Path(snake_case__			).mkdir(exist_ok=snake_case__			)
		with open(F"{pytorch_dump_folder_path}/mapping.json" ,			"w"			) as txtfile:
				json.dump(snake_case__ ,			snake_case__			)

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

		return weight_dict


if __name__ == "__main__":
			__A						       =      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.",
			)
			__A						       =      parser.parse_args()
			convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)





"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A						       =      logging.get_logger(__name__)

__A						       =      {
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  """data2vec-vision"""

				def __init__(     self							:    Optional[int]					,  UpperCamelCase__							:    str=7_6_8					,  UpperCamelCase__							:    Any=1_2					,  UpperCamelCase__							:    str=1_2					,  UpperCamelCase__							:    Optional[Any]=3_0_7_2					,  UpperCamelCase__							:    Union[str, Any]="gelu"					,  UpperCamelCase__							:    List[Any]=0.0					,  UpperCamelCase__							:    Dict=0.0					,  UpperCamelCase__							:    Union[str, Any]=0.02					,  UpperCamelCase__							:    str=1e-12					,  UpperCamelCase__							:    Dict=2_2_4					,  UpperCamelCase__							:    List[Any]=1_6					,  UpperCamelCase__							:    Optional[Any]=3					,  UpperCamelCase__							:    List[str]=False					,  UpperCamelCase__							:    List[Any]=False					,  UpperCamelCase__							:    str=False					,  UpperCamelCase__							:    List[str]=False					,  UpperCamelCase__							:    int=0.1					,  UpperCamelCase__							:    List[Any]=0.1					,  UpperCamelCase__							:    Optional[Any]=True					,  UpperCamelCase__							:    List[Any]=[3, 5, 7, 1_1]					,  UpperCamelCase__							:    List[str]=[1, 2, 3, 6]					,  UpperCamelCase__							:    Union[str, Any]=True					,  UpperCamelCase__							:    Any=0.4					,  UpperCamelCase__							:    Union[str, Any]=2_5_6					,  UpperCamelCase__							:    List[Any]=1					,  UpperCamelCase__							:    str=False					,  UpperCamelCase__							:    Optional[int]=2_5_5					,  **UpperCamelCase__							:    Dict					,  )->       List[str]:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)

						__lowerCAmelCase:	List[str]   				=      hidden_size
						__lowerCAmelCase:	Union[str, Any]   				=      num_hidden_layers
						__lowerCAmelCase:	Dict   				=      num_attention_heads
						__lowerCAmelCase:	Optional[int]   				=      intermediate_size
						__lowerCAmelCase:	int   				=      hidden_act
						__lowerCAmelCase:	Union[str, Any]   				=      hidden_dropout_prob
						__lowerCAmelCase:	Any   				=      attention_probs_dropout_prob
						__lowerCAmelCase:	Dict   				=      initializer_range
						__lowerCAmelCase:	Any   				=      layer_norm_eps

						__lowerCAmelCase:	Union[str, Any]   				=      image_size
						__lowerCAmelCase:	Tuple   				=      patch_size
						__lowerCAmelCase:	List[str]   				=      num_channels
						__lowerCAmelCase:	Optional[Any]   				=      use_mask_token
						__lowerCAmelCase:	str   				=      use_absolute_position_embeddings
						__lowerCAmelCase:	Optional[int]   				=      use_relative_position_bias
						__lowerCAmelCase:	str   				=      use_shared_relative_position_bias
						__lowerCAmelCase:	Union[str, Any]   				=      layer_scale_init_value
						__lowerCAmelCase:	Any   				=      drop_path_rate
						__lowerCAmelCase:	Dict   				=      use_mean_pooling
						# decode head attributes (semantic segmentation)
						__lowerCAmelCase:	int   				=      out_indices
						__lowerCAmelCase:	Any   				=      pool_scales
						# auxiliary head attributes (semantic segmentation)
						__lowerCAmelCase:	List[Any]   				=      use_auxiliary_head
						__lowerCAmelCase:	int   				=      auxiliary_loss_weight
						__lowerCAmelCase:	Dict   				=      auxiliary_channels
						__lowerCAmelCase:	Any   				=      auxiliary_num_convs
						__lowerCAmelCase:	Any   				=      auxiliary_concat_input
						__lowerCAmelCase:	Any   				=      semantic_loss_ignore_index





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Dict       				=  version.parse("""1.11"""  )

				@property
				def 							lowercase_					(     self							:    Optional[Any])->       Mapping[str, Mapping[int, str]]:



						'''simple docstring'''


						return OrderedDict(
						    [
						        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
						    ])




				@property
				def 							lowercase_					(     self							:    Any)->       float:



						'''simple docstring'''


						return 1e-4


