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


__magic_name__       =			"https://openaipublic.azureedge.net/jukebox/models/"
__magic_name__       =			{
    "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 						_lowerCAmelCase				(				UpperCamelCase_				):
		if key.endswith(""".model.1.bias"""				) and len(key.split("""."""				)				) > 10:
				__SCREAMING_SNAKE_CASE            = key.replace(""".model.1.bias"""  ,				""".conv1d_1.bias"""				)
		elif key.endswith(""".model.1.weight"""				) and len(key.split("""."""				)				) > 10:
				__SCREAMING_SNAKE_CASE            = key.replace(""".model.1.weight"""  ,				""".conv1d_1.weight"""				)
		elif key.endswith(""".model.3.bias"""				) and len(key.split("""."""				)				) > 10:
				__SCREAMING_SNAKE_CASE            = key.replace(""".model.3.bias"""  ,				""".conv1d_2.bias"""				)
		elif key.endswith(""".model.3.weight"""				) and len(key.split("""."""				)				) > 10:
				__SCREAMING_SNAKE_CASE            = key.replace(""".model.3.weight"""  ,				""".conv1d_2.weight"""				)

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

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

		if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
				__SCREAMING_SNAKE_CASE            = 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:
				__SCREAMING_SNAKE_CASE            = 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 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = {}
		import re

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

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

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

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

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

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

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

				elif re_decoder_block_proj_in.fullmatch(UpperCamelCase_				):
						__SCREAMING_SNAKE_CASE            = re_decoder_block_proj_in.match(UpperCamelCase_				)
						__SCREAMING_SNAKE_CASE            = regex_match.groups()
						__SCREAMING_SNAKE_CASE            = f"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
						__SCREAMING_SNAKE_CASE            = re_decoder_block_proj_in.sub(UpperCamelCase_  ,				UpperCamelCase_				)

				# rename prior cond.model to upsampler.upsample_block and resnet
				elif re_prior_cond_conv_out.fullmatch(UpperCamelCase_				):
						__SCREAMING_SNAKE_CASE            = re_prior_cond_conv_out.match(UpperCamelCase_				)
						__SCREAMING_SNAKE_CASE            = regex_match.groups()
						__SCREAMING_SNAKE_CASE            = int(groups[1]				) * 2 + int(groups[2]				) - 2
						__SCREAMING_SNAKE_CASE            = f"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
						__SCREAMING_SNAKE_CASE            = re_prior_cond_conv_out.sub(UpperCamelCase_  ,				UpperCamelCase_				)

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

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

				# keep original key
				else:
						__SCREAMING_SNAKE_CASE            = original_key

				__SCREAMING_SNAKE_CASE            = replace_key(UpperCamelCase_				)

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

				__SCREAMING_SNAKE_CASE            = original_key
				__SCREAMING_SNAKE_CASE            = value

		return new_dict



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

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

		__SCREAMING_SNAKE_CASE            = JukeboxConfig.from_pretrained(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = JukeboxModel(UpperCamelCase_				)

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

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

				__SCREAMING_SNAKE_CASE            = """vqvae""" if i == 0 else f"priors.{3 - i}"
				__SCREAMING_SNAKE_CASE            = fix_jukebox_keys(UpperCamelCase_  ,				model.state_dict()  ,				UpperCamelCase_  ,				UpperCamelCase_				)
				weight_dict.append(UpperCamelCase_				)

		__SCREAMING_SNAKE_CASE            = weight_dict.pop(0				)
		model.vqvae.load_state_dict(UpperCamelCase_				)
		for i in range(len(UpperCamelCase_				)				):
				model.priors[i].load_state_dict(weight_dict[2 - i]				)

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

		print(f"Saving model {model_name} to {pytorch_dump_folder_path}"				)
		model.save_pretrained(UpperCamelCase_				)

		return weight_dict


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






"""simple docstring"""





from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__       =			logging.get_logger(__name__)

__magic_name__       =			{
    "huggingface/informer-tourism-monthly": (
        "https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Dict						   =		'''informer'''
		__lowercase							:  Union[str, Any]						   =		{
		    '''hidden_size''': '''d_model''',
		    '''num_attention_heads''': '''encoder_attention_heads''',
		    '''num_hidden_layers''': '''encoder_layers''',
		}




		def __init__(     self							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = "student_t"							,  lowerCAmelCase__ = "nll"							,  lowerCAmelCase__ = 1							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = "mean"							,  lowerCAmelCase__ = 0							,  lowerCAmelCase__ = 0							,  lowerCAmelCase__ = 0							,  lowerCAmelCase__ = 0							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = 6_4							,  lowerCAmelCase__ = 3_2							,  lowerCAmelCase__ = 3_2							,  lowerCAmelCase__ = 2							,  lowerCAmelCase__ = 2							,  lowerCAmelCase__ = 2							,  lowerCAmelCase__ = 2							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = "gelu"							,  lowerCAmelCase__ = 0.05							,  lowerCAmelCase__ = 0.1							,  lowerCAmelCase__ = 0.1							,  lowerCAmelCase__ = 0.1							,  lowerCAmelCase__ = 0.1							,  lowerCAmelCase__ = 1_0_0							,  lowerCAmelCase__ = 0.02							,  lowerCAmelCase__=True							,  lowerCAmelCase__ = "prob"							,  lowerCAmelCase__ = 5							,  lowerCAmelCase__ = True							,  **lowerCAmelCase__							,  ):
				# time series specific configuration
				__SCREAMING_SNAKE_CASE            = prediction_length
				__SCREAMING_SNAKE_CASE            = context_length or prediction_length
				__SCREAMING_SNAKE_CASE            = distribution_output
				__SCREAMING_SNAKE_CASE            = loss
				__SCREAMING_SNAKE_CASE            = input_size
				__SCREAMING_SNAKE_CASE            = num_time_features
				__SCREAMING_SNAKE_CASE            = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
				__SCREAMING_SNAKE_CASE            = scaling
				__SCREAMING_SNAKE_CASE            = num_dynamic_real_features
				__SCREAMING_SNAKE_CASE            = num_static_real_features
				__SCREAMING_SNAKE_CASE            = num_static_categorical_features

				# set cardinality
				if cardinality and num_static_categorical_features > 0:
						if len(lowerCAmelCase__) != num_static_categorical_features:
								raise ValueError(
								    """The cardinality should be a list of the same length as `num_static_categorical_features`""")
						__SCREAMING_SNAKE_CASE            = cardinality
				else:
						__SCREAMING_SNAKE_CASE            = [0]

				# set embedding_dimension
				if embedding_dimension and num_static_categorical_features > 0:
						if len(lowerCAmelCase__) != num_static_categorical_features:
								raise ValueError(
								    """The embedding dimension should be a list of the same length as `num_static_categorical_features`""")
						__SCREAMING_SNAKE_CASE            = embedding_dimension
				else:
						__SCREAMING_SNAKE_CASE            = [min(5_0							,  (cat + 1) // 2) for cat in self.cardinality]

				__SCREAMING_SNAKE_CASE            = num_parallel_samples

				# Transformer architecture configuration
				__SCREAMING_SNAKE_CASE            = input_size * len(self.lags_sequence) + self._number_of_features
				__SCREAMING_SNAKE_CASE            = d_model
				__SCREAMING_SNAKE_CASE            = encoder_attention_heads
				__SCREAMING_SNAKE_CASE            = decoder_attention_heads
				__SCREAMING_SNAKE_CASE            = encoder_ffn_dim
				__SCREAMING_SNAKE_CASE            = decoder_ffn_dim
				__SCREAMING_SNAKE_CASE            = encoder_layers
				__SCREAMING_SNAKE_CASE            = decoder_layers

				__SCREAMING_SNAKE_CASE            = dropout
				__SCREAMING_SNAKE_CASE            = attention_dropout
				__SCREAMING_SNAKE_CASE            = activation_dropout
				__SCREAMING_SNAKE_CASE            = encoder_layerdrop
				__SCREAMING_SNAKE_CASE            = decoder_layerdrop

				__SCREAMING_SNAKE_CASE            = activation_function
				__SCREAMING_SNAKE_CASE            = init_std

				__SCREAMING_SNAKE_CASE            = use_cache

				# Informer
				__SCREAMING_SNAKE_CASE            = attention_type
				__SCREAMING_SNAKE_CASE            = sampling_factor
				__SCREAMING_SNAKE_CASE            = distil

				super().__init__(is_encoder_decoder=lowerCAmelCase__							,  **lowerCAmelCase__)




		@property
		def 	snake_case_     (     self):
				return (
				    sum(self.embedding_dimension)
				    + self.num_dynamic_real_features
				    + self.num_time_features
				    + self.num_static_real_features
				    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
				)




