




import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD





def        __UpperCAmelCase						(							)       ->  Tuple:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Dict					  =      'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      Image.open(requests.get(lowerCamelCase_  ,							stream=lowerCamelCase_  ).raw  ).convert('RGB'  )

			return image





def        __UpperCAmelCase						(							lowerCamelCase_ :     int  )       ->  Any:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : int					  =      []
			# fmt: off

			# vision encoder
			rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding')  )
			rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding')  )
			rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight')  )
			rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias')  )
			rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight')  )
			rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias')  )

			for i in range(config.vision_config.num_hidden_layers  ):
						rename_keys.append((F'visual_encoder.blocks.{i}.norm1.weight', F'vision_model.encoder.layers.{i}.layer_norm1.weight')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.norm1.bias', F'vision_model.encoder.layers.{i}.layer_norm1.bias')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.norm2.weight', F'vision_model.encoder.layers.{i}.layer_norm2.weight')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.norm2.bias', F'vision_model.encoder.layers.{i}.layer_norm2.bias')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.attn.qkv.weight', F'vision_model.encoder.layers.{i}.self_attn.qkv.weight')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.attn.proj.weight', F'vision_model.encoder.layers.{i}.self_attn.projection.weight',)  )
						rename_keys.append((F'visual_encoder.blocks.{i}.attn.proj.bias', F'vision_model.encoder.layers.{i}.self_attn.projection.bias')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc1.weight', F'vision_model.encoder.layers.{i}.mlp.fc1.weight')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc1.bias', F'vision_model.encoder.layers.{i}.mlp.fc1.bias')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc2.weight', F'vision_model.encoder.layers.{i}.mlp.fc2.weight')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc2.bias', F'vision_model.encoder.layers.{i}.mlp.fc2.bias')  )

			# QFormer
			rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.embeddings.layernorm.weight')  )
			rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias')  )

			# fmt: on
			return rename_keys





def        __UpperCAmelCase						(							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     Dict  )       ->  Any:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : List[Any]					  =      dct.pop(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      val





def        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     Optional[Any]  )       ->  Optional[Any]:







			"""simple docstring"""





			for i in range(config.vision_config.num_hidden_layers  ):
						# read in original q and v biases
						SCREAMING_SNAKE_CASE_   : str					  =      state_dict.pop(F'visual_encoder.blocks.{i}.attn.q_bias'  )
						SCREAMING_SNAKE_CASE_   : Dict					  =      state_dict.pop(F'visual_encoder.blocks.{i}.attn.v_bias'  )

						# next, set bias in the state dict
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      torch.cat((q_bias, torch.zeros_like(lowerCamelCase_  ,							requires_grad=lowerCamelCase_  ), v_bias)  )
						SCREAMING_SNAKE_CASE_   : List[str]					  =      qkv_bias





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  )       ->  Any:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      3_64 if 'coco' in model_name else 2_24
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      InstructBlipVisionConfig(image_size=lowerCamelCase_  ).to_dict()

			# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
			# seems like flan-T5 models don't have bos_token_id properly set?
			if "t5-xl" in model_name:
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      TaConfig.from_pretrained('google/flan-t5-xl'  ,							dense_act_fn='gelu'  ,							bos_token_id=1  ).to_dict()
			elif "t5-xxl" in model_name:
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      TaConfig.from_pretrained('google/flan-t5-xxl'  ,							dense_act_fn='gelu'  ,							bos_token_id=1  ).to_dict()
			elif "vicuna-7b" in model_name:
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'  ,							vocab_size=3_20_01  ).to_dict()
			elif "vicuna-13b" in model_name:
						SCREAMING_SNAKE_CASE_   : Dict					  =      LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf'  ,							vocab_size=3_20_01  ).to_dict()
			else:
						raise ValueError('Model name not supported'  )

			# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      InstructBlipQFormerConfig(vocab_size=3_05_23  ).to_dict()
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      InstructBlipConfig(vision_config=lowerCamelCase_  ,							text_config=lowerCamelCase_  ,							qformer_config=lowerCamelCase_  )

			return config, image_size





@torch.no_grad()
def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     Any=None  ,							lowerCamelCase_ :     Any=False  )       ->  Union[str, Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      AutoTokenizer.from_pretrained('bert-base-uncased'  ,							truncation_side='left'  )
			qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}  )

			if "t5" in model_name:
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      TaTokenizerFast.from_pretrained('google/flan-t5-xl'  ,							truncation_side='left'  )
			elif "vicuna" in model_name:
						# the following was used in the original implementation:
						# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
						# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
						# tokenizer.add_special_tokens({"bos_token": "</s>"})
						# tokenizer.add_special_tokens({"eos_token": "</s>"})
						# tokenizer.add_special_tokens({"unk_token": "</s>"})
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      LlamaTokenizerFast.from_pretrained(
						    'huggyllama/llama-7b'  ,							truncation_side='left'  ,							bos_token='</s>'  ,							unk_token='</s>'  )
						tokenizer.add_special_tokens({'pad_token': '[PAD]'}  )

			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      get_blipa_config(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : int					  =      InstructBlipForConditionalGeneration(lowerCamelCase_  ).eval()

			SCREAMING_SNAKE_CASE_   : str					  =      {
			    'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'),
			    'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'),
			    'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'),
			    'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'),
			}

			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[int]					  =      model_name_to_original[model_name]

			# load original model
			print('Loading original model...'  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      'cuda:1' if torch.cuda.is_available() else 'cpu'
			SCREAMING_SNAKE_CASE_   : Any					  =      'cuda:2' if torch.cuda.is_available() else 'cpu'
			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      load_model_and_preprocess(
			    name=lowerCamelCase_  ,							model_type=lowerCamelCase_  ,							is_eval=lowerCamelCase_  ,							device=lowerCamelCase_  )
			original_model.eval()
			print('Done!'  )

			# update state dict keys
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      original_model.state_dict()
			SCREAMING_SNAKE_CASE_   : Dict					  =      create_rename_keys(lowerCamelCase_  )
			for src, dest in rename_keys:
						rename_key(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  )

			# some keys can be renamed efficiently
			for key, val in state_dict.copy().items():
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      state_dict.pop(lowerCamelCase_  )
						if key.startswith('Qformer.bert'  ):
									SCREAMING_SNAKE_CASE_   : Tuple					  =      key.replace('Qformer.bert'  ,							'qformer'  )
						if "attention.self" in key:
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      key.replace('self'  ,							'attention'  )
						if "llm_proj" in key:
									SCREAMING_SNAKE_CASE_   : Any					  =      key.replace('llm_proj'  ,							'language_projection'  )
						if "t5_proj" in key:
									SCREAMING_SNAKE_CASE_   : str					  =      key.replace('t5_proj'  ,							'language_projection'  )
						if key.startswith('llm_model'  ):
									SCREAMING_SNAKE_CASE_   : Any					  =      key.replace('llm_model'  ,							'language_model'  )
						if key.startswith('t5'  ):
									SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      key.replace('t5'  ,							'language'  )
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      val

			# read in qv biases
			read_in_q_v_bias(lowerCamelCase_  ,							lowerCamelCase_  )

			# note: weights get loaded in torch.float32 by default
			hf_model.load_state_dict(lowerCamelCase_  ,							strict=lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : str					  =      load_demo_image()
			SCREAMING_SNAKE_CASE_   : Any					  =      'What is unusual about this image?'

			# create processor
			SCREAMING_SNAKE_CASE_   : Dict					  =      BlipImageProcessor(
			    size={'height': image_size, 'width': image_size}  ,							image_mean=lowerCamelCase_  ,							image_std=lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Dict					  =      InstructBlipProcessor(
			    image_processor=lowerCamelCase_  ,							tokenizer=lowerCamelCase_  ,							qformer_tokenizer=lowerCamelCase_  ,							)
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      processor(images=lowerCamelCase_  ,							text=lowerCamelCase_  ,							return_tensors='pt'  ).to(lowerCamelCase_  )

			# make sure processor creates exact same pixel values
			SCREAMING_SNAKE_CASE_   : str					  =      vis_processors['eval'](lowerCamelCase_  ).unsqueeze(0  ).to(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[str]					  =      inputs.pixel_values
			assert torch.allclose(original_pixel_values.to(pixel_values.device  )  ,							lowerCamelCase_  )

			original_model.to(lowerCamelCase_  )
			hf_model.to(lowerCamelCase_  )
			with torch.no_grad():
						if "vicuna" in model_name:
									SCREAMING_SNAKE_CASE_   : str					  =      original_model({'image': original_pixel_values, 'text_input': [prompt]}  ).logits
									SCREAMING_SNAKE_CASE_   : Tuple					  =      hf_model(**lowerCamelCase_  ).logits
						else:
									SCREAMING_SNAKE_CASE_   : Any					  =      original_model(
									    {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}  ).logits
									SCREAMING_SNAKE_CASE_   : str					  =      tokenizer('\n'  ,							return_tensors='pt'  ).input_ids.to(lowerCamelCase_  )
									SCREAMING_SNAKE_CASE_   : str					  =      label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id  ,							-1_00  )
									SCREAMING_SNAKE_CASE_   : Dict					  =      hf_model(**lowerCamelCase_  ,							labels=lowerCamelCase_  ).logits

			print('First values of original logits:'  ,							original_logits[0, :3, :3]  )
			print('First values of HF logits:'  ,							logits[0, :3, :3]  )

			# assert values
			assert original_logits.shape == logits.shape
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      1E-4 if 'vicuna' in model_name else 1E-5
			assert torch.allclose(original_logits.to(logits.device  )  ,							lowerCamelCase_  ,							atol=lowerCamelCase_  )
			print('Looks ok!'  )

			print('Generating with original model...'  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      original_model.generate({'image': original_pixel_values, 'prompt': prompt}  ,							num_beams=5  )

			# important: we need to cast the weights of the HF model to the appropriate type
			print('Generating with HF model...'  )
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      hf_model.generate(
			    **lowerCamelCase_  ,							do_sample=lowerCamelCase_  ,							num_beams=5  ,							max_length=2_56  ,							min_length=1  ,							top_p=0.9  ,							repetition_penalty=1.5  ,							length_penalty=1.0  ,							temperature=1  ,							)
			if "vicuna" in model_name:
						# convert output id 0 to 2 (eos_token_id)
						# TODO add this in the generate method?
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      2
			print('Original generation:'  ,							lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      processor.batch_decode(lowerCamelCase_  ,							skip_special_tokens=lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      [text.strip() for text in output_text]
			print('HF generation:'  ,							lowerCamelCase_  )

			if pytorch_dump_folder_path is not None:
						processor.save_pretrained(lowerCamelCase_  )
						hf_model.save_pretrained(lowerCamelCase_  )

			if push_to_hub:
						processor.push_to_hub(F'Salesforce/{model_name}'  )
						hf_model.push_to_hub(F'Salesforce/{model_name}'  )


if __name__ == "__main__":
						UpperCamelCase__ :   int						=							argparse.ArgumentParser()
						UpperCamelCase__ :   int						=							[
						    '''instructblip-vicuna-7b''',
						    '''instructblip-vicuna-13b''',
						    '''instructblip-flan-t5-xl''',
						    '''instructblip-flan-t5-xxl''',
						]
						parser.add_argument(
						    '''--model_name''',
						    default='''instructblip-flan-t5-xl''',
						    choices=choices,
						    type=str,
						    help='''Path to hf config.json of model to convert''',
						)
						parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						parser.add_argument(
						    '''--push_to_hub''',
						    action='''store_true''',
						    help='''Whether to push the model and processor to the hub after converting''',
						)

						UpperCamelCase__ :   Optional[Any]						=							parser.parse_args()

						convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









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__ :   Tuple						=							logging.get_logger(__name__)


UpperCamelCase__ :   Optional[int]						=							'''https://openaipublic.azureedge.net/jukebox/models/'''
UpperCamelCase__ :   Optional[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_ :     List[str]  )       ->  int:







			"""simple docstring"""





			if key.endswith('.model.1.bias'  ) and len(key.split('.'  )  ) > 10:
						SCREAMING_SNAKE_CASE_   : List[str]					  =      key.replace('.model.1.bias'  ,							'.conv1d_1.bias'  )
			elif key.endswith('.model.1.weight'  ) and len(key.split('.'  )  ) > 10:
						SCREAMING_SNAKE_CASE_   : Any					  =      key.replace('.model.1.weight'  ,							'.conv1d_1.weight'  )
			elif key.endswith('.model.3.bias'  ) and len(key.split('.'  )  ) > 10:
						SCREAMING_SNAKE_CASE_   : Tuple					  =      key.replace('.model.3.bias'  ,							'.conv1d_2.bias'  )
			elif key.endswith('.model.3.weight'  ) and len(key.split('.'  )  ) > 10:
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      key.replace('.model.3.weight'  ,							'.conv1d_2.weight'  )

			if "conditioner_blocks.0." in key:
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      key.replace('conditioner_blocks.0'  ,							'conditioner_blocks'  )

			if "prime_prior" in key:
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      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_   : 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:
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      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_ :     Dict  ,							lowerCamelCase_ :     List[Any]  ,							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     List[Any]  )       ->  List[Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      {}
			import re

			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'  )
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      re.compile(
			    R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'  )
			SCREAMING_SNAKE_CASE_   : str					  =      re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'  )

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

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

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

						elif re_encoder_block_proj_out.fullmatch(lowerCamelCase_  ):
									SCREAMING_SNAKE_CASE_   : Tuple					  =      re_encoder_block_proj_out.match(lowerCamelCase_  )
									SCREAMING_SNAKE_CASE_   : int					  =      regex_match.groups()
									SCREAMING_SNAKE_CASE_   : int					  =      F'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
									SCREAMING_SNAKE_CASE_   : Tuple					  =      re_encoder_block_proj_out.sub(lowerCamelCase_  ,							lowerCamelCase_  )

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

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

						elif re_decoder_block_proj_in.fullmatch(lowerCamelCase_  ):
									SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      re_decoder_block_proj_in.match(lowerCamelCase_  )
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      regex_match.groups()
									SCREAMING_SNAKE_CASE_   : int					  =      F'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
									SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      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_  ):
									SCREAMING_SNAKE_CASE_   : List[str]					  =      re_prior_cond_conv_out.match(lowerCamelCase_  )
									SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      regex_match.groups()
									SCREAMING_SNAKE_CASE_   : str					  =      int(groups[1]  ) * 2 + int(groups[2]  ) - 2
									SCREAMING_SNAKE_CASE_   : Any					  =      F'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      re_prior_cond_conv_out.sub(lowerCamelCase_  ,							lowerCamelCase_  )

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

						elif re_prior_cond_proj_in.fullmatch(lowerCamelCase_  ):
									SCREAMING_SNAKE_CASE_   : Any					  =      re_prior_cond_proj_in.match(lowerCamelCase_  )
									SCREAMING_SNAKE_CASE_   : List[str]					  =      regex_match.groups()
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      F'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      re_prior_cond_proj_in.sub(lowerCamelCase_  ,							lowerCamelCase_  )

						# keep original key
						else:
									SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      original_key

						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      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:
									SCREAMING_SNAKE_CASE_   : str					  =      model_state_dict[F'{key_prefix}.{key}']
									print(F'{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'  )
									SCREAMING_SNAKE_CASE_   : Dict					  =      original_key

						SCREAMING_SNAKE_CASE_   : int					  =      original_key
						SCREAMING_SNAKE_CASE_   : int					  =      value

			return new_dict





@torch.no_grad()
def        __UpperCAmelCase						(							lowerCamelCase_ :     int=None  ,							lowerCamelCase_ :     int=None  )       ->  Dict:







			"""simple docstring"""





			for file in MODEL_MAPPING[model_name]:
						if not os.path.isfile(F'{pytorch_dump_folder_path}/{file.split("/"  )[-1]}'  ):
									SCREAMING_SNAKE_CASE_   : int					  =      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  )

			SCREAMING_SNAKE_CASE_   : List[str]					  =      MODEL_MAPPING[model_name.split('/'  )[-1]]

			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      JukeboxConfig.from_pretrained(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : str					  =      JukeboxModel(lowerCamelCase_  )

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

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

						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      'vqvae' if i == 0 else F'priors.{3 - i}'
						SCREAMING_SNAKE_CASE_   : Any					  =      fix_jukebox_keys(lowerCamelCase_  ,							model.state_dict()  ,							lowerCamelCase_  ,							lowerCamelCase_  )
						weight_dict.append(lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : Tuple					  =      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__ :   Optional[int]						=							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__ :   str						=							parser.parse_args()
						convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



