import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)



def 	__A		(      _A       ):



 """simple docstring"""


 if model.config.model_type == "gpt2":
  return model.transformer.h[0].mlp.c_fc
 return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
  import torch
  import torch.nn as nn
  class  A_			(							nn.Module			):

    def __init__(		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   nn.Module  ,				__SCREAMING_SNAKE_CASE			:   int							):
     super().__init__()
     __a							=	module
     __a							=	nn.Sequential(
         nn.Linear(module.in_features  ,				__SCREAMING_SNAKE_CASE  ,				bias=__SCREAMING_SNAKE_CASE							)  ,				nn.Linear(__SCREAMING_SNAKE_CASE  ,				module.out_features  ,				bias=__SCREAMING_SNAKE_CASE							)  ,				)
     __a							=	(2.0 / (5 * min(module.in_features  ,				module.out_features							))) ** 0.5
     nn.init.normal_(self.adapter[0].weight  ,				std=__SCREAMING_SNAKE_CASE							)
     nn.init.zeros_(self.adapter[1].weight							)
     self.adapter.to(module.weight.device							)

    def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   str  ,				*__SCREAMING_SNAKE_CASE			:   str  ,				**__SCREAMING_SNAKE_CASE			:   Tuple							):
     return self.module(__SCREAMING_SNAKE_CASE  ,				*__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							) + self.adapter(__SCREAMING_SNAKE_CASE							)
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class  A_			(							unittest.TestCase			):
  # We keep the constants inside the init function and model loading inside setUp function

  # We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
  # Therefore here we use only bloom-1b3 to test our module
  _SCREAMING_SNAKE_CASE     					=      """bigscience/bloom-1b7"""

  # Constant values
  _SCREAMING_SNAKE_CASE     					=      2.1_0_9_6_5_9_5_5_2_6_9_2_5_7_4

  _SCREAMING_SNAKE_CASE     					=      """Hello my name is"""
  _SCREAMING_SNAKE_CASE     					=      set()
  EXPECTED_OUTPUTS.add("""Hello my name is John and I am a professional photographer. I"""			)
  EXPECTED_OUTPUTS.add("""Hello my name is John.\nI am a friend of your father.\n"""			)
  EXPECTED_OUTPUTS.add("""Hello my name is John Doe, I am a student at the University"""			)
  _SCREAMING_SNAKE_CASE     					=      10

  def 		_UpperCAmelCase (		self			:   List[str]							):
   # Models and tokenizer
   __a							=	AutoTokenizer.from_pretrained(self.model_name							)
class  A_			(							a_			):

  def 		_UpperCAmelCase (		self			:   int							):
   super().setUp()

   # Models and tokenizer
   __a							=	AutoModelForCausalLM.from_pretrained(
       self.model_name  ,				torch_dtype=torch.floataa  ,				device_map="auto"							)
   __a							=	AutoModelForCausalLM.from_pretrained(self.model_name  ,				load_in_abit=__SCREAMING_SNAKE_CASE  ,				device_map="auto"							)

  def 		_UpperCAmelCase (		self			:   Dict							):
   del self.model_fpaa
   del self.model_abit

   gc.collect()
   torch.cuda.empty_cache()

  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	self.model_abit.config

   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,				"quantization_config"							)							)

   __a							=	config.to_dict()
   __a							=	config.to_diff_dict()

   __a							=	config.to_json_string()

  def 		_UpperCAmelCase (		self			:   Tuple							):
   from bitsandbytes.nn import Paramsabit

   __a							=	self.model_fpaa.get_memory_footprint()
   __a							=	self.model_abit.get_memory_footprint()

   self.assertAlmostEqual(mem_fpaa / mem_abit  ,				self.EXPECTED_RELATIVE_DIFFERENCE							)
   __a							=	get_some_linear_layer(self.model_abit							)
   self.assertTrue(linear.weight.__class__ == Paramsabit							)

  def 		_UpperCAmelCase (		self			:   str							):
   from transformers import TaPreTrainedModel

   self.model_fpaa.get_memory_footprint()
   self.model_abit.get_memory_footprint()

   for name, module in self.model_abit.named_modules():
    if isinstance(__SCREAMING_SNAKE_CASE  ,				torch.nn.Linear							):
     if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
      # 4-bit parameters are packed in uint8 variables
      self.assertTrue(module.weight.dtype == torch.uinta							)

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	self.tokenizer(self.input_text  ,				return_tensors="pt"							)
   __a							=	self.model_abit.generate(input_ids=encoded_input["input_ids"].to(0							)  ,				max_new_tokens=10							)

   self.assertIn(self.tokenizer.decode(output_sequences[0]  ,				skip_special_tokens=__SCREAMING_SNAKE_CASE							)  ,				self.EXPECTED_OUTPUTS							)

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	BitsAndBytesConfig()
   __a							=	True

   __a							=	AutoModelForCausalLM.from_pretrained(
       self.model_name  ,				quantization_config=__SCREAMING_SNAKE_CASE  ,				device_map="auto"							)

   __a							=	self.tokenizer(self.input_text  ,				return_tensors="pt"							)
   __a							=	model_abit_from_config.generate(
       input_ids=encoded_input["input_ids"].to(0							)  ,				max_new_tokens=10							)

   self.assertIn(self.tokenizer.decode(output_sequences[0]  ,				skip_special_tokens=__SCREAMING_SNAKE_CASE							)  ,				self.EXPECTED_OUTPUTS							)

  def 		_UpperCAmelCase (		self			:   List[str]							):
   with self.assertRaises(__SCREAMING_SNAKE_CASE							), tempfile.TemporaryDirectory() as tmpdirname:
    self.model_abit.save_pretrained(__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Tuple							):
   __a							=	BitsAndBytesConfig()

   with self.assertRaises(__SCREAMING_SNAKE_CASE							):
    __a							=	AutoModelForCausalLM.from_pretrained(
        self.model_name  ,				quantization_config=__SCREAMING_SNAKE_CASE  ,				load_in_abit=__SCREAMING_SNAKE_CASE  ,				device_map="auto"  ,				bnb_abit_quant_type="nf4"  ,				)

  def 		_UpperCAmelCase (		self			:   str							):
   with self.assertRaises(__SCREAMING_SNAKE_CASE							):
    # Tries with `str`
    self.model_abit.to("cpu"							)

   with self.assertRaises(__SCREAMING_SNAKE_CASE							):
    # Tries with a `dtype``
    self.model_abit.to(torch.floataa							)

   with self.assertRaises(__SCREAMING_SNAKE_CASE							):
    # Tries with a `device`
    self.model_abit.to(torch.device("cuda:0"							)							)

   with self.assertRaises(__SCREAMING_SNAKE_CASE							):
    # Tries with a `device`
    self.model_abit.float()

   with self.assertRaises(__SCREAMING_SNAKE_CASE							):
    # Tries with a `device`
    self.model_abit.half()

   # Test if we did not break anything
   __a							=	self.tokenizer(self.input_text  ,				return_tensors="pt"							)

   __a							=	self.model_fpaa.to(torch.floataa							)
   __a							=	self.model_fpaa.generate(input_ids=encoded_input["input_ids"].to(0							)  ,				max_new_tokens=10							)

   # Check this does not throw an error
   __a							=	self.model_fpaa.to("cpu"							)

   # Check this does not throw an error
   __a							=	self.model_fpaa.half()

   # Check this does not throw an error
   __a							=	self.model_fpaa.float()

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	AutoModelForSeqaSeqLM.from_pretrained("t5-small"  ,				load_in_abit=__SCREAMING_SNAKE_CASE  ,				device_map="auto"							)
   self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa							)
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class  A_			(							unittest.TestCase			):

  @classmethod
  def 		_UpperCAmelCase (		cls			:   Optional[Any]							):
   __a							=	"t5-small"
   __a							=	"google/flan-t5-small"  # flan-t5 uses dense-act instead of dense-relu-dense
   __a							=	AutoTokenizer.from_pretrained(cls.model_name							)
   __a							=	"Translate in German: Hello, my dog is cute"

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   gc.collect()
   torch.cuda.empty_cache()

  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   from transformers import TaForConditionalGeneration

   __a							=	TaForConditionalGeneration._keep_in_fpaa_modules
   __a							=	None

   # test with `t5-small`
   __a							=	TaForConditionalGeneration.from_pretrained(self.model_name  ,				load_in_abit=__SCREAMING_SNAKE_CASE  ,				device_map="auto"							)
   __a							=	self.tokenizer(self.input_text  ,				return_tensors="pt"							).to(0							)
   __a							=	model.generate(**__SCREAMING_SNAKE_CASE							)

   # test with `flan-t5-small`
   __a							=	TaForConditionalGeneration.from_pretrained(
       self.dense_act_model_name  ,				load_in_abit=__SCREAMING_SNAKE_CASE  ,				device_map="auto"							)
   __a							=	self.tokenizer(self.input_text  ,				return_tensors="pt"							).to(0							)
   __a							=	model.generate(**__SCREAMING_SNAKE_CASE							)
   __a							=	modules

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   import bitsandbytes as bnb

   from transformers import TaForConditionalGeneration

   # test with `t5-small`
   __a							=	TaForConditionalGeneration.from_pretrained(self.model_name  ,				load_in_abit=__SCREAMING_SNAKE_CASE  ,				device_map="auto"							)

   # there was a bug with decoders - this test checks that it is fixed
   self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q  ,				bnb.nn.Linearabit							)							)

   __a							=	self.tokenizer(self.input_text  ,				return_tensors="pt"							).to(0							)
   __a							=	model.generate(**__SCREAMING_SNAKE_CASE							)

   # test with `flan-t5-small`
   __a							=	TaForConditionalGeneration.from_pretrained(
       self.dense_act_model_name  ,				load_in_abit=__SCREAMING_SNAKE_CASE  ,				device_map="auto"							)
   __a							=	self.tokenizer(self.input_text  ,				return_tensors="pt"							).to(0							)
   __a							=	model.generate(**__SCREAMING_SNAKE_CASE							)
class  A_			(							a_			):

  def 		_UpperCAmelCase (		self			:   List[str]							):
   super().setUp()
   # model_name
   __a							=	"bigscience/bloom-560m"
   __a							=	"t5-small"

   # Different types of model

   __a							=	AutoModel.from_pretrained(self.model_name  ,				load_in_abit=__SCREAMING_SNAKE_CASE  ,				device_map="auto"							)
   # Sequence classification model
   __a							=	AutoModelForSequenceClassification.from_pretrained(
       self.model_name  ,				load_in_abit=__SCREAMING_SNAKE_CASE  ,				device_map="auto"							)
   # CausalLM model
   __a							=	AutoModelForCausalLM.from_pretrained(self.model_name  ,				load_in_abit=__SCREAMING_SNAKE_CASE  ,				device_map="auto"							)
   # Seq2seq model
   __a							=	AutoModelForSeqaSeqLM.from_pretrained(
       self.seq_to_seq_name  ,				load_in_abit=__SCREAMING_SNAKE_CASE  ,				device_map="auto"							)

  def 		_UpperCAmelCase (		self			:   Any							):
   del self.base_model
   del self.sequence_model
   del self.model_abit
   del self.seq_to_seq_model

   gc.collect()
   torch.cuda.empty_cache()

  def 		_UpperCAmelCase (		self			:   str							):
   from bitsandbytes.nn import Paramsabit

   self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit							)

   # Other heads should be nn.Parameter
   self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter							)
   self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter							)
   self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter							)
class  A_			(							a_			):

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   super().setUp()

  def 		_UpperCAmelCase (		self			:   List[str]							):
   del self.pipe

   gc.collect()
   torch.cuda.empty_cache()

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   __a							=	pipeline(
       "text-generation"  ,				model=self.model_name  ,				model_kwargs={"device_map": "auto", "load_in_4bit": True, "torch_dtype": torch.floataa}  ,				max_new_tokens=self.MAX_NEW_TOKENS  ,				)

   # Real second forward pass
   __a							=	self.pipe(self.input_text							)
   self.assertIn(pipeline_output[0]["generated_text"]  ,				self.EXPECTED_OUTPUTS							)
@require_torch_multi_gpu
class  A_			(							a_			):

  def 		_UpperCAmelCase (		self			:   int							):
   super().setUp()

  def 		_UpperCAmelCase (		self			:   List[str]							):
   __a							=	AutoModelForCausalLM.from_pretrained(
       self.model_name  ,				load_in_abit=__SCREAMING_SNAKE_CASE  ,				device_map="balanced"							)

   # Check correct device map
   self.assertEqual(set(model_parallel.hf_device_map.values()							)  ,				{0, 1}							)

   # Check that inference pass works on the model
   __a							=	self.tokenizer(self.input_text  ,				return_tensors="pt"							)

   # Second real batch
   __a							=	model_parallel.generate(input_ids=encoded_input["input_ids"].to(0							)  ,				max_new_tokens=10							)
   self.assertIn(self.tokenizer.decode(output_parallel[0]  ,				skip_special_tokens=__SCREAMING_SNAKE_CASE							)  ,				self.EXPECTED_OUTPUTS							)
class  A_			(							a_			):

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   __a							=	"facebook/opt-350m"
   super().setUp()

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   if version.parse(importlib.metadata.version("bitsandbytes"							)							) < version.parse("0.37.0"							):
    return

   # Step 1: freeze all parameters
   __a							=	AutoModelForCausalLM.from_pretrained(self.model_name  ,				load_in_abit=__SCREAMING_SNAKE_CASE							)

   self.assertEqual(set(model.hf_device_map.values()							)  ,				{torch.cuda.current_device()}							)

   for param in model.parameters():
    __a							=	False  # freeze the model - train adapters later
    if param.ndim == 1:
     # cast the small parameters (e.g. layernorm) to fp32 for stability
     __a							=	param.data.to(torch.floataa							)

        # Step 2: add adapters
   for _, module in model.named_modules():
    if "OPTAttention" in repr(type(__SCREAMING_SNAKE_CASE							)							):
     __a							=	LoRALayer(module.q_proj  ,				rank=16							)
     __a							=	LoRALayer(module.k_proj  ,				rank=16							)
     __a							=	LoRALayer(module.v_proj  ,				rank=16							)

        # Step 3: dummy batch
   __a							=	self.tokenizer("Test batch "  ,				return_tensors="pt"							).to(0							)

   # Step 4: Check if the gradient is not None
   with torch.cuda.amp.autocast():
    __a							=	model.forward(**__SCREAMING_SNAKE_CASE							)
    out.logits.norm().backward()

   for module in model.modules():
    if isinstance(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							):
     self.assertTrue(module.adapter[1].weight.grad is not None							)
     self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0							)
    elif isinstance(__SCREAMING_SNAKE_CASE  ,				nn.Embedding							):
     self.assertTrue(module.weight.grad is None							)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """gpt2-xl"""
  _SCREAMING_SNAKE_CASE     					=      3.3_1_9_1_8_5_4_8_5_4_1_5_2_1_8_7



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)


