



'''simple docstring'''

import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


__lowerCAmelCase									=   '''sshleifer/mar_enro_6_3_student'''

class 	__magic_name__						(    _UpperCamelCase   ):







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

									_a     :      Any        =							cached_path(
									    'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'	,extract_compressed_file=_UpperCAmelCase	,)
									_a     :      List[str]        =							F"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""







							@slow
							@require_torch_gpu
							def 							__lowercase			(							self					:       Dict		):
									MarianMTModel.from_pretrained(_UpperCAmelCase		)







							@slow
							@require_torch_gpu
							def 							__lowercase			(							self					:       Any		):
									_a     :      Any        =							{
									    '$MAX_LEN': 64,
									    '$BS': 64,
									    '$GAS': 1,
									    '$ENRO_DIR': self.data_dir,
									    'facebook/mbart-large-cc25': MARIAN_MODEL,
									    # "val_check_interval=0.25": "val_check_interval=1.0",
									    '--learning_rate=3e-5': '--learning_rate 3e-4',
									    '--num_train_epochs 6': '--num_train_epochs 1',
									}

									# Clean up bash script
									_a     :      Union[str, Any]        =							(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'		)[1].strip()
									_a     :      Union[str, Any]        =							bash_script.replace('\\\n'	,''		).strip().replace('"$@"'	,''		)
									for k, v in env_vars_to_replace.items():
											_a     :      int        =							bash_script.replace(_UpperCAmelCase	,str(_UpperCAmelCase		)		)
									_a     :      List[str]        =							self.get_auto_remove_tmp_dir()

									# bash_script = bash_script.replace("--fp16 ", "")
									_a     :      Any        =							F"""
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        """.split()
									# XXX: args.gpus > 1 : handle multi_gpu in the future

									_a     :      List[str]        =							['finetune.py'] + bash_script.split() + args
									with patch.object(_UpperCAmelCase	,'argv'	,_UpperCAmelCase		):
											_a     :      str        =							argparse.ArgumentParser()
											_a     :      Any        =							pl.Trainer.add_argparse_args(_UpperCAmelCase		)
											_a     :      List[Any]        =							SummarizationModule.add_model_specific_args(_UpperCAmelCase	,os.getcwd()		)
											_a     :      Optional[int]        =							parser.parse_args()
											_a     :      int        =							main(_UpperCAmelCase		)

									# Check metrics
									_a     :      Any        =							load_json(model.metrics_save_path		)
									_a     :      int        =							metrics['val'][0]
									_a     :      Tuple        =							metrics['val'][-1]
									self.assertEqual(len(metrics['val']		)	,(args.max_epochs / args.val_check_interval)		)
									assert isinstance(last_step_stats[F"""val_avg_{model.val_metric}"""]	,_UpperCAmelCase		)

									self.assertGreater(last_step_stats['val_avg_gen_time']	,0.01		)
									# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
									self.assertLessEqual(last_step_stats['val_avg_gen_time']	,1.0		)

									# test learning requirements:

									# 1. BLEU improves over the course of training by more than 2 pts
									self.assertGreater(last_step_stats['val_avg_bleu'] - first_step_stats['val_avg_bleu']	,2		)

									# 2. BLEU finishes above 17
									self.assertGreater(last_step_stats['val_avg_bleu']	,17		)

									# 3. test BLEU and val BLEU within ~1.1 pt.
									self.assertLess(abs(metrics['val'][-1]['val_avg_bleu'] - metrics['test'][-1]['test_avg_bleu']		)	,1.1		)

									# check lightning ckpt can be loaded and has a reasonable statedict
									_a     :      Optional[Any]        =							os.listdir(_UpperCAmelCase		)
									_a     :      Optional[Any]        =							[x for x in contents if x.endswith('.ckpt'		)][0]
									_a     :      List[str]        =							os.path.join(args.output_dir	,_UpperCAmelCase		)
									_a     :      Optional[int]        =							torch.load(_UpperCAmelCase	,map_location='cpu'		)
									_a     :      List[str]        =							'model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
									assert expected_key in ckpt["state_dict"]
									assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

									# TODO: turn on args.do_predict when PL bug fixed.
									if args.do_predict:
											_a     :      Optional[Any]        =							{os.path.basename(_UpperCAmelCase		) for p in contents}
											assert "test_generations.txt" in contents
											assert "test_results.txt" in contents
											# assert len(metrics["val"]) ==  desired_n_evals
											assert len(metrics['test']		) == 1

class 	__magic_name__						(    _UpperCamelCase   ):







							@timeout_decorator.timeout(600		)
							@slow
							@require_torch_gpu
							def 							__lowercase			(							self					:       Any		):
									_a     :      Any        =							F"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
									_a     :      Union[str, Any]        =							{
									    '--fp16_opt_level=O1': '',
									    '$MAX_LEN': 128,
									    '$BS': 16,
									    '$GAS': 1,
									    '$ENRO_DIR': data_dir,
									    '$m': 'sshleifer/student_marian_en_ro_6_1',
									    'val_check_interval=0.25': 'val_check_interval=1.0',
									}

									# Clean up bash script
									_a     :      Any        =							(
									    (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'		)[1].strip()
									)
									_a     :      Optional[int]        =							bash_script.replace('\\\n'	,''		).strip().replace('"$@"'	,''		)
									_a     :      Tuple        =							bash_script.replace('--fp16 '	,' '		)

									for k, v in env_vars_to_replace.items():
											_a     :      Tuple        =							bash_script.replace(_UpperCAmelCase	,str(_UpperCAmelCase		)		)
									_a     :      Dict        =							self.get_auto_remove_tmp_dir()
									_a     :      int        =							bash_script.replace('--fp16'	,''		)
									_a     :      Any        =							6
									_a     :      int        =							(
									    ['distillation.py']
									    + bash_script.split()
									    + [
									        F"""--output_dir={output_dir}""",
									        '--gpus=1',
									        '--learning_rate=1e-3',
									        F"""--num_train_epochs={epochs}""",
									        '--warmup_steps=10',
									        '--val_check_interval=1.0',
									        '--do_predict',
									    ]
									)
									with patch.object(_UpperCAmelCase	,'argv'	,_UpperCAmelCase		):
											_a     :      List[str]        =							argparse.ArgumentParser()
											_a     :      Union[str, Any]        =							pl.Trainer.add_argparse_args(_UpperCAmelCase		)
											_a     :      Any        =							SummarizationDistiller.add_model_specific_args(_UpperCAmelCase	,os.getcwd()		)
											_a     :      Dict        =							parser.parse_args()
											# assert args.gpus == gpus THIS BREAKS for multi_gpu

											_a     :      Dict        =							distill_main(_UpperCAmelCase		)

									# Check metrics
									_a     :      Optional[Any]        =							load_json(model.metrics_save_path		)
									_a     :      Optional[Any]        =							metrics['val'][0]
									_a     :      Union[str, Any]        =							metrics['val'][-1]
									assert len(metrics['val']		) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

									assert last_step_stats["val_avg_gen_time"] >= 0.01

									assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
									assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
									assert isinstance(last_step_stats[F"""val_avg_{model.val_metric}"""]	,_UpperCAmelCase		)

									# check lightning ckpt can be loaded and has a reasonable statedict
									_a     :      Tuple        =							os.listdir(_UpperCAmelCase		)
									_a     :      Dict        =							[x for x in contents if x.endswith('.ckpt'		)][0]
									_a     :      Union[str, Any]        =							os.path.join(args.output_dir	,_UpperCAmelCase		)
									_a     :      str        =							torch.load(_UpperCAmelCase	,map_location='cpu'		)
									_a     :      Union[str, Any]        =							'model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
									assert expected_key in ckpt["state_dict"]
									assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

									# TODO: turn on args.do_predict when PL bug fixed.
									if args.do_predict:
											_a     :      int        =							{os.path.basename(_UpperCAmelCase		) for p in contents}
											assert "test_generations.txt" in contents
											assert "test_results.txt" in contents
											# assert len(metrics["val"]) ==  desired_n_evals
											assert len(metrics['test']		) == 1












import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)






def 				lowerCamelCase__      (    __lowerCamelCase			:     Dict  ,		__lowerCamelCase			:     int		):
  __UpperCAmelCase							:	List[Any]           =  checkpoint

  __UpperCAmelCase							:	Tuple           =  {}

  __UpperCAmelCase							:	Dict           =  vae_state_dict["""encoder.conv_in.weight"""]
  __UpperCAmelCase							:	Optional[Any]           =  vae_state_dict["""encoder.conv_in.bias"""]
  __UpperCAmelCase							:	Dict           =  vae_state_dict["""encoder.conv_out.weight"""]
  __UpperCAmelCase							:	Union[str, Any]           =  vae_state_dict["""encoder.conv_out.bias"""]
  __UpperCAmelCase							:	List[Any]           =  vae_state_dict["""encoder.norm_out.weight"""]
  __UpperCAmelCase							:	Tuple           =  vae_state_dict["""encoder.norm_out.bias"""]

  __UpperCAmelCase							:	Dict           =  vae_state_dict["""decoder.conv_in.weight"""]
  __UpperCAmelCase							:	Tuple           =  vae_state_dict["""decoder.conv_in.bias"""]
  __UpperCAmelCase							:	Optional[int]           =  vae_state_dict["""decoder.conv_out.weight"""]
  __UpperCAmelCase							:	Optional[int]           =  vae_state_dict["""decoder.conv_out.bias"""]
  __UpperCAmelCase							:	Optional[Any]           =  vae_state_dict["""decoder.norm_out.weight"""]
  __UpperCAmelCase							:	Union[str, Any]           =  vae_state_dict["""decoder.norm_out.bias"""]

  __UpperCAmelCase							:	Optional[int]           =  vae_state_dict["""quant_conv.weight"""]
  __UpperCAmelCase							:	int           =  vae_state_dict["""quant_conv.bias"""]
  __UpperCAmelCase							:	Union[str, Any]           =  vae_state_dict["""post_quant_conv.weight"""]
  __UpperCAmelCase							:	Any           =  vae_state_dict["""post_quant_conv.bias"""]

  # Retrieves the keys for the encoder down blocks only
  __UpperCAmelCase							:	int           =  len({""".""".join(layer.split("""."""		)[:3]		) for layer in vae_state_dict if """encoder.down""" in layer}		)
  __UpperCAmelCase							:	Optional[Any]           =  {
      layer_id: [key for key in vae_state_dict if f"""down.{layer_id}""" in key] for layer_id in range(__lowerCamelCase		)
  }

  # Retrieves the keys for the decoder up blocks only
  __UpperCAmelCase							:	Dict           =  len({""".""".join(layer.split("""."""		)[:3]		) for layer in vae_state_dict if """decoder.up""" in layer}		)
  __UpperCAmelCase							:	Optional[int]           =  {
      layer_id: [key for key in vae_state_dict if f"""up.{layer_id}""" in key] for layer_id in range(__lowerCamelCase		)
  }

  for i in range(__lowerCamelCase		):
    __UpperCAmelCase							:	List[Any]           =  [key for key in down_blocks[i] if f"""down.{i}""" in key and f"""down.{i}.downsample""" not in key]

    if f"""encoder.down.{i}.downsample.conv.weight""" in vae_state_dict:
      __UpperCAmelCase							:	Optional[Any]           =  vae_state_dict.pop(
          f"""encoder.down.{i}.downsample.conv.weight"""		)
      __UpperCAmelCase							:	int           =  vae_state_dict.pop(
          f"""encoder.down.{i}.downsample.conv.bias"""		)

    __UpperCAmelCase							:	Optional[int]           =  renew_vae_resnet_paths(__lowerCamelCase		)
    __UpperCAmelCase							:	Optional[Any]           =  {"""old""": f"""down.{i}.block""", """new""": f"""down_blocks.{i}.resnets"""}
    assign_to_checkpoint(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		additional_replacements=[meta_path]  ,		config=__lowerCamelCase		)

  __UpperCAmelCase							:	Tuple           =  [key for key in vae_state_dict if """encoder.mid.block""" in key]
  __UpperCAmelCase							:	Optional[Any]           =  2
  for i in range(1  ,		num_mid_res_blocks + 1		):
    __UpperCAmelCase							:	Dict           =  [key for key in mid_resnets if f"""encoder.mid.block_{i}""" in key]

    __UpperCAmelCase							:	Tuple           =  renew_vae_resnet_paths(__lowerCamelCase		)
    __UpperCAmelCase							:	Tuple           =  {"""old""": f"""mid.block_{i}""", """new""": f"""mid_block.resnets.{i - 1}"""}
    assign_to_checkpoint(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		additional_replacements=[meta_path]  ,		config=__lowerCamelCase		)

  __UpperCAmelCase							:	List[Any]           =  [key for key in vae_state_dict if """encoder.mid.attn""" in key]
  __UpperCAmelCase							:	str           =  renew_vae_attention_paths(__lowerCamelCase		)
  __UpperCAmelCase							:	List[str]           =  {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
  assign_to_checkpoint(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		additional_replacements=[meta_path]  ,		config=__lowerCamelCase		)
  conv_attn_to_linear(__lowerCamelCase		)

  for i in range(__lowerCamelCase		):
    __UpperCAmelCase							:	Optional[Any]           =  num_up_blocks - 1 - i
    __UpperCAmelCase							:	Union[str, Any]           =  [
        key for key in up_blocks[block_id] if f"""up.{block_id}""" in key and f"""up.{block_id}.upsample""" not in key
    ]

    if f"""decoder.up.{block_id}.upsample.conv.weight""" in vae_state_dict:
      __UpperCAmelCase							:	int           =  vae_state_dict[
          f"""decoder.up.{block_id}.upsample.conv.weight"""
      ]
      __UpperCAmelCase							:	Dict           =  vae_state_dict[
          f"""decoder.up.{block_id}.upsample.conv.bias"""
      ]

    __UpperCAmelCase							:	Dict           =  renew_vae_resnet_paths(__lowerCamelCase		)
    __UpperCAmelCase							:	Optional[Any]           =  {"""old""": f"""up.{block_id}.block""", """new""": f"""up_blocks.{i}.resnets"""}
    assign_to_checkpoint(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		additional_replacements=[meta_path]  ,		config=__lowerCamelCase		)

  __UpperCAmelCase							:	Tuple           =  [key for key in vae_state_dict if """decoder.mid.block""" in key]
  __UpperCAmelCase							:	Union[str, Any]           =  2
  for i in range(1  ,		num_mid_res_blocks + 1		):
    __UpperCAmelCase							:	Dict           =  [key for key in mid_resnets if f"""decoder.mid.block_{i}""" in key]

    __UpperCAmelCase							:	List[Any]           =  renew_vae_resnet_paths(__lowerCamelCase		)
    __UpperCAmelCase							:	int           =  {"""old""": f"""mid.block_{i}""", """new""": f"""mid_block.resnets.{i - 1}"""}
    assign_to_checkpoint(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		additional_replacements=[meta_path]  ,		config=__lowerCamelCase		)

  __UpperCAmelCase							:	Dict           =  [key for key in vae_state_dict if """decoder.mid.attn""" in key]
  __UpperCAmelCase							:	List[Any]           =  renew_vae_attention_paths(__lowerCamelCase		)
  __UpperCAmelCase							:	List[str]           =  {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
  assign_to_checkpoint(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		additional_replacements=[meta_path]  ,		config=__lowerCamelCase		)
  conv_attn_to_linear(__lowerCamelCase		)
  return new_checkpoint






def 				lowerCamelCase__      (    __lowerCamelCase			:     str  ,		__lowerCamelCase			:     str  ,		):
  # Only support V1
  __UpperCAmelCase							:	Optional[int]           =  requests.get(
      """ https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"""		)
  __UpperCAmelCase							:	Optional[int]           =  io.BytesIO(r.content		)

  __UpperCAmelCase							:	Dict           =  OmegaConf.load(__lowerCamelCase		)
  __UpperCAmelCase							:	str           =  512
  __UpperCAmelCase							:	Any           =  """cuda""" if torch.cuda.is_available() else """cpu"""
  if checkpoint_path.endswith("""safetensors"""		):
    from safetensors import safe_open

    __UpperCAmelCase							:	List[Any]           =  {}
    with safe_open(__lowerCamelCase  ,		framework="""pt"""  ,		device="""cpu"""		) as f:
      for key in f.keys():
        __UpperCAmelCase							:	str           =  f.get_tensor(__lowerCamelCase		)
  else:
    __UpperCAmelCase							:	Optional[int]           =  torch.load(__lowerCamelCase  ,		map_location=__lowerCamelCase		)["""state_dict"""]

  # Convert the VAE model.
  __UpperCAmelCase							:	Optional[int]           =  create_vae_diffusers_config(__lowerCamelCase  ,		image_size=__lowerCamelCase		)
  __UpperCAmelCase							:	Union[str, Any]           =  custom_convert_ldm_vae_checkpoint(__lowerCamelCase  ,		__lowerCamelCase		)

  __UpperCAmelCase							:	Optional[int]           =  AutoencoderKL(**__lowerCamelCase		)
  vae.load_state_dict(__lowerCamelCase		)
  vae.save_pretrained(__lowerCamelCase		)


if __name__ == "__main__":
       a		:       List[Any]			     = argparse.ArgumentParser()

       parser.add_argument("--vae_pt_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
       parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")

       a		:       Optional[int]			     = parser.parse_args()

       vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)


