



'''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							:Dict				    =					'''sshleifer/mar_enro_6_3_student'''







class 	_lowerCAmelCase							(			SCREAMING_SNAKE_CASE_ ):


	def     _a	(self							):
				super().setUp()

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


	@slow
	@require_torch_gpu
	def     _a	(self							):
				MarianMTModel.from_pretrained(_a							)


	@slow
	@require_torch_gpu
	def     _a	(self							):
				A_	:						Optional[int]	=     {
				    '$MAX_LEN': 64,
				    '$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_	:						int	=     (self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split("""finetune.py"""							)[1].strip()
				A_	:						Dict	=     bash_script.replace("""\\\n"""       ,      """"""							).strip().replace("""\"$@\""""       ,      """"""							)
				for k, v in env_vars_to_replace.items():
							A_	:						Optional[int]	=     bash_script.replace(_a       ,      str(_a							)							)
				A_	:						List[Any]	=     self.get_auto_remove_tmp_dir()

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

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

				# Check metrics
				A_	:						Dict	=     load_json(model.metrics_save_path							)
				A_	:						Tuple	=     metrics['val'][0]
				A_	:						str	=     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}']       ,      _a							)

				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_	:						Any	=     os.listdir(_a							)
				A_	:						List[str]	=     [x for x in contents if x.endswith(""".ckpt"""							)][0]
				A_	:						str	=     os.path.join(args.output_dir       ,      _a							)
				A_	:						List[Any]	=     torch.load(_a       ,      map_location="""cpu"""							)
				A_	:						List[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_	:						Tuple	=     {os.path.basename(_a							) 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 	_lowerCAmelCase							(			SCREAMING_SNAKE_CASE_ ):


	@timeout_decorator.timeout(600							)
	@slow
	@require_torch_gpu
	def     _a	(self							):
				A_	:						Tuple	=     F'{self.test_file_dir_str}/test_data/wmt_en_ro'
				A_	:						List[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_	:						Union[str, Any]	=     bash_script.replace("""\\\n"""       ,      """"""							).strip().replace("""\"$@\""""       ,      """"""							)
				A_	:						str	=     bash_script.replace("""--fp16 """       ,      """ """							)

				for k, v in env_vars_to_replace.items():
							A_	:						Any	=     bash_script.replace(_a       ,      str(_a							)							)
				A_	:						Union[str, Any]	=     self.get_auto_remove_tmp_dir()
				A_	:						Tuple	=     bash_script.replace("""--fp16"""       ,      """"""							)
				A_	:						List[str]	=     6
				A_	:						str	=     (
				    ['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(_a       ,      """argv"""       ,      _a							):
							A_	:						Union[str, Any]	=     argparse.ArgumentParser()
							A_	:						int	=     pl.Trainer.add_argparse_args(_a							)
							A_	:						List[Any]	=     SummarizationDistiller.add_model_specific_args(_a       ,      os.getcwd()							)
							A_	:						int	=     parser.parse_args()
							# assert args.gpus == gpus THIS BREAKS for multi_gpu

							A_	:						Union[str, Any]	=     distill_main(_a							)

				# Check metrics
				A_	:						Optional[int]	=     load_json(model.metrics_save_path							)
				A_	:						Tuple	=     metrics['val'][0]
				A_	:						int	=     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}']       ,      _a							)

				# check lightning ckpt can be loaded and has a reasonable statedict
				A_	:						Union[str, Any]	=     os.listdir(_a							)
				A_	:						List[str]	=     [x for x in contents if x.endswith(""".ckpt"""							)][0]
				A_	:						List[str]	=     os.path.join(args.output_dir       ,      _a							)
				A_	:						str	=     torch.load(_a       ,      map_location="""cpu"""							)
				A_	:						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_	:						List[Any]	=     {os.path.basename(_a							) 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




'''simple docstring'''





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        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Dict:
  __lowerCamelCase       :       Any           = checkpoint

  __lowerCamelCase       :       List[str]           = {}

  __lowerCamelCase       :       List[str]           = vae_state_dict['encoder.conv_in.weight']
  __lowerCamelCase       :       Dict           = vae_state_dict['encoder.conv_in.bias']
  __lowerCamelCase       :       Optional[Any]           = vae_state_dict['encoder.conv_out.weight']
  __lowerCamelCase       :       Optional[int]           = vae_state_dict['encoder.conv_out.bias']
  __lowerCamelCase       :       Optional[int]           = vae_state_dict['encoder.norm_out.weight']
  __lowerCamelCase       :       Optional[int]           = vae_state_dict['encoder.norm_out.bias']

  __lowerCamelCase       :       Dict           = vae_state_dict['decoder.conv_in.weight']
  __lowerCamelCase       :       List[str]           = vae_state_dict['decoder.conv_in.bias']
  __lowerCamelCase       :       Tuple           = vae_state_dict['decoder.conv_out.weight']
  __lowerCamelCase       :       List[str]           = vae_state_dict['decoder.conv_out.bias']
  __lowerCamelCase       :       Dict           = vae_state_dict['decoder.norm_out.weight']
  __lowerCamelCase       :       Union[str, Any]           = vae_state_dict['decoder.norm_out.bias']

  __lowerCamelCase       :       Any           = vae_state_dict['quant_conv.weight']
  __lowerCamelCase       :       Union[str, Any]           = vae_state_dict['quant_conv.bias']
  __lowerCamelCase       :       Tuple           = vae_state_dict['post_quant_conv.weight']
  __lowerCamelCase       :       List[Any]           = vae_state_dict['post_quant_conv.bias']

  # Retrieves the keys for the encoder down blocks only
  __lowerCamelCase       :       str           = len({'.'.join(layer.split('.'       )[:3]       ) for layer in vae_state_dict if 'encoder.down' in layer}       )
  __lowerCamelCase       :       int           = {
      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
  __lowerCamelCase       :       Dict           = len({'.'.join(layer.split('.'       )[:3]       ) for layer in vae_state_dict if 'decoder.up' in layer}       )
  __lowerCamelCase       :       Dict           = {
      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       ):
    __lowerCamelCase       :       List[str]           = [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:
      __lowerCamelCase       :       Any           = vae_state_dict.pop(
          F'encoder.down.{i}.downsample.conv.weight'       )
      __lowerCamelCase       :       Any           = vae_state_dict.pop(
          F'encoder.down.{i}.downsample.conv.bias'       )

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

  __lowerCamelCase       :       List[Any]           = [key for key in vae_state_dict if 'encoder.mid.block' in key]
  __lowerCamelCase       :       str           = 2
  for i in range(1       ,num_mid_res_blocks + 1       ):
    __lowerCamelCase       :       int           = [key for key in mid_resnets if F'encoder.mid.block_{i}' in key]

    __lowerCamelCase       :       Optional[Any]           = renew_vae_resnet_paths(_lowerCAmelCase       )
    __lowerCamelCase       :       Dict           = {'old': F'mid.block_{i}', 'new': F'mid_block.resnets.{i - 1}'}
    assign_to_checkpoint(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,additional_replacements=[meta_path]       ,config=_lowerCAmelCase       )

  __lowerCamelCase       :       Optional[Any]           = [key for key in vae_state_dict if 'encoder.mid.attn' in key]
  __lowerCamelCase       :       int           = renew_vae_attention_paths(_lowerCAmelCase       )
  __lowerCamelCase       :       Optional[Any]           = {'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       ):
    __lowerCamelCase       :       Optional[Any]           = num_up_blocks - 1 - i
    __lowerCamelCase       :       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:
      __lowerCamelCase       :       Optional[int]           = vae_state_dict[
          F'decoder.up.{block_id}.upsample.conv.weight'
      ]
      __lowerCamelCase       :       Optional[Any]           = vae_state_dict[
          F'decoder.up.{block_id}.upsample.conv.bias'
      ]

    __lowerCamelCase       :       Tuple           = renew_vae_resnet_paths(_lowerCAmelCase       )
    __lowerCamelCase       :       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       )

  __lowerCamelCase       :       Optional[Any]           = [key for key in vae_state_dict if 'decoder.mid.block' in key]
  __lowerCamelCase       :       Union[str, Any]           = 2
  for i in range(1       ,num_mid_res_blocks + 1       ):
    __lowerCamelCase       :       List[str]           = [key for key in mid_resnets if F'decoder.mid.block_{i}' in key]

    __lowerCamelCase       :       str           = renew_vae_resnet_paths(_lowerCAmelCase       )
    __lowerCamelCase       :       Any           = {'old': F'mid.block_{i}', 'new': F'mid_block.resnets.{i - 1}'}
    assign_to_checkpoint(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,additional_replacements=[meta_path]       ,config=_lowerCAmelCase       )

  __lowerCamelCase       :       Any           = [key for key in vae_state_dict if 'decoder.mid.attn' in key]
  __lowerCamelCase       :       List[str]           = renew_vae_attention_paths(_lowerCAmelCase       )
  __lowerCamelCase       :       Optional[Any]           = {'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        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,)      ->      List[Any]:
  # Only support V1
  __lowerCamelCase       :       Any           = requests.get(
      ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'       )
  __lowerCamelCase       :       str           = io.BytesIO(r.content       )

  __lowerCamelCase       :       Any           = OmegaConf.load(_lowerCAmelCase       )
  __lowerCamelCase       :       List[Any]           = 512
  __lowerCamelCase       :       Dict           = 'cuda' if torch.cuda.is_available() else 'cpu'
  if checkpoint_path.endswith('safetensors'       ):
    from safetensors import safe_open

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

  # Convert the VAE model.
  __lowerCamelCase       :       int           = create_vae_diffusers_config(_lowerCAmelCase       ,image_size=_lowerCAmelCase       )
  __lowerCamelCase       :       int           = custom_convert_ldm_vae_checkpoint(_lowerCAmelCase       ,_lowerCAmelCase       )

  __lowerCamelCase       :       Any           = AutoencoderKL(**_lowerCAmelCase       )
  vae.load_state_dict(_lowerCAmelCase       )
  vae.save_pretrained(_lowerCAmelCase       )


if __name__ == "__main__":
   _UpperCamelCase											=						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.')

   _UpperCamelCase											=						parser.parse_args()

   vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)






