


"""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     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[int] ,			SCREAMING_SNAKE_CASE_		:				Any  )   ->						Dict:
						SCREAMING_SNAKE_CASE													=					checkpoint

						SCREAMING_SNAKE_CASE													=					{}

						SCREAMING_SNAKE_CASE													=					vae_state_dict['encoder.conv_in.weight']
						SCREAMING_SNAKE_CASE													=					vae_state_dict['encoder.conv_in.bias']
						SCREAMING_SNAKE_CASE													=					vae_state_dict['encoder.conv_out.weight']
						SCREAMING_SNAKE_CASE													=					vae_state_dict['encoder.conv_out.bias']
						SCREAMING_SNAKE_CASE													=					vae_state_dict['encoder.norm_out.weight']
						SCREAMING_SNAKE_CASE													=					vae_state_dict['encoder.norm_out.bias']

						SCREAMING_SNAKE_CASE													=					vae_state_dict['decoder.conv_in.weight']
						SCREAMING_SNAKE_CASE													=					vae_state_dict['decoder.conv_in.bias']
						SCREAMING_SNAKE_CASE													=					vae_state_dict['decoder.conv_out.weight']
						SCREAMING_SNAKE_CASE													=					vae_state_dict['decoder.conv_out.bias']
						SCREAMING_SNAKE_CASE													=					vae_state_dict['decoder.norm_out.weight']
						SCREAMING_SNAKE_CASE													=					vae_state_dict['decoder.norm_out.bias']

						SCREAMING_SNAKE_CASE													=					vae_state_dict['quant_conv.weight']
						SCREAMING_SNAKE_CASE													=					vae_state_dict['quant_conv.bias']
						SCREAMING_SNAKE_CASE													=					vae_state_dict['post_quant_conv.weight']
						SCREAMING_SNAKE_CASE													=					vae_state_dict['post_quant_conv.bias']

						# Retrieves the keys for the encoder down blocks only
						SCREAMING_SNAKE_CASE													=					len({'.'.join(layer.split('.'  )[:3]  ) for layer in vae_state_dict if 'encoder.down' in layer}  )
						SCREAMING_SNAKE_CASE													=					{
						    layer_id: [key for key in vae_state_dict if F'down.{layer_id}' in key] for layer_id in range(SCREAMING_SNAKE_CASE_  )
						}

						# Retrieves the keys for the decoder up blocks only
						SCREAMING_SNAKE_CASE													=					len({'.'.join(layer.split('.'  )[:3]  ) for layer in vae_state_dict if 'decoder.up' in layer}  )
						SCREAMING_SNAKE_CASE													=					{
						    layer_id: [key for key in vae_state_dict if F'up.{layer_id}' in key] for layer_id in range(SCREAMING_SNAKE_CASE_  )
						}

						for i in range(SCREAMING_SNAKE_CASE_  ):
												SCREAMING_SNAKE_CASE													=					[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:
																		SCREAMING_SNAKE_CASE													=					vae_state_dict.pop(
																		    F'encoder.down.{i}.downsample.conv.weight'  )
																		SCREAMING_SNAKE_CASE													=					vae_state_dict.pop(
																		    F'encoder.down.{i}.downsample.conv.bias'  )

												SCREAMING_SNAKE_CASE													=					renew_vae_resnet_paths(SCREAMING_SNAKE_CASE_  )
												SCREAMING_SNAKE_CASE													=					{'old': F'down.{i}.block', 'new': F'down_blocks.{i}.resnets'}
												assign_to_checkpoint(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			additional_replacements=[meta_path] ,			config=SCREAMING_SNAKE_CASE_  )

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

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

						SCREAMING_SNAKE_CASE													=					[key for key in vae_state_dict if 'encoder.mid.attn' in key]
						SCREAMING_SNAKE_CASE													=					renew_vae_attention_paths(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					{'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
						assign_to_checkpoint(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			additional_replacements=[meta_path] ,			config=SCREAMING_SNAKE_CASE_  )
						conv_attn_to_linear(SCREAMING_SNAKE_CASE_  )

						for i in range(SCREAMING_SNAKE_CASE_  ):
												SCREAMING_SNAKE_CASE													=					num_up_blocks - 1 - i
												SCREAMING_SNAKE_CASE													=					[
												    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:
																		SCREAMING_SNAKE_CASE													=					vae_state_dict[
																		    F'decoder.up.{block_id}.upsample.conv.weight'
																		]
																		SCREAMING_SNAKE_CASE													=					vae_state_dict[
																		    F'decoder.up.{block_id}.upsample.conv.bias'
																		]

												SCREAMING_SNAKE_CASE													=					renew_vae_resnet_paths(SCREAMING_SNAKE_CASE_  )
												SCREAMING_SNAKE_CASE													=					{'old': F'up.{block_id}.block', 'new': F'up_blocks.{i}.resnets'}
												assign_to_checkpoint(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			additional_replacements=[meta_path] ,			config=SCREAMING_SNAKE_CASE_  )

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

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

						SCREAMING_SNAKE_CASE													=					[key for key in vae_state_dict if 'decoder.mid.attn' in key]
						SCREAMING_SNAKE_CASE													=					renew_vae_attention_paths(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					{'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
						assign_to_checkpoint(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			additional_replacements=[meta_path] ,			config=SCREAMING_SNAKE_CASE_  )
						conv_attn_to_linear(SCREAMING_SNAKE_CASE_  )
						return new_checkpoint


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				str ,			)   ->						Union[str, Any]:
						# Only support V1
						SCREAMING_SNAKE_CASE													=					requests.get(
						    ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'  )
						SCREAMING_SNAKE_CASE													=					io.BytesIO(r.content  )

						SCREAMING_SNAKE_CASE													=					OmegaConf.load(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					5_12
						SCREAMING_SNAKE_CASE													=					'cuda' if torch.cuda.is_available() else 'cpu'
						if checkpoint_path.endswith('safetensors'  ):
												from safetensors import safe_open

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

						# Convert the VAE model.
						SCREAMING_SNAKE_CASE													=					create_vae_diffusers_config(SCREAMING_SNAKE_CASE_ ,			image_size=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					custom_convert_ldm_vae_checkpoint(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					AutoencoderKL(**SCREAMING_SNAKE_CASE_  )
						vae.load_state_dict(SCREAMING_SNAKE_CASE_  )
						vae.save_pretrained(SCREAMING_SNAKE_CASE_  )


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)






"""simple docstring"""




# Usage:
# ./gen-card-facebook-wmt19.py

import os
from pathlib import Path


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				List[str] ,			SCREAMING_SNAKE_CASE_		:				int  )   ->						List[str]:
						SCREAMING_SNAKE_CASE													=					{
						    'en': 'Machine learning is great, isn\'t it?',
						    'ru': 'Машинное обучение - это здорово, не так ли?',
						    'de': 'Maschinelles Lernen ist großartig, oder?',
						}

						# BLUE scores as follows:
						# "pair": [fairseq, transformers]
						SCREAMING_SNAKE_CASE													=					{
						    'ru-en': ['[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)', '39.20'],
						    'en-ru': ['[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)', '33.47'],
						    'en-de': ['[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)', '42.83'],
						    'de-en': ['[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)', '41.35'],
						}
						SCREAMING_SNAKE_CASE													=					F'{src_lang}-{tgt_lang}'

						SCREAMING_SNAKE_CASE													=					F'\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = "facebook/wmt19-{src_lang}-{tgt_lang}"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = "{texts[src_lang]}"\ninput_ids = tokenizer.encode(input, return_tensors="pt")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair   | fairseq | transformers\n-------|---------|----------\n{pair}  | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n  year={{2020}},\n  title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},\n  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n  booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n'
						os.makedirs(SCREAMING_SNAKE_CASE_ ,			exist_ok=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					os.path.join(SCREAMING_SNAKE_CASE_ ,			'README.md'  )
						print(F'Generating {path}'  )
						with open(SCREAMING_SNAKE_CASE_ ,			'w' ,			encoding='utf-8'  ) as f:
												f.write(SCREAMING_SNAKE_CASE_  )


# make sure we are under the root of the project
__UpperCamelCase						=      Path(__file__).resolve().parent.parent.parent
__UpperCamelCase						=      repo_dir / '''model_cards'''

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
	__UpperCamelCase,__UpperCamelCase,__UpperCamelCase						=      model_name.split('''-''')
	__UpperCamelCase						=      model_cards_dir / '''facebook''' / model_name
	write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)


