






import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


_a				     =  '''pcuenq/k-upscaler'''


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		*, __lowerCAmelCase			,		__lowerCAmelCase     )->  Dict:





						"""simple docstring"""



						_UpperCAmelCase     =      {
						    # norm1
						    F"""{diffusers_resnet_prefix}.norm1.linear.weight""": checkpoint[F"""{resnet_prefix}.main.0.mapper.weight"""],
						    F"""{diffusers_resnet_prefix}.norm1.linear.bias""": checkpoint[F"""{resnet_prefix}.main.0.mapper.bias"""],
						    # conv1
						    F"""{diffusers_resnet_prefix}.conv1.weight""": checkpoint[F"""{resnet_prefix}.main.2.weight"""],
						    F"""{diffusers_resnet_prefix}.conv1.bias""": checkpoint[F"""{resnet_prefix}.main.2.bias"""],
						    # norm2
						    F"""{diffusers_resnet_prefix}.norm2.linear.weight""": checkpoint[F"""{resnet_prefix}.main.4.mapper.weight"""],
						    F"""{diffusers_resnet_prefix}.norm2.linear.bias""": checkpoint[F"""{resnet_prefix}.main.4.mapper.bias"""],
						    # conv2
						    F"""{diffusers_resnet_prefix}.conv2.weight""": checkpoint[F"""{resnet_prefix}.main.6.weight"""],
						    F"""{diffusers_resnet_prefix}.conv2.bias""": checkpoint[F"""{resnet_prefix}.main.6.bias"""],
						}

						if resnet.conv_shortcut is not None:
												rv.update(
												    {
												        F"""{diffusers_resnet_prefix}.conv_shortcut.weight""": checkpoint[F"""{resnet_prefix}.skip.weight"""],
												    }     )

						return rv


def __A   (				__lowerCAmelCase			,		*, __lowerCAmelCase			,		__lowerCAmelCase     )->  Union[str, Any]:





						"""simple docstring"""



						_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase     =      checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3			,		dim=0     )
						_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase     =      checkpoint[F"""{attention_prefix}.qkv_proj.bias"""].chunk(3			,		dim=0     )
						_UpperCAmelCase     =      {
						    # norm
						    F"""{diffusers_attention_prefix}.norm1.linear.weight""": checkpoint[F"""{attention_prefix}.norm_in.mapper.weight"""],
						    F"""{diffusers_attention_prefix}.norm1.linear.bias""": checkpoint[F"""{attention_prefix}.norm_in.mapper.bias"""],
						    # to_q
						    F"""{diffusers_attention_prefix}.attn1.to_q.weight""": weight_q.squeeze(-1     ).squeeze(-1     ),
						    F"""{diffusers_attention_prefix}.attn1.to_q.bias""": bias_q,
						    # to_k
						    F"""{diffusers_attention_prefix}.attn1.to_k.weight""": weight_k.squeeze(-1     ).squeeze(-1     ),
						    F"""{diffusers_attention_prefix}.attn1.to_k.bias""": bias_k,
						    # to_v
						    F"""{diffusers_attention_prefix}.attn1.to_v.weight""": weight_v.squeeze(-1     ).squeeze(-1     ),
						    F"""{diffusers_attention_prefix}.attn1.to_v.bias""": bias_v,
						    # to_out
						    F"""{diffusers_attention_prefix}.attn1.to_out.0.weight""": checkpoint[F"""{attention_prefix}.out_proj.weight"""]
						    .squeeze(-1     )
						    .squeeze(-1     ),
						    F"""{diffusers_attention_prefix}.attn1.to_out.0.bias""": checkpoint[F"""{attention_prefix}.out_proj.bias"""],
						}

						return rv


def __A   (				__lowerCAmelCase			,		*, __lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  Any:





						"""simple docstring"""



						_UpperCAmelCase ,			_UpperCAmelCase     =      checkpoint[F"""{attention_prefix}.kv_proj.weight"""].chunk(2			,		dim=0     )
						_UpperCAmelCase ,			_UpperCAmelCase     =      checkpoint[F"""{attention_prefix}.kv_proj.bias"""].chunk(2			,		dim=0     )

						_UpperCAmelCase     =      {
						    # norm2 (ada groupnorm)
						    F"""{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight""": checkpoint[
						        F"""{attention_prefix}.norm_dec.mapper.weight"""
						    ],
						    F"""{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias""": checkpoint[
						        F"""{attention_prefix}.norm_dec.mapper.bias"""
						    ],
						    # layernorm on encoder_hidden_state
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight""": checkpoint[
						        F"""{attention_prefix}.norm_enc.weight"""
						    ],
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias""": checkpoint[
						        F"""{attention_prefix}.norm_enc.bias"""
						    ],
						    # to_q
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight""": checkpoint[
						        F"""{attention_prefix}.q_proj.weight"""
						    ]
						    .squeeze(-1     )
						    .squeeze(-1     ),
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias""": checkpoint[
						        F"""{attention_prefix}.q_proj.bias"""
						    ],
						    # to_k
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight""": weight_k.squeeze(-1     ).squeeze(-1     ),
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias""": bias_k,
						    # to_v
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight""": weight_v.squeeze(-1     ).squeeze(-1     ),
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias""": bias_v,
						    # to_out
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight""": checkpoint[
						        F"""{attention_prefix}.out_proj.weight"""
						    ]
						    .squeeze(-1     )
						    .squeeze(-1     ),
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias""": checkpoint[
						        F"""{attention_prefix}.out_proj.bias"""
						    ],
						}

						return rv


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  Optional[int]:





						"""simple docstring"""



						_UpperCAmelCase     =      'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
						_UpperCAmelCase     =      F"""{block_prefix}.{block_idx}"""

						_UpperCAmelCase     =      {}

						if not hasattr(__lowerCAmelCase			,		'attentions'     ):
												_UpperCAmelCase     =      1  # resnet only
						elif not block.attentions[0].add_self_attention:
												_UpperCAmelCase     =      2  # resnet -> cross-attention
						else:
												_UpperCAmelCase     =      3  # resnet -> self-attention -> cross-attention)

						for resnet_idx, resnet in enumerate(block.resnets     ):
												# diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
												_UpperCAmelCase     =      F"""{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"""
												_UpperCAmelCase     =      n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
												_UpperCAmelCase     =      F"""{block_prefix}.{idx}""" if block_type == 'up' else F"""{block_prefix}.{idx}"""

												diffusers_checkpoint.update(
												    resnet_to_diffusers_checkpoint(
												        __lowerCAmelCase			,		__lowerCAmelCase			,		diffusers_resnet_prefix=__lowerCAmelCase			,		resnet_prefix=__lowerCAmelCase     )     )

						if hasattr(__lowerCAmelCase			,		'attentions'     ):
												for attention_idx, attention in enumerate(block.attentions     ):
																		_UpperCAmelCase     =      F"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
																		_UpperCAmelCase     =      n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
																		_UpperCAmelCase     =      F"""{block_prefix}.{idx}"""
																		_UpperCAmelCase     =      F"""{block_prefix}.{idx }"""
																		_UpperCAmelCase     =      1 if not attention.add_self_attention else 2
																		_UpperCAmelCase     =      (
																		    n * attention_idx + cross_attention_index
																		    if block_type == 'up'
																		    else n * attention_idx + cross_attention_index + 1
																		)
																		_UpperCAmelCase     =      F"""{block_prefix}.{idx }"""

																		diffusers_checkpoint.update(
																		    cross_attn_to_diffusers_checkpoint(
																		        __lowerCAmelCase			,		diffusers_attention_prefix=__lowerCAmelCase			,		diffusers_attention_index=2			,		attention_prefix=__lowerCAmelCase			,		)     )

																		if attention.add_self_attention is True:
																								diffusers_checkpoint.update(
																								    self_attn_to_diffusers_checkpoint(
																								        __lowerCAmelCase			,		diffusers_attention_prefix=__lowerCAmelCase			,		attention_prefix=__lowerCAmelCase			,		)     )

						return diffusers_checkpoint


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  Union[str, Any]:





						"""simple docstring"""



						_UpperCAmelCase     =      {}

						# pre-processing
						diffusers_checkpoint.update(
						    {
						        'conv_in.weight': checkpoint['inner_model.proj_in.weight'],
						        'conv_in.bias': checkpoint['inner_model.proj_in.bias'],
						    }     )

						# timestep and class embedding
						diffusers_checkpoint.update(
						    {
						        'time_proj.weight': checkpoint['inner_model.timestep_embed.weight'].squeeze(-1     ),
						        'time_embedding.linear_1.weight': checkpoint['inner_model.mapping.0.weight'],
						        'time_embedding.linear_1.bias': checkpoint['inner_model.mapping.0.bias'],
						        'time_embedding.linear_2.weight': checkpoint['inner_model.mapping.2.weight'],
						        'time_embedding.linear_2.bias': checkpoint['inner_model.mapping.2.bias'],
						        'time_embedding.cond_proj.weight': checkpoint['inner_model.mapping_cond.weight'],
						    }     )

						# down_blocks
						for down_block_idx, down_block in enumerate(model.down_blocks     ):
												diffusers_checkpoint.update(block_to_diffusers_checkpoint(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		'down'     )     )

						# up_blocks
						for up_block_idx, up_block in enumerate(model.up_blocks     ):
												diffusers_checkpoint.update(block_to_diffusers_checkpoint(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		'up'     )     )

						# post-processing
						diffusers_checkpoint.update(
						    {
						        'conv_out.weight': checkpoint['inner_model.proj_out.weight'],
						        'conv_out.bias': checkpoint['inner_model.proj_out.bias'],
						    }     )

						return diffusers_checkpoint


def __A   (				__lowerCAmelCase     )->  str:





						"""simple docstring"""



						_UpperCAmelCase     =      original_config['input_channels'] + original_config['unet_cond_dim']
						_UpperCAmelCase     =      original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

						_UpperCAmelCase     =      original_config['channels']

						assert (
						    len(set(original_config['depths']     )     ) == 1
						), "UNet2DConditionModel currently do not support blocks with different number of layers"
						_UpperCAmelCase     =      original_config['depths'][0]

						_UpperCAmelCase     =      original_config['mapping_cond_dim']
						_UpperCAmelCase     =      original_config['cross_cond_dim']

						_UpperCAmelCase     =      []
						_UpperCAmelCase     =      []
						for s, c in zip(original_config['self_attn_depths']			,		original_config['cross_attn_depths']     ):
												if s:
																		_UpperCAmelCase     =      'self'
																		_UpperCAmelCase     =      'cross' if c else None
												elif c:
																		_UpperCAmelCase     =      'cross'
																		_UpperCAmelCase     =      None
												else:
																		_UpperCAmelCase     =      None
																		_UpperCAmelCase     =      None
												attna_types.append(__lowerCAmelCase     )
												attna_types.append(__lowerCAmelCase     )

						_UpperCAmelCase     =      UNetaDConditionModel(
						    in_channels=__lowerCAmelCase			,		out_channels=__lowerCAmelCase			,		down_block_types=('KDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D')			,		mid_block_type=__lowerCAmelCase			,		up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')			,		block_out_channels=__lowerCAmelCase			,		layers_per_block=__lowerCAmelCase			,		act_fn='gelu'			,		norm_num_groups=__lowerCAmelCase			,		cross_attention_dim=__lowerCAmelCase			,		attention_head_dim=64			,		time_cond_proj_dim=__lowerCAmelCase			,		resnet_time_scale_shift='scale_shift'			,		time_embedding_type='fourier'			,		timestep_post_act='gelu'			,		conv_in_kernel=1			,		conv_out_kernel=1			,		)

						return unet


def __A   (				__lowerCAmelCase     )->  Union[str, Any]:





						"""simple docstring"""



						_UpperCAmelCase     =      torch.device('cuda' if torch.cuda.is_available() else 'cpu'     )

						_UpperCAmelCase     =      huggingface_hub.hf_hub_download(__lowerCAmelCase			,		'config_laion_text_cond_latent_upscaler_2.json'     )
						_UpperCAmelCase     =      huggingface_hub.hf_hub_download(
						    __lowerCAmelCase			,		'laion_text_cond_latent_upscaler_2_1_00470000_slim.pth'     )
						print(F"""loading original model configuration from {orig_config_path}"""     )
						print(F"""loading original model checkpoint from {orig_weights_path}"""     )

						print('converting to diffusers unet'     )
						_UpperCAmelCase     =      K.config.load_config(open(__lowerCAmelCase     )     )['model']
						_UpperCAmelCase     =      unet_model_from_original_config(__lowerCAmelCase     )

						_UpperCAmelCase     =      torch.load(__lowerCAmelCase			,		map_location=__lowerCAmelCase     )['model_ema']
						_UpperCAmelCase     =      unet_to_diffusers_checkpoint(__lowerCAmelCase			,		__lowerCAmelCase     )

						model.load_state_dict(__lowerCAmelCase			,		strict=__lowerCAmelCase     )
						model.save_pretrained(args.dump_path     )
						print(F"""saving converted unet model in {args.dump_path}"""     )


if __name__ == "__main__":
		_a				     =  argparse.ArgumentParser()

		parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
		_a				     =  parser.parse_args()

		main(args)








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

import os
from pathlib import Path


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  List[Any]:





						"""simple docstring"""



						_UpperCAmelCase     =      {
						    'en': 'Machine learning is great, isn\'t it?',
						    'ru': 'Машинное обучение - это здорово, не так ли?',
						    'de': 'Maschinelles Lernen ist großartig, nicht wahr?',
						}

						# BLUE scores as follows:
						# "pair": [fairseq, transformers]
						_UpperCAmelCase     =      {
						    'wmt19-de-en-6-6-base': [0, 38.37],
						    'wmt19-de-en-6-6-big': [0, 39.90],
						}
						_UpperCAmelCase     =      F"""{src_lang}-{tgt_lang}"""

						_UpperCAmelCase     =      F"""
---

language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- allenai
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.

For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).

2 models are available:

* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)
* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)


## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"allenai/{model_name}\"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

input = \"{texts[src_lang]}\"
input_ids = tokenizer.encode(input, return_tensors=\"pt\")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}

```

#### Limitations and bias


## Training data

Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).

## Eval results

Here are the BLEU scores:

model   |  transformers
-------|---------
{model_name}  |  {scores[model_name][1]}

The score was calculated using this code:

```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=5
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py allenai/{model_name} $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
```

## Data Sources

- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### BibTeX entry and citation info

```
@misc{{kasai2020deep,
    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
    year={{2020}},
    eprint={{2006.10369}},
    archivePrefix={{arXiv}},
    primaryClass={{cs.CL}}
}}
```

"""
						model_card_dir.mkdir(parents=__lowerCAmelCase			,		exist_ok=__lowerCAmelCase     )
						_UpperCAmelCase     =      os.path.join(__lowerCAmelCase			,		'README.md'     )
						print(F"""Generating {path}"""     )
						with open(__lowerCAmelCase			,		'w'			,		encoding='utf-8'     ) as f:
												f.write(__lowerCAmelCase     )


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

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
		_a				     =  model_cards_dir / '''allenai''' / model_name
		write_model_card(model_card_dir, src_lang='''de''', tgt_lang='''en''', model_name=model_name)
