

'''simple docstring'''





import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)

os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)






def 				_lowerCAmelCase			(   __magic_name__						:     Union[str, Any]						)				->  Dict:
						if hor == 128:
												lowercase :							Dict    		=('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
												lowercase :							Tuple    		=(32, 128, 256)
												lowercase :							Optional[int]    		=('''UpResnetBlock1D''', '''UpResnetBlock1D''')

						elif hor == 32:
												lowercase :							Union[str, Any]    		=('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
												lowercase :							str    		=(32, 64, 128, 256)
												lowercase :							Any    		=('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
						lowercase :							str    		=torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''						)
						lowercase :							Optional[int]    		=model.state_dict()
						lowercase :							Optional[Any]    		={
						    '''down_block_types''': down_block_types,
						    '''block_out_channels''': block_out_channels,
						    '''up_block_types''': up_block_types,
						    '''layers_per_block''': 1,
						    '''use_timestep_embedding''': True,
						    '''out_block_type''': '''OutConv1DBlock''',
						    '''norm_num_groups''': 8,
						    '''downsample_each_block''': False,
						    '''in_channels''': 14,
						    '''out_channels''': 14,
						    '''extra_in_channels''': 0,
						    '''time_embedding_type''': '''positional''',
						    '''flip_sin_to_cos''': False,
						    '''freq_shift''': 1,
						    '''sample_size''': 65536,
						    '''mid_block_type''': '''MidResTemporalBlock1D''',
						    '''act_fn''': '''mish''',
						}
						lowercase :							List[Any]    		=UNetaDModel(**__magic_name__						)
						print(f'''length of state dict: {len(state_dict.keys()						)}'''						)
						print(f'''length of value function dict: {len(hf_value_function.state_dict().keys()						)}'''						)
						lowercase :							Dict    		=dict(zip(model.state_dict().keys()   , hf_value_function.state_dict().keys()						)						)
						for k, v in mapping.items():
												lowercase :							List[str]    		=state_dict.pop(__magic_name__						)
						hf_value_function.load_state_dict(__magic_name__						)

						torch.save(hf_value_function.state_dict()   , f'''hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin'''						)
						with open(f'''hub/hopper-medium-v2/unet/hor{hor}/config.json'''   , '''w'''						) as f:
												json.dump(__magic_name__   , __magic_name__						)






def 				_lowerCAmelCase			(   )				->  Any:
						lowercase :							List[str]    		={
						    '''in_channels''': 14,
						    '''down_block_types''': ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''),
						    '''up_block_types''': (),
						    '''out_block_type''': '''ValueFunction''',
						    '''mid_block_type''': '''ValueFunctionMidBlock1D''',
						    '''block_out_channels''': (32, 64, 128, 256),
						    '''layers_per_block''': 1,
						    '''downsample_each_block''': True,
						    '''sample_size''': 65536,
						    '''out_channels''': 14,
						    '''extra_in_channels''': 0,
						    '''time_embedding_type''': '''positional''',
						    '''use_timestep_embedding''': True,
						    '''flip_sin_to_cos''': False,
						    '''freq_shift''': 1,
						    '''norm_num_groups''': 8,
						    '''act_fn''': '''mish''',
						}

						lowercase :							int    		=torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'''						)
						lowercase :							Optional[int]    		=model
						lowercase :							Union[str, Any]    		=UNetaDModel(**__magic_name__						)
						print(f'''length of state dict: {len(state_dict.keys()						)}'''						)
						print(f'''length of value function dict: {len(hf_value_function.state_dict().keys()						)}'''						)

						lowercase :							Optional[Any]    		=dict(zip(state_dict.keys()   , hf_value_function.state_dict().keys()						)						)
						for k, v in mapping.items():
												lowercase :							Union[str, Any]    		=state_dict.pop(__magic_name__						)

						hf_value_function.load_state_dict(__magic_name__						)

						torch.save(hf_value_function.state_dict()   , '''hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin'''						)
						with open('''hub/hopper-medium-v2/value_function/config.json'''   , '''w'''						) as f:
												json.dump(__magic_name__   , __magic_name__						)


if __name__ == "__main__":
							unet(32)
							# unet(128)
							value_function()









'''simple docstring'''
# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path




def 	_A  (       A							,A							,A							,A							)  ->				List[Any]:
	lowercase						:		Optional[int]   					=		{
	    "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]
	lowercase						:		Optional[int]   					=		{
	    "wmt16-en-de-dist-12-1": [28.3, 27.52],
	    "wmt16-en-de-dist-6-1": [27.4, 27.11],
	    "wmt16-en-de-12-1": [26.9, 25.75],
	}
	lowercase						:		Dict   					=		F'''{src_lang}-{tgt_lang}'''

	lowercase						:		Union[str, Any]   					=		F'''
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt16 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).

All 3 models are available:

* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)


## 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   | fairseq | transformers
-------|---------|----------
{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}

The score is slightly below the score reported in the paper, as the researchers don\'t use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.

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 wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -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/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)


### 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=A							,exist_ok=A							)
	lowercase						:		str   					=		os.path.join(A							,"README.md"							)
	print(F'''Generating {path}'''							)
	with open(A							,"w"							,encoding="utf-8"							) as f:
		f.write(A							)


# make sure we are under the root of the project
lowerCAmelCase   : Union[str, Any]             =				Path(__file__).resolve().parent.parent.parent
lowerCAmelCase   : Union[str, Any]             =				repo_dir / """model_cards"""

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
					lowerCAmelCase   : Optional[Any]             =				model_cards_dir / """allenai""" / model_name
					write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)


