



'''simple docstring'''






import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm




def 					snake_case_      (  lowerCAmelCase_ ,     lowerCAmelCase_ = False	)->	List[str]:





							'''simple docstring'''


							if device_specific:
														seed += AcceleratorState().process_index
							random.seed(snake_case_	)
							np.random.seed(snake_case_	)
							torch.manual_seed(snake_case_	)
							if is_xpu_available():
														torch.xpu.manual_seed_all(snake_case_	)
							elif is_npu_available():
														torch.npu.manual_seed_all(snake_case_	)
							else:
														torch.cuda.manual_seed_all(snake_case_	)
							# ^^ safe to call this function even if cuda is not available
							if is_tpu_available():
														xm.set_rng_state(snake_case_	)




def 					snake_case_      (  lowerCAmelCase_ = None ,     lowerCAmelCase_ = None	)->	Dict:





							'''simple docstring'''


							if rng_type == RNGType.TORCH:
														_UpperCAmelCase						:		int      =				torch.get_rng_state()
							elif rng_type == RNGType.CUDA:
														_UpperCAmelCase						:		Optional[Any]      =				torch.cuda.get_rng_state()
							elif rng_type == RNGType.XLA:
														assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
														_UpperCAmelCase						:		Dict      =				torch.tensor(xm.get_rng_state()	)
							elif rng_type == RNGType.NPU:
														assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
														_UpperCAmelCase						:		str      =				torch.npu.get_rng_state()
							elif rng_type == RNGType.XPU:
														assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
														_UpperCAmelCase						:		Any      =				torch.xpu.get_rng_state()
							elif rng_type == RNGType.GENERATOR:
														assert generator is not None, "Need a generator to synchronize its seed."
														_UpperCAmelCase						:		Tuple      =				generator.get_state()

							# Broadcast the rng state from device 0 to other devices
							_UpperCAmelCase						:		int      =				AcceleratorState()
							if state.distributed_type == DistributedType.TPU:
														_UpperCAmelCase						:		Optional[int]      =				rng_state.to(xm.xla_device()	)
														xm.collective_broadcast([rng_state]	)
														xm.mark_step()
														_UpperCAmelCase						:		int      =				rng_state.cpu()
							elif (
							    state.distributed_type in CUDA_DISTRIBUTED_TYPES
							    or state.distributed_type == DistributedType.MULTI_NPU
							    or state.distributed_type == DistributedType.MULTI_XPU
							):
														_UpperCAmelCase						:		List[str]      =				rng_state.to(state.device	)
														torch.distributed.broadcast(snake_case_ ,     0	)
														_UpperCAmelCase						:		Dict      =				rng_state.cpu()
							elif state.distributed_type == DistributedType.MULTI_CPU:
														torch.distributed.broadcast(snake_case_ ,     0	)

							# Set the broadcast rng state
							if rng_type == RNGType.TORCH:
														torch.set_rng_state(snake_case_	)
							elif rng_type == RNGType.CUDA:
														torch.cuda.set_rng_state(snake_case_	)
							elif rng_type == RNGType.NPU:
														torch.npu.set_rng_state(snake_case_	)
							elif rng_type == RNGType.XPU:
														torch.xpu.set_rng_state(snake_case_	)
							elif rng_type == RNGType.XLA:
														xm.set_rng_state(rng_state.item()	)
							elif rng_type == RNGType.GENERATOR:
														generator.set_state(snake_case_	)




def 					snake_case_      (  lowerCAmelCase_ ,     lowerCAmelCase_ = None	)->	Tuple:





							'''simple docstring'''


							for rng_type in rng_types:
														synchronize_rng_state(RNGType(snake_case_	) ,     generator=snake_case_	)








'''simple docstring'''






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

import os
from pathlib import Path




def 					snake_case_      (  lowerCAmelCase_ ,     lowerCAmelCase_ ,     lowerCAmelCase_ ,     lowerCAmelCase_	)->	str:





       '''simple docstring'''


       _UpperCAmelCase						:		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]
       _UpperCAmelCase						:		List[str]      =				{
           """wmt19-de-en-6-6-base""": [0, 3_8.3_7],
           """wmt19-de-en-6-6-big""": [0, 3_9.9_0],
       }
       _UpperCAmelCase						:		str      =				F'''{src_lang}-{tgt_lang}'''

       _UpperCAmelCase						:		List[Any]      =				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						:		Optional[int]      =				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_							:				Dict						=			Path(__file__).resolve().parent.parent.parent
A_							:				Tuple						=			repo_dir / """model_cards"""

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



