



import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
snake_case     :    List[Any]						=    logging.getLogger(__name__)
def 						snake_case__							(       __lowercase							)		->		Any:







						"""simple docstring"""

						np.random.seed(args.seed							)
						torch.manual_seed(args.seed							)
						if args.n_gpu > 0:
												torch.cuda.manual_seed_all(args.seed							)
def 						snake_case__							(       )		->		Dict:







						"""simple docstring"""

						A__						:				List[Any]         =			argparse.ArgumentParser()
						parser.add_argument(
						    "--model_name_or_path"				,  default=lowerCAmelCase__				,  type=lowerCAmelCase__				,  required=lowerCAmelCase__				,  )
						parser.add_argument("--prompt"				,  type=lowerCAmelCase__				,  default=""							)
						parser.add_argument("--length"				,  type=lowerCAmelCase__				,  default=2_0							)
						parser.add_argument("--stop_token"				,  type=lowerCAmelCase__				,  default=lowerCAmelCase__				,  help="Token at which text generation is stopped"							)
						parser.add_argument(
						    "--temperature"				,  type=lowerCAmelCase__				,  default=1.0				,  help="temperature of 1.0 has no effect, lower tend toward greedy sampling"				,  )
						parser.add_argument(
						    "--repetition_penalty"				,  type=lowerCAmelCase__				,  default=1.0				,  help="primarily useful for CTRL model; in that case, use 1.2"							)
						parser.add_argument("--k"				,  type=lowerCAmelCase__				,  default=0							)
						parser.add_argument("--penalty_alpha"				,  type=lowerCAmelCase__				,  default=0.0							)
						parser.add_argument("--p"				,  type=lowerCAmelCase__				,  default=0.9							)

						parser.add_argument("--prefix"				,  type=lowerCAmelCase__				,  default=""				,  help="Text added prior to input."							)
						parser.add_argument("--padding_text"				,  type=lowerCAmelCase__				,  default=""				,  help="Deprecated, the use of `--prefix` is preferred."							)
						parser.add_argument("--xlm_language"				,  type=lowerCAmelCase__				,  default=""				,  help="Optional language when used with the XLM model."							)

						parser.add_argument("--seed"				,  type=lowerCAmelCase__				,  default=4_2				,  help="random seed for initialization"							)
						parser.add_argument("--no_cuda"				,  action="store_true"				,  help="Avoid using CUDA when available"							)
						parser.add_argument(
						    "--fp16"				,  action="store_true"				,  help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"				,  )
						A__						:				Optional[Any]         =			parser.parse_args()

						A__						:				List[Any]         =			torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"							)
						A__						:				List[str]         =			0 if args.no_cuda else torch.cuda.device_count()

						logger.warning(F'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'							)

						set_seed(lowerCAmelCase__							)

						# Initialize the model and tokenizer
						A__						:				Dict         =			AutoTokenizer.from_pretrained(args.model_name_or_path							)
						A__						:				Union[str, Any]         =			AutoModelForCausalLM.from_pretrained(args.model_name_or_path							)

						# tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
						# model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
						model.to(args.device							)

						if args.fpaa:
												model.half()

						logger.info(lowerCAmelCase__							)
						A__						:				Optional[int]         =			args.prompt if args.prompt else input("Model prompt >>> "							)

						A__						:				Dict         =			tokenizer(lowerCAmelCase__				,  return_tensors="pt"				,  add_special_tokens=lowerCAmelCase__							)
						A__						:				Tuple         =			{key: value.to(args.device							) for key, value in inputs.items()}

						A__						:				int         =			model.generate(
						    **lowerCAmelCase__				,  max_length=args.length + len(inputs["input_ids"][0]							)				,  penalty_alpha=args.penalty_alpha				,  top_k=args.k				,  )

						A__						:				List[str]         =			[]
						for generated_sequence_idx, generated_sequence in enumerate(lowerCAmelCase__							):
												print(F'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='							)
												A__						:				Optional[int]         =			generated_sequence.tolist()

												# Decode text
												A__						:				str         =			tokenizer.decode(lowerCAmelCase__				,  clean_up_tokenization_spaces=lowerCAmelCase__				,  add_special_tokens=lowerCAmelCase__							)

												# Remove all text after the stop token
												A__						:				Tuple         =			text[: text.find(args.stop_token							) if args.stop_token else None]

												# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
												A__						:				Optional[Any]         =			(
												    prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0]				,  clean_up_tokenization_spaces=lowerCAmelCase__							)							) :]
												)

												generated_sequences.append(lowerCAmelCase__							)
												print(lowerCAmelCase__							)

						return generated_sequences


if __name__ == "__main__":
		main()







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

import os
from pathlib import Path



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							{
		    "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]
		__A    =							{
		    "wmt19-de-en-6-6-base": [0, 38.37],
		    "wmt19-de-en-6-6-big": [0, 39.90],
		}
		__A    =							F"""{src_lang}-{tgt_lang}"""

		__A    =							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__	)
		__A    =							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
snake_case_   :	Optional[int]							      =Path(__file__).resolve().parent.parent.parent
snake_case_   :	Dict							      =repo_dir / '''model_cards'''

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

