
'''simple docstring'''



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

import os
from pathlib import Path
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       Optional[Any]:




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

   _SCREAMING_SNAKE_CASE       	=			F"\n---\n\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- allenai\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-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.\n\nFor more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).\n\n2 models are available:\n\n* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)\n* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)\n\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"allenai/{model_name}\"\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\n## Training data\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).\n\n## Eval results\n\nHere are the BLEU scores:\n\nmodel   |  transformers\n-------|---------\n{model_name}  |  {scores[model_name][1]}\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=5\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 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\n```\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```\n@misc{{kasai2020deep,\n    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},\n    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},\n    year={{2020}},\n    eprint={{2006.10369}},\n    archivePrefix={{arXiv}},\n    primaryClass={{cs.CL}}\n}}\n```\n\n"
   model_card_dir.mkdir(parents=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__ :						List[Any]    							=					Path(__file__).resolve().parent.parent.parent
UpperCamelCase__ :						int    							=					repo_dir / "model_cards"

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
     UpperCamelCase__ :						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)








'''simple docstring'''



import argparse
import time

import torch
import transformers
from measures_util import end_measure, log_measures, start_measure
from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer

from accelerate.utils import compute_module_sizes


UpperCamelCase__ :						Tuple    							=					{
    "gpt-j-6b": {"is_causal": True, "model": "sgugger/sharded-gpt-j-6B", "tokenizer": "EleutherAI/gpt-j-6B"},
    "gpt-neox": {"is_causal": True, "model": "EleutherAI/gpt-neox-20b"},
    "opt": {"is_causal": True, "model": "facebook/opt-30b"},
    "T0pp": {"is_causal": False, "model": "bigscience/T0pp", "model_revision": "sharded"},
}

UpperCamelCase__ :						int    							=					[
    "Hello, my name is",
    "Are unicorns real? Unicorns are",
    "For the first time in several years,",
    "My name is Julien and I am",
    "The goal of life is",
    "Whenever I'm sad, I like to",
]
def 				lowerCAmelCase_		(			)       ->       str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			argparse.ArgumentParser(description="""Run and time generations on a big model using Accelerate."""  )
   parser.add_argument("""model_name"""      , type=SCREAMING_SNAKE_CASE_      , default=SCREAMING_SNAKE_CASE_      , help="""The name of the model to try."""  )
   parser.add_argument(
       """--tokenizer_name"""      , type=SCREAMING_SNAKE_CASE_      , default=SCREAMING_SNAKE_CASE_      , help="""The name of the tokenizer (if different from the model."""  )
   parser.add_argument("""--is_causal"""      , type=SCREAMING_SNAKE_CASE_      , default=SCREAMING_SNAKE_CASE_      , help="""Whether or not the model is causal."""  )
   parser.add_argument(
       """--model_revision"""      , type=SCREAMING_SNAKE_CASE_      , default=SCREAMING_SNAKE_CASE_      , help="""The revision to use for the model checkpoint."""  )
   parser.add_argument("""--torch_dtype"""      , type=SCREAMING_SNAKE_CASE_      , default=SCREAMING_SNAKE_CASE_      , help="""The dtype for the model."""  )
   parser.add_argument("""--disk_offload"""      , action="""store_true"""  )

   _SCREAMING_SNAKE_CASE       	=			parser.parse_args()

   # Sanitize args
   if args.model_name in DEFAULT_MODELS:
      _SCREAMING_SNAKE_CASE       	=			DEFAULT_MODELS[args.model_name]
      _SCREAMING_SNAKE_CASE       	=			defaults["""model"""]
      if args.tokenizer_name is None:
         _SCREAMING_SNAKE_CASE       	=			defaults.get("""tokenizer"""      , args.model_name  )
      if args.is_causal is None:
         _SCREAMING_SNAKE_CASE       	=			defaults["""is_causal"""]
      if args.model_revision is None:
         _SCREAMING_SNAKE_CASE       	=			defaults.get("""model_revision"""      , """main"""  )

   if args.is_causal is None:
      raise ValueError("""Could not infer the default for `--is_causal`, pass either True or False for it."""  )
   if args.tokenizer_name is None:
      _SCREAMING_SNAKE_CASE       	=			args.model_name
   if args.model_revision is None:
      _SCREAMING_SNAKE_CASE       	=			"""main"""

   return args
def 				lowerCAmelCase_		(			)       ->       Optional[int]:




   """simple docstring"""
   transformers.utils.logging.set_verbosity_error()
   _SCREAMING_SNAKE_CASE       	=			parse_args()

   if args.torch_dtype is None:
      _SCREAMING_SNAKE_CASE       	=			AutoConfig.from_pretrained(args.model_name  )
      _SCREAMING_SNAKE_CASE       	=			getattr(SCREAMING_SNAKE_CASE_      , """torch_dtype"""      , torch.floataa  )
   else:
      _SCREAMING_SNAKE_CASE       	=			getattr(SCREAMING_SNAKE_CASE_      , args.torch_dtype  )
   _SCREAMING_SNAKE_CASE       	=			AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
   _SCREAMING_SNAKE_CASE       	=			{
       """torch_dtype""": torch_dtype,
       """revision""": args.model_revision,
   }
   if args.disk_offload:
      _SCREAMING_SNAKE_CASE       	=			"""tmp_offload"""
      _SCREAMING_SNAKE_CASE       	=			True

   _SCREAMING_SNAKE_CASE       	=			start_measure()
   _SCREAMING_SNAKE_CASE       	=			model_cls.from_pretrained(args.model_name      , device_map="""auto"""      , **SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE       	=			end_measure(SCREAMING_SNAKE_CASE_  )
   log_measures(SCREAMING_SNAKE_CASE_      , """Model loading"""  )

   _SCREAMING_SNAKE_CASE       	=			compute_module_sizes(SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE       	=			{v: 0 for v in model.hf_device_map.values()}
   for module, device in model.hf_device_map.items():
      device_size[device] += module_sizes[module]
   _SCREAMING_SNAKE_CASE       	=			"""\n""".join([F"- {device}: {size // 2**20}MiB" for device, size in device_size.items()]  )
   print(F"\nTheoretical use:\n{message}"  )

   _SCREAMING_SNAKE_CASE       	=			AutoTokenizer.from_pretrained(args.tokenizer_name  )

   _SCREAMING_SNAKE_CASE       	=			start_measure()
   _SCREAMING_SNAKE_CASE       	=			[]
   _SCREAMING_SNAKE_CASE       	=			[]
   _SCREAMING_SNAKE_CASE       	=			[]
   for prompt in PROMPTS:
      _SCREAMING_SNAKE_CASE       	=			tokenizer(SCREAMING_SNAKE_CASE_      , return_tensors="""pt"""  ).to(0  )
      _SCREAMING_SNAKE_CASE       	=			inputs["""input_ids"""][0].tolist()
      _SCREAMING_SNAKE_CASE       	=			time.time()
      _SCREAMING_SNAKE_CASE       	=			model.generate(inputs["""input_ids"""]  )
      _SCREAMING_SNAKE_CASE       	=			time.time()
      _SCREAMING_SNAKE_CASE       	=			outputs[0].tolist()
      _SCREAMING_SNAKE_CASE       	=			len(SCREAMING_SNAKE_CASE_  ) if outputs[: len(SCREAMING_SNAKE_CASE_  )] != tokens else len(SCREAMING_SNAKE_CASE_  ) - len(SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       	=			after_generate - before_generate

      _SCREAMING_SNAKE_CASE       	=			tokenizer.decode(SCREAMING_SNAKE_CASE_      , skip_special_tokens=SCREAMING_SNAKE_CASE_  )
      texts_outs.append(SCREAMING_SNAKE_CASE_  )
      generation_times.append(SCREAMING_SNAKE_CASE_  )
      gen_tokens.append(SCREAMING_SNAKE_CASE_  )
      print(F"Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n"  )

   _SCREAMING_SNAKE_CASE       	=			end_measure(SCREAMING_SNAKE_CASE_  )
   log_measures(SCREAMING_SNAKE_CASE_      , """Model generation"""  )

   _SCREAMING_SNAKE_CASE       	=			[gen / tok for gen, tok in zip(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )]
   _SCREAMING_SNAKE_CASE       	=			sum(SCREAMING_SNAKE_CASE_  ) / len(SCREAMING_SNAKE_CASE_  )
   print(F"Average time of generation per token: {avg_gen:.2f}s"  )
   print(F"First generation (avg time per token): {generation_times_per_token[0]:.2f}s"  )
   _SCREAMING_SNAKE_CASE       	=			sum(generation_times_per_token[1:]  ) / (len(SCREAMING_SNAKE_CASE_  ) - 1)
   print(F"Average time of generation per token (excluding the first): {avg_gen:.2f}s"  )


if __name__ == "__main__":
     main()






