




import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


UpperCAmelCase_ 		=      logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm
  import torch_xla.debug.metrics as met





class    __lowercase						(  _snake_case ):







   def __init__(	self      ,							*UpperCamelCase      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							**UpperCamelCase      )				->    Optional[Any]:
     super().__init__(*lowerCAmelCase__      ,							**lowerCAmelCase__      )
     __a			=					eval_examples
     __a			=					post_process_function
     __a			=					quant_trainer_args
     __a			=					128  # default number of calibration samples







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase=None      )				->    Optional[int]:
     if calib_dataset is None and self.calib_dataset is None:
       raise ValueError('Trainer: calibration requires an calib_dataset.'      )
     __a			=					calib_dataset if calib_dataset is not None else self.calib_dataset

     __a			=					self._remove_unused_columns(lowerCAmelCase__      ,							description='Calibration'      )

     return DataLoader(
         lowerCAmelCase__      ,							batch_size=self.args.eval_batch_size      ,							collate_fn=self.data_collator      ,							drop_last=self.args.dataloader_drop_last      ,							num_workers=self.args.dataloader_num_workers      ,							pin_memory=self.args.dataloader_pin_memory      ,							shuffle=lowerCAmelCase__      ,							)







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase=None      )				->    int:
     __a			=					self.train_dataset if calib_dataset is None else calib_dataset
     __a			=					self.get_calib_dataloader(lowerCAmelCase__      )

     __a			=					self.model
     quant_trainer.configure_model(lowerCAmelCase__      ,							self.quant_trainer_args      ,							calib=lowerCAmelCase__      )
     model.eval()
     quant_trainer.enable_calibration(lowerCAmelCase__      )

     logger.info('***** Running calibration *****'      )
     logger.info(f"  Num examples = {self.calib_num}"      )
     logger.info(f"  Batch size = {calib_dataloader.batch_size}"      )

     for step, inputs in enumerate(lowerCAmelCase__      ):
       # Prediction step
       __a	,       __a	,       __a			=					self.prediction_step(lowerCAmelCase__      ,							lowerCAmelCase__      ,							prediction_loss_only=lowerCAmelCase__      )
       if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
         break

     quant_trainer.finish_calibration(lowerCAmelCase__      ,							self.quant_trainer_args      )
     __a			=					model







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase = "eval"      )				->    List[Any]:
     __a			=					self.eval_dataset if eval_dataset is None else eval_dataset
     __a			=					self.get_eval_dataloader(lowerCAmelCase__      )
     __a			=					self.eval_examples if eval_examples is None else eval_examples

     # Temporarily disable metric computation, we will do it in the loop here.
     __a			=					self.compute_metrics
     __a			=					None
     __a			=					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
       __a			=					eval_loop(
           lowerCAmelCase__      ,							description='Evaluation'      ,							prediction_loss_only=True if compute_metrics is None else None      ,							ignore_keys=lowerCAmelCase__      ,							)
     finally:
       __a			=					compute_metrics

     if self.post_process_function is not None and self.compute_metrics is not None:
       __a			=					self.post_process_function(lowerCAmelCase__      ,							lowerCAmelCase__      ,							output.predictions      )
       __a			=					self.compute_metrics(lowerCAmelCase__      )

       # Prefix all keys with metric_key_prefix + '_'
       for key in list(metrics.keys()      ):
         if not key.startswith(f"{metric_key_prefix}_"      ):
           __a			=					metrics.pop(lowerCAmelCase__      )

       self.log(lowerCAmelCase__      )
     else:
       __a			=					{}

     if self.args.tpu_metrics_debug or self.args.debug:
       # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
       xm.master_print(met.metrics_report()      )

     __a			=					self.callback_handler.on_evaluate(self.args      ,							self.state      ,							self.control      ,							lowerCAmelCase__      )
     return metrics







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase=None      ,							UpperCamelCase = "test"      )				->    Any:
     __a			=					self.get_test_dataloader(lowerCAmelCase__      )

     # Temporarily disable metric computation, we will do it in the loop here.
     __a			=					self.compute_metrics
     __a			=					None
     __a			=					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
       __a			=					eval_loop(
           lowerCAmelCase__      ,							description='Prediction'      ,							prediction_loss_only=True if compute_metrics is None else None      ,							ignore_keys=lowerCAmelCase__      ,							)
     finally:
       __a			=					compute_metrics

     if self.post_process_function is None or self.compute_metrics is None:
       return output

     __a			=					self.post_process_function(lowerCAmelCase__      ,							lowerCAmelCase__      ,							output.predictions      ,							'predict'      )
     __a			=					self.compute_metrics(lowerCAmelCase__      )

     # Prefix all keys with metric_key_prefix + '_'
     for key in list(metrics.keys()      ):
       if not key.startswith(f"{metric_key_prefix}_"      ):
         __a			=					metrics.pop(lowerCAmelCase__      )

     return PredictionOutput(predictions=predictions.predictions      ,							label_ids=predictions.label_ids      ,							metrics=lowerCAmelCase__      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase="./"      )				->    Optional[int]:
     __a			=					self.eval_dataset
     __a			=					self.get_eval_dataloader(lowerCAmelCase__      )

     __a			=					next(iter(lowerCAmelCase__      )      )

     # saving device - to make it consistent
     __a			=					torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )

     # convert to tuple
     __a			=					tuple(v.to(lowerCAmelCase__      ) for k, v in batch.items()      )

     logger.info('Converting model to be onnx compatible'      )
     from pytorch_quantization.nn import TensorQuantizer

     __a			=					True

     __a			=					self.model.to(lowerCAmelCase__      )

     model.eval()
     model.float()

     __a			=					model.module if hasattr(lowerCAmelCase__      ,							'module'      ) else model
     quant_trainer.configure_model(lowerCAmelCase__      ,							self.quant_trainer_args      )

     __a			=					os.path.join(lowerCAmelCase__      ,							'model.onnx'      )
     logger.info(f"exporting model to {output_model_file}"      )

     __a			=					{0: 'batch_size', 1: 'seq_len'}

     torch.onnx.export(
         lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__      ,							export_params=lowerCAmelCase__      ,							opset_version=13      ,							do_constant_folding=lowerCAmelCase__      ,							input_names=['input_ids', 'attention_mask', 'token_type_ids']      ,							output_names=['output_start_logits', 'output_end_logits']      ,							dynamic_axes={
             'input_ids': axes,
             'attention_mask': axes,
             'token_type_ids': axes,
             'output_start_logits': axes,
             'output_end_logits': axes,
         }      ,							verbose=lowerCAmelCase__      ,							)
     logger.info('onnx export finished'      )









'''simple docstring'''


# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path


def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[Any]				,					a_	:							int				,					a_	:							Tuple				,					a_	:							Tuple			):
  __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			=					{
      '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],
  }
  __a			=					f"{src_lang}-{tgt_lang}"

  __a			=					f"\n---\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt16\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt16\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt16 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\nAll 3 models are available:\n\n* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)\n* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)\n* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)\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   | fairseq | transformers\n-------|---------|----------\n{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}\n\nThe 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.\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 wmt16 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt16 -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/wmt16/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)\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=a_				,					exist_ok=a_			)
  __a			=					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
UpperCAmelCase_ 		=      Path(__file__).resolve().parent.parent.parent
UpperCAmelCase_ 		=      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"]:
  UpperCAmelCase_ 		=      model_cards_dir / "allenai" / model_name
  write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)



