




import os
from pathlib import Path
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      {
      '''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]
  lowerCAmelCase__										=      {
      '''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],
  }
  lowerCAmelCase__										=      F"""{src_lang}-{tgt_lang}"""

  lowerCAmelCase__										=      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				)
  lowerCAmelCase__										=      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
__magic_name__  :  Union[str, Any]					=       Path(__file__).resolve().parent.parent.parent
__magic_name__  :  Optional[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"]:
      __magic_name__  :  Optional[int]					=       model_cards_dir / """allenai""" / model_name
      write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)


'''simple docstring'''




import math
import time

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


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






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			def __init__(  self			,       *__lowerCAmelCase			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       **__lowerCAmelCase):




					"""simple docstring"""


					super().__init__(*__lowerCAmelCase			,       **__lowerCAmelCase)
					lowerCAmelCase     =    eval_examples
					lowerCAmelCase     =    post_process_function



			def 				a_			(  self			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       __lowerCAmelCase = "eval"):




					"""simple docstring"""


					lowerCAmelCase     =    self.eval_dataset if eval_dataset is None else eval_dataset
					lowerCAmelCase     =    self.get_eval_dataloader(__lowerCAmelCase)
					lowerCAmelCase     =    self.eval_examples if eval_examples is None else eval_examples

					# Temporarily disable metric computation, we will do it in the loop here.
					lowerCAmelCase     =    self.compute_metrics
					lowerCAmelCase     =    None
					lowerCAmelCase     =    self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					lowerCAmelCase     =    time.time()
					try:
							lowerCAmelCase     =    eval_loop(
							    __lowerCAmelCase			,       description="""Evaluation"""			,       prediction_loss_only=True if compute_metrics is None else None			,       ignore_keys=__lowerCAmelCase			,       metric_key_prefix=__lowerCAmelCase			,       )
					finally:
							lowerCAmelCase     =    compute_metrics
					lowerCAmelCase     =    self.args.eval_batch_size * self.args.world_size
					if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
							start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
					output.metrics.update(
					    speed_metrics(
					        __lowerCAmelCase			,       __lowerCAmelCase			,       num_samples=output.num_samples			,       num_steps=math.ceil(output.num_samples / total_batch_size)			,       ))
					if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
							# Only the main node write the results by default
							lowerCAmelCase     =    self.post_process_function(__lowerCAmelCase			,       __lowerCAmelCase			,       output.predictions)
							lowerCAmelCase     =    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}_"):
											lowerCAmelCase     =    metrics.pop(__lowerCAmelCase)
							metrics.update(output.metrics)
					else:
							lowerCAmelCase     =    output.metrics

					if self.args.should_log:
							# Only the main node log the results by default
							self.log(__lowerCAmelCase)

					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())

					lowerCAmelCase     =    self.callback_handler.on_evaluate(self.args			,       self.state			,       self.control			,       __lowerCAmelCase)
					return metrics







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase=None			,       __lowerCAmelCase = "test"):




					"""simple docstring"""


					lowerCAmelCase     =    self.get_test_dataloader(__lowerCAmelCase)

					# Temporarily disable metric computation, we will do it in the loop here.
					lowerCAmelCase     =    self.compute_metrics
					lowerCAmelCase     =    None
					lowerCAmelCase     =    self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					lowerCAmelCase     =    time.time()
					try:
							lowerCAmelCase     =    eval_loop(
							    __lowerCAmelCase			,       description="""Prediction"""			,       prediction_loss_only=True if compute_metrics is None else None			,       ignore_keys=__lowerCAmelCase			,       metric_key_prefix=__lowerCAmelCase			,       )
					finally:
							lowerCAmelCase     =    compute_metrics
					lowerCAmelCase     =    self.args.eval_batch_size * self.args.world_size
					if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
							start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
					output.metrics.update(
					    speed_metrics(
					        __lowerCAmelCase			,       __lowerCAmelCase			,       num_samples=output.num_samples			,       num_steps=math.ceil(output.num_samples / total_batch_size)			,       ))

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

					lowerCAmelCase     =    self.post_process_function(__lowerCAmelCase			,       __lowerCAmelCase			,       output.predictions			,       """predict""")
					lowerCAmelCase     =    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}_"):
									lowerCAmelCase     =    metrics.pop(__lowerCAmelCase)
					metrics.update(output.metrics)
					return PredictionOutput(predictions=predictions.predictions			,       label_ids=predictions.label_ids			,       metrics=__lowerCAmelCase)




