


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

import os
from pathlib import Path







def  __UpperCamelCase    (						A ,							A ,							A ,							A							):
     UpperCamelCase__										=      {
         '''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__										=      {
         '''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],
     }
     UpperCamelCase__										=      f"{src_lang}-{tgt_lang}"

     UpperCamelCase__										=      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							)
     UpperCamelCase__										=      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__								=Path(__file__).resolve().parent.parent.parent
__magic_name__								=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__								=model_cards_dir / '''allenai''' / model_name
     write_model_card(model_card_dir, src_lang='''en''', tgt_lang='''de''', model_name=model_name)




import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM



@require_torch
class     _A   :






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=99   ,				SCREAMING_SNAKE_CASE_=13   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=7   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=False   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=2   ,				SCREAMING_SNAKE_CASE_=32   ,				SCREAMING_SNAKE_CASE_=4   ,				SCREAMING_SNAKE_CASE_=4   ,				SCREAMING_SNAKE_CASE_=30   ,				SCREAMING_SNAKE_CASE_=0   ,				SCREAMING_SNAKE_CASE_=1   ,				SCREAMING_SNAKE_CASE_=2   ,				SCREAMING_SNAKE_CASE_=None   ,				)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      parent
       UpperCamelCase__										=      batch_size
       UpperCamelCase__										=      decoder_seq_length
       # For common tests
       UpperCamelCase__										=      self.decoder_seq_length
       UpperCamelCase__										=      is_training
       UpperCamelCase__										=      use_attention_mask
       UpperCamelCase__										=      use_labels

       UpperCamelCase__										=      vocab_size
       UpperCamelCase__										=      d_model
       UpperCamelCase__										=      d_model
       UpperCamelCase__										=      decoder_layers
       UpperCamelCase__										=      decoder_layers
       UpperCamelCase__										=      decoder_ffn_dim
       UpperCamelCase__										=      decoder_attention_heads
       UpperCamelCase__										=      decoder_attention_heads
       UpperCamelCase__										=      eos_token_id
       UpperCamelCase__										=      bos_token_id
       UpperCamelCase__										=      pad_token_id
       UpperCamelCase__										=      decoder_start_token_id
       UpperCamelCase__										=      use_cache
       UpperCamelCase__										=      max_position_embeddings

       UpperCamelCase__										=      None
       UpperCamelCase__										=      decoder_seq_length
       UpperCamelCase__										=      2
       UpperCamelCase__										=      1






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      ids_tensor([self.batch_size, self.decoder_seq_length]   ,				self.vocab_size	)

       UpperCamelCase__										=      None
       if self.use_attention_mask:
            UpperCamelCase__										=      ids_tensor([self.batch_size, self.decoder_seq_length]   ,				vocab_size=2	)

       UpperCamelCase__										=      None
       if self.use_labels:
            UpperCamelCase__										=      ids_tensor([self.batch_size, self.decoder_seq_length]   ,				self.vocab_size	)

       UpperCamelCase__										=      TrOCRConfig(
           vocab_size=self.vocab_size   ,				d_model=self.d_model   ,				decoder_layers=self.decoder_layers   ,				decoder_ffn_dim=self.decoder_ffn_dim   ,				decoder_attention_heads=self.decoder_attention_heads   ,				eos_token_id=self.eos_token_id   ,				bos_token_id=self.bos_token_id   ,				use_cache=self.use_cache   ,				pad_token_id=self.pad_token_id   ,				decoder_start_token_id=self.decoder_start_token_id   ,				max_position_embeddings=self.max_position_embeddings   ,				)

       return (config, input_ids, attention_mask, lm_labels)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      True
       UpperCamelCase__										=      TrOCRDecoder(config=SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	).eval()
       UpperCamelCase__										=      input_ids[:2]

       input_ids[input_ids == 0] += 1
       # first forward pass
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				use_cache=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				use_cache=SCREAMING_SNAKE_CASE_	)

       self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_	) == len(SCREAMING_SNAKE_CASE_	)	)
       self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_	) == len(SCREAMING_SNAKE_CASE_	) + 1	)

       UpperCamelCase__										=      outputs['''past_key_values''']

       # create hypothetical next token and extent to next_input_ids
       UpperCamelCase__										=      ids_tensor((2, 1)   ,				config.vocab_size - 1	) + 1

       # append to next input_ids and
       UpperCamelCase__										=      torch.cat([input_ids, next_tokens]   ,				dim=-1	)

       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_	)['''last_hidden_state''']
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				past_key_values=SCREAMING_SNAKE_CASE_	)['''last_hidden_state''']

       # select random slice
       UpperCamelCase__										=      ids_tensor((1,)   ,				output_from_past.shape[-1]	).item()
       UpperCamelCase__										=      output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
       UpperCamelCase__										=      output_from_past[:, 0, random_slice_idx].detach()

       # test that outputs are equal for slice
       assert torch.allclose(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				atol=1E-3	)






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.prepare_config_and_inputs()
       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__										=      config_and_inputs

       UpperCamelCase__										=      {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict



@require_torch
class     _A   (    __UpperCamelCase	, __UpperCamelCase	, __UpperCamelCase	, unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	Tuple						=(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE_	:	Any						=(TrOCRForCausalLM,) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE_	:	Optional[int]						={"text-generation": TrOCRForCausalLM} if is_torch_available() else {}
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=True
  SCREAMING_SNAKE_CASE_	:	Dict						=False






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      TrOCRStandaloneDecoderModelTester(self   ,				is_training=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      ConfigTester(self   ,				config_class=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       self.config_tester.run_common_tests()






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past(*SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       return






  @unittest.skip('''The model doesn\'t support left padding'''	)  # and it's not used enough to be worth fixing :)
  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       pass



