





from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers.models.distilbert.modeling_tf_distilbert import (
          TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDistilBertForMaskedLM,
          TFDistilBertForMultipleChoice,
          TFDistilBertForQuestionAnswering,
          TFDistilBertForSequenceClassification,
          TFDistilBertForTokenClassification,
          TFDistilBertModel,
      )




class 			UpperCAmelCase					:




      def __init__(      self      :			str      ,      __snake_case      :			str      ,      )    ->				int:
            _lowerCAmelCase												=							parent
            _lowerCAmelCase												=							13
            _lowerCAmelCase												=							7
            _lowerCAmelCase												=							True
            _lowerCAmelCase												=							True
            _lowerCAmelCase												=							False
            _lowerCAmelCase												=							True
            _lowerCAmelCase												=							99
            _lowerCAmelCase												=							32
            _lowerCAmelCase												=							2
            _lowerCAmelCase												=							4
            _lowerCAmelCase												=							37
            _lowerCAmelCase												=							"""gelu"""
            _lowerCAmelCase												=							0.1
            _lowerCAmelCase												=							0.1
            _lowerCAmelCase												=							5_12
            _lowerCAmelCase												=							16
            _lowerCAmelCase												=							2
            _lowerCAmelCase												=							0.02
            _lowerCAmelCase												=							3
            _lowerCAmelCase												=							4
            _lowerCAmelCase												=							None




      def     lowercase__   (      self      :			Union[str, Any]  )    ->				Dict:
            _lowerCAmelCase												=							ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size  )

            _lowerCAmelCase												=							None
            if self.use_input_mask:
                  _lowerCAmelCase												=							random_attention_mask([self.batch_size, self.seq_length]  )

            _lowerCAmelCase												=							None
            _lowerCAmelCase												=							None
            _lowerCAmelCase												=							None
            if self.use_labels:
                  _lowerCAmelCase												=							ids_tensor([self.batch_size]      ,      self.type_sequence_label_size  )
                  _lowerCAmelCase												=							ids_tensor([self.batch_size, self.seq_length]      ,      self.num_labels  )
                  _lowerCAmelCase												=							ids_tensor([self.batch_size]      ,      self.num_choices  )

            _lowerCAmelCase												=							DistilBertConfig(
                vocab_size=self.vocab_size      ,      dim=self.hidden_size      ,      n_layers=self.num_hidden_layers      ,      n_heads=self.num_attention_heads      ,      hidden_dim=self.intermediate_size      ,      hidden_act=self.hidden_act      ,      dropout=self.hidden_dropout_prob      ,      attention_dropout=self.attention_probs_dropout_prob      ,      max_position_embeddings=self.max_position_embeddings      ,      initializer_range=self.initializer_range      ,      )

            return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




      def     lowercase__   (      self      :			List[str]      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			int      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			List[str]  )    ->				List[Any]:
            _lowerCAmelCase												=							TFDistilBertModel(config=SCREAMING_SNAKE_CASE_  )
            _lowerCAmelCase												=							{"""input_ids""": input_ids, """attention_mask""": input_mask}

            _lowerCAmelCase												=							model(SCREAMING_SNAKE_CASE_  )

            _lowerCAmelCase												=							[input_ids, input_mask]

            _lowerCAmelCase												=							model(SCREAMING_SNAKE_CASE_  )

            self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)  )




      def     lowercase__   (      self      :			Optional[int]      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			Tuple      ,      __snake_case      :			Dict      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			Optional[Any]  )    ->				Optional[Any]:
            _lowerCAmelCase												=							TFDistilBertForMaskedLM(config=SCREAMING_SNAKE_CASE_  )
            _lowerCAmelCase												=							{"""input_ids""": input_ids, """attention_mask""": input_mask}
            _lowerCAmelCase												=							model(SCREAMING_SNAKE_CASE_  )
            self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.vocab_size)  )




      def     lowercase__   (      self      :			Optional[Any]      ,      __snake_case      :			Any      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			List[str]      ,      __snake_case      :			str      ,      __snake_case      :			Dict  )    ->				Optional[Any]:
            _lowerCAmelCase												=							TFDistilBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_  )
            _lowerCAmelCase												=							{
                """input_ids""": input_ids,
                """attention_mask""": input_mask,
            }
            _lowerCAmelCase												=							model(SCREAMING_SNAKE_CASE_  )
            self.parent.assertEqual(result.start_logits.shape      ,      (self.batch_size, self.seq_length)  )
            self.parent.assertEqual(result.end_logits.shape      ,      (self.batch_size, self.seq_length)  )




      def     lowercase__   (      self      :			Optional[int]      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			int      ,      __snake_case      :			str      ,      __snake_case      :			int      ,      __snake_case      :			str      ,      __snake_case      :			List[Any]  )    ->				int:
            _lowerCAmelCase												=							self.num_labels
            _lowerCAmelCase												=							TFDistilBertForSequenceClassification(SCREAMING_SNAKE_CASE_  )
            _lowerCAmelCase												=							{"""input_ids""": input_ids, """attention_mask""": input_mask}
            _lowerCAmelCase												=							model(SCREAMING_SNAKE_CASE_  )
            self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_labels)  )




      def     lowercase__   (      self      :			Dict      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			str      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			str      ,      __snake_case      :			Tuple      ,      __snake_case      :			Optional[Any]  )    ->				List[Any]:
            _lowerCAmelCase												=							self.num_choices
            _lowerCAmelCase												=							TFDistilBertForMultipleChoice(SCREAMING_SNAKE_CASE_  )
            _lowerCAmelCase												=							tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_      ,      1  )      ,      (1, self.num_choices, 1)  )
            _lowerCAmelCase												=							tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_      ,      1  )      ,      (1, self.num_choices, 1)  )
            _lowerCAmelCase												=							{
                """input_ids""": multiple_choice_inputs_ids,
                """attention_mask""": multiple_choice_input_mask,
            }
            _lowerCAmelCase												=							model(SCREAMING_SNAKE_CASE_  )
            self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_choices)  )




      def     lowercase__   (      self      :			Tuple      ,      __snake_case      :			Dict      ,      __snake_case      :			List[Any]      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			Tuple      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			Optional[int]  )    ->				str:
            _lowerCAmelCase												=							self.num_labels
            _lowerCAmelCase												=							TFDistilBertForTokenClassification(SCREAMING_SNAKE_CASE_  )
            _lowerCAmelCase												=							{"""input_ids""": input_ids, """attention_mask""": input_mask}
            _lowerCAmelCase												=							model(SCREAMING_SNAKE_CASE_  )
            self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.num_labels)  )




      def     lowercase__   (      self      :			Optional[Any]  )    ->				int:
            _lowerCAmelCase												=							self.prepare_config_and_inputs()
            (_lowerCAmelCase)												=							config_and_inputs
            _lowerCAmelCase												=							{"""input_ids""": input_ids, """attention_mask""": input_mask}
            return config, inputs_dict




@require_tf
class 			UpperCAmelCase					(      _UpperCAmelCase							,		_UpperCAmelCase							,		unittest.TestCase       ):
      _lowercase:      Tuple											=       (
          (
              TFDistilBertModel,
              TFDistilBertForMaskedLM,
              TFDistilBertForQuestionAnswering,
              TFDistilBertForSequenceClassification,
              TFDistilBertForTokenClassification,
              TFDistilBertForMultipleChoice,
          )
          if is_tf_available()
          else None
      )
      _lowercase:      Tuple											=       (
          {
              'feature-extraction': TFDistilBertModel,
              'fill-mask': TFDistilBertForMaskedLM,
              'question-answering': TFDistilBertForQuestionAnswering,
              'text-classification': TFDistilBertForSequenceClassification,
              'token-classification': TFDistilBertForTokenClassification,
              'zero-shot': TFDistilBertForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      _lowercase:      Any											=       False
      _lowercase:      int											=       False




      def     lowercase__   (      self      :			List[str]  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							TFDistilBertModelTester(self  )
            _lowerCAmelCase												=							ConfigTester(self      ,      config_class=SCREAMING_SNAKE_CASE_      ,      dim=37  )




      def     lowercase__   (      self      :			Optional[int]  )    ->				List[Any]:
            self.config_tester.run_common_tests()




      def     lowercase__   (      self      :			Union[str, Any]  )    ->				Optional[Any]:
            _lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_distilbert_model(*SCREAMING_SNAKE_CASE_  )




      def     lowercase__   (      self      :			Optional[Any]  )    ->				Optional[Any]:
            _lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_distilbert_for_masked_lm(*SCREAMING_SNAKE_CASE_  )




      def     lowercase__   (      self      :			str  )    ->				Optional[int]:
            _lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_distilbert_for_question_answering(*SCREAMING_SNAKE_CASE_  )




      def     lowercase__   (      self      :			str  )    ->				List[str]:
            _lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_distilbert_for_sequence_classification(*SCREAMING_SNAKE_CASE_  )




      def     lowercase__   (      self      :			Any  )    ->				List[Any]:
            _lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_distilbert_for_multiple_choice(*SCREAMING_SNAKE_CASE_  )




      def     lowercase__   (      self      :			Tuple  )    ->				Tuple:
            _lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_distilbert_for_token_classification(*SCREAMING_SNAKE_CASE_  )




      @slow
      def     lowercase__   (      self      :			Optional[int]  )    ->				Tuple:
            for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]  ):
                  _lowerCAmelCase												=							TFDistilBertModel.from_pretrained(SCREAMING_SNAKE_CASE_  )
                  self.assertIsNotNone(SCREAMING_SNAKE_CASE_  )




@require_tf
class 			UpperCAmelCase					(      unittest.TestCase       ):




      @slow
      def     lowercase__   (      self      :			Any  )    ->				Tuple:
            _lowerCAmelCase												=							TFDistilBertModel.from_pretrained("""distilbert-base-uncased"""  )
            _lowerCAmelCase												=							tf.constant([[0, 1, 2, 3, 4, 5]]  )
            _lowerCAmelCase												=							model(SCREAMING_SNAKE_CASE_  )[0]

            _lowerCAmelCase												=							[1, 6, 7_68]
            self.assertEqual(output.shape      ,      SCREAMING_SNAKE_CASE_  )

            _lowerCAmelCase												=							tf.constant(
                [
                    [
                        [0.19_26_18_85, -0.13_73_29_55, 0.4_11_97_99],
                        [0.22_15_01_56, -0.07_42_26_61, 0.39_03_72_04],
                        [0.22_75_60_18, -0.0_89_64_14, 0.3_70_14_67],
                    ]
                ]  )
            tf.debugging.assert_near(output[:, :3, :3]      ,      SCREAMING_SNAKE_CASE_      ,      atol=1E-4  )













'''simple docstring'''






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

import os
from pathlib import Path
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							{
          """en""": """Machine learning is great, isn't it?""",
          """ru""": """Машинное обучение - это здорово, не так ли?""",
          """de""": """Maschinelles Lernen ist großartig, oder?""",
      }

      # BLUE scores as follows:
      # "pair": [fairseq, transformers]
      _lowerCAmelCase												=							{
          """ru-en""": ["""[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)""", """39.20"""],
          """en-ru""": ["""[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)""", """33.47"""],
          """en-de""": ["""[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)""", """42.83"""],
          """de-en""": ["""[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)""", """41.35"""],
      }
      _lowerCAmelCase												=							f"{src_lang}-{tgt_lang}"

      _lowerCAmelCase												=							f"\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\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 wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"\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- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair   | fairseq | transformers\n-------|---------|----------\n{pair}  | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\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=15\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 $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $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```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\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```bibtex\n@inproceedings{{...,\n  year={{2020}},\n  title={{Facebook FAIR's WMT19 News Translation Task Submission}},\n  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n  booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n"
      os.makedirs(lowerCAmelCase							,     exist_ok=lowerCAmelCase  )
      _lowerCAmelCase												=							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
A__    :       Optional[int]              =Path(__file__).resolve().parent.parent.parent
A__    :       Union[str, Any]              =repo_dir / '''model_cards'''

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
      A__       ,						A__       ,						A__    :       Optional[Any]              =model_name.split('''-''')
      A__    :       List[str]              =model_cards_dir / '''facebook''' / model_name
      write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)






