

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

import os
from pathlib import Path

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     Tuple     ,					lowerCAmelCase:     Any     ,					lowerCAmelCase:     str     ,					lowerCAmelCase:     List[str]       )      ->				Any:
				_UpperCAmelCase				:						Tuple       						=			{
				    "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				:						int       						=			{
				    "wmt19-de-en-6-6-base": [0, 38.37],
				    "wmt19-de-en-6-6-big": [0, 39.90],
				}
				_UpperCAmelCase				:						Union[str, Any]       						=			F'{src_lang}-{tgt_lang}'

				_UpperCAmelCase				:						Dict       						=			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=lowerCAmelCase     ,					exist_ok=lowerCAmelCase       )
				_UpperCAmelCase				:						List[str]       						=			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
SCREAMING_SNAKE_CASE_						=					Path(__file__).resolve().parent.parent.parent
SCREAMING_SNAKE_CASE_						=					repo_dir / 'model_cards'

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
					SCREAMING_SNAKE_CASE_						=					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 concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class    UpperCamelCase     (			snake_case ,					unittest.TestCase    ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_				:    str		    =   PreTrainedTokenizerFast
	SCREAMING_SNAKE_CASE_				:    Optional[Any]		    =   False
	SCREAMING_SNAKE_CASE_				:    Optional[int]		    =   True
	SCREAMING_SNAKE_CASE_				:    int		    =   "tokenizer_file"






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Optional[int]			=					False  # because we don't have pretrained_vocab_files_map
			super().setUp()
			_lowercase							:      Tuple			=					True

			_lowercase							:      Optional[Any]			=					["""robot-test/dummy-tokenizer-fast""", """robot-test/dummy-tokenizer-wordlevel"""]
			_lowercase							:      Dict			=					"""SaulLu/dummy-tokenizer-bytelevel-bpe"""

			# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
			_lowercase							:      Dict			=					[(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

			_lowercase							:      Any			=					PreTrainedTokenizerFast.from_pretrained(model_paths[0]					)
			tokenizer.save_pretrained(self.tmpdirname					)






	def 					lowerCamelCase__ (			self					):
			# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
			# model
			pass






	def 					lowerCamelCase__ (			self					):
			# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
			# model
			pass






	def 					lowerCamelCase__ (			self					):
			# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
			# model
			pass






	def 					lowerCamelCase__ (			self					):
			# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
			pass






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      List[str]			=					self.tmpdirname
			# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""					):
							try:
									_lowercase							:      Optional[Any]			=					tempfile.mkdtemp()
									_lowercase							:      Any			=					self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_							,**UpperCAmelCase_					)

									tokenizer.save_pretrained(self.tmpdirname					)
									super().test_training_new_tokenizer()
							finally:
									# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
									# is restored
									shutil.rmtree(self.tmpdirname					)
									_lowercase							:      List[Any]			=					tmpdirname_orig






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      List[str]			=					self.tmpdirname
			# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""					):
							try:
									_lowercase							:      Optional[int]			=					tempfile.mkdtemp()
									_lowercase							:      Tuple			=					self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_							,**UpperCAmelCase_					)

									tokenizer.save_pretrained(self.tmpdirname					)
									super().test_training_new_tokenizer_with_special_tokens_change()
							finally:
									# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
									# is restored
									shutil.rmtree(self.tmpdirname					)
									_lowercase							:      Optional[int]			=					tmpdirname_orig






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      List[Any]			=					self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name					)

			_lowercase							:      List[str]			=					("""a""" for _ in range(10_00					))
			_lowercase							:      Dict			=					tokenizer.train_new_from_iterator(text_iterator=UpperCAmelCase_							,length=10_00							,vocab_size=50					)

			_lowercase							:      List[str]			=					new_tokenizer.encode("""a🤗"""					)
			self.assertEqual(UpperCAmelCase_							,[64, 1_72, 2_53, 97, 2_45]					)







@require_tokenizers
class    UpperCamelCase     (			unittest.TestCase    ):




	"""simple docstring"""






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      List[Any]			=					AutoTokenizer.from_pretrained("""bert-base-cased"""					)
			_lowercase							:      Optional[Any]			=					json.loads(tokenizer._tokenizer.to_str()					)
			_lowercase							:      Dict			=					len(UpperCAmelCase_					)

			with tempfile.TemporaryDirectory() as tmp_dir:
					# Hack to save this in the tokenizer_config.json
					_lowercase							:      str			=					["""tokenizer.4.0.0.json"""]
					tokenizer.save_pretrained(UpperCAmelCase_					)
					json.dump(UpperCAmelCase_							,open(os.path.join(UpperCAmelCase_							,"""tokenizer.4.0.0.json"""					)							,"""w"""					)					)

					# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
					_lowercase							:      int			=					AutoTokenizer.from_pretrained(UpperCAmelCase_					)
					self.assertEqual(len(UpperCAmelCase_					)							,len(UpperCAmelCase_					) + 1					)
					_lowercase							:      List[str]			=					json.loads(new_tokenizer._tokenizer.to_str()					)
					self.assertIn("""huggingface"""							,json_tokenizer["""model"""]["""vocab"""]					)

					# Will need to be adjusted if we reach v42 and this test is still here.
					# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
					shutil.move(os.path.join(UpperCAmelCase_							,"""tokenizer.4.0.0.json"""					)							,os.path.join(UpperCAmelCase_							,"""tokenizer.42.0.0.json"""					)					)
					_lowercase							:      Optional[int]			=					["""tokenizer.42.0.0.json"""]
					tokenizer.save_pretrained(UpperCAmelCase_					)
					_lowercase							:      List[Any]			=					AutoTokenizer.from_pretrained(UpperCAmelCase_					)
					self.assertEqual(len(UpperCAmelCase_					)							,len(UpperCAmelCase_					)					)
					_lowercase							:      List[Any]			=					json.loads(new_tokenizer._tokenizer.to_str()					)
					self.assertNotIn("""huggingface"""							,json_tokenizer["""model"""]["""vocab"""]					)






	def 					lowerCamelCase__ (			self					):
			# This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
			_lowercase							:      Tuple			=					"""hf-internal-testing/test-two-tokenizers"""

			# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
			_lowercase							:      List[Any]			=					AutoTokenizer.from_pretrained(UpperCAmelCase_					)
			self.assertEqual(len(UpperCAmelCase_					)							,2_89_97					)
			_lowercase							:      List[Any]			=					json.loads(tokenizer._tokenizer.to_str()					)
			self.assertIn("""huggingface"""							,json_tokenizer["""model"""]["""vocab"""]					)

			# Testing an older version by monkey-patching the version in the module it's used.
			import transformers as old_transformers

			_lowercase							:      Optional[Any]			=					"""3.0.0"""
			_lowercase							:      int			=					old_transformers.models.auto.AutoTokenizer.from_pretrained(UpperCAmelCase_					)
			self.assertEqual(len(UpperCAmelCase_					)							,2_89_96					)
			_lowercase							:      str			=					json.loads(old_tokenizer._tokenizer.to_str()					)
			self.assertNotIn("""huggingface"""							,json_tokenizer["""model"""]["""vocab"""]					)







@require_tokenizers
class    UpperCamelCase     (			unittest.TestCase    ):




	"""simple docstring"""






	def 					lowerCamelCase__ (			self					):
			# See https://github.com/huggingface/transformers/pull/12550
			# and https://github.com/huggingface/tokenizers/issues/537
			_lowercase							:      Optional[int]			=					PreTrainedTokenizerFast.from_pretrained("""robot-test/dummy-tokenizer-wordlevel"""					)
			_lowercase							:      List[str]			=					"""The Matrix is a 1999 science fiction action film."""

			with concurrent.futures.ThreadPoolExecutor() as executor:
					_lowercase							:      Tuple			=					[executor.submit(self.fetch							,UpperCAmelCase_							,UpperCAmelCase_					) for i in range(10					)]
					_lowercase							:      str			=					[future.result() for future in futures]
					self.assertEqual(UpperCAmelCase_							,[[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10					)]					)






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_							,UpperCAmelCase_					):
			return tokenizer.encode(UpperCAmelCase_							,truncation="""longest_first"""							,padding="""longest"""					)



