





import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


_a						=   get_tests_dir("fixtures")
_a						=   get_tests_dir("fixtures/dummy_feature_extractor_config.json")
_a						=   get_tests_dir("fixtures/dummy-config.json")






class       __A					(					unittest.TestCase				):

			'''simple docstring'''







			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	0





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h'''							)
				self.assertIsInstance(A__ ,						A__							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	AutoFeatureExtractor.from_pretrained(A__							)
				self.assertIsInstance(A__ ,						A__							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				with tempfile.TemporaryDirectory() as tmpdirname:
					lowerCamelCase__              =	WavaVecaConfig()

					# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
					lowerCamelCase__              =	AutoFeatureExtractor.from_pretrained(A__							).to_dict()

					config_dict.pop('''feature_extractor_type'''							)
					lowerCamelCase__              =	WavaVecaFeatureExtractor(**A__							)

					# save in new folder
					model_config.save_pretrained(A__							)
					config.save_pretrained(A__							)

					lowerCamelCase__              =	AutoFeatureExtractor.from_pretrained(A__							)

					# make sure private variable is not incorrectly saved
					lowerCamelCase__              =	json.loads(config.to_json_string()							)
					self.assertTrue('''_processor_class''' not in dict_as_saved							)

				self.assertIsInstance(A__ ,						A__							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	AutoFeatureExtractor.from_pretrained(A__							)
				self.assertIsInstance(A__ ,						A__							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				with self.assertRaisesRegex(
				    A__ ,						'''bert-base is not a local folder and is not a valid model identifier'''							):
					lowerCamelCase__              =	AutoFeatureExtractor.from_pretrained('''bert-base'''							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				with self.assertRaisesRegex(
				    A__ ,						r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''							):
					lowerCamelCase__              =	AutoFeatureExtractor.from_pretrained(A__ ,						revision='''aaaaaa'''							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				with self.assertRaisesRegex(
				    A__ ,						'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' ,						):
					lowerCamelCase__              =	AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model'''							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				with self.assertRaises(A__							):
					lowerCamelCase__              =	AutoFeatureExtractor.from_pretrained(
					    '''hf-internal-testing/test_dynamic_feature_extractor'''							)
				# If remote code is disabled, we can't load this config.
				with self.assertRaises(A__							):
					lowerCamelCase__              =	AutoFeatureExtractor.from_pretrained(
					    '''hf-internal-testing/test_dynamic_feature_extractor''' ,						trust_remote_code=A__							)

				lowerCamelCase__              =	AutoFeatureExtractor.from_pretrained(
				    '''hf-internal-testing/test_dynamic_feature_extractor''' ,						trust_remote_code=A__							)
				self.assertEqual(feature_extractor.__class__.__name__ ,						'''NewFeatureExtractor'''							)

				# Test feature extractor can be reloaded.
				with tempfile.TemporaryDirectory() as tmp_dir:
					feature_extractor.save_pretrained(A__							)
					lowerCamelCase__              =	AutoFeatureExtractor.from_pretrained(A__ ,						trust_remote_code=A__							)
				self.assertEqual(reloaded_feature_extractor.__class__.__name__ ,						'''NewFeatureExtractor'''							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				try:
					AutoConfig.register('''custom''' ,						A__							)
					AutoFeatureExtractor.register(A__ ,						A__							)
					# Trying to register something existing in the Transformers library will raise an error
					with self.assertRaises(A__							):
						AutoFeatureExtractor.register(A__ ,						A__							)

					# Now that the config is registered, it can be used as any other config with the auto-API
					lowerCamelCase__              =	CustomFeatureExtractor.from_pretrained(A__							)
					with tempfile.TemporaryDirectory() as tmp_dir:
						feature_extractor.save_pretrained(A__							)
						lowerCamelCase__              =	AutoFeatureExtractor.from_pretrained(A__							)
						self.assertIsInstance(A__ ,						A__							)

				finally:
					if "custom" in CONFIG_MAPPING._extra_content:
						del CONFIG_MAPPING._extra_content["custom"]
					if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
						del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				class       __A					(					lowerCAmelCase				):

							'''simple docstring'''







							lowerCAmelCase_							  =		True

				try:
					AutoConfig.register('''custom''' ,						A__							)
					AutoFeatureExtractor.register(A__ ,						A__							)
					# If remote code is not set, the default is to use local
					lowerCamelCase__              =	AutoFeatureExtractor.from_pretrained(
					    '''hf-internal-testing/test_dynamic_feature_extractor'''							)
					self.assertEqual(feature_extractor.__class__.__name__ ,						'''NewFeatureExtractor'''							)
					self.assertTrue(feature_extractor.is_local							)

					# If remote code is disabled, we load the local one.
					lowerCamelCase__              =	AutoFeatureExtractor.from_pretrained(
					    '''hf-internal-testing/test_dynamic_feature_extractor''' ,						trust_remote_code=A__							)
					self.assertEqual(feature_extractor.__class__.__name__ ,						'''NewFeatureExtractor'''							)
					self.assertTrue(feature_extractor.is_local							)

					# If remote is enabled, we load from the Hub
					lowerCamelCase__              =	AutoFeatureExtractor.from_pretrained(
					    '''hf-internal-testing/test_dynamic_feature_extractor''' ,						trust_remote_code=A__							)
					self.assertEqual(feature_extractor.__class__.__name__ ,						'''NewFeatureExtractor'''							)
					self.assertTrue(not hasattr(A__ ,						'''is_local'''							)							)

				finally:
					if "custom" in CONFIG_MAPPING._extra_content:
						del CONFIG_MAPPING._extra_content["custom"]
					if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
						del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]








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

import os
from pathlib import Path





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   Any:







	'''simple docstring'''







	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''': [2_8.3, 2_7.5_2],
	    '''wmt16-en-de-dist-6-1''': [2_7.4, 2_7.1_1],
	    '''wmt16-en-de-12-1''': [2_6.9, 2_5.7_5],
	}
	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=__snake_case ,exist_ok=__snake_case							)
	lowerCamelCase__              =	os.path.join(__snake_case ,'''README.md'''							)
	print(F'Generating {path}'							)
	with open(__snake_case ,'''w''' ,encoding='''utf-8'''							) as f:
		f.write(__snake_case							)


# make sure we are under the root of the project
_a						=   Path(__file__).resolve().parent.parent.parent
_a						=   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"]:
			_a						=   model_cards_dir / "allenai" / model_name
			write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)



