





import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
		from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


SCREAMING_SNAKE_CASE_      :			Union[str, Any]		  =      get_tests_dir('''fixtures/test_sentencepiece.model''')

@require_torch
@require_torchaudio
@require_sentencepiece
class 		snake_case_      (       unittest.TestCase	):







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    List[Any]      )					->   str:

			'''simple docstring'''




			__lowercase					=    tempfile.mkdtemp()

			__lowercase					=    ['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
			__lowercase					=    dict(zip(__lowerCamelCase				,			range(len(__lowerCamelCase      )      )      )      )
			__lowercase					=    Path(self.tmpdirname      )
			save_json(__lowerCamelCase				,			save_dir / VOCAB_FILES_NAMES['vocab_file']      )
			if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
					copyfile(__lowerCamelCase				,			save_dir / VOCAB_FILES_NAMES['spm_file']      )

			__lowercase					=    SpeechaTextTokenizer.from_pretrained(self.tmpdirname      )
			tokenizer.save_pretrained(self.tmpdirname      )

			__lowercase					=    {
			    'feature_size': 24,
			    'num_mel_bins': 24,
			    'padding_value': 0.0,
			    'sampling_rate': 16_000,
			    'return_attention_mask': False,
			    'do_normalize': True,
			}
			save_json(__lowerCamelCase				,			save_dir / FEATURE_EXTRACTOR_NAME      )




	def       UpperCAmelCase (	self       :    Dict				,			**__lowerCamelCase       :    Dict      )					->   Union[str, Any]:

			'''simple docstring'''




			return SpeechaTextTokenizer.from_pretrained(self.tmpdirname				,			**__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    str				,			**__lowerCamelCase       :    List[str]      )					->   Tuple:

			'''simple docstring'''




			return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname				,			**__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   str:

			'''simple docstring'''




			shutil.rmtree(self.tmpdirname      )




	def       UpperCAmelCase (	self       :    str      )					->   Any:

			'''simple docstring'''




			__lowercase					=    self.get_tokenizer()
			__lowercase					=    self.get_feature_extractor()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			processor.save_pretrained(self.tmpdirname      )
			__lowercase					=    SpeechaTextProcessor.from_pretrained(self.tmpdirname      )

			self.assertEqual(processor.tokenizer.get_vocab()				,			tokenizer.get_vocab()      )
			self.assertIsInstance(processor.tokenizer				,			__lowerCamelCase      )

			self.assertEqual(processor.feature_extractor.to_json_string()				,			feature_extractor.to_json_string()      )
			self.assertIsInstance(processor.feature_extractor				,			__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    List[str]      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase					=    SpeechaTextProcessor(
			    tokenizer=self.get_tokenizer()				,			feature_extractor=self.get_feature_extractor()      )
			processor.save_pretrained(self.tmpdirname      )

			__lowercase					=    self.get_tokenizer(bos_token='(BOS)'				,			eos_token='(EOS)'      )
			__lowercase					=    self.get_feature_extractor(do_normalize=__lowerCamelCase				,			padding_value=1.0      )

			__lowercase					=    SpeechaTextProcessor.from_pretrained(
			    self.tmpdirname				,			bos_token='(BOS)'				,			eos_token='(EOS)'				,			do_normalize=__lowerCamelCase				,			padding_value=1.0      )

			self.assertEqual(processor.tokenizer.get_vocab()				,			tokenizer_add_kwargs.get_vocab()      )
			self.assertIsInstance(processor.tokenizer				,			__lowerCamelCase      )

			self.assertEqual(processor.feature_extractor.to_json_string()				,			feature_extractor_add_kwargs.to_json_string()      )
			self.assertIsInstance(processor.feature_extractor				,			__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Dict      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			__lowercase					=    floats_list((3, 1_000)      )

			__lowercase					=    feature_extractor(__lowerCamelCase				,			return_tensors='np'      )
			__lowercase					=    processor(__lowerCamelCase				,			return_tensors='np'      )

			for key in input_feat_extract.keys():
					self.assertAlmostEqual(input_feat_extract[key].sum()				,			input_processor[key].sum()				,			delta=1E-2      )




	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			__lowercase					=    'This is a test string'

			__lowercase					=    processor(text=__lowerCamelCase      )

			__lowercase					=    tokenizer(__lowerCamelCase      )

			for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]				,			encoded_processor[key]      )




	def       UpperCAmelCase (	self       :    str      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			__lowercase					=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			__lowercase					=    processor.batch_decode(__lowerCamelCase      )
			__lowercase					=    tokenizer.batch_decode(__lowerCamelCase      )

			self.assertListEqual(__lowerCamelCase				,			__lowerCamelCase      )






	def       UpperCAmelCase (	self       :    Tuple      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			self.assertListEqual(
			    processor.model_input_names				,			feature_extractor.model_input_names				,			msg='`processor` and `feature_extractor` model input names do not match'				,			)











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

import os
from pathlib import Path



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case	,      snake_case       )		->						Optional[Any]:
		__lowercase					=    {
		    '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]
		__lowercase					=    {
		    'wmt19-de-en-6-6-base': [0, 38.37],
		    'wmt19-de-en-6-6-big': [0, 39.90],
		}
		__lowercase					=    F"{src_lang}-{tgt_lang}"

		__lowercase					=    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=snake_case	,      exist_ok=snake_case       )
		__lowercase					=    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
SCREAMING_SNAKE_CASE_      :			Tuple		  =      Path(__file__).resolve().parent.parent.parent
SCREAMING_SNAKE_CASE_      :			Any		  =      repo_dir / '''model_cards'''

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
		SCREAMING_SNAKE_CASE_      :			Any		  =      model_cards_dir / '''allenai''' / model_name
		write_model_card(model_card_dir, src_lang='''de''', tgt_lang='''en''', model_name=model_name)




