import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
						from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

						from .test_feature_extraction_speechta import floats_list


lowerCAmelCase__  	=      get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")
@require_torch
class 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	def __lowerCAmelCase      (   self       ):
		_lowercase   =tempfile.mkdtemp()

		_lowercase   =SpeechTaTokenizer(lowerCAmelCase_       )
		tokenizer.save_pretrained(self.tmpdirname       )

		_lowercase   ={
		    "feature_size": 1,
		    "padding_value": 0.0,
		    "sampling_rate": 16000,
		    "do_normalize": False,
		    "num_mel_bins": 80,
		    "hop_length": 16,
		    "win_length": 64,
		    "win_function": "hann_window",
		    "fmin": 80,
		    "fmax": 7600,
		    "mel_floor": 1e-10,
		    "reduction_factor": 2,
		    "return_attention_mask": True,
		}

		_lowercase   =os.path.join(self.tmpdirname       ,  lowerCAmelCase_       )
		with open(self.feature_extraction_file       ,  "w"       ,  encoding="utf-8"       ) as fp:
			fp.write(json.dumps(lowerCAmelCase_       ) + "\n"       )


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		return SpeechTaTokenizer.from_pretrained(self.tmpdirname       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		shutil.rmtree(self.tmpdirname       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_tokenizer()
		_lowercase   =self.get_feature_extractor()

		_lowercase   =SpeechTaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		processor.save_pretrained(self.tmpdirname       )
		_lowercase   =SpeechTaProcessor.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 __lowerCAmelCase      (   self       ):
		_lowercase   =SpeechTaProcessor(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   =SpeechTaProcessor.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 __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =SpeechTaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		_lowercase   =floats_list((3, 1000)       )

		_lowercase   =feature_extractor(audio=lowerCAmelCase_       ,  return_tensors="np"       )
		_lowercase   =processor(audio=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 __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =SpeechTaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		_lowercase   =floats_list((3, 1000)       )

		_lowercase   =feature_extractor(audio_target=lowerCAmelCase_       ,  return_tensors="np"       )
		_lowercase   =processor(audio_target=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 __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =SpeechTaProcessor(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 __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =SpeechTaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		_lowercase   ="This is a test string"

		_lowercase   =processor(text_target=lowerCAmelCase_       )
		_lowercase   =tokenizer(lowerCAmelCase_       )

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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =SpeechTaProcessor(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 __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =SpeechTaProcessor(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      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							{
		    "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]
		__A    =							{
		    "wmt19-de-en-6-6-base": [0, 38.37],
		    "wmt19-de-en-6-6-big": [0, 39.90],
		}
		__A    =							F"""{src_lang}-{tgt_lang}"""

		__A    =							F"""
---

language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- allenai
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.

For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).

2 models are available:

* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)
* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)


## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"allenai/{model_name}\"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

input = \"{texts[src_lang]}\"
input_ids = tokenizer.encode(input, return_tensors=\"pt\")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}

```

#### Limitations and bias


## Training data

Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).

## Eval results

Here are the BLEU scores:

model   |  transformers
-------|---------
{model_name}  |  {scores[model_name][1]}

The score was calculated using this code:

```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=5
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH=\"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
```

## Data Sources

- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### BibTeX entry and citation info

```
@misc{{kasai2020deep,
    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
    year={{2020}},
    eprint={{2006.10369}},
    archivePrefix={{arXiv}},
    primaryClass={{cs.CL}}
}}
```

"""
		model_card_dir.mkdir(parents=lowerCAmelCase__					,     exist_ok=lowerCAmelCase__	)
		__A    =							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
snake_case_   :	Optional[int]							      =Path(__file__).resolve().parent.parent.parent
snake_case_   :	Dict							      =repo_dir / '''model_cards'''

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

