
"""simple docstring"""







from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


lowerCamelCase__        =		logging.get_logger(__name__)




class 			A__	(     SCREAMING_SNAKE_CASE__):
		A_						:	Tuple           =     ['input_features', 'attention_mask']

		def __init__(				self	,		_SCREAMING_SNAKE_CASE=80	,		_SCREAMING_SNAKE_CASE=1_60_00	,		_SCREAMING_SNAKE_CASE=0.0	,		_SCREAMING_SNAKE_CASE=10	,		_SCREAMING_SNAKE_CASE=25	,		_SCREAMING_SNAKE_CASE="hamming_window"	,		_SCREAMING_SNAKE_CASE=3_27_68.0	,		_SCREAMING_SNAKE_CASE=0.97	,		_SCREAMING_SNAKE_CASE=1.0	,		_SCREAMING_SNAKE_CASE=True	,		_SCREAMING_SNAKE_CASE=True	,		_SCREAMING_SNAKE_CASE=False	,		**_SCREAMING_SNAKE_CASE	,		):
				super().__init__(feature_size=_SCREAMING_SNAKE_CASE	,		sampling_rate=_SCREAMING_SNAKE_CASE	,		padding_value=_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			)

				__lowerCAmelCase      :						Any						      =				feature_size
				__lowerCAmelCase      :						Optional[int]						      =				sampling_rate
				__lowerCAmelCase      :						List[Any]						      =				padding_value
				__lowerCAmelCase      :						List[Any]						      =				hop_length
				__lowerCAmelCase      :						List[str]						      =				win_length
				__lowerCAmelCase      :						Union[str, Any]						      =				frame_signal_scale
				__lowerCAmelCase      :						List[Any]						      =				preemphasis_coeff
				__lowerCAmelCase      :						List[str]						      =				mel_floor
				__lowerCAmelCase      :						str						      =				normalize_means
				__lowerCAmelCase      :						Dict						      =				normalize_vars
				__lowerCAmelCase      :						Dict						      =				win_function
				__lowerCAmelCase      :						Tuple						      =				return_attention_mask

				__lowerCAmelCase      :						List[Any]						      =				win_length * sampling_rate // 10_00
				__lowerCAmelCase      :						Union[str, Any]						      =				hop_length * sampling_rate // 10_00

				__lowerCAmelCase      :						Tuple						      =				optimal_fft_length(self.sample_size			)
				__lowerCAmelCase      :						Union[str, Any]						      =				(self.n_fft // 2) + 1

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
				if self.win_function == "hamming_window":
						__lowerCAmelCase      :						Union[str, Any]						      =				window_function(window_length=self.sample_size	,		name=self.win_function	,		periodic=_SCREAMING_SNAKE_CASE			)
				else:
						__lowerCAmelCase      :						Any						      =				window_function(window_length=self.sample_size	,		name=self.win_function			)

				__lowerCAmelCase      :						Union[str, Any]						      =				mel_filter_bank(
				    num_frequency_bins=self.n_freqs	,		num_mel_filters=self.feature_size	,		min_frequency=0.0	,		max_frequency=self.sampling_rate / 2.0	,		sampling_rate=self.sampling_rate	,		)

				__lowerCAmelCase      :						str						      =				spectrogram(
				    one_waveform * self.frame_signal_scale	,		window=_SCREAMING_SNAKE_CASE	,		frame_length=self.sample_size	,		hop_length=self.sample_stride	,		fft_length=self.n_fft	,		center=_SCREAMING_SNAKE_CASE	,		preemphasis=self.preemphasis_coeff	,		mel_filters=_SCREAMING_SNAKE_CASE	,		mel_floor=self.mel_floor	,		log_mel='log'	,		)
				return msfc_features.T

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
				# make sure we normalize float32 arrays
				if self.normalize_means:
						__lowerCAmelCase      :						Optional[Any]						      =				x[:input_length].mean(axis=0			)
						__lowerCAmelCase      :						Union[str, Any]						      =				np.subtract(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
				if self.normalize_vars:
						__lowerCAmelCase      :						Dict						      =				x[:input_length].std(axis=0			)
						__lowerCAmelCase      :						Optional[Any]						      =				np.divide(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

				if input_length < x.shape[0]:
						__lowerCAmelCase      :						Optional[Any]						      =				padding_value

				# make sure array is in float32
				__lowerCAmelCase      :						Tuple						      =				x.astype(np.floataa			)

				return x

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None			):
				__lowerCAmelCase      :						Any						      =				attention_mask.sum(-1			) if attention_mask is not None else [x.shape[0] for x in input_features]
				return [self._normalize_one(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		self.padding_value			) for x, n in zip(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)]







		def __call__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		**_SCREAMING_SNAKE_CASE	,		):

				if sampling_rate is not None:
						if sampling_rate != self.sampling_rate:
								raise ValueError(
								    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
								    f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
								    f" {self.sampling_rate} and not {sampling_rate}."			)
				else:
						logger.warning(
						    'It is strongly recommended to pass the ``sampling_rate`` argument to this function. '
						    'Failing to do so can result in silent errors that might be hard to debug.'			)

				__lowerCAmelCase      :						Any						      =				isinstance(_SCREAMING_SNAKE_CASE	,		np.ndarray			) and len(raw_speech.shape			) > 1
				if is_batched_numpy and len(raw_speech.shape			) > 2:
						raise ValueError(f"Only mono-channel audio is supported for input to {self}"			)
				__lowerCAmelCase      :						int						      =				is_batched_numpy or (
				    isinstance(_SCREAMING_SNAKE_CASE	,		(list, tuple)			) and (isinstance(raw_speech[0]	,		(np.ndarray, tuple, list)			))
				)

				if is_batched:
						__lowerCAmelCase      :						Tuple						      =				[np.asarray(_SCREAMING_SNAKE_CASE	,		dtype=np.floataa			) for speech in raw_speech]
				elif not is_batched and not isinstance(_SCREAMING_SNAKE_CASE	,		np.ndarray			):
						__lowerCAmelCase      :						List[Any]						      =				np.asarray(_SCREAMING_SNAKE_CASE	,		dtype=np.floataa			)
				elif isinstance(_SCREAMING_SNAKE_CASE	,		np.ndarray			) and raw_speech.dtype is np.dtype(np.floataa			):
						__lowerCAmelCase      :						Optional[int]						      =				raw_speech.astype(np.floataa			)

				# always return batch
				if not is_batched:
						__lowerCAmelCase      :						Any						      =				[raw_speech]

				# extract fbank features
				__lowerCAmelCase      :						str						      =				[self._extract_mfsc_features(_SCREAMING_SNAKE_CASE			) for one_waveform in raw_speech]

				# convert into correct format for padding
				__lowerCAmelCase      :						Dict						      =				BatchFeature({'input_features': features}			)

				__lowerCAmelCase      :						str						      =				self.pad(
				    _SCREAMING_SNAKE_CASE	,		padding=_SCREAMING_SNAKE_CASE	,		max_length=_SCREAMING_SNAKE_CASE	,		truncation=_SCREAMING_SNAKE_CASE	,		pad_to_multiple_of=_SCREAMING_SNAKE_CASE	,		return_attention_mask=_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE	,		)
				# make sure list is in array format
				__lowerCAmelCase      :						Any						      =				padded_inputs.get('input_features'			)
				if isinstance(input_features[0]	,		_SCREAMING_SNAKE_CASE			):
						__lowerCAmelCase      :						str						      =				[np.asarray(_SCREAMING_SNAKE_CASE	,		dtype=np.floataa			) for feature in input_features]

				__lowerCAmelCase      :						Optional[int]						      =				padded_inputs.get('attention_mask'			)
				if attention_mask is not None:
						__lowerCAmelCase      :						Optional[Any]						      =				[np.asarray(_SCREAMING_SNAKE_CASE	,		dtype=np.intaa			) for array in attention_mask]

				if self.normalize_means or self.normalize_vars:
						__lowerCAmelCase      :						Optional[int]						      =				(
						    np.array(_SCREAMING_SNAKE_CASE	,		dtype=np.intaa			)
						    if self._get_padding_strategies(_SCREAMING_SNAKE_CASE	,		max_length=_SCREAMING_SNAKE_CASE			) is not PaddingStrategy.DO_NOT_PAD
						    and padding
						    else None
						)
						__lowerCAmelCase      :						int						      =				self.normalize(
						    padded_inputs['input_features']	,		attention_mask=_SCREAMING_SNAKE_CASE			)

				if return_tensors is not None:
						__lowerCAmelCase      :						Optional[Any]						      =				padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE			)

				return padded_inputs

"""simple docstring"""







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

import os
from pathlib import Path
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						str						      =				{
	    '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      :						Union[str, Any]						      =				{
	    '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      :						List[str]						      =				F"{src_lang}-{tgt_lang}"

	__lowerCAmelCase      :						Tuple						      =				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(_UpperCamelCase	,  exist_ok=_UpperCamelCase		)
	__lowerCAmelCase      :						Any						      =				os.path.join(_UpperCamelCase	,  'README.md'		)
	print(F"Generating {path}"		)
	with open(_UpperCamelCase	,  'w'	,  encoding='utf-8'		) as f:
		f.write(_UpperCamelCase		)


# make sure we are under the root of the project
lowerCamelCase__        =		Path(__file__).resolve().parent.parent.parent
lowerCamelCase__        =		repo_dir / """model_cards"""

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
							lowerCamelCase__ ,						lowerCamelCase__ ,						lowerCamelCase__        =		model_name.split("""-""")
							lowerCamelCase__        =		model_cards_dir / """facebook""" / model_name
							write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)