
"""simple docstring"""
from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							def    A__						( self    ,				snake_case_				)       ->  float:
													return 0.0

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  min([-20, np.min(fft_results[1 : samplerate // 2 - 1]						)]						)
						__lowerCAmelCase     =  max([20, np.max(fft_results[1 : samplerate // 2 - 1]						)]						)
						return lowest, highest

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  512
						__lowerCAmelCase     =  [1] + [0] * (size - 1)
						__lowerCAmelCase     =  [filter_type.process(_lowerCAmelCase						) for item in inputs]

						__lowerCAmelCase     =  [0] * (samplerate - size)  # zero-padding
						outputs += filler
						__lowerCAmelCase     =  np.abs(np.fft.fft(_lowerCAmelCase						)						)
						__lowerCAmelCase     =  20 * np.logaa(_lowerCAmelCase						)

						# Frequencies on log scale from 24 to nyquist frequency
						plt.xlim(24 ,    samplerate / 2 - 1						)
						plt.xlabel("""Frequency (Hz)"""						)
						plt.xscale("""log"""						)

						# Display within reasonable bounds
						__lowerCAmelCase     =  get_bounds(_lowerCAmelCase ,    _lowerCAmelCase						)
						plt.ylim(max([-80, bounds[0]]						) ,    min([80, bounds[1]]						)						)
						plt.ylabel("""Gain (dB)"""						)

						plt.plot(_lowerCAmelCase						)
						plt.show()



def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  512
						__lowerCAmelCase     =  [1] + [0] * (size - 1)
						__lowerCAmelCase     =  [filter_type.process(_lowerCAmelCase						) for item in inputs]

						__lowerCAmelCase     =  [0] * (samplerate - size)  # zero-padding
						outputs += filler
						__lowerCAmelCase     =  np.angle(np.fft.fft(_lowerCAmelCase						)						)

						# Frequencies on log scale from 24 to nyquist frequency
						plt.xlim(24 ,    samplerate / 2 - 1						)
						plt.xlabel("""Frequency (Hz)"""						)
						plt.xscale("""log"""						)

						plt.ylim(-2 * pi ,    2 * pi						)
						plt.ylabel("""Phase shift (Radians)"""						)
						plt.plot(np.unwrap(_lowerCAmelCase ,    -2 * pi						)						)
						plt.show()


"""simple docstring"""
# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						__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""": [28.3, 27.52],
						    """wmt16-en-de-dist-6-1""": [27.4, 27.11],
						    """wmt16-en-de-12-1""": [26.9, 25.75],
						}
						__lowerCAmelCase     =  f"""{src_lang}-{tgt_lang}"""

						__lowerCAmelCase     =  f"""
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt16 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).

All 3 models are available:

* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)


## 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   | fairseq | transformers
-------|---------|----------
{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}

The 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.

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 wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -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/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)


### 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						)
						__lowerCAmelCase     =  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 ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
			SCREAMING_SNAKE_CASE_					     =	model_cards_dir / '''allenai''' / model_name
			write_model_card(model_card_dir, src_lang='''en''', tgt_lang='''de''', model_name=model_name)
