


import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt
class 		__magic_name__						:





     '''simple docstring'''



     def __init__(					self:Optional[int]   ):
       snake_case__						= ''''''
       snake_case__						= ''''''
       snake_case__						= []
       snake_case__						= 0
       snake_case__						= 2_56
       snake_case__						= 0
       snake_case__						= 0
       snake_case__						= 0
       snake_case__						= 0

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:Dict   ):
       snake_case__						= cva.imread(snake_case_     ,		0   )
       snake_case__						= copy.deepcopy(self.img   )
       snake_case__				,				snake_case__				,				snake_case__						= plt.hist(self.img.ravel()     ,		2_56     ,		[0, 2_56]     ,		label='''x'''   )
       snake_case__						= np.sum(snake_case_   )
       for i in range(len(snake_case_   )   ):
         snake_case__						= x[i] / self.k
         self.sk += prk
         snake_case__						= (self.L - 1) * self.sk
         if self.rem != 0:
           snake_case__						= int(last % last   )
         snake_case__						= int(last + 1 if self.rem >= 0.5 else last   )
         self.last_list.append(snake_case_   )
         snake_case__						= int(np.ma.count(self.img   ) / self.img[1].size   )
         snake_case__						= self.img[1].size
       for i in range(self.number_of_cols   ):
         for j in range(self.number_of_rows   ):
           snake_case__						= self.img[j][i]
           if num != self.last_list[num]:
             snake_case__						= self.last_list[num]
       cva.imwrite('''output_data/output.jpg'''     ,		self.img   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       plt.hist(self.img.ravel()     ,		2_56     ,		[0, 2_56]   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       cva.imshow('''Output-Image'''     ,		self.img   )
       cva.imshow('''Input-Image'''     ,		self.original_image   )
       cva.waitKey(50_00   )
       cva.destroyAllWindows()


if __name__ == "__main__":
   lowerCamelCase__							:						Union[str, Any]							     =   os.path.join(os.path.basename(__file__), """image_data/input.jpg""")
   lowerCamelCase__							:						Dict							     =   ConstantStretch()
   stretcher.stretch(file_path)
   stretcher.plot_histogram()
   stretcher.show_image()



"""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)
