

'''simple docstring'''
import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


lowerCamelCase :      List[Any]					 =     "sshleifer/mar_enro_6_3_student"







class 							A__    (       A__  ):





  def 						A  (		self      :	Tuple )	->    Tuple:





       '''simple docstring'''






       super().setUp()

       _SCREAMING_SNAKE_CASE							    =cached_path(
           'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'							,					extract_compressed_file=_a							,					)
       _SCREAMING_SNAKE_CASE							    =f"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"





  @slow
  @require_torch_gpu
  def 						A  (		self      :	List[Any] )	->    str:





       '''simple docstring'''






       MarianMTModel.from_pretrained(_a )





  @slow
  @require_torch_gpu
  def 						A  (		self      :	int )	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    ={
           '$MAX_LEN': 64,
           '$BS': 64,
           '$GAS': 1,
           '$ENRO_DIR': self.data_dir,
           'facebook/mbart-large-cc25': MARIAN_MODEL,
           # "val_check_interval=0.25": "val_check_interval=1.0",
           '--learning_rate=3e-5': '--learning_rate 3e-4',
           '--num_train_epochs 6': '--num_train_epochs 1',
       }

       # Clean up bash script
       _SCREAMING_SNAKE_CASE							    =(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py' )[1].strip()
       _SCREAMING_SNAKE_CASE							    =bash_script.replace('\\\n'							,					'' ).strip().replace('"$@"'							,					'' )
       for k, v in env_vars_to_replace.items():
            _SCREAMING_SNAKE_CASE							    =bash_script.replace(_a							,					str(_a ) )
       _SCREAMING_SNAKE_CASE							    =self.get_auto_remove_tmp_dir()

       # bash_script = bash_script.replace("--fp16 ", "")
       _SCREAMING_SNAKE_CASE							    =f"\n            --output_dir {output_dir}\n            --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n            --sortish_sampler\n            --do_predict\n            --gpus 1\n            --freeze_encoder\n            --n_train 40000\n            --n_val 500\n            --n_test 500\n            --fp16_opt_level O1\n            --num_sanity_val_steps 0\n            --eval_beams 2\n        ".split()
       # XXX: args.gpus > 1 : handle multi_gpu in the future

       _SCREAMING_SNAKE_CASE							    =['finetune.py'] + bash_script.split() + args
       with patch.object(_a							,					'argv'							,					_a ):
            _SCREAMING_SNAKE_CASE							    =argparse.ArgumentParser()
            _SCREAMING_SNAKE_CASE							    =pl.Trainer.add_argparse_args(_a )
            _SCREAMING_SNAKE_CASE							    =SummarizationModule.add_model_specific_args(_a							,					os.getcwd() )
            _SCREAMING_SNAKE_CASE							    =parser.parse_args()
            _SCREAMING_SNAKE_CASE							    =main(_a )

       # Check metrics
       _SCREAMING_SNAKE_CASE							    =load_json(model.metrics_save_path )
       _SCREAMING_SNAKE_CASE							    =metrics['val'][0]
       _SCREAMING_SNAKE_CASE							    =metrics['val'][-1]
       self.assertEqual(len(metrics['val'] )							,					(args.max_epochs / args.val_check_interval) )
       assert isinstance(last_step_stats[f"val_avg_{model.val_metric}"]							,					_a )

       self.assertGreater(last_step_stats['val_avg_gen_time']							,					0.01 )
       # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
       self.assertLessEqual(last_step_stats['val_avg_gen_time']							,					1.0 )

       # test learning requirements:

       # 1. BLEU improves over the course of training by more than 2 pts
       self.assertGreater(last_step_stats['val_avg_bleu'] - first_step_stats['val_avg_bleu']							,					2 )

       # 2. BLEU finishes above 17
       self.assertGreater(last_step_stats['val_avg_bleu']							,					17 )

       # 3. test BLEU and val BLEU within ~1.1 pt.
       self.assertLess(abs(metrics['val'][-1]['val_avg_bleu'] - metrics['test'][-1]['test_avg_bleu'] )							,					1.1 )

       # check lightning ckpt can be loaded and has a reasonable statedict
       _SCREAMING_SNAKE_CASE							    =os.listdir(_a )
       _SCREAMING_SNAKE_CASE							    =[x for x in contents if x.endswith('.ckpt' )][0]
       _SCREAMING_SNAKE_CASE							    =os.path.join(args.output_dir							,					_a )
       _SCREAMING_SNAKE_CASE							    =torch.load(_a							,					map_location='cpu' )
       _SCREAMING_SNAKE_CASE							    ='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
       assert expected_key in ckpt["state_dict"]
       assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

       # TODO: turn on args.do_predict when PL bug fixed.
       if args.do_predict:
            _SCREAMING_SNAKE_CASE							    ={os.path.basename(_a ) for p in contents}
            assert "test_generations.txt" in contents
            assert "test_results.txt" in contents
            # assert len(metrics["val"]) ==  desired_n_evals
            assert len(metrics['test'] ) == 1







class 							A__    (       A__  ):





  @timeout_decorator.timeout(600 )
  @slow
  @require_torch_gpu
  def 						A  (		self      :	List[str] )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =f"{self.test_file_dir_str}/test_data/wmt_en_ro"
       _SCREAMING_SNAKE_CASE							    ={
           '--fp16_opt_level=O1': '',
           '$MAX_LEN': 128,
           '$BS': 16,
           '$GAS': 1,
           '$ENRO_DIR': data_dir,
           '$m': 'sshleifer/student_marian_en_ro_6_1',
           'val_check_interval=0.25': 'val_check_interval=1.0',
       }

       # Clean up bash script
       _SCREAMING_SNAKE_CASE							    =(
           (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py' )[1].strip()
       )
       _SCREAMING_SNAKE_CASE							    =bash_script.replace('\\\n'							,					'' ).strip().replace('"$@"'							,					'' )
       _SCREAMING_SNAKE_CASE							    =bash_script.replace('--fp16 '							,					' ' )

       for k, v in env_vars_to_replace.items():
            _SCREAMING_SNAKE_CASE							    =bash_script.replace(_a							,					str(_a ) )
       _SCREAMING_SNAKE_CASE							    =self.get_auto_remove_tmp_dir()
       _SCREAMING_SNAKE_CASE							    =bash_script.replace('--fp16'							,					'' )
       _SCREAMING_SNAKE_CASE							    =6
       _SCREAMING_SNAKE_CASE							    =(
           ['distillation.py']
           + bash_script.split()
           + [
               f"--output_dir={output_dir}",
               '--gpus=1',
               '--learning_rate=1e-3',
               f"--num_train_epochs={epochs}",
               '--warmup_steps=10',
               '--val_check_interval=1.0',
               '--do_predict',
           ]
       )
       with patch.object(_a							,					'argv'							,					_a ):
            _SCREAMING_SNAKE_CASE							    =argparse.ArgumentParser()
            _SCREAMING_SNAKE_CASE							    =pl.Trainer.add_argparse_args(_a )
            _SCREAMING_SNAKE_CASE							    =SummarizationDistiller.add_model_specific_args(_a							,					os.getcwd() )
            _SCREAMING_SNAKE_CASE							    =parser.parse_args()
            # assert args.gpus == gpus THIS BREAKS for multi_gpu

            _SCREAMING_SNAKE_CASE							    =distill_main(_a )

       # Check metrics
       _SCREAMING_SNAKE_CASE							    =load_json(model.metrics_save_path )
       _SCREAMING_SNAKE_CASE							    =metrics['val'][0]
       _SCREAMING_SNAKE_CASE							    =metrics['val'][-1]
       assert len(metrics['val'] ) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

       assert last_step_stats["val_avg_gen_time"] >= 0.01

       assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
       assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
       assert isinstance(last_step_stats[f"val_avg_{model.val_metric}"]							,					_a )

       # check lightning ckpt can be loaded and has a reasonable statedict
       _SCREAMING_SNAKE_CASE							    =os.listdir(_a )
       _SCREAMING_SNAKE_CASE							    =[x for x in contents if x.endswith('.ckpt' )][0]
       _SCREAMING_SNAKE_CASE							    =os.path.join(args.output_dir							,					_a )
       _SCREAMING_SNAKE_CASE							    =torch.load(_a							,					map_location='cpu' )
       _SCREAMING_SNAKE_CASE							    ='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
       assert expected_key in ckpt["state_dict"]
       assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

       # TODO: turn on args.do_predict when PL bug fixed.
       if args.do_predict:
            _SCREAMING_SNAKE_CASE							    ={os.path.basename(_a ) for p in contents}
            assert "test_generations.txt" in contents
            assert "test_results.txt" in contents
            # assert len(metrics["val"]) ==  desired_n_evals
            assert len(metrics['test'] ) == 1





"""simple docstring"""

import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt




class        _UpperCAmelCase       :

 def __init__(     self		:       List[Any]				):
     snake_case_ :     List[str]				=	''''''
     snake_case_ :     Tuple				=	''''''
     snake_case_ :     int				=	[]
     snake_case_ :     Optional[int]				=	0
     snake_case_ :     Optional[Any]				=	256
     snake_case_ :     Tuple				=	0
     snake_case_ :     Tuple				=	0
     snake_case_ :     Optional[Any]				=	0
     snake_case_ :     Any				=	0

 def 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       List[Any]				):
     snake_case_ :     List[Any]				=	cva.imread(lowercase_  ,     0				)
     snake_case_ :     Tuple				=	copy.deepcopy(self.img				)
     snake_case_,	snake_case_,	snake_case_ :     List[Any]				=	plt.hist(self.img.ravel()  ,     256  ,     [0, 256]  ,     label='''x'''				)
     snake_case_ :     str				=	np.sum(lowercase_				)
     for i in range(len(lowercase_				)				):
         snake_case_ :     Optional[Any]				=	x[i] / self.k
         self.sk += prk
         snake_case_ :     Any				=	(self.L - 1) * self.sk
         if self.rem != 0:
             snake_case_ :     Dict				=	int(last % last				)
         snake_case_ :     Union[str, Any]				=	int(last + 1 if self.rem >= 0.5 else last				)
         self.last_list.append(lowercase_				)
         snake_case_ :     int				=	int(np.ma.count(self.img				) / self.img[1].size				)
         snake_case_ :     Tuple				=	self.img[1].size
     for i in range(self.number_of_cols				):
         for j in range(self.number_of_rows				):
             snake_case_ :     Union[str, Any]				=	self.img[j][i]
             if num != self.last_list[num]:
                 snake_case_ :     List[str]				=	self.last_list[num]
     cva.imwrite('''output_data/output.jpg'''  ,     self.img				)

 def 						_snake_case					(     self		:       Tuple				):
     plt.hist(self.img.ravel()  ,     256  ,     [0, 256]				)






 def 						_snake_case					(     self		:       int				):
     cva.imshow('''Output-Image'''  ,     self.img				)
     cva.imshow('''Input-Image'''  ,     self.original_image				)
     cva.waitKey(5000				)
     cva.destroyAllWindows()


if __name__ == "__main__":
     lowercase__    :  Any							    =	os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
     lowercase__    :  Any							    =	ConstantStretch()
     stretcher.stretch(file_path)
     stretcher.plot_histogram()
     stretcher.show_image()





