


'''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


lowercase				='sshleifer/mar_enro_6_3_student'






class    __magic_name__				(						lowerCAmelCase							):






       def      lowerCAmelCase						(     self)		->    Dict:




          '''simple docstring'''
          super().setUp()

          _UpperCAmelCase  :  Optional[Any]				       =cached_path(
              'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'    ,				extract_compressed_file=snake_case    ,				)
          _UpperCAmelCase  :  Union[str, Any]				       =f"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"






       @slow
       @require_torch_gpu
       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          MarianMTModel.from_pretrained(snake_case)






       @slow
       @require_torch_gpu
       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          _UpperCAmelCase  :  str				       ={
              '$MAX_LEN': 6_4,
              '$BS': 6_4,
              '$GAS': 1,
              '$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
          _UpperCAmelCase  :  int				       =(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py')[1].strip()
          _UpperCAmelCase  :  Optional[int]				       =bash_script.replace('\\\n'    ,				'').strip().replace('"$@"'    ,				'')
          for k, v in env_vars_to_replace.items():
             _UpperCAmelCase  :  Any				       =bash_script.replace(snake_case    ,				str(snake_case))
          _UpperCAmelCase  :  Optional[int]				       =self.get_auto_remove_tmp_dir()

          # bash_script = bash_script.replace("--fp16 ", "")
          _UpperCAmelCase  :  Optional[int]				       =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

          _UpperCAmelCase  :  Any				       =['finetune.py'] + bash_script.split() + args
          with patch.object(snake_case    ,				'argv'    ,				snake_case):
             _UpperCAmelCase  :  int				       =argparse.ArgumentParser()
             _UpperCAmelCase  :  List[str]				       =pl.Trainer.add_argparse_args(snake_case)
             _UpperCAmelCase  :  int				       =SummarizationModule.add_model_specific_args(snake_case    ,				os.getcwd())
             _UpperCAmelCase  :  str				       =parser.parse_args()
             _UpperCAmelCase  :  Tuple				       =main(snake_case)

          # Check metrics
          _UpperCAmelCase  :  Any				       =load_json(model.metrics_save_path)
          _UpperCAmelCase  :  Optional[Any]				       =metrics['val'][0]
          _UpperCAmelCase  :  Union[str, Any]				       =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}"]    ,				snake_case)

          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']    ,				1_7)

          # 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
          _UpperCAmelCase  :  Optional[Any]				       =os.listdir(snake_case)
          _UpperCAmelCase  :  Tuple				       =[x for x in contents if x.endswith('.ckpt')][0]
          _UpperCAmelCase  :  Union[str, Any]				       =os.path.join(args.output_dir    ,				snake_case)
          _UpperCAmelCase  :  Any				       =torch.load(snake_case    ,				map_location='cpu')
          _UpperCAmelCase  :  Dict				       ='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:
             _UpperCAmelCase  :  Union[str, Any]				       ={os.path.basename(snake_case) 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    __magic_name__				(						lowerCAmelCase							):






       @timeout_decorator.timeout(6_0_0)
       @slow
       @require_torch_gpu
       def      lowerCAmelCase						(     self)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =f"{self.test_file_dir_str}/test_data/wmt_en_ro"
          _UpperCAmelCase  :  int				       ={
              '--fp16_opt_level=O1': '',
              '$MAX_LEN': 1_2_8,
              '$BS': 1_6,
              '$GAS': 1,
              '$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
          _UpperCAmelCase  :  Dict				       =(
              (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py')[1].strip()
          )
          _UpperCAmelCase  :  Any				       =bash_script.replace('\\\n'    ,				'').strip().replace('"$@"'    ,				'')
          _UpperCAmelCase  :  int				       =bash_script.replace('--fp16 '    ,				' ')

          for k, v in env_vars_to_replace.items():
             _UpperCAmelCase  :  List[Any]				       =bash_script.replace(snake_case    ,				str(snake_case))
          _UpperCAmelCase  :  Optional[int]				       =self.get_auto_remove_tmp_dir()
          _UpperCAmelCase  :  Optional[Any]				       =bash_script.replace('--fp16'    ,				'')
          _UpperCAmelCase  :  Union[str, Any]				       =6
          _UpperCAmelCase  :  List[str]				       =(
              ['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(snake_case    ,				'argv'    ,				snake_case):
             _UpperCAmelCase  :  Optional[Any]				       =argparse.ArgumentParser()
             _UpperCAmelCase  :  List[Any]				       =pl.Trainer.add_argparse_args(snake_case)
             _UpperCAmelCase  :  Any				       =SummarizationDistiller.add_model_specific_args(snake_case    ,				os.getcwd())
             _UpperCAmelCase  :  Union[str, Any]				       =parser.parse_args()
             # assert args.gpus == gpus THIS BREAKS for multi_gpu

             _UpperCAmelCase  :  Optional[Any]				       =distill_main(snake_case)

          # Check metrics
          _UpperCAmelCase  :  Union[str, Any]				       =load_json(model.metrics_save_path)
          _UpperCAmelCase  :  Dict				       =metrics['val'][0]
          _UpperCAmelCase  :  List[Any]				       =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}"]    ,				snake_case)

          # check lightning ckpt can be loaded and has a reasonable statedict
          _UpperCAmelCase  :  Tuple				       =os.listdir(snake_case)
          _UpperCAmelCase  :  str				       =[x for x in contents if x.endswith('.ckpt')][0]
          _UpperCAmelCase  :  int				       =os.path.join(args.output_dir    ,				snake_case)
          _UpperCAmelCase  :  List[Any]				       =torch.load(snake_case    ,				map_location='cpu')
          _UpperCAmelCase  :  List[str]				       ='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:
             _UpperCAmelCase  :  List[str]				       ={os.path.basename(snake_case) 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 hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
 from PIL import Image
else:






 class    __magic_name__				:






        @staticmethod
        def      lowerCAmelCase						(     *snake_case    ,				**snake_case)		->    str:




           '''simple docstring'''
           pass





def lowerCamelCase__     (		__lowerCamelCase			:				Image				):







   '''simple docstring'''




   _UpperCAmelCase  :  Optional[Any]				       =hashlib.mda(image.tobytes()				)
   return m.hexdigest()[:1_0]





def lowerCamelCase__     (		__lowerCamelCase			:				Image				):







   '''simple docstring'''




   _UpperCAmelCase  :  List[str]				       =np.array(__lowerCamelCase				)
   _UpperCAmelCase  :  List[str]				       =npimg.shape
   return {"hash": hashimage(__lowerCamelCase				), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class    __magic_name__				(						unittest.TestCase							):
       UpperCAmelCase		 =dict(
           (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if MODEL_FOR_MASK_GENERATION_MAPPING else [])							)
       UpperCAmelCase		 =dict(
           (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])							)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       =MaskGenerationPipeline(model=snake_case    ,				image_processor=snake_case)
          return image_segmenter, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case)		->    Optional[Any]:




          '''simple docstring'''
          pass






       @require_tf
       @unittest.skip('Image segmentation not implemented in TF')
       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          pass






       @slow
       @require_torch
       def      lowerCAmelCase						(     self)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =pipeline('mask-generation'    ,				model='facebook/sam-vit-huge')

          _UpperCAmelCase  :  Union[str, Any]				       =image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'    ,				points_per_batch=2_5_6)

          # Shortening by hashing
          _UpperCAmelCase  :  str				       =[]
          for i, o in enumerate(outputs['masks']):
             new_outupt += [{"mask": mask_to_test_readable(snake_case), "scores": outputs["scores"][i]}]

          # fmt: off
          self.assertEqual(
              nested_simplify(snake_case    ,				decimals=4)    ,				[
                  {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.04_44},
                  {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_21},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_67},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_32},
                  {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.00_53},
                  {'mask': {'hash': 'e2d0b7a0b7', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.99_67},
                  {'mask': {'hash': '453c7844bd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_93},
                  {'mask': {'hash': '3d44f2926d', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.99_09},
                  {'mask': {'hash': '64033ddc3f', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.98_79},
                  {'mask': {'hash': '801064ff79', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.98_34},
                  {'mask': {'hash': '6172f276ef', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.97_16},
                  {'mask': {'hash': 'b49e60e084', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.96_12},
                  {'mask': {'hash': 'a811e775fd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_99},
                  {'mask': {'hash': 'a6a8ebcf4b', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_52},
                  {'mask': {'hash': '9d8257e080', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_32},
                  {'mask': {'hash': '32de6454a8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_16},
                  {'mask': {'hash': 'af3d4af2c8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_99},
                  {'mask': {'hash': '3c6db475fb', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_83},
                  {'mask': {'hash': 'c290813fb9', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_64},
                  {'mask': {'hash': 'b6f0b8f606', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_43},
                  {'mask': {'hash': '92ce16bfdf', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_43},
                  {'mask': {'hash': 'c749b25868', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_08},
                  {'mask': {'hash': 'efb6cab859', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.93_35},
                  {'mask': {'hash': '1ff2eafb30', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.93_26},
                  {'mask': {'hash': '788b798e24', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.92_62},
                  {'mask': {'hash': 'abea804f0e', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.89_99},
                  {'mask': {'hash': '7b9e8ddb73', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.89_86},
                  {'mask': {'hash': 'cd24047c8a', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.89_84},
                  {'mask': {'hash': '6943e6bcbd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.88_73},
                  {'mask': {'hash': 'b5f47c9191', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.88_71}
              ]    ,				)
          # fmt: on






       @require_torch
       @slow
       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       ='facebook/sam-vit-huge'
          _UpperCAmelCase  :  Optional[int]				       =pipeline('mask-generation'    ,				model=snake_case)

          _UpperCAmelCase  :  str				       =image_segmenter(
              'http://images.cocodataset.org/val2017/000000039769.jpg'    ,				pred_iou_thresh=1    ,				points_per_batch=2_5_6)

          # Shortening by hashing
          _UpperCAmelCase  :  Tuple				       =[]
          for i, o in enumerate(outputs['masks']):
             new_outupt += [{"mask": mask_to_test_readable(snake_case), "scores": outputs["scores"][i]}]

          self.assertEqual(
              nested_simplify(snake_case    ,				decimals=4)    ,				[
                  {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.04_44},
                  {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.02_10},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_67},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_32},
                  {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.00_53},
              ]    ,				)

