





'''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


snake_case_     =	"""sshleifer/mar_enro_6_3_student"""


class   _lowercase						(							a				):


   def 					snake_case				( self					):
       super().setUp()

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


   @slow
   @require_torch_gpu
   def 					snake_case				( self					):
       MarianMTModel.from_pretrained(_UpperCAmelCase					)


   @slow
   @require_torch_gpu
   def 					snake_case				( self					):
       A       :       List[Any]							=						{
           '''$MAX_LEN''': 64,
           '''$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
       A       :       Optional[Any]							=						(self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''					)[1].strip()
       A       :       Any							=						bash_script.replace('''\\\n'''    ,   ''''''					).strip().replace('''"$@"'''    ,   ''''''					)
       for k, v in env_vars_to_replace.items():
           A       :       str							=						bash_script.replace(_UpperCAmelCase    ,   str(_UpperCAmelCase					)					)
       A       :       str							=						self.get_auto_remove_tmp_dir()

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

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

       # Check metrics
       A       :       List[Any]							=						load_json(model.metrics_save_path					)
       A       :       List[Any]							=						metrics['''val'''][0]
       A       :       Tuple							=						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}''']    ,   _UpperCAmelCase					)

       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
       A       :       Tuple							=						os.listdir(_UpperCAmelCase					)
       A       :       int							=						[x for x in contents if x.endswith('''.ckpt'''					)][0]
       A       :       Optional[Any]							=						os.path.join(args.output_dir    ,   _UpperCAmelCase					)
       A       :       Union[str, Any]							=						torch.load(_UpperCAmelCase    ,   map_location='''cpu'''					)
       A       :       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:
           A       :       List[str]							=						{os.path.basename(_UpperCAmelCase					) 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   _lowercase						(							a				):


   @timeout_decorator.timeout(600					)
   @slow
   @require_torch_gpu
   def 					snake_case				( self					):
       A       :       List[Any]							=						f'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
       A       :       Dict							=						{
           '''--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
       A       :       Union[str, Any]							=						(
           (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''					)[1].strip()
       )
       A       :       Union[str, Any]							=						bash_script.replace('''\\\n'''    ,   ''''''					).strip().replace('''"$@"'''    ,   ''''''					)
       A       :       List[str]							=						bash_script.replace('''--fp16 '''    ,   ''' '''					)

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

           A       :       str							=						distill_main(_UpperCAmelCase					)

       # Check metrics
       A       :       Tuple							=						load_json(model.metrics_save_path					)
       A       :       List[str]							=						metrics['''val'''][0]
       A       :       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}''']    ,   _UpperCAmelCase					)

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






from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
     import PIL

snake_case_     =	logging.get_logger(__name__)
def 							_lowerCamelCase(  UpperCamelCase__    : int		)		->  List[List[ImageInput]]:
    if isinstance(UpperCamelCase__    ,       (list, tuple)		) and isinstance(videos[0]    ,       (list, tuple)		) and is_valid_image(videos[0][0]		):
        return videos

    elif isinstance(UpperCamelCase__    ,       (list, tuple)		) and is_valid_image(videos[0]		):
        return [videos]

    elif is_valid_image(UpperCamelCase__		):
        return [[videos]]

    raise ValueError(F'''Could not make batched video from {videos}'''		)





class   _lowercase						(							a				):
   _UpperCamelCase       		=   ["""pixel_values"""]


   def __init__( self    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = PILImageResampling.BILINEAR    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = 1 / 255    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       super().__init__(**_UpperCAmelCase					)
       A       :       int							=						size if size is not None else {'''shortest_edge''': 256}
       A       :       str							=						get_size_dict(_UpperCAmelCase    ,   default_to_square=_UpperCAmelCase					)
       A       :       List[Any]							=						crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       A       :       List[Any]							=						get_size_dict(_UpperCAmelCase    ,   param_name='''crop_size'''					)

       A       :       Dict							=						do_resize
       A       :       Union[str, Any]							=						size
       A       :       int							=						do_center_crop
       A       :       Union[str, Any]							=						crop_size
       A       :       int							=						resample
       A       :       Any							=						do_rescale
       A       :       Optional[Any]							=						rescale_factor
       A       :       Any							=						offset
       A       :       int							=						do_normalize
       A       :       Optional[Any]							=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       A       :       str							=						image_std if image_std is not None else IMAGENET_STANDARD_STD


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = PILImageResampling.BILINEAR    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       A       :       str							=						get_size_dict(_UpperCAmelCase    ,   default_to_square=_UpperCAmelCase					)
       if "shortest_edge" in size:
           A       :       Union[str, Any]							=						get_resize_output_image_size(_UpperCAmelCase    ,   size['''shortest_edge''']    ,   default_to_square=_UpperCAmelCase					)
       elif "height" in size and "width" in size:
           A       :       Optional[Any]							=						(size['''height'''], size['''width'''])
       else:
           raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''					)
       return resize(_UpperCAmelCase    ,   size=_UpperCAmelCase    ,   resample=_UpperCAmelCase    ,   data_format=_UpperCAmelCase    ,   **_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       A       :       Optional[int]							=						get_size_dict(_UpperCAmelCase					)
       if "height" not in size or "width" not in size:
           raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''					)
       return center_crop(_UpperCAmelCase    ,   size=(size['''height'''], size['''width'''])    ,   data_format=_UpperCAmelCase    ,   **_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       A       :       Dict							=						image.astype(np.floataa					)
       if offset:
           A       :       List[str]							=						image - (scale / 2)
       return rescale(_UpperCAmelCase    ,   scale=_UpperCAmelCase    ,   data_format=_UpperCAmelCase    ,   **_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       return normalize(_UpperCAmelCase    ,   mean=_UpperCAmelCase    ,   std=_UpperCAmelCase    ,   data_format=_UpperCAmelCase    ,   **_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = ChannelDimension.FIRST    ,   ):
       if do_resize and size is None or resample is None:
           raise ValueError('''Size and resample must be specified if do_resize is True.'''					)

       if do_center_crop and crop_size is None:
           raise ValueError('''Crop size must be specified if do_center_crop is True.'''					)

       if do_rescale and rescale_factor is None:
           raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

       if do_normalize and (image_mean is None or image_std is None):
           raise ValueError('''Image mean and std must be specified if do_normalize is True.'''					)

       if offset and not do_rescale:
           raise ValueError('''For offset, do_rescale must also be set to True.'''					)

       # All transformations expect numpy arrays.
       A       :       Dict							=						to_numpy_array(_UpperCAmelCase					)

       if do_resize:
           A       :       int							=						self.resize(image=_UpperCAmelCase    ,   size=_UpperCAmelCase    ,   resample=_UpperCAmelCase					)

       if do_center_crop:
           A       :       Optional[int]							=						self.center_crop(_UpperCAmelCase    ,   size=_UpperCAmelCase					)

       if do_rescale:
           A       :       int							=						self.rescale(image=_UpperCAmelCase    ,   scale=_UpperCAmelCase    ,   offset=_UpperCAmelCase					)

       if do_normalize:
           A       :       List[Any]							=						self.normalize(image=_UpperCAmelCase    ,   mean=_UpperCAmelCase    ,   std=_UpperCAmelCase					)

       A       :       Optional[int]							=						to_channel_dimension_format(_UpperCAmelCase    ,   _UpperCAmelCase					)
       return image


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = ChannelDimension.FIRST    ,   **_UpperCAmelCase    ,   ):
       A       :       Union[str, Any]							=						do_resize if do_resize is not None else self.do_resize
       A       :       Optional[Any]							=						resample if resample is not None else self.resample
       A       :       List[str]							=						do_center_crop if do_center_crop is not None else self.do_center_crop
       A       :       Union[str, Any]							=						do_rescale if do_rescale is not None else self.do_rescale
       A       :       Dict							=						rescale_factor if rescale_factor is not None else self.rescale_factor
       A       :       List[Any]							=						offset if offset is not None else self.offset
       A       :       str							=						do_normalize if do_normalize is not None else self.do_normalize
       A       :       List[str]							=						image_mean if image_mean is not None else self.image_mean
       A       :       Optional[Any]							=						image_std if image_std is not None else self.image_std

       A       :       List[Any]							=						size if size is not None else self.size
       A       :       Any							=						get_size_dict(_UpperCAmelCase    ,   default_to_square=_UpperCAmelCase					)
       A       :       Optional[int]							=						crop_size if crop_size is not None else self.crop_size
       A       :       int							=						get_size_dict(_UpperCAmelCase    ,   param_name='''crop_size'''					)

       if not valid_images(_UpperCAmelCase					):
           raise ValueError(
               '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
               '''torch.Tensor, tf.Tensor or jax.ndarray.'''					)

       A       :       List[Any]							=						make_batched(_UpperCAmelCase					)

       A       :       List[Any]							=						[
           [
               self._preprocess_image(
                   image=_UpperCAmelCase    ,   do_resize=_UpperCAmelCase    ,   size=_UpperCAmelCase    ,   resample=_UpperCAmelCase    ,   do_center_crop=_UpperCAmelCase    ,   crop_size=_UpperCAmelCase    ,   do_rescale=_UpperCAmelCase    ,   rescale_factor=_UpperCAmelCase    ,   offset=_UpperCAmelCase    ,   do_normalize=_UpperCAmelCase    ,   image_mean=_UpperCAmelCase    ,   image_std=_UpperCAmelCase    ,   data_format=_UpperCAmelCase    ,   )
               for img in video
           ]
           for video in videos
       ]

       A       :       Any							=						{'''pixel_values''': videos}
       return BatchFeature(data=_UpperCAmelCase    ,   tensor_type=_UpperCAmelCase					)
