'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


lowercase_     		= logging.get_logger(__name__)
class   SCREAMING_SNAKE_CASE			(						__lowercase):







  """simple docstring"""
  lowercase   :      Union[str, Any]       =							['pixel_values']






  def __init__(   self		,						__A = True		,						__A = None		,						__A = 0.9		,						__A = PILImageResampling.BICUBIC		,						__A = True		,						__A = None		,						__A = 1 / 255		,						__A = True		,						__A = True		,						__A = None		,						__A = None		,						**__A		,						) ->     None:
        super().__init__(**__A				)
        _lowerCAmelCase										=size if size is not None else {'shortest_edge': 224}
        _lowerCAmelCase										=get_size_dict(__A		,						default_to_square=__A				)
        _lowerCAmelCase										=crop_size if crop_size is not None else {'height': 224, 'width': 224}
        _lowerCAmelCase										=get_size_dict(__A		,						param_name='crop_size'				)

        _lowerCAmelCase										=do_resize
        _lowerCAmelCase										=size
        _lowerCAmelCase										=crop_pct
        _lowerCAmelCase										=resample
        _lowerCAmelCase										=do_center_crop
        _lowerCAmelCase										=crop_size
        _lowerCAmelCase										=do_rescale
        _lowerCAmelCase										=rescale_factor
        _lowerCAmelCase										=do_normalize
        _lowerCAmelCase										=image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        _lowerCAmelCase										=image_std if image_std is not None else IMAGENET_DEFAULT_STD






  def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A = None		,						__A = PILImageResampling.BICUBIC		,						__A = None		,						**__A		,						) ->     np.ndarray:
        _lowerCAmelCase										=get_size_dict(__A		,						default_to_square=__A				)
        if "shortest_edge" not in size and ("height" not in size or "width" not in size):
              raise ValueError(F'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''				)
        if crop_pct is not None:
              if "shortest_edge" in size:
                    _lowerCAmelCase										=int(size['shortest_edge'] / crop_pct				)
              elif "height" in size and "width" in size:
                    if size["height"] == size["width"]:
                          _lowerCAmelCase										=int(size['height'] / crop_pct				)
                    else:
                          _lowerCAmelCase										=(int(size['height'] / crop_pct				), int(size['width'] / crop_pct				))
              else:
                    raise ValueError('Invalid size for resize: {}'.format(__A				)				)

              _lowerCAmelCase										=get_resize_output_image_size(__A		,						size=__A		,						default_to_square=__A				)
        else:
              if "shortest_edge" in size:
                    _lowerCAmelCase										=get_resize_output_image_size(__A		,						size=size['shortest_edge']		,						default_to_square=__A				)
              elif "height" in size and "width" in size:
                    _lowerCAmelCase										=(size['height'], size['width'])
              else:
                    raise ValueError('Invalid size for resize: {}'.format(__A				)				)

        return resize(__A		,						size=__A		,						resample=__A		,						data_format=__A		,						**__A				)






  def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A = None		,						**__A		,						) ->     np.ndarray:
        _lowerCAmelCase										=get_size_dict(__A				)
        if "height" not in size or "width" not in size:
              raise ValueError(F'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''				)
        return center_crop(__A		,						size=(size['height'], size['width'])		,						data_format=__A		,						**__A				)






  def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A = None		,						**__A		,						) ->     int:
        return rescale(__A		,						scale=__A		,						data_format=__A		,						**__A				)






  def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A = None		,						**__A		,						) ->     np.ndarray:
        return normalize(__A		,						mean=__A		,						std=__A		,						data_format=__A		,						**__A				)






  def 		UpperCamelCase__       (   self		,						__A		,						__A = None		,						__A = None		,						__A = None		,						__A = None		,						__A = None		,						__A = None		,						__A = None		,						__A = None		,						__A = None		,						__A = None		,						__A = None		,						__A = None		,						__A = ChannelDimension.FIRST		,						**__A		,						) ->     PIL.Image.Image:
        _lowerCAmelCase										=do_resize if do_resize is not None else self.do_resize
        _lowerCAmelCase										=crop_pct if crop_pct is not None else self.crop_pct
        _lowerCAmelCase										=resample if resample is not None else self.resample
        _lowerCAmelCase										=do_center_crop if do_center_crop is not None else self.do_center_crop
        _lowerCAmelCase										=do_rescale if do_rescale is not None else self.do_rescale
        _lowerCAmelCase										=rescale_factor if rescale_factor is not None else self.rescale_factor
        _lowerCAmelCase										=do_normalize if do_normalize is not None else self.do_normalize
        _lowerCAmelCase										=image_mean if image_mean is not None else self.image_mean
        _lowerCAmelCase										=image_std if image_std is not None else self.image_std

        _lowerCAmelCase										=size if size is not None else self.size
        _lowerCAmelCase										=get_size_dict(__A		,						default_to_square=__A				)
        _lowerCAmelCase										=crop_size if crop_size is not None else self.crop_size
        _lowerCAmelCase										=get_size_dict(__A		,						param_name='crop_size'				)

        _lowerCAmelCase										=make_list_of_images(__A				)

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

        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_pct is None:
              raise ValueError('Crop_pct 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.'				)

        # All transformations expect numpy arrays.
        _lowerCAmelCase										=[to_numpy_array(__A				) for image in images]

        if do_resize:
              _lowerCAmelCase										=[self.resize(image=__A		,						size=__A		,						crop_pct=__A		,						resample=__A				) for image in images]

        if do_center_crop:
              _lowerCAmelCase										=[self.center_crop(image=__A		,						size=__A				) for image in images]

        if do_rescale:
              _lowerCAmelCase										=[self.rescale(image=__A		,						scale=__A				) for image in images]

        if do_normalize:
              _lowerCAmelCase										=[self.normalize(image=__A		,						mean=__A		,						std=__A				) for image in images]

        _lowerCAmelCase										=[to_channel_dimension_format(__A		,						__A				) for image in images]

        _lowerCAmelCase										={'pixel_values': images}
        return BatchFeature(data=__A		,						tensor_type=__A				)






'''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   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								super().setUp()

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






		@slow
		@require_torch_gpu
		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								MarianMTModel.from_pretrained(__A				)






		@slow
		@require_torch_gpu
		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								_lowerCAmelCase										={
								    '$MAX_LEN': 64,
								    '$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
								_lowerCAmelCase										=(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'				)[1].strip()
								_lowerCAmelCase										=bash_script.replace('\\\n'		,						''				).strip().replace('"$@"'		,						''				)
								for k, v in env_vars_to_replace.items():
														_lowerCAmelCase										=bash_script.replace(__A		,						str(__A				)				)
								_lowerCAmelCase										=self.get_auto_remove_tmp_dir()

								# bash_script = bash_script.replace("--fp16 ", "")
								_lowerCAmelCase										=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

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

								# Check metrics
								_lowerCAmelCase										=load_json(model.metrics_save_path				)
								_lowerCAmelCase										=metrics['val'][0]
								_lowerCAmelCase										=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
								_lowerCAmelCase										=os.listdir(__A				)
								_lowerCAmelCase										=[x for x in contents if x.endswith('.ckpt'				)][0]
								_lowerCAmelCase										=os.path.join(args.output_dir		,						__A				)
								_lowerCAmelCase										=torch.load(__A		,						map_location='cpu'				)
								_lowerCAmelCase										='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:
														_lowerCAmelCase										={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   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		@timeout_decorator.timeout(600				)
		@slow
		@require_torch_gpu
		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
								_lowerCAmelCase										={
								    '--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
								_lowerCAmelCase										=(
								    (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'				)[1].strip()
								)
								_lowerCAmelCase										=bash_script.replace('\\\n'		,						''				).strip().replace('"$@"'		,						''				)
								_lowerCAmelCase										=bash_script.replace('--fp16 '		,						' '				)

								for k, v in env_vars_to_replace.items():
														_lowerCAmelCase										=bash_script.replace(__A		,						str(__A				)				)
								_lowerCAmelCase										=self.get_auto_remove_tmp_dir()
								_lowerCAmelCase										=bash_script.replace('--fp16'		,						''				)
								_lowerCAmelCase										=6
								_lowerCAmelCase										=(
								    ['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				):
														_lowerCAmelCase										=argparse.ArgumentParser()
														_lowerCAmelCase										=pl.Trainer.add_argparse_args(__A				)
														_lowerCAmelCase										=SummarizationDistiller.add_model_specific_args(__A		,						os.getcwd()				)
														_lowerCAmelCase										=parser.parse_args()
														# assert args.gpus == gpus THIS BREAKS for multi_gpu

														_lowerCAmelCase										=distill_main(__A				)

								# Check metrics
								_lowerCAmelCase										=load_json(model.metrics_save_path				)
								_lowerCAmelCase										=metrics['val'][0]
								_lowerCAmelCase										=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
								_lowerCAmelCase										=os.listdir(__A				)
								_lowerCAmelCase										=[x for x in contents if x.endswith('.ckpt'				)][0]
								_lowerCAmelCase										=os.path.join(args.output_dir		,						__A				)
								_lowerCAmelCase										=torch.load(__A		,						map_location='cpu'				)
								_lowerCAmelCase										='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:
														_lowerCAmelCase										={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





