



"""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  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

								super().setUp()

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





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







								'''simple docstring'''

								MarianMTModel.from_pretrained(_lowercase )





		@slow
		@require_torch_gpu
		def  UpperCAmelCase__				(       self )			->    int:







								'''simple docstring'''

								snake_case_ :			Tuple            =      {
								    """$MAX_LEN""": 6_4,
								    """$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
								snake_case_ :			List[str]            =      (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py""" )[1].strip()
								snake_case_ :			List[str]            =      bash_script.replace("""\\\n"""				,     """""" ).strip().replace("""\"$@\""""				,     """""" )
								for k, v in env_vars_to_replace.items():
														snake_case_ :			Union[str, Any]            =      bash_script.replace(_lowercase				,     str(_lowercase ) )
								snake_case_ :			List[Any]            =      self.get_auto_remove_tmp_dir()

								# bash_script = bash_script.replace("--fp16 ", "")
								snake_case_ :			Optional[Any]            =      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

								snake_case_ :			Union[str, Any]            =      ["""finetune.py"""] + bash_script.split() + args
								with patch.object(_lowercase				,     """argv"""				,     _lowercase ):
														snake_case_ :			Dict            =      argparse.ArgumentParser()
														snake_case_ :			List[Any]            =      pl.Trainer.add_argparse_args(_lowercase )
														snake_case_ :			List[Any]            =      SummarizationModule.add_model_specific_args(_lowercase				,     os.getcwd() )
														snake_case_ :			int            =      parser.parse_args()
														snake_case_ :			List[Any]            =      main(_lowercase )

								# Check metrics
								snake_case_ :			Union[str, Any]            =      load_json(model.metrics_save_path )
								snake_case_ :			Optional[int]            =      metrics["""val"""][0]
								snake_case_ :			List[str]            =      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}']				,     _lowercase )

								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
								snake_case_ :			Optional[Any]            =      os.listdir(_lowercase )
								snake_case_ :			Union[str, Any]            =      [x for x in contents if x.endswith(""".ckpt""" )][0]
								snake_case_ :			Any            =      os.path.join(args.output_dir				,     _lowercase )
								snake_case_ :			str            =      torch.load(_lowercase				,     map_location="""cpu""" )
								snake_case_ :			Tuple            =      """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:
														snake_case_ :			Dict            =      {os.path.basename(_lowercase ) 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  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		@timeout_decorator.timeout(6_0_0 )
		@slow
		@require_torch_gpu
		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

								snake_case_ :			List[str]            =      f'{self.test_file_dir_str}/test_data/wmt_en_ro'
								snake_case_ :			List[Any]            =      {
								    """--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
								snake_case_ :			Any            =      (
								    (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py""" )[1].strip()
								)
								snake_case_ :			Union[str, Any]            =      bash_script.replace("""\\\n"""				,     """""" ).strip().replace("""\"$@\""""				,     """""" )
								snake_case_ :			int            =      bash_script.replace("""--fp16 """				,     """ """ )

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

														snake_case_ :			int            =      distill_main(_lowercase )

								# Check metrics
								snake_case_ :			Dict            =      load_json(model.metrics_save_path )
								snake_case_ :			int            =      metrics["""val"""][0]
								snake_case_ :			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}']				,     _lowercase )

								# check lightning ckpt can be loaded and has a reasonable statedict
								snake_case_ :			int            =      os.listdir(_lowercase )
								snake_case_ :			Any            =      [x for x in contents if x.endswith(""".ckpt""" )][0]
								snake_case_ :			List[str]            =      os.path.join(args.output_dir				,     _lowercase )
								snake_case_ :			Dict            =      torch.load(_lowercase				,     map_location="""cpu""" )
								snake_case_ :			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:
														snake_case_ :			Tuple            =      {os.path.basename(_lowercase ) 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 ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[Any]						=			['''pixel_values''']



			def __init__(  self			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = PILImageResampling.BICUBIC			,       __lowerCAmelCase = True			,       __lowerCAmelCase = 1 / 255			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = True			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)
					lowerCAmelCase     =    size if size is not None else {"""height""": 384, """width""": 384}
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase			,       default_to_square=__lowerCAmelCase)

					lowerCAmelCase     =    do_resize
					lowerCAmelCase     =    size
					lowerCAmelCase     =    resample
					lowerCAmelCase     =    do_rescale
					lowerCAmelCase     =    rescale_factor
					lowerCAmelCase     =    do_normalize
					lowerCAmelCase     =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
					lowerCAmelCase     =    image_std if image_std is not None else OPENAI_CLIP_STD
					lowerCAmelCase     =    do_convert_rgb



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = PILImageResampling.BICUBIC			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase			,       default_to_square=__lowerCAmelCase)
					if "height" not in size or "width" not in size:
							raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
					lowerCAmelCase     =    (size["""height"""], size["""width"""])
					return resize(__lowerCAmelCase			,       size=__lowerCAmelCase			,       resample=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					return rescale(__lowerCAmelCase			,       scale=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					return normalize(__lowerCAmelCase			,       mean=__lowerCAmelCase			,       std=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = ChannelDimension.FIRST			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    do_resize if do_resize is not None else self.do_resize
					lowerCAmelCase     =    resample if resample is not None else self.resample
					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     =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					lowerCAmelCase     =    size if size is not None else self.size
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase			,       default_to_square=__lowerCAmelCase)

					lowerCAmelCase     =    make_list_of_images(__lowerCAmelCase)

					if not valid_images(__lowerCAmelCase):
							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_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.""")

					# PIL RGBA images are converted to RGB
					if do_convert_rgb:
							lowerCAmelCase     =    [convert_to_rgb(__lowerCAmelCase) for image in images]

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

					if do_resize:
							lowerCAmelCase     =    [self.resize(image=__lowerCAmelCase			,       size=__lowerCAmelCase			,       resample=__lowerCAmelCase) for image in images]

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

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

					lowerCAmelCase     =    [to_channel_dimension_format(__lowerCAmelCase			,       __lowerCAmelCase) for image in images]

					lowerCAmelCase     =    BatchFeature(data={"""pixel_values""": images}			,       tensor_type=__lowerCAmelCase)

					return encoded_outputs




