

"""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


__A						       =      "sshleifer/mar_enro_6_3_student"





class    snake_case     (    __snake_case  ):

				def 							lowercase_					(     self							:    Any)->       List[str]:



						'''simple docstring'''


						super().setUp()

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

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



						'''simple docstring'''


						MarianMTModel.from_pretrained(UpperCamelCase__)




				@slow
				@require_torch_gpu
				def 							lowercase_					(     self							:    str)->       Any:



						'''simple docstring'''


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

						# bash_script = bash_script.replace("--fp16 ", "")
						__lowerCAmelCase:	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

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

						# Check metrics
						__lowerCAmelCase:	str   				=      load_json(model.metrics_save_path)
						__lowerCAmelCase:	Optional[int]   				=      metrics["val"][0]
						__lowerCAmelCase:	int   				=      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"]					,  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
						__lowerCAmelCase:	Dict   				=      os.listdir(UpperCamelCase__)
						__lowerCAmelCase:	List[str]   				=      [x for x in contents if x.endswith(".ckpt")][0]
						__lowerCAmelCase:	Any   				=      os.path.join(args.output_dir					,  UpperCamelCase__)
						__lowerCAmelCase:	int   				=      torch.load(UpperCamelCase__					,  map_location="cpu")
						__lowerCAmelCase:	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:
								__lowerCAmelCase:	int   				=      {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    snake_case     (    __snake_case  ):

				@timeout_decorator.timeout(6_0_0)
				@slow
				@require_torch_gpu
				def 							lowercase_					(     self							:    Optional[Any])->       str:



						'''simple docstring'''


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

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

								__lowerCAmelCase:	Tuple   				=      distill_main(UpperCamelCase__)

						# Check metrics
						__lowerCAmelCase:	Any   				=      load_json(model.metrics_save_path)
						__lowerCAmelCase:	Tuple   				=      metrics["val"][0]
						__lowerCAmelCase:	int   				=      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
						__lowerCAmelCase:	Tuple   				=      os.listdir(UpperCamelCase__)
						__lowerCAmelCase:	Tuple   				=      [x for x in contents if x.endswith(".ckpt")][0]
						__lowerCAmelCase:	str   				=      os.path.join(args.output_dir					,  UpperCamelCase__)
						__lowerCAmelCase:	int   				=      torch.load(UpperCamelCase__					,  map_location="cpu")
						__lowerCAmelCase:	Optional[int]   				=      "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:	Tuple   				=      {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 pathlib import Path

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






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     np.ndarray:
		__lowerCAmelCase:	List[Any]   				=      cva.getAffineTransform(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		return cva.warpAffine(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			(rows, cols)			)


if __name__ == "__main__":
			# read original image
			__A						       =      cva.imread(
			    str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
			)
			# turn image in gray scale value
			__A						       =      cva.cvtColor(image, cva.COLOR_BGR2GRAY)
			# get image shape
			__A       ,    __A						       =      gray_img.shape

			# set different points to rotate image
			__A						       =      np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
			__A						       =      np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
			__A						       =      np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
			__A						       =      np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

			# add all rotated images in a list
			__A						       =      [
			    gray_img,
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			]

			# plot different image rotations
			__A						       =      plt.figure(1)
			__A						       =      ["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
			for i, image in enumerate(images):
						plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
						plt.title(titles[i])
						plt.axis("off")
						plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
			plt.show()


