



"""simple docstring"""




import random
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	import PIL

	from transformers import FlavaImageProcessor
	from transformers.image_utils import PILImageResampling
	from transformers.models.flava.image_processing_flava import (
	    FLAVA_CODEBOOK_MEAN,
	    FLAVA_CODEBOOK_STD,
	    FLAVA_IMAGE_MEAN,
	    FLAVA_IMAGE_STD,
	)
else:
	_lowerCAmelCase       :      str							=							None






class 			UpperCAmelCase_						(       unittest.TestCase		):



						def __init__(  self  :			Union[str, Any]  ,			A  :			List[Any]  ,			A  :			List[Any]=7  ,			A  :			List[str]=3  ,			A  :			int=3_0  ,			A  :			Optional[int]=4_0_0  ,			A  :			Optional[Any]=True  ,			A  :			Optional[int]=None  ,			A  :			Union[str, Any]=True  ,			A  :			Union[str, Any]=None  ,			A  :			str=None  ,			A  :			Optional[Any]=True  ,			A  :			Union[str, Any]=1 / 2_5_5  ,			A  :			Dict=True  ,			A  :			Dict=FLAVA_IMAGE_MEAN  ,			A  :			Optional[int]=FLAVA_IMAGE_STD  ,			A  :			Tuple=1_4  ,			A  :			int=7_5  ,			A  :			str=None  ,			A  :			str=1_6  ,			A  :			str=0.3  ,			A  :			List[str]=None  ,			A  :			Tuple=True  ,			A  :			Optional[Any]=None  ,			A  :			Union[str, Any]=None  ,			A  :			Optional[Any]=True  ,			A  :			List[str]=None  ,			A  :			Any=True  ,			A  :			Any=True  ,			A  :			Optional[Any]=FLAVA_CODEBOOK_MEAN  ,			A  :			Any=FLAVA_CODEBOOK_STD  ,			):
											_UpperCAmelCase    :			Union[str, Any]              =					size if size is not None else {"height": 2_2_4, "width": 2_2_4}
											_UpperCAmelCase    :			str              =					crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
											_UpperCAmelCase    :			Tuple              =					codebook_size if codebook_size is not None else {"height": 1_1_2, "width": 1_1_2}
											_UpperCAmelCase    :			Union[str, Any]              =					codebook_crop_size if codebook_crop_size is not None else {"height": 1_1_2, "width": 1_1_2}

											_UpperCAmelCase    :			Optional[Any]              =					parent
											_UpperCAmelCase    :			Any              =					batch_size
											_UpperCAmelCase    :			Union[str, Any]              =					num_channels
											_UpperCAmelCase    :			Tuple              =					do_resize
											_UpperCAmelCase    :			str              =					do_rescale
											_UpperCAmelCase    :			int              =					rescale_factor
											_UpperCAmelCase    :			Optional[Any]              =					min_resolution
											_UpperCAmelCase    :			str              =					max_resolution
											_UpperCAmelCase    :			Optional[int]              =					size
											_UpperCAmelCase    :			int              =					resample if resample is not None else PILImageResampling.BICUBIC
											_UpperCAmelCase    :			List[Any]              =					do_normalize
											_UpperCAmelCase    :			Tuple              =					image_mean
											_UpperCAmelCase    :			Optional[int]              =					image_std
											_UpperCAmelCase    :			str              =					do_center_crop
											_UpperCAmelCase    :			Optional[Any]              =					crop_size

											_UpperCAmelCase    :			Optional[int]              =					input_size_patches
											_UpperCAmelCase    :			str              =					total_mask_patches
											_UpperCAmelCase    :			Tuple              =					mask_group_max_patches
											_UpperCAmelCase    :			Optional[Any]              =					mask_group_min_patches
											_UpperCAmelCase    :			Optional[Any]              =					mask_group_min_aspect_ratio
											_UpperCAmelCase    :			Any              =					mask_group_max_aspect_ratio

											_UpperCAmelCase    :			Any              =					codebook_do_resize
											_UpperCAmelCase    :			int              =					codebook_size
											_UpperCAmelCase    :			Optional[int]              =					codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
											_UpperCAmelCase    :			List[str]              =					codebook_do_center_crop
											_UpperCAmelCase    :			Optional[int]              =					codebook_crop_size
											_UpperCAmelCase    :			Tuple              =					codebook_do_map_pixels
											_UpperCAmelCase    :			List[Any]              =					codebook_do_normalize
											_UpperCAmelCase    :			List[str]              =					codebook_image_mean
											_UpperCAmelCase    :			List[str]              =					codebook_image_std



						def 		snake_case_      (  self  :			Tuple		):
											return {
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_normalize": self.do_normalize,
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "resample": self.resample,
											    "do_rescale": self.do_rescale,
											    "rescale_factor": self.rescale_factor,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											    "input_size_patches": self.input_size_patches,
											    "total_mask_patches": self.total_mask_patches,
											    "mask_group_max_patches": self.mask_group_max_patches,
											    "mask_group_min_patches": self.mask_group_min_patches,
											    "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
											    "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
											    "codebook_do_resize": self.codebook_do_resize,
											    "codebook_size": self.codebook_size,
											    "codebook_resample": self.codebook_resample,
											    "codebook_do_center_crop": self.codebook_do_center_crop,
											    "codebook_crop_size": self.codebook_crop_size,
											    "codebook_do_map_pixels": self.codebook_do_map_pixels,
											    "codebook_do_normalize": self.codebook_do_normalize,
											    "codebook_image_mean": self.codebook_image_mean,
											    "codebook_image_std": self.codebook_image_std,
											}



						def 		snake_case_      (  self  :			List[str]		):
											return (self.size["height"], self.size["width"])



						def 		snake_case_      (  self  :			str		):
											return (
											    (self.input_size_patches, self.input_size_patches)
											    if not isinstance(self.input_size_patches  ,			A		)
											    else self.input_size_patches
											)



						def 		snake_case_      (  self  :			List[Any]		):
											return (self.codebook_size["height"], self.codebook_size["width"])







@require_torch
@require_vision
class 			UpperCAmelCase_						(       _a ,    unittest.TestCase		):
						__SCREAMING_SNAKE_CASE		:     Any				  =				FlavaImageProcessor if is_vision_available() else None
						__SCREAMING_SNAKE_CASE		:     str				  =				None



						def 		snake_case_      (  self  :			Optional[int]		):
											_UpperCAmelCase    :			List[Any]              =					FlavaImageProcessingTester(self		)



						@property
						def 		snake_case_      (  self  :			int		):
											return self.image_processor_tester.prepare_image_processor_dict()



						def 		snake_case_      (  self  :			List[str]		):
											_UpperCAmelCase    :			Optional[Any]              =					self.image_processing_class(**self.image_processor_dict		)
											self.assertTrue(hasattr(A  ,			"image_mean"		)		)
											self.assertTrue(hasattr(A  ,			"image_std"		)		)
											self.assertTrue(hasattr(A  ,			"do_normalize"		)		)
											self.assertTrue(hasattr(A  ,			"do_resize"		)		)
											self.assertTrue(hasattr(A  ,			"resample"		)		)
											self.assertTrue(hasattr(A  ,			"crop_size"		)		)
											self.assertTrue(hasattr(A  ,			"do_center_crop"		)		)
											self.assertTrue(hasattr(A  ,			"do_rescale"		)		)
											self.assertTrue(hasattr(A  ,			"rescale_factor"		)		)
											self.assertTrue(hasattr(A  ,			"masking_generator"		)		)
											self.assertTrue(hasattr(A  ,			"codebook_do_resize"		)		)
											self.assertTrue(hasattr(A  ,			"codebook_size"		)		)
											self.assertTrue(hasattr(A  ,			"codebook_resample"		)		)
											self.assertTrue(hasattr(A  ,			"codebook_do_center_crop"		)		)
											self.assertTrue(hasattr(A  ,			"codebook_crop_size"		)		)
											self.assertTrue(hasattr(A  ,			"codebook_do_map_pixels"		)		)
											self.assertTrue(hasattr(A  ,			"codebook_do_normalize"		)		)
											self.assertTrue(hasattr(A  ,			"codebook_image_mean"		)		)
											self.assertTrue(hasattr(A  ,			"codebook_image_std"		)		)



						def 		snake_case_      (  self  :			Any		):
											_UpperCAmelCase    :			Optional[int]              =					self.image_processing_class.from_dict(self.image_processor_dict		)
											self.assertEqual(image_processor.size  ,			{"height": 2_2_4, "width": 2_2_4}		)
											self.assertEqual(image_processor.crop_size  ,			{"height": 2_2_4, "width": 2_2_4}		)
											self.assertEqual(image_processor.codebook_size  ,			{"height": 1_1_2, "width": 1_1_2}		)
											self.assertEqual(image_processor.codebook_crop_size  ,			{"height": 1_1_2, "width": 1_1_2}		)

											_UpperCAmelCase    :			List[str]              =					self.image_processing_class.from_dict(
											    self.image_processor_dict  ,			size=4_2  ,			crop_size=8_4  ,			codebook_size=3_3  ,			codebook_crop_size=6_6		)
											self.assertEqual(image_processor.size  ,			{"height": 4_2, "width": 4_2}		)
											self.assertEqual(image_processor.crop_size  ,			{"height": 8_4, "width": 8_4}		)
											self.assertEqual(image_processor.codebook_size  ,			{"height": 3_3, "width": 3_3}		)
											self.assertEqual(image_processor.codebook_crop_size  ,			{"height": 6_6, "width": 6_6}		)



						def 		snake_case_      (  self  :			Tuple		):
											pass



						def 		snake_case_      (  self  :			List[str]		):
											# Initialize image_processing
											_UpperCAmelCase    :			Optional[Any]              =					self.image_processing_class(**self.image_processor_dict		)
											# create random PIL images
											_UpperCAmelCase    :			List[Any]              =					prepare_image_inputs(self.image_processor_tester  ,			equal_resolution=A		)
											for image in image_inputs:
																self.assertIsInstance(A  ,			PIL.Image.Image		)

											# Test not batched input
											_UpperCAmelCase    :			List[str]              =					image_processing(image_inputs[0]  ,			return_tensors="pt"		)

											# Test no bool masked pos
											self.assertFalse("bool_masked_pos" in encoded_images		)

											_UpperCAmelCase       , _UpperCAmelCase    :			List[str]              =					self.image_processor_tester.get_expected_image_size()

											self.assertEqual(
											    encoded_images.pixel_values.shape  ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,			)

											# Test batched
											_UpperCAmelCase    :			List[str]              =					image_processing(A  ,			return_tensors="pt"		)
											_UpperCAmelCase       , _UpperCAmelCase    :			str              =					self.image_processor_tester.get_expected_image_size()

											# Test no bool masked pos
											self.assertFalse("bool_masked_pos" in encoded_images		)

											self.assertEqual(
											    encoded_images.pixel_values.shape  ,			(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )  ,			)



						def 		snake_case_      (  self  :			Dict  ,			A  :			Tuple  ,			A  :			int		):
											# Initialize image_processing
											_UpperCAmelCase    :			Dict              =					self.image_processing_class(**self.image_processor_dict		)
											# create random tensors
											_UpperCAmelCase    :			Optional[int]              =					prepare_image_inputs(self.image_processor_tester  ,			equal_resolution=A  ,			**A		)
											for image in image_inputs:
																self.assertIsInstance(A  ,			A		)

											# Test not batched input
											_UpperCAmelCase    :			Optional[int]              =					image_processing(image_inputs[0]  ,			return_tensors="pt"		)

											_UpperCAmelCase       , _UpperCAmelCase    :			str              =					self.image_processor_tester.get_expected_image_size()
											self.assertEqual(
											    encoded_images.pixel_values.shape  ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,			)

											_UpperCAmelCase    :			int              =					image_processing(A  ,			return_image_mask=A  ,			return_tensors="pt"		)

											_UpperCAmelCase       , _UpperCAmelCase    :			List[str]              =					self.image_processor_tester.get_expected_image_size()
											self.assertEqual(
											    encoded_images.pixel_values.shape  ,			(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )  ,			)

											_UpperCAmelCase       , _UpperCAmelCase    :			Dict              =					self.image_processor_tester.get_expected_mask_size()
											self.assertEqual(
											    encoded_images.bool_masked_pos.shape  ,			(
											        self.image_processor_tester.batch_size,
											        expected_height,
											        expected_width,
											    )  ,			)

											# Test batched
											_UpperCAmelCase    :			Dict              =					image_processing(A  ,			return_tensors="pt"		).pixel_values

											_UpperCAmelCase       , _UpperCAmelCase    :			Dict              =					self.image_processor_tester.get_expected_image_size()
											self.assertEqual(
											    encoded_images.shape  ,			(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )  ,			)

											# Test masking
											_UpperCAmelCase    :			List[Any]              =					image_processing(A  ,			return_image_mask=A  ,			return_tensors="pt"		)

											_UpperCAmelCase       , _UpperCAmelCase    :			Any              =					self.image_processor_tester.get_expected_image_size()
											self.assertEqual(
											    encoded_images.pixel_values.shape  ,			(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )  ,			)

											_UpperCAmelCase       , _UpperCAmelCase    :			Optional[int]              =					self.image_processor_tester.get_expected_mask_size()
											self.assertEqual(
											    encoded_images.bool_masked_pos.shape  ,			(
											        self.image_processor_tester.batch_size,
											        expected_height,
											        expected_width,
											    )  ,			)



						def 		snake_case_      (  self  :			Union[str, Any]		):
											self._test_call_framework(np.ndarray  ,			prepare_kwargs={"numpify": True}		)



						def 		snake_case_      (  self  :			Dict		):
											self._test_call_framework(torch.Tensor  ,			prepare_kwargs={"torchify": True}		)



						def 		snake_case_      (  self  :			Dict		):
											# Initialize image_processing
											random.seed(1_2_3_4		)
											_UpperCAmelCase    :			Optional[Any]              =					self.image_processing_class(**self.image_processor_dict		)
											_UpperCAmelCase    :			List[Any]              =					prepare_image_inputs(self.image_processor_tester  ,			equal_resolution=A  ,			torchify=A		)

											# Test not batched input
											_UpperCAmelCase    :			List[str]              =					image_processing(image_inputs[0]  ,			return_image_mask=A  ,			return_tensors="pt"		)
											self.assertEqual(encoded_images.bool_masked_pos.sum().item()  ,			7_5		)



						def 		snake_case_      (  self  :			Tuple		):
											# Initialize image_processing
											_UpperCAmelCase    :			Any              =					self.image_processing_class(**self.image_processor_dict		)
											# create random PIL images
											_UpperCAmelCase    :			List[Any]              =					prepare_image_inputs(self.image_processor_tester  ,			equal_resolution=A		)
											for image in image_inputs:
																self.assertIsInstance(A  ,			PIL.Image.Image		)

											# Test not batched input
											_UpperCAmelCase    :			int              =					image_processing(image_inputs[0]  ,			return_codebook_pixels=A  ,			return_tensors="pt"		)
											_UpperCAmelCase       , _UpperCAmelCase    :			Tuple              =					self.image_processor_tester.get_expected_codebook_image_size()
											self.assertEqual(
											    encoded_images.codebook_pixel_values.shape  ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,			)

											# Test batched
											_UpperCAmelCase    :			str              =					image_processing(A  ,			return_codebook_pixels=A  ,			return_tensors="pt"		)
											_UpperCAmelCase       , _UpperCAmelCase    :			Optional[Any]              =					self.image_processor_tester.get_expected_codebook_image_size()
											self.assertEqual(
											    encoded_images.codebook_pixel_values.shape  ,			(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )  ,			)





"""simple docstring"""




import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


_lowerCAmelCase       :      Optional[Any]							=							torch.cuda.is_available()
_lowerCAmelCase       :      str							=							[" Sam ate lunch today.", "Sams lunch ingredients."]
_lowerCAmelCase       :      Dict							=							["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
_lowerCAmelCase       :      Union[str, Any]							=							{
    "max_tokens_per_batch": None,
    "supervise_forward": True,
    "normalize_hidden": True,
    "label_smoothing": 0.2,
    "eval_max_gen_length": None,
    "eval_beams": 1,
    "val_metric": "loss",
    "save_top_k": 1,
    "adafactor": True,
    "early_stopping_patience": 2,
    "logger_name": "default",
    "length_penalty": 0.5,
    "cache_dir": "",
    "task": "summarization",
    "num_workers": 2,
    "alpha_hid": 0,
    "freeze_embeds": True,
    "enc_only": False,
    "tgt_suffix": "",
    "resume_from_checkpoint": None,
    "sortish_sampler": True,
    "student_decoder_layers": 1,
    "val_check_interval": 1.0,
    "output_dir": "",
    "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    "no_teacher": False,
    "fp16_opt_level": "O1",
    "gpus": 1 if CUDA_AVAILABLE else 0,
    "n_tpu_cores": 0,
    "max_grad_norm": 1.0,
    "do_train": True,
    "do_predict": True,
    "accumulate_grad_batches": 1,
    "server_ip": "",
    "server_port": "",
    "seed": 42,
    "model_name_or_path": "sshleifer/bart-tiny-random",
    "config_name": "",
    "tokenizer_name": "facebook/bart-large",
    "do_lower_case": False,
    "learning_rate": 0.3,
    "lr_scheduler": "linear",
    "weight_decay": 0.0,
    "adam_epsilon": 1E-08,
    "warmup_steps": 0,
    "max_epochs": 1,
    "train_batch_size": 2,
    "eval_batch_size": 2,
    "max_source_length": 12,
    "max_target_length": 12,
    "val_max_target_length": 12,
    "test_max_target_length": 12,
    "fast_dev_run": False,
    "no_cache": False,
    "n_train": -1,
    "n_val": -1,
    "n_test": -1,
    "student_encoder_layers": 1,
    "freeze_encoder": False,
    "auto_scale_batch_size": False,
    "overwrite_output_dir": False,
    "student": None,
}

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Path		,						SCREAMING_SNAKE_CASE__			:		list       )						->     Dict:





     '''simple docstring'''

     _UpperCAmelCase    :			Tuple              =					"\n".join(SCREAMING_SNAKE_CASE__       )
     Path(SCREAMING_SNAKE_CASE__       ).open("w"       ).writelines(SCREAMING_SNAKE_CASE__       )

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]       )						->     Union[str, Any]:





     '''simple docstring'''

     for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(SCREAMING_SNAKE_CASE__		,						f'{split}.source'       )		,						SCREAMING_SNAKE_CASE__       )
          _dump_articles(os.path.join(SCREAMING_SNAKE_CASE__		,						f'{split}.target'       )		,						SCREAMING_SNAKE_CASE__       )
     return tmp_dir






class 			UpperCAmelCase_						(       _UpperCamelCase		):



      @classmethod
      def 		snake_case_      (  cls  :			Optional[int]		):
           return cls



      @require_torch_multi_gpu
      def 		snake_case_      (  self  :			Union[str, Any]		):
           _UpperCAmelCase    :			Dict              =					{
               "no_teacher": True,
               "freeze_encoder": True,
               "gpus": 2,
               "overwrite_output_dir": True,
               "sortish_sampler": True,
           }
           self._test_distiller_cli_fork(A  ,			check_contents=A		)



      def 		snake_case_      (  self  :			List[str]  ,			A  :			List[Any]  ,			A  :			Optional[Any]=True		):
           _UpperCAmelCase    :			int              =					{
               "label_smoothing": 0.0,
               "early_stopping_patience": -1,
               "train_batch_size": 1,
               "eval_batch_size": 2,
               "max_epochs": 2,
               "alpha_mlm": 0.2,
               "alpha_ce": 0.8,
               "do_predict": True,
               "model_name_or_path": "sshleifer/tinier_bart",
               "teacher": CHEAP_ARGS["model_name_or_path"],
               "val_check_interval": 0.5,
           }
           default_updates.update(A		)
           _UpperCAmelCase    :			dict              =					CHEAP_ARGS.copy()
           _UpperCAmelCase    :			Union[str, Any]              =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
           _UpperCAmelCase    :			List[Any]              =					self.get_auto_remove_tmp_dir()
           args_d.update(data_dir=A  ,			output_dir=A  ,			**A		)

           def convert(A  :			List[Any]  ,			A  :			Optional[int]		):
                if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
                     return ""
                if v is False or v is None:
                     return ""
                if v is True:  # or len(str(v))==0:
                     return f'--{k}'
                return f'--{k}={v}'

           _UpperCAmelCase    :			Tuple              =					[x for x in (convert(A  ,			A		) for k, v in args_d.items()) if len(A		)]
           _UpperCAmelCase    :			Union[str, Any]              =					[sys.executable, f'{self.test_file_dir}/distillation.py'] + cli_args
           execute_subprocess_async(A  ,			env=self.get_env()		)

           _UpperCAmelCase    :			int              =					os.listdir(A		)
           _UpperCAmelCase    :			List[Any]              =					{os.path.basename(A		) for p in contents}
           _UpperCAmelCase    :			List[Any]              =					[p for p in contents if p.endswith("ckpt"		)]
           assert len(A		) > 0

           self.assertIn("test_generations.txt"  ,			A		)
           self.assertIn("test_results.txt"  ,			A		)

           # get the following from the module, (we don't have access to `model` here)
           _UpperCAmelCase    :			List[Any]              =					os.path.join(A  ,			"metrics.json"		)
           _UpperCAmelCase    :			str              =					"rouge2"

           _UpperCAmelCase    :			List[Any]              =					load_json(A		)
           # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
           print(A		)
           _UpperCAmelCase    :			List[str]              =					metrics["val"][-1]
           self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]  ,			0.01		)
           self.assertIsInstance(last_step_stats[f'val_avg_{val_metric}']  ,			A		)
           self.assertEqual(len(metrics["test"]		)  ,			1		)
           _UpperCAmelCase    :			Dict              =					int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1		)
           self.assertEqual(len(metrics["val"]		)  ,			A		)
