



'''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__	  =		None

class 	_lowerCAmelCase     (    unittest.TestCase       ):






		'''simple docstring'''
		def __init__(						self							:       Dict      ,		UpperCamelCase_							:       List[str]      ,		UpperCamelCase_							:       Any=7      ,		UpperCamelCase_							:       str=3      ,		UpperCamelCase_							:       int=30      ,		UpperCamelCase_							:       List[Any]=400      ,		UpperCamelCase_							:       Union[str, Any]=True      ,		UpperCamelCase_							:       Optional[Any]=None      ,		UpperCamelCase_							:       List[Any]=True      ,		UpperCamelCase_							:       Dict=None      ,		UpperCamelCase_							:       Optional[int]=None      ,		UpperCamelCase_							:       Dict=True      ,		UpperCamelCase_							:       Optional[Any]=1 / 255      ,		UpperCamelCase_							:       Union[str, Any]=True      ,		UpperCamelCase_							:       Union[str, Any]=FLAVA_IMAGE_MEAN      ,		UpperCamelCase_							:       Tuple=FLAVA_IMAGE_STD      ,		UpperCamelCase_							:       Optional[Any]=14      ,		UpperCamelCase_							:       Any=75      ,		UpperCamelCase_							:       Optional[int]=None      ,		UpperCamelCase_							:       Optional[Any]=16      ,		UpperCamelCase_							:       str=0.3      ,		UpperCamelCase_							:       Dict=None      ,		UpperCamelCase_							:       Optional[Any]=True      ,		UpperCamelCase_							:       Dict=None      ,		UpperCamelCase_							:       Any=None      ,		UpperCamelCase_							:       Any=True      ,		UpperCamelCase_							:       Union[str, Any]=None      ,		UpperCamelCase_							:       Tuple=True      ,		UpperCamelCase_							:       List[Any]=True      ,		UpperCamelCase_							:       Optional[int]=FLAVA_CODEBOOK_MEAN      ,		UpperCamelCase_							:       Optional[int]=FLAVA_CODEBOOK_STD      ,		)      ->			int:







									'''simple docstring'''





									_lowercase							:		str				=   size if size is not None else {'''height''': 224, '''width''': 224}
									_lowercase							:		Dict				=   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
									_lowercase							:		Optional[int]				=   codebook_size if codebook_size is not None else {'''height''': 112, '''width''': 112}
									_lowercase							:		str				=   codebook_crop_size if codebook_crop_size is not None else {'''height''': 112, '''width''': 112}

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

									_lowercase							:		Union[str, Any]				=   input_size_patches
									_lowercase							:		int				=   total_mask_patches
									_lowercase							:		List[Any]				=   mask_group_max_patches
									_lowercase							:		Optional[Any]				=   mask_group_min_patches
									_lowercase							:		List[str]				=   mask_group_min_aspect_ratio
									_lowercase							:		Tuple				=   mask_group_max_aspect_ratio

									_lowercase							:		List[str]				=   codebook_do_resize
									_lowercase							:		Dict				=   codebook_size
									_lowercase							:		List[Any]				=   codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
									_lowercase							:		List[Any]				=   codebook_do_center_crop
									_lowercase							:		Tuple				=   codebook_crop_size
									_lowercase							:		List[str]				=   codebook_do_map_pixels
									_lowercase							:		Tuple				=   codebook_do_normalize
									_lowercase							:		int				=   codebook_image_mean
									_lowercase							:		Dict				=   codebook_image_std
		def 						__lowercase      (						self							:       str		)      ->			Optional[Any]:







									'''simple docstring'''





									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 						__lowercase      (						self							:       int		)      ->			List[str]:







									'''simple docstring'''





									return (self.size["height"], self.size["width"])
		def 						__lowercase      (						self							:       int		)      ->			Dict:







									'''simple docstring'''





									return (
									    (self.input_size_patches, self.input_size_patches)
									    if not isinstance(self.input_size_patches      ,		UpperCAmelCase_		)
									    else self.input_size_patches
									)





		def 						__lowercase      (						self							:       Optional[Any]		)      ->			Optional[Any]:







									'''simple docstring'''





									return (self.codebook_size["height"], self.codebook_size["width"])




@require_torch
@require_vision
class 	_lowerCAmelCase     (    __A     ,		unittest.TestCase       ):






		'''simple docstring'''
		snake_case_							= FlavaImageProcessor if is_vision_available() else None
		snake_case_							= None
		def 						__lowercase      (						self							:       Tuple		)      ->			Any:







									'''simple docstring'''





									_lowercase							:		int				=   FlavaImageProcessingTester(self		)
		@property
		def 						__lowercase      (						self							:       Dict		)      ->			Union[str, Any]:







									'''simple docstring'''





									return self.image_processor_tester.prepare_image_processor_dict()
		def 						__lowercase      (						self							:       Union[str, Any]		)      ->			List[Any]:







									'''simple docstring'''





									_lowercase							:		int				=   self.image_processing_class(**self.image_processor_dict		)
									self.assertTrue(hasattr(UpperCAmelCase_      ,		'''image_mean'''		)		)
									self.assertTrue(hasattr(UpperCAmelCase_      ,		'''image_std'''		)		)
									self.assertTrue(hasattr(UpperCAmelCase_      ,		'''do_normalize'''		)		)
									self.assertTrue(hasattr(UpperCAmelCase_      ,		'''do_resize'''		)		)
									self.assertTrue(hasattr(UpperCAmelCase_      ,		'''resample'''		)		)
									self.assertTrue(hasattr(UpperCAmelCase_      ,		'''crop_size'''		)		)
									self.assertTrue(hasattr(UpperCAmelCase_      ,		'''do_center_crop'''		)		)
									self.assertTrue(hasattr(UpperCAmelCase_      ,		'''do_rescale'''		)		)
									self.assertTrue(hasattr(UpperCAmelCase_      ,		'''rescale_factor'''		)		)
									self.assertTrue(hasattr(UpperCAmelCase_      ,		'''masking_generator'''		)		)
									self.assertTrue(hasattr(UpperCAmelCase_      ,		'''codebook_do_resize'''		)		)
									self.assertTrue(hasattr(UpperCAmelCase_      ,		'''codebook_size'''		)		)
									self.assertTrue(hasattr(UpperCAmelCase_      ,		'''codebook_resample'''		)		)
									self.assertTrue(hasattr(UpperCAmelCase_      ,		'''codebook_do_center_crop'''		)		)
									self.assertTrue(hasattr(UpperCAmelCase_      ,		'''codebook_crop_size'''		)		)
									self.assertTrue(hasattr(UpperCAmelCase_      ,		'''codebook_do_map_pixels'''		)		)
									self.assertTrue(hasattr(UpperCAmelCase_      ,		'''codebook_do_normalize'''		)		)
									self.assertTrue(hasattr(UpperCAmelCase_      ,		'''codebook_image_mean'''		)		)
									self.assertTrue(hasattr(UpperCAmelCase_      ,		'''codebook_image_std'''		)		)
		def 						__lowercase      (						self							:       Dict		)      ->			List[Any]:







									'''simple docstring'''





									_lowercase							:		Tuple				=   self.image_processing_class.from_dict(self.image_processor_dict		)
									self.assertEqual(image_processor.size      ,		{'''height''': 224, '''width''': 224}		)
									self.assertEqual(image_processor.crop_size      ,		{'''height''': 224, '''width''': 224}		)
									self.assertEqual(image_processor.codebook_size      ,		{'''height''': 112, '''width''': 112}		)
									self.assertEqual(image_processor.codebook_crop_size      ,		{'''height''': 112, '''width''': 112}		)

									_lowercase							:		Optional[Any]				=   self.image_processing_class.from_dict(
									    self.image_processor_dict      ,		size=42      ,		crop_size=84      ,		codebook_size=33      ,		codebook_crop_size=66		)
									self.assertEqual(image_processor.size      ,		{'''height''': 42, '''width''': 42}		)
									self.assertEqual(image_processor.crop_size      ,		{'''height''': 84, '''width''': 84}		)
									self.assertEqual(image_processor.codebook_size      ,		{'''height''': 33, '''width''': 33}		)
									self.assertEqual(image_processor.codebook_crop_size      ,		{'''height''': 66, '''width''': 66}		)
		def 						__lowercase      (						self							:       Union[str, Any]		)      ->			Dict:







									'''simple docstring'''





									pass
		def 						__lowercase      (						self							:       Dict		)      ->			Any:







									'''simple docstring'''





									_lowercase							:		List[Any]				=   self.image_processing_class(**self.image_processor_dict		)
									# create random PIL images
									_lowercase							:		Optional[int]				=   prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=UpperCAmelCase_		)
									for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase_      ,		PIL.Image.Image		)

									# Test not batched input
									_lowercase							:		List[Any]				=   image_processing(image_inputs[0]      ,		return_tensors='''pt'''		)

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

									_lowercase			, _lowercase							:		List[Any]				=   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
									_lowercase							:		List[str]				=   image_processing(UpperCAmelCase_      ,		return_tensors='''pt'''		)
									_lowercase			, _lowercase							:		Dict				=   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 						__lowercase      (						self							:       Tuple      ,		UpperCamelCase_							:       List[Any]      ,		UpperCamelCase_							:       Tuple		)      ->			Any:







									'''simple docstring'''





									_lowercase							:		Tuple				=   self.image_processing_class(**self.image_processor_dict		)
									# create random tensors
									_lowercase							:		List[Any]				=   prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=UpperCAmelCase_      ,		**UpperCAmelCase_		)
									for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase_      ,		UpperCAmelCase_		)

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

									_lowercase			, _lowercase							:		Optional[Any]				=   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)      ,		)

									_lowercase							:		Any				=   image_processing(UpperCAmelCase_      ,		return_image_mask=UpperCAmelCase_      ,		return_tensors='''pt'''		)

									_lowercase			, _lowercase							:		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,
									    )      ,		)

									_lowercase			, _lowercase							:		Union[str, Any]				=   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
									_lowercase							:		int				=   image_processing(UpperCAmelCase_      ,		return_tensors='''pt'''		).pixel_values

									_lowercase			, _lowercase							:		Union[str, Any]				=   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
									_lowercase							:		Tuple				=   image_processing(UpperCAmelCase_      ,		return_image_mask=UpperCAmelCase_      ,		return_tensors='''pt'''		)

									_lowercase			, _lowercase							:		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,
									    )      ,		)

									_lowercase			, _lowercase							:		Union[str, Any]				=   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 						__lowercase      (						self							:       List[Any]		)      ->			List[Any]:







									'''simple docstring'''





									self._test_call_framework(np.ndarray      ,		prepare_kwargs={'''numpify''': True}		)
		def 						__lowercase      (						self							:       List[Any]		)      ->			Optional[Any]:







									'''simple docstring'''





									self._test_call_framework(torch.Tensor      ,		prepare_kwargs={'''torchify''': True}		)
		def 						__lowercase      (						self							:       str		)      ->			List[str]:







									'''simple docstring'''





									random.seed(1_234		)
									_lowercase							:		Dict				=   self.image_processing_class(**self.image_processor_dict		)
									_lowercase							:		List[str]				=   prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=UpperCAmelCase_      ,		torchify=UpperCAmelCase_		)

									# Test not batched input
									_lowercase							:		str				=   image_processing(image_inputs[0]      ,		return_image_mask=UpperCAmelCase_      ,		return_tensors='''pt'''		)
									self.assertEqual(encoded_images.bool_masked_pos.sum().item()      ,		75		)





		def 						__lowercase      (						self							:       Tuple		)      ->			Tuple:







									'''simple docstring'''





									_lowercase							:		Any				=   self.image_processing_class(**self.image_processor_dict		)
									# create random PIL images
									_lowercase							:		Tuple				=   prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=UpperCAmelCase_		)
									for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase_      ,		PIL.Image.Image		)

									# Test not batched input
									_lowercase							:		Optional[Any]				=   image_processing(image_inputs[0]      ,		return_codebook_pixels=UpperCAmelCase_      ,		return_tensors='''pt'''		)
									_lowercase			, _lowercase							:		Any				=   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
									_lowercase							:		List[Any]				=   image_processing(UpperCAmelCase_      ,		return_codebook_pixels=UpperCAmelCase_      ,		return_tensors='''pt'''		)
									_lowercase			, _lowercase							:		str				=   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


a__			:						Union[str, Any]							=			torch.cuda.is_available()
a__			:						Union[str, Any]							=			[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
a__			:						List[Any]							=			["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
a__			:						str							=			{
    """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 					__UpperCamelCase (     a__:			Path			,						a__:			list      )					->      List[str]:




					"""simple docstring"""



					__lowercase        =      '''\n'''.join(a__      )
					Path(a__      ).open('''w'''      ).writelines(a__      )





def 					__UpperCamelCase (     a__:			int      )					->      List[str]:




					"""simple docstring"""



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


class       _snake_case  (    UpperCamelCase							):







						@classmethod
						def 							_UpperCamelCase     (cls				)      ->    List[str]:
											return cls







						@require_torch_multi_gpu
						def 							_UpperCamelCase     (self				)      ->    Dict:
											__lowercase        =      {
											    '''no_teacher''': True,
											    '''freeze_encoder''': True,
											    '''gpus''': 2,
											    '''overwrite_output_dir''': True,
											    '''sortish_sampler''': True,
											}
											self._test_distiller_cli_fork(UpperCAmelCase_  ,				check_contents=UpperCAmelCase_				)







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_=True				)      ->    Tuple:
											__lowercase        =      {
											    '''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(UpperCAmelCase_				)
											__lowercase        =      CHEAP_ARGS.copy()
											__lowercase        =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
											__lowercase        =      self.get_auto_remove_tmp_dir()
											args_d.update(data_dir=UpperCAmelCase_  ,				output_dir=UpperCAmelCase_  ,				**UpperCAmelCase_				)

											def convert(UpperCAmelCase_  ,				UpperCAmelCase_				):
																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}'''

											__lowercase        =      [x for x in (convert(UpperCAmelCase_  ,				UpperCAmelCase_				) for k, v in args_d.items()) if len(UpperCAmelCase_				)]
											__lowercase        =      [sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
											execute_subprocess_async(UpperCAmelCase_  ,				env=self.get_env()				)

											__lowercase        =      os.listdir(UpperCAmelCase_				)
											__lowercase        =      {os.path.basename(UpperCAmelCase_				) for p in contents}
											__lowercase        =      [p for p in contents if p.endswith('''ckpt'''				)]
											assert len(UpperCAmelCase_				) > 0

											self.assertIn('''test_generations.txt'''  ,				UpperCAmelCase_				)
											self.assertIn('''test_results.txt'''  ,				UpperCAmelCase_				)

											# get the following from the module, (we don't have access to `model` here)
											__lowercase        =      os.path.join(UpperCAmelCase_  ,				'''metrics.json'''				)
											__lowercase        =      '''rouge2'''

											__lowercase        =      load_json(UpperCAmelCase_				)
											# {'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(UpperCAmelCase_				)
											__lowercase        =      metrics['''val'''][-1]
											self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']  ,				0.0_1				)
											self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']  ,				UpperCAmelCase_				)
											self.assertEqual(len(metrics['''test''']				)  ,				1				)
											__lowercase        =      int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1				)
											self.assertEqual(len(metrics['''val''']				)  ,				UpperCAmelCase_				)


