

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__							=			torch.cuda.is_available()
lowerCAmelCase__							=			[" Sam ate lunch today.", "Sams lunch ingredients."]
lowerCAmelCase__							=			["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
lowerCAmelCase__							=			{
    "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   _lowerCAmelCase(						__A ,							__A      ):
  UpperCAmelCase          =	"\n".join(__A      )
  Path(__A      ).open("w"      ).writelines(__A      )
def   _lowerCAmelCase(						__A      ):
  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     __magic_name__     (							snake_case__     ):


 @classmethod
 def _UpperCamelCase						(    cls		:			Optional[Any]     )     ->   Optional[Any]:
   return cls


 @require_torch_multi_gpu
 def _UpperCamelCase						(    self		:			List[str]     )     ->   Tuple:
   UpperCAmelCase          =	{
       "no_teacher": True,
       "freeze_encoder": True,
       "gpus": 2,
       "overwrite_output_dir": True,
       "sortish_sampler": True,
   }
   self._test_distiller_cli_fork(_SCREAMING_SNAKE_CASE      ,    check_contents=_SCREAMING_SNAKE_CASE     )


 def _UpperCamelCase						(    self		:			str      ,    lowerCAmelCase__		:			Optional[int]      ,    lowerCAmelCase__		:			int=True     )     ->   Tuple:
   UpperCAmelCase          =	{
       "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(_SCREAMING_SNAKE_CASE     )
   UpperCAmelCase          =	CHEAP_ARGS.copy()
   UpperCAmelCase          =	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
   UpperCAmelCase          =	self.get_auto_remove_tmp_dir()
   args_d.update(data_dir=_SCREAMING_SNAKE_CASE      ,    output_dir=_SCREAMING_SNAKE_CASE      ,    **_SCREAMING_SNAKE_CASE     )

   def convert(lowerCAmelCase__		:			Optional[Any]      ,    lowerCAmelCase__		:			Union[str, Any]     ):
     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          =	[x for x in (convert(_SCREAMING_SNAKE_CASE      ,    _SCREAMING_SNAKE_CASE     ) for k, v in args_d.items()) if len(_SCREAMING_SNAKE_CASE     )]
   UpperCAmelCase          =	[sys.executable, f"{self.test_file_dir}/distillation.py"] + cli_args
   execute_subprocess_async(_SCREAMING_SNAKE_CASE      ,    env=self.get_env()     )

   UpperCAmelCase          =	os.listdir(_SCREAMING_SNAKE_CASE     )
   UpperCAmelCase          =	{os.path.basename(_SCREAMING_SNAKE_CASE     ) for p in contents}
   UpperCAmelCase          =	[p for p in contents if p.endswith("ckpt"     )]
   assert len(_SCREAMING_SNAKE_CASE     ) > 0

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

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

   UpperCAmelCase          =	load_json(_SCREAMING_SNAKE_CASE     )
   # {'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(_SCREAMING_SNAKE_CASE     )
   UpperCAmelCase          =	metrics["val"][-1]
   self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]      ,    0.01     )
   self.assertIsInstance(last_step_stats[f"val_avg_{val_metric}"]      ,    _SCREAMING_SNAKE_CASE     )
   self.assertEqual(len(metrics["test"]     )      ,    1     )
   UpperCAmelCase          =	int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1     )
   self.assertEqual(len(metrics["val"]     )      ,    _SCREAMING_SNAKE_CASE     )











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


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import CLIPImageProcessor
class   UpperCamelCase			(					unittest.TestCase			):







		"""simple docstring"""



		def __init__(	self		:							int    ,_SCREAMING_SNAKE_CASE		:							Any    ,_SCREAMING_SNAKE_CASE		:							Optional[int]=7    ,_SCREAMING_SNAKE_CASE		:							List[Any]=3    ,_SCREAMING_SNAKE_CASE		:							int=1_8    ,_SCREAMING_SNAKE_CASE		:							Tuple=3_0    ,_SCREAMING_SNAKE_CASE		:							Optional[Any]=4_0_0    ,_SCREAMING_SNAKE_CASE		:							Dict=True    ,_SCREAMING_SNAKE_CASE		:							int=None    ,_SCREAMING_SNAKE_CASE		:							Tuple=True    ,_SCREAMING_SNAKE_CASE		:							Any=None    ,_SCREAMING_SNAKE_CASE		:							Dict=True    ,_SCREAMING_SNAKE_CASE		:							List[str]=[0.48145466, 0.4578275, 0.40821073]    ,_SCREAMING_SNAKE_CASE		:							int=[0.26862954, 0.26130258, 0.27577711]    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any]=True    ,)     -> List[str]:


						'''simple docstring'''
						A									=   size if size is not None else {'shortest_edge': 2_0}
						A									=   crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
						A									=   parent
						A									=   batch_size
						A									=   num_channels
						A									=   image_size
						A									=   min_resolution
						A									=   max_resolution
						A									=   do_resize
						A									=   size
						A									=   do_center_crop
						A									=   crop_size
						A									=   do_normalize
						A									=   image_mean
						A									=   image_std
						A									=   do_convert_rgb
		def    A(	self		:							List[str]		)     -> Optional[int]:


						'''simple docstring'''
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_convert_rgb": self.do_convert_rgb,
						}







		def    A(	self		:							Optional[int]    ,_SCREAMING_SNAKE_CASE		:							str=False    ,_SCREAMING_SNAKE_CASE		:							List[str]=False    ,_SCREAMING_SNAKE_CASE		:							Optional[Any]=False		)     -> str:


						'''simple docstring'''
						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						if equal_resolution:
										A									=   []
										for i in range(self.batch_size		):
														image_inputs.append(
														    np.random.randint(
														        2_5_5    ,size=(self.num_channels, self.max_resolution, self.max_resolution)    ,dtype=np.uinta		)		)
						else:
										A									=   []
										for i in range(self.batch_size		):
														A			,		A									=   np.random.choice(np.arange(self.min_resolution    ,self.max_resolution		)    ,2		)
														image_inputs.append(np.random.randint(2_5_5    ,size=(self.num_channels, width, height)    ,dtype=np.uinta		)		)

						if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										A									=   [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE    ,0    ,-1		)		) for x in image_inputs]

						if torchify:
										A									=   [torch.from_numpy(_SCREAMING_SNAKE_CASE		) for x in image_inputs]

						return image_inputs


@require_torch
@require_vision
class   UpperCamelCase			(					snake_case__      ,							unittest.TestCase			):







		"""simple docstring"""



		snake_case			 = CLIPImageProcessor if is_vision_available() else None
		def    A(	self		:							Optional[Any]		)     -> Union[str, Any]:


						'''simple docstring'''
						A									=   CLIPImageProcessingTester(self		)
		@property
		def    A(	self		:							Optional[int]		)     -> Any:


						'''simple docstring'''
						return self.image_processor_tester.prepare_image_processor_dict()
		def    A(	self		:							Optional[int]		)     -> Tuple:


						'''simple docstring'''
						A									=   self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_resize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'size'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_center_crop'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'center_crop'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_normalize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_mean'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_std'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_convert_rgb'		)		)
		def    A(	self		:							Optional[Any]		)     -> int:


						'''simple docstring'''
						A									=   self.image_processing_class.from_dict(self.image_processor_dict		)
						self.assertEqual(image_processor.size    ,{'shortest_edge': 2_0}		)
						self.assertEqual(image_processor.crop_size    ,{'height': 1_8, 'width': 1_8}		)

						A									=   self.image_processing_class.from_dict(self.image_processor_dict    ,size=4_2    ,crop_size=8_4		)
						self.assertEqual(image_processor.size    ,{'shortest_edge': 4_2}		)
						self.assertEqual(image_processor.crop_size    ,{'height': 8_4, 'width': 8_4}		)
		def    A(	self		:							Any		)     -> Union[str, Any]:


						'''simple docstring'''
						pass
		def    A(	self		:							Tuple		)     -> Optional[int]:


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						A									=   self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,Image.Image		)

						# Test not batched input
						A									=   image_processing(image_inputs[0]    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)

						# Test batched
						A									=   image_processing(_SCREAMING_SNAKE_CASE    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)
		def    A(	self		:							int		)     -> Tuple:


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						A									=   self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE    ,numpify=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,np.ndarray		)

						# Test not batched input
						A									=   image_processing(image_inputs[0]    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)

						# Test batched
						A									=   image_processing(_SCREAMING_SNAKE_CASE    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)







		def    A(	self		:							str		)     -> str:


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						A									=   self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE    ,torchify=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,torch.Tensor		)

						# Test not batched input
						A									=   image_processing(image_inputs[0]    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)

						# Test batched
						A									=   image_processing(_SCREAMING_SNAKE_CASE    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)


@require_torch
@require_vision
class   UpperCamelCase			(					snake_case__      ,							unittest.TestCase			):







		"""simple docstring"""



		snake_case			 = CLIPImageProcessor if is_vision_available() else None
		def    A(	self		:							Tuple		)     -> Union[str, Any]:


						'''simple docstring'''
						A									=   CLIPImageProcessingTester(self    ,num_channels=4		)
						A									=   3
		@property
		def    A(	self		:							str		)     -> Dict:


						'''simple docstring'''
						return self.image_processor_tester.prepare_image_processor_dict()
		def    A(	self		:							Any		)     -> int:


						'''simple docstring'''
						A									=   self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_resize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'size'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_center_crop'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'center_crop'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_normalize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_mean'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_std'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_convert_rgb'		)		)
		def    A(	self		:							Any		)     -> str:


						'''simple docstring'''
						pass







		def    A(	self		:							Union[str, Any]		)     -> Dict:


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						A									=   self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,Image.Image		)

						# Test not batched input
						A									=   image_processing(image_inputs[0]    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        1,
						        self.expected_encoded_image_num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)

						# Test batched
						A									=   image_processing(_SCREAMING_SNAKE_CASE    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        self.image_processor_tester.batch_size,
						        self.expected_encoded_image_num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)





