






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


snake_case			 =						torch.cuda.is_available()
snake_case			 =						[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
snake_case			 =						["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
snake_case			 =						{
    """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__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					List[str]		  =			"\n".join(lowercase )
  Path(lowercase ).open("w" ).writelines(lowercase )


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






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


class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase    ):
     '''simple docstring'''


     @classmethod
     def  _A				( cls				:		Union[str, Any]					):
       return cls


     @require_torch_multi_gpu
     def  _A				( self				:		List[str]					):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			{
           "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  _A				( self				:		Optional[Any]    ,       UpperCAmelCase_				:		str    ,       UpperCAmelCase_				:		int=True					):
       SCREAMING_SNAKE_CASE				:					Optional[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(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					dict		  =			CHEAP_ARGS.copy()
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
       SCREAMING_SNAKE_CASE				:					str		  =			self.get_auto_remove_tmp_dir()
       args_d.update(data_dir=UpperCAmelCase_    ,       output_dir=UpperCAmelCase_    ,       **UpperCAmelCase_					)

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

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

       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			os.listdir(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					str		  =			{os.path.basename(UpperCAmelCase_					) for p in contents}
       SCREAMING_SNAKE_CASE				:					str		  =			[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)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			os.path.join(UpperCAmelCase_    ,       "metrics.json"					)
       SCREAMING_SNAKE_CASE				:					Any		  =			"rouge2"

       SCREAMING_SNAKE_CASE				:					int		  =			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_					)
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			metrics["val"][-1]
       self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]    ,       0.01					)
       self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']    ,       UpperCAmelCase_					)
       self.assertEqual(len(metrics["test"]					)    ,       1					)
       SCREAMING_SNAKE_CASE				:					List[str]		  =			int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1					)
       self.assertEqual(len(metrics["val"]					)    ,       UpperCAmelCase_					)














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():
   from PIL import Image

   from transformers import ConvNextImageProcessor


class        SCREAMING_SNAKE_CASE	(	unittest.TestCase    ):
     '''simple docstring'''


     def __init__( self				:		Optional[Any]    ,       UpperCAmelCase_				:		Optional[int]    ,       UpperCAmelCase_				:		Any=7    ,       UpperCAmelCase_				:		Union[str, Any]=3    ,       UpperCAmelCase_				:		List[str]=18    ,       UpperCAmelCase_				:		Optional[int]=30    ,       UpperCAmelCase_				:		int=400    ,       UpperCAmelCase_				:		int=True    ,       UpperCAmelCase_				:		List[Any]=None    ,       UpperCAmelCase_				:		Tuple=0.875    ,       UpperCAmelCase_				:		str=True    ,       UpperCAmelCase_				:		Optional[Any]=[0.5, 0.5, 0.5]    ,       UpperCAmelCase_				:		List[Any]=[0.5, 0.5, 0.5]    ,       ):
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			size if size is not None else {"shortest_edge": 20}
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			parent
       SCREAMING_SNAKE_CASE				:					int		  =			batch_size
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			num_channels
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			image_size
       SCREAMING_SNAKE_CASE				:					Any		  =			min_resolution
       SCREAMING_SNAKE_CASE				:					List[str]		  =			max_resolution
       SCREAMING_SNAKE_CASE				:					int		  =			do_resize
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			size
       SCREAMING_SNAKE_CASE				:					Any		  =			crop_pct
       SCREAMING_SNAKE_CASE				:					Dict		  =			do_normalize
       SCREAMING_SNAKE_CASE				:					List[str]		  =			image_mean
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			image_std






     def  _A				( self				:		str					):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_pct": self.crop_pct,
       }





@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase					,						unittest.TestCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						str								=		ConvNextImageProcessor if is_vision_available() else None


     def  _A				( self				:		Optional[int]					):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			ConvNextImageProcessingTester(self					)


     @property
     def  _A				( self				:		str					):
       return self.image_processor_tester.prepare_image_processor_dict()


     def  _A				( self				:		str					):
       SCREAMING_SNAKE_CASE				:					str		  =			self.image_processing_class(**self.image_processor_dict					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "do_resize"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "size"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "crop_pct"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "do_normalize"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "image_mean"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "image_std"					)					)


     def  _A				( self				:		Union[str, Any]					):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.image_processing_class.from_dict(self.image_processor_dict					)
       self.assertEqual(image_processor.size    ,       {"shortest_edge": 20}					)

       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42					)
       self.assertEqual(image_processor.size    ,       {"shortest_edge": 42}					)


     def  _A				( self				:		List[str]					):
       pass


     def  _A				( self				:		Optional[Any]					):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCAmelCase_					)
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_    ,       Image.Image					)

       # Test not batched input
       SCREAMING_SNAKE_CASE				:					Dict		  =			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.size["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )

       # Test batched
       SCREAMING_SNAKE_CASE				:					str		  =			image_processing(UpperCAmelCase_    ,       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.size["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )


     def  _A				( self				:		Optional[Any]					):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE				:					List[str]		  =			self.image_processing_class(**self.image_processor_dict					)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCAmelCase_    ,       numpify=UpperCAmelCase_					)
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_    ,       np.ndarray					)

       # Test not batched input
       SCREAMING_SNAKE_CASE				:					str		  =			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.size["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )

       # Test batched
       SCREAMING_SNAKE_CASE				:					Dict		  =			image_processing(UpperCAmelCase_    ,       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.size["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )






     def  _A				( self				:		Tuple					):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE				:					str		  =			self.image_processing_class(**self.image_processor_dict					)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE				:					Tuple		  =			prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCAmelCase_    ,       torchify=UpperCAmelCase_					)
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_    ,       torch.Tensor					)

       # Test not batched input
       SCREAMING_SNAKE_CASE				:					int		  =			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.size["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )

       # Test batched
       SCREAMING_SNAKE_CASE				:					Any		  =			image_processing(UpperCAmelCase_    ,       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.size["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )






