'''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


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


       """simple docstring"""







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

def      UpperCamelCase_							( _UpperCAmelCase  :				List[Any]   )   ->   Optional[int]:


       """simple docstring"""







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


class      lowerCamelCase_     (snake_case__   ):
      '''simple docstring'''

      @classmethod
      def 					_A     (	cls						:    Any	):
             return cls




      @require_torch_multi_gpu
      def 					_A     (	self						:    Optional[int]	):
             _UpperCAmelCase					:  str											=   {
                 "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 					_A     (	self						:    List[str]						, A						:    List[str]						, A						:    int=True	):
             _UpperCAmelCase					:  List[Any]											=   {
                 "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					:  int											=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
             _UpperCAmelCase					:  Any											=   self.get_auto_remove_tmp_dir()
             args_d.update(data_dir=A						, output_dir=A						, **A	)

             def convert(A						:    Dict						, A						:    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}"""

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

             _UpperCAmelCase					:  Optional[int]											=   os.listdir(A	)
             _UpperCAmelCase					:  List[Any]											=   {os.path.basename(A	) for p in contents}
             _UpperCAmelCase					:  Optional[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					:  int											=   "rouge2"

             _UpperCAmelCase					:  Tuple											=   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					:  int											=   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					:  str											=   int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1	)
             self.assertEqual(len(metrics["val"]	)						, A	)


'''simple docstring'''




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class      lowerCamelCase_     (unittest.TestCase   ):
      '''simple docstring'''

      def 					_A     (	self						:    Any	):
             _UpperCAmelCase					:  List[Any]											=   tempfile.mkdtemp()

             _UpperCAmelCase					:  Any											=   CLIPImageProcessor()
             _UpperCAmelCase					:  Optional[int]											=   BertTokenizer.from_pretrained(
                 "hf-internal-testing/tiny-random-BertModel"						, model_input_names=["input_ids", "attention_mask"]	)

             _UpperCAmelCase					:  Dict											=   GitProcessor(A						, A	)

             processor.save_pretrained(self.tmpdirname	)




      def 					_A     (	self						:    Dict						, **A						:    Tuple	):
             return AutoProcessor.from_pretrained(self.tmpdirname						, **A	).tokenizer




      def 					_A     (	self						:    List[str]						, **A						:    str	):
             return AutoProcessor.from_pretrained(self.tmpdirname						, **A	).image_processor




      def 					_A     (	self						:    Optional[int]	):
             shutil.rmtree(self.tmpdirname	)




      def 					_A     (	self						:    Tuple	):
             _UpperCAmelCase					:  Dict											=   [np.random.randint(255						, size=(3, 30, 400)						, dtype=np.uinta	)]

             _UpperCAmelCase					:  Optional[Any]											=   [Image.fromarray(np.moveaxis(A						, 0						, -1	)	) for x in image_inputs]

             return image_inputs




      def 					_A     (	self						:    Optional[int]	):
             _UpperCAmelCase					:  Optional[int]											=   GitProcessor(tokenizer=self.get_tokenizer()						, image_processor=self.get_image_processor()	)
             processor.save_pretrained(self.tmpdirname	)

             _UpperCAmelCase					:  Dict											=   self.get_tokenizer(bos_token="(BOS)"						, eos_token="(EOS)"	)
             _UpperCAmelCase					:  Optional[Any]											=   self.get_image_processor(do_normalize=A						, padding_value=1.0	)

             _UpperCAmelCase					:  Union[str, Any]											=   GitProcessor.from_pretrained(
                 self.tmpdirname						, bos_token="(BOS)"						, eos_token="(EOS)"						, do_normalize=A						, padding_value=1.0	)

             self.assertEqual(processor.tokenizer.get_vocab()						, tokenizer_add_kwargs.get_vocab()	)
             self.assertIsInstance(processor.tokenizer						, A	)

             self.assertEqual(processor.image_processor.to_json_string()						, image_processor_add_kwargs.to_json_string()	)
             self.assertIsInstance(processor.image_processor						, A	)




      def 					_A     (	self						:    Union[str, Any]	):
             _UpperCAmelCase					:  Union[str, Any]											=   self.get_image_processor()
             _UpperCAmelCase					:  List[Any]											=   self.get_tokenizer()

             _UpperCAmelCase					:  int											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  List[Any]											=   self.prepare_image_inputs()

             _UpperCAmelCase					:  Optional[Any]											=   image_processor(A						, return_tensors="np"	)
             _UpperCAmelCase					:  int											=   processor(images=A						, return_tensors="np"	)

             for key in input_feat_extract.keys():
                    self.assertAlmostEqual(input_feat_extract[key].sum()						, input_processor[key].sum()						, delta=1E-2	)




      def 					_A     (	self						:    Optional[Any]	):
             _UpperCAmelCase					:  str											=   self.get_image_processor()
             _UpperCAmelCase					:  Dict											=   self.get_tokenizer()

             _UpperCAmelCase					:  List[Any]											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  List[str]											=   "lower newer"

             _UpperCAmelCase					:  Optional[int]											=   processor(text=A	)

             _UpperCAmelCase					:  Tuple											=   tokenizer(A						, return_token_type_ids=A	)

             for key in encoded_tok.keys():
                    self.assertListEqual(encoded_tok[key]						, encoded_processor[key]	)




      def 					_A     (	self						:    List[str]	):
             _UpperCAmelCase					:  Union[str, Any]											=   self.get_image_processor()
             _UpperCAmelCase					:  Dict											=   self.get_tokenizer()

             _UpperCAmelCase					:  Any											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  Optional[Any]											=   "lower newer"
             _UpperCAmelCase					:  Optional[int]											=   self.prepare_image_inputs()

             _UpperCAmelCase					:  Any											=   processor(text=A						, images=A	)

             self.assertListEqual(list(inputs.keys()	)						, ["input_ids", "attention_mask", "pixel_values"]	)

             # test if it raises when no input is passed
             with pytest.raises(A	):
                    processor()




      def 					_A     (	self						:    Optional[Any]	):
             _UpperCAmelCase					:  List[str]											=   self.get_image_processor()
             _UpperCAmelCase					:  List[Any]											=   self.get_tokenizer()

             _UpperCAmelCase					:  List[str]											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  List[str]											=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             _UpperCAmelCase					:  Dict											=   processor.batch_decode(A	)
             _UpperCAmelCase					:  Dict											=   tokenizer.batch_decode(A	)

             self.assertListEqual(A						, A	)





      def 					_A     (	self						:    Any	):
             _UpperCAmelCase					:  List[Any]											=   self.get_image_processor()
             _UpperCAmelCase					:  Union[str, Any]											=   self.get_tokenizer()

             _UpperCAmelCase					:  Optional[int]											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  Dict											=   "lower newer"
             _UpperCAmelCase					:  Tuple											=   self.prepare_image_inputs()

             _UpperCAmelCase					:  Dict											=   processor(text=A						, images=A	)

             # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
             self.assertListEqual(list(inputs.keys()	)						, ["input_ids", "attention_mask", "pixel_values"]	)

