






'''simple docstring'''







import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


UpperCamelCase_					=				[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-generation',
        'text-classification',
        'token-classification',
        'language-modeling',
        'multiple-choice',
        'question-answering',
        'summarization',
        'translation',
        'image-classification',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
  import run_clm
  import run_image_classification
  import run_mlm
  import run_ner
  import run_qa as run_squad
  import run_summarization
  import run_swag
  import run_text_classification
  import run_translation


logging.basicConfig(level=logging.DEBUG)

UpperCamelCase_					=				logging.getLogger()






def  lowerCAmelCase__		(					)   -> List[Any]:
       UpperCAmelCase__	:   List[str]												=	argparse.ArgumentParser()
       parser.add_argument('''-f'''			)
       UpperCAmelCase__	:   Union[str, Any]												=	parser.parse_args()
       return args.f






def  lowerCAmelCase__		(					a_  :     List[str]			)   -> Optional[Any]:
       UpperCAmelCase__	:   Union[str, Any]												=	{}
       UpperCAmelCase__	:   Optional[Any]												=	os.path.join(__SCREAMING_SNAKE_CASE			,						'''all_results.json'''			)
       if os.path.exists(__SCREAMING_SNAKE_CASE			):
              with open(__SCREAMING_SNAKE_CASE			,						'''r'''			) as f:
                     UpperCAmelCase__	:   int												=	json.load(__SCREAMING_SNAKE_CASE			)
       else:
              raise ValueError(f"""can't find {path}"""			)
       return results






def  lowerCAmelCase__		(					)   -> Dict:
       return bool(tf.config.list_physical_devices('''GPU'''			)			)


UpperCamelCase_					=				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class 					__UpperCAmelCase (		_SCREAMING_SNAKE_CASE	):

      '''simple docstring'''





      @skip('''Skipping until shape inference for to_tf_dataset PR is merged.'''						)
      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   str												=	self.get_auto_remove_tmp_dir()
             UpperCAmelCase__	:   str												=	F"""\n            run_text_classification.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            """.split()

             if is_cuda_available():
                    testargs.append('''--fp16'''						)

             with patch.object(_UpperCAmelCase  ,		'''argv'''  ,		_UpperCAmelCase						):
                    run_text_classification.main()
                    # Reset the mixed precision policy so we don't break other tests
                    tf.keras.mixed_precision.set_global_policy('''float32'''						)
                    UpperCAmelCase__	:   Optional[int]												=	get_results(_UpperCAmelCase						)
                    self.assertGreaterEqual(result['''eval_accuracy''']  ,		0.75						)




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Tuple												=	self.get_auto_remove_tmp_dir()
             UpperCAmelCase__	:   Optional[int]												=	F"""\n            run_clm.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            """.split()

             if len(tf.config.list_physical_devices('''GPU'''						)						) > 1:
                    # Skipping because there are not enough batches to train the model + would need a drop_last to work.
                    return

             with patch.object(_UpperCAmelCase  ,		'''argv'''  ,		_UpperCAmelCase						):
                    run_clm.main()
                    UpperCAmelCase__	:   int												=	get_results(_UpperCAmelCase						)
                    self.assertLess(result['''eval_perplexity''']  ,		100						)




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Optional[Any]												=	self.get_auto_remove_tmp_dir()
             UpperCAmelCase__	:   Optional[int]												=	F"""\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --max_seq_length 64\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --prediction_loss_only\n            --num_train_epochs=1\n            --learning_rate=1e-4\n        """.split()

             with patch.object(_UpperCAmelCase  ,		'''argv'''  ,		_UpperCAmelCase						):
                    run_mlm.main()
                    UpperCAmelCase__	:   Optional[Any]												=	get_results(_UpperCAmelCase						)
                    self.assertLess(result['''eval_perplexity''']  ,		42						)




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   int												=	7 if get_gpu_count() > 1 else 2

             UpperCAmelCase__	:   Optional[Any]												=	self.get_auto_remove_tmp_dir()
             UpperCAmelCase__	:   Optional[Any]												=	F"""\n            run_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        """.split()

             with patch.object(_UpperCAmelCase  ,		'''argv'''  ,		_UpperCAmelCase						):
                    run_ner.main()
                    UpperCAmelCase__	:   List[Any]												=	get_results(_UpperCAmelCase						)
                    self.assertGreaterEqual(result['''accuracy''']  ,		0.75						)




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Tuple												=	self.get_auto_remove_tmp_dir()
             UpperCAmelCase__	:   Any												=	F"""\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        """.split()

             with patch.object(_UpperCAmelCase  ,		'''argv'''  ,		_UpperCAmelCase						):
                    run_squad.main()
                    UpperCAmelCase__	:   Dict												=	get_results(_UpperCAmelCase						)
                    self.assertGreaterEqual(result['''f1''']  ,		30						)
                    self.assertGreaterEqual(result['''exact''']  ,		30						)




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Union[str, Any]												=	self.get_auto_remove_tmp_dir()
             UpperCAmelCase__	:   Dict												=	F"""\n            run_swag.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=20\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        """.split()

             with patch.object(_UpperCAmelCase  ,		'''argv'''  ,		_UpperCAmelCase						):
                    run_swag.main()
                    UpperCAmelCase__	:   int												=	get_results(_UpperCAmelCase						)
                    self.assertGreaterEqual(result['''val_accuracy''']  ,		0.8						)




      @slow
      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Tuple												=	self.get_auto_remove_tmp_dir()
             UpperCAmelCase__	:   List[Any]												=	F"""\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        """.split()

             with patch.object(_UpperCAmelCase  ,		'''argv'''  ,		_UpperCAmelCase						):
                    run_summarization.main()
                    UpperCAmelCase__	:   Any												=	get_results(_UpperCAmelCase						)
                    self.assertGreaterEqual(result['''rouge1''']  ,		10						)
                    self.assertGreaterEqual(result['''rouge2''']  ,		2						)
                    self.assertGreaterEqual(result['''rougeL''']  ,		7						)
                    self.assertGreaterEqual(result['''rougeLsum''']  ,		7						)




      @slow
      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Optional[int]												=	self.get_auto_remove_tmp_dir()
             UpperCAmelCase__	:   Tuple												=	F"""\n            run_translation.py\n            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n        """.split()

             with patch.object(_UpperCAmelCase  ,		'''argv'''  ,		_UpperCAmelCase						):
                    run_translation.main()
                    UpperCAmelCase__	:   int												=	get_results(_UpperCAmelCase						)
                    self.assertGreaterEqual(result['''bleu''']  ,		30						)




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Tuple												=	self.get_auto_remove_tmp_dir()
             UpperCAmelCase__	:   Tuple												=	F"""\n            run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --model_name_or_path microsoft/resnet-18\n            --do_train\n            --do_eval\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --dataloader_num_workers 16\n            --num_train_epochs 2\n            --train_val_split 0.1\n            --seed 42\n            --ignore_mismatched_sizes True\n            """.split()

             with patch.object(_UpperCAmelCase  ,		'''argv'''  ,		_UpperCAmelCase						):
                    run_image_classification.main()
                    UpperCAmelCase__	:   Optional[int]												=	get_results(_UpperCAmelCase						)
                    self.assertGreaterEqual(result['''accuracy''']  ,		0.7						)

'''simple docstring'''
A__ :		Optional[Any]					=[chr(i) for i in range(32, 126)]
A__ :		Tuple					=list(range(len(alphabets)))
A__ :		int					=list(range(len(alphabets)))
A__ :		Optional[Any]					=list(range(len(alphabets)))
A__ :		Any					=list(reversed(range(len(alphabets))))
A__ :		Optional[int]					=[]
A__ :		Tuple					=0







def 							A_  (    )					->							Any:





  """simple docstring"""

  global gear_one_pos
  global gear_two_pos
  global gear_three_pos
  __A		:  Any       				=		gear_one[0]
  gear_one.append(__SCREAMING_SNAKE_CASE			)
  del gear_one[0]
  gear_one_pos += 1
  if gear_one_pos % int(len(__SCREAMING_SNAKE_CASE			)			) == 0:
    __A		:  Any       				=		gear_two[0]
    gear_two.append(__SCREAMING_SNAKE_CASE			)
    del gear_two[0]
    gear_two_pos += 1
    if gear_two_pos % int(len(__SCREAMING_SNAKE_CASE			)			) == 0:
      __A		:  str       				=		gear_three[0]
      gear_three.append(__SCREAMING_SNAKE_CASE			)
      del gear_three[0]
      gear_three_pos += 1







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str			)					->							List[str]:





  """simple docstring"""

  __A		:  Union[str, Any]       				=		alphabets.index(__SCREAMING_SNAKE_CASE			)
  __A		:  Dict       				=		gear_one[target]
  __A		:  int       				=		gear_two[target]
  __A		:  List[str]       				=		gear_three[target]
  __A		:  List[Any]       				=		reflector[target]
  __A		:  List[Any]       				=		gear_three.index(__SCREAMING_SNAKE_CASE			)
  __A		:  Tuple       				=		gear_two.index(__SCREAMING_SNAKE_CASE			)
  __A		:  List[str]       				=		gear_one.index(__SCREAMING_SNAKE_CASE			)
  code.append(alphabets[target]			)
  rotator()


if __name__ == "__main__":
     A__ :		int					=list(input('Type your message:\n'))
     while True:
          try:
               A__ :		Any					=int(input('Please set token:(must be only digits)\n'))
               break
          except Exception as error:
               print(error)
     for _ in range(token):
          rotator()
     for j in decode:
          engine(j)
     print('\n' + ''.join(code))
     print(
         F"\nYour Token is {token} please write it down.\nIf you want to decode "
         'this message again you should input same digits as token!'
     )



