





"""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			:		Dict		    =   [
    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			:		Any		    =   logging.getLogger()

def 						_SCREAMING_SNAKE_CASE       ():
 lowerCAmelCase  							=      argparse.ArgumentParser()
 parser.add_argument('-f'       )
 lowerCAmelCase  							=      parser.parse_args()
 return args.f

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  int       ):
 lowerCAmelCase  							=      {}
 lowerCAmelCase  							=      os.path.join(_UpperCAmelCase   ,			'all_results.json'       )
 if os.path.exists(_UpperCAmelCase       ):
  with open(_UpperCAmelCase   ,			'r'       ) as f:
   lowerCAmelCase  							=      json.load(_UpperCAmelCase       )
 else:
  raise ValueError(F'can\'t find {path}'       )
 return results

def 						_SCREAMING_SNAKE_CASE       ():
 return bool(tf.config.list_physical_devices('GPU'       )       )


__UpperCamelCase			:		List[str]		    =   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 	a				(					a__   ):



 @skip('Skipping until shape inference for to_tf_dataset PR is merged.'			)
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.get_auto_remove_tmp_dir()
  lowerCAmelCase  							=      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(_snake_case							,    'argv'							,    _snake_case			):
   run_text_classification.main()
   # Reset the mixed precision policy so we don't break other tests
   tf.keras.mixed_precision.set_global_policy('float32'			)
   lowerCAmelCase  							=      get_results(_snake_case			)
   self.assertGreaterEqual(result['eval_accuracy']							,    0.75			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.get_auto_remove_tmp_dir()
  lowerCAmelCase  							=      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(_snake_case							,    'argv'							,    _snake_case			):
   run_clm.main()
   lowerCAmelCase  							=      get_results(_snake_case			)
   self.assertLess(result['eval_perplexity']							,    1_00			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.get_auto_remove_tmp_dir()
  lowerCAmelCase  							=      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(_snake_case							,    'argv'							,    _snake_case			):
   run_mlm.main()
   lowerCAmelCase  							=      get_results(_snake_case			)
   self.assertLess(result['eval_perplexity']							,    42			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      7 if get_gpu_count() > 1 else 2

  lowerCAmelCase  							=      self.get_auto_remove_tmp_dir()
  lowerCAmelCase  							=      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(_snake_case							,    'argv'							,    _snake_case			):
   run_ner.main()
   lowerCAmelCase  							=      get_results(_snake_case			)
   self.assertGreaterEqual(result['accuracy']							,    0.75			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.get_auto_remove_tmp_dir()
  lowerCAmelCase  							=      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(_snake_case							,    'argv'							,    _snake_case			):
   run_squad.main()
   lowerCAmelCase  							=      get_results(_snake_case			)
   self.assertGreaterEqual(result['f1']							,    30			)
   self.assertGreaterEqual(result['exact']							,    30			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.get_auto_remove_tmp_dir()
  lowerCAmelCase  							=      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(_snake_case							,    'argv'							,    _snake_case			):
   run_swag.main()
   lowerCAmelCase  							=      get_results(_snake_case			)
   self.assertGreaterEqual(result['val_accuracy']							,    0.8			)



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.get_auto_remove_tmp_dir()
  lowerCAmelCase  							=      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(_snake_case							,    'argv'							,    _snake_case			):
   run_summarization.main()
   lowerCAmelCase  							=      get_results(_snake_case			)
   self.assertGreaterEqual(result['rouge1']							,    10			)
   self.assertGreaterEqual(result['rouge2']							,    2			)
   self.assertGreaterEqual(result['rougeL']							,    7			)
   self.assertGreaterEqual(result['rougeLsum']							,    7			)



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.get_auto_remove_tmp_dir()
  lowerCAmelCase  							=      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(_snake_case							,    'argv'							,    _snake_case			):
   run_translation.main()
   lowerCAmelCase  							=      get_results(_snake_case			)
   self.assertGreaterEqual(result['bleu']							,    30			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.get_auto_remove_tmp_dir()
  lowerCAmelCase  							=      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(_snake_case							,    'argv'							,    _snake_case			):
   run_image_classification.main()
   lowerCAmelCase  							=      get_results(_snake_case			)
   self.assertGreaterEqual(result['accuracy']							,    0.7			)













def 		_UpperCamelCase      (			UpperCamelCase_				:    str						)     ->      int:

						"""simple docstring"""



						lowerCAmelCase__			  =  len(UpperCamelCase_						)
						lowerCAmelCase__			  =  [0] * length
						lowerCAmelCase__			  =  [[False for i in range(UpperCamelCase_						)] for j in range(UpperCamelCase_						)]
						for i, c in enumerate(UpperCamelCase_						):
												lowerCAmelCase__			  =  i
												for j in range(i + 1						):
																		if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
																								lowerCAmelCase__			  =  True
																								lowerCAmelCase__			  =  min(UpperCamelCase_			,				0 if j == 0 else (cut[j - 1] + 1)						)
												lowerCAmelCase__			  =  mincut
						return cut[length - 1]


if __name__ == "__main__":
					__snake_case			:			Optional[Any]			   =   input("""Enter the string: """).strip()
					__snake_case			:			List[Any]			   =   find_minimum_partitions(s)
					print(f'Minimum number of partitions required for the \'{s}\' is {ans}')





