


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


a					:				Optional[Any]       =      [
    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)

a					:				Optional[int]       =      logging.getLogger()






def  _UpperCAmelCase						( )		->Optional[int]:


 '''simple docstring'''
 snake_case :			int							=   argparse.ArgumentParser()
 parser.add_argument('''-f'''							)
 snake_case :			Union[str, Any]							=   parser.parse_args()
 return args.f






def  _UpperCAmelCase						( __lowerCamelCase							)		->Tuple:


 '''simple docstring'''
 snake_case :			List[Any]							=   {}
 snake_case :			Optional[int]							=   os.path.join(__lowerCamelCase			,  '''all_results.json'''							)
 if os.path.exists(__lowerCamelCase							):
  with open(__lowerCamelCase			,  '''r'''							) as f:
   snake_case :			int							=   json.load(__lowerCamelCase							)
 else:
  raise ValueError(f'''can\'t find {path}'''							)
 return results






def  _UpperCAmelCase						( )		->str:


 '''simple docstring'''
 return bool(tf.config.list_physical_devices('''GPU'''							)							)


a					:				str       =      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):

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






        '''simple docstring'''






        snake_case :			Optional[int]							=   self.get_auto_remove_tmp_dir()
        snake_case :			str							=   f'''
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

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

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

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			List[str]							=   self.get_auto_remove_tmp_dir()
        snake_case :			Tuple							=   f'''
            run_clm.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.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(a__						,    '''argv'''						,    a__       ):
         run_clm.main()
         snake_case :			Any							=   get_results(a__       )
         self.assertLess(result['''eval_perplexity''']						,    1_00       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			int							=   self.get_auto_remove_tmp_dir()
        snake_case :			str							=   f'''
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        '''.split()

        with patch.object(a__						,    '''argv'''						,    a__       ):
         run_mlm.main()
         snake_case :			List[str]							=   get_results(a__       )
         self.assertLess(result['''eval_perplexity''']						,    42       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Tuple							=   7 if get_gpu_count() > 1 else 2

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

        with patch.object(a__						,    '''argv'''						,    a__       ):
         run_ner.main()
         snake_case :			int							=   get_results(a__       )
         self.assertGreaterEqual(result['''accuracy''']						,    0.75       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			str							=   self.get_auto_remove_tmp_dir()
        snake_case :			Any							=   f'''
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

        with patch.object(a__						,    '''argv'''						,    a__       ):
         run_squad.main()
         snake_case :			Optional[Any]							=   get_results(a__       )
         self.assertGreaterEqual(result['''f1''']						,    30       )
         self.assertGreaterEqual(result['''exact''']						,    30       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			List[Any]							=   self.get_auto_remove_tmp_dir()
        snake_case :			Dict							=   f'''
            run_swag.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

        with patch.object(a__						,    '''argv'''						,    a__       ):
         run_swag.main()
         snake_case :			Tuple							=   get_results(a__       )
         self.assertGreaterEqual(result['''val_accuracy''']						,    0.8       )

       @slow
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Tuple							=   self.get_auto_remove_tmp_dir()
        snake_case :			Union[str, Any]							=   f'''
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

        with patch.object(a__						,    '''argv'''						,    a__       ):
         run_summarization.main()
         snake_case :			Any							=   get_results(a__       )
         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'''






        snake_case :			Union[str, Any]							=   self.get_auto_remove_tmp_dir()
        snake_case :			Dict							=   f'''
            run_translation.py
            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        '''.split()

        with patch.object(a__						,    '''argv'''						,    a__       ):
         run_translation.main()
         snake_case :			Any							=   get_results(a__       )
         self.assertGreaterEqual(result['''bleu''']						,    30       )



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Union[str, Any]							=   self.get_auto_remove_tmp_dir()
        snake_case :			int							=   f'''
            run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --model_name_or_path microsoft/resnet-18
            --do_train
            --do_eval
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --dataloader_num_workers 16
            --num_train_epochs 2
            --train_val_split 0.1
            --seed 42
            --ignore_mismatched_sizes True
            '''.split()

        with patch.object(a__						,    '''argv'''						,    a__       ):
         run_image_classification.main()
         snake_case :			str							=   get_results(a__       )
         self.assertGreaterEqual(result['''accuracy''']						,    0.7       )












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


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






