






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 		A_			(		)							->str:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						argparse.ArgumentParser()
 parser.add_argument('-f'	)
 SCREAMING_SNAKE_CASE							  =						parser.parse_args()
 return args.f





def 		A_			(		lowercase_	)							->str:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						{}
 SCREAMING_SNAKE_CASE							  =						os.path.join(lowercase_					,       'all_results.json'	)
 if os.path.exists(lowercase_	):
  with open(lowercase_					,       'r'	) as f:
   SCREAMING_SNAKE_CASE							  =						json.load(lowercase_	)
 else:
  raise ValueError(f'''can\'t find {path}'''	)
 return results





def 		A_			(		)							->Any:







 """simple docstring"""

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


__UpperCAmelCase      				=							logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 					a_(						lowercase__					):



     """simple docstring"""



     @skip('Skipping until shape inference for to_tf_dataset PR is merged.')
     def   __UpperCamelCase		(						self			:					Optional[int])    ->					List[Any]:

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





     def   __UpperCamelCase		(						self			:					Union[str, Any])    ->					Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.get_auto_remove_tmp_dir()
      SCREAMING_SNAKE_CASE							  =						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(lowerCAmelCase__       ,       'argv'       ,       lowerCAmelCase__):
       run_clm.main()
       SCREAMING_SNAKE_CASE							  =						get_results(lowerCAmelCase__)
       self.assertLess(result['eval_perplexity']       ,       1_0_0)





     def   __UpperCamelCase		(						self			:					int)    ->					str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.get_auto_remove_tmp_dir()
      SCREAMING_SNAKE_CASE							  =						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(lowerCAmelCase__       ,       'argv'       ,       lowerCAmelCase__):
       run_mlm.main()
       SCREAMING_SNAKE_CASE							  =						get_results(lowerCAmelCase__)
       self.assertLess(result['eval_perplexity']       ,       4_2)





     def   __UpperCamelCase		(						self			:					List[str])    ->					List[Any]:

      """simple docstring"""
      # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
      SCREAMING_SNAKE_CASE							  =						7 if get_gpu_count() > 1 else 2

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





     def   __UpperCamelCase		(						self			:					Optional[Any])    ->					Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.get_auto_remove_tmp_dir()
      SCREAMING_SNAKE_CASE							  =						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(lowerCAmelCase__       ,       'argv'       ,       lowerCAmelCase__):
       run_squad.main()
       SCREAMING_SNAKE_CASE							  =						get_results(lowerCAmelCase__)
       self.assertGreaterEqual(result['f1']       ,       3_0)
       self.assertGreaterEqual(result['exact']       ,       3_0)





     def   __UpperCamelCase		(						self			:					Tuple)    ->					Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.get_auto_remove_tmp_dir()
      SCREAMING_SNAKE_CASE							  =						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(lowerCAmelCase__       ,       'argv'       ,       lowerCAmelCase__):
       run_swag.main()
       SCREAMING_SNAKE_CASE							  =						get_results(lowerCAmelCase__)
       self.assertGreaterEqual(result['val_accuracy']       ,       0.8)





     @slow
     def   __UpperCamelCase		(						self			:					Dict)    ->					Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.get_auto_remove_tmp_dir()
      SCREAMING_SNAKE_CASE							  =						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(lowerCAmelCase__       ,       'argv'       ,       lowerCAmelCase__):
       run_summarization.main()
       SCREAMING_SNAKE_CASE							  =						get_results(lowerCAmelCase__)
       self.assertGreaterEqual(result['rouge1']       ,       1_0)
       self.assertGreaterEqual(result['rouge2']       ,       2)
       self.assertGreaterEqual(result['rougeL']       ,       7)
       self.assertGreaterEqual(result['rougeLsum']       ,       7)





     @slow
     def   __UpperCamelCase		(						self			:					int)    ->					Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.get_auto_remove_tmp_dir()
      SCREAMING_SNAKE_CASE							  =						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(lowerCAmelCase__       ,       'argv'       ,       lowerCAmelCase__):
       run_translation.main()
       SCREAMING_SNAKE_CASE							  =						get_results(lowerCAmelCase__)
       self.assertGreaterEqual(result['bleu']       ,       3_0)





     def   __UpperCamelCase		(						self			:					Any)    ->					List[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.get_auto_remove_tmp_dir()
      SCREAMING_SNAKE_CASE							  =						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(lowerCAmelCase__       ,       'argv'       ,       lowerCAmelCase__):
       run_image_classification.main()
       SCREAMING_SNAKE_CASE							  =						get_results(lowerCAmelCase__)
       self.assertGreaterEqual(result['accuracy']       ,       0.7)












import math





def 		A_			(		lowercase_	)							->List[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						0
 if n % 2 == 0:
  count += 1
  while n % 2 == 0:
   SCREAMING_SNAKE_CASE							  =						int(n / 2	)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

 SCREAMING_SNAKE_CASE							  =						3

 while i <= int(math.sqrt(lowercase_	)	):
  if n % i == 0:
   count += 1
   while n % i == 0:
    SCREAMING_SNAKE_CASE							  =						int(n / i	)
  SCREAMING_SNAKE_CASE							  =						i + 2

 # this condition checks the prime
 # number n is greater than 2

 if n > 2:
  count += 1
 return count


if __name__ == "__main__":
      __UpperCAmelCase      				=							5_1_2_4_2_1_8_3
      print(f'The number of distinct prime factors is/are {exact_prime_factor_count(n)}')
      print(f'The value of log(log(n)) is {math.log(math.log(n)):.4f}')




