'''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


__SCREAMING_SNAKE_CASE	:					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)

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

def      UpperCamelCase_							( )   ->   str:


       """simple docstring"""







       _UpperCAmelCase					:  Tuple											=   argparse.ArgumentParser()
       parser.add_argument("-f"   )
       _UpperCAmelCase					:  Union[str, Any]											=   parser.parse_args()
       return args.f

def      UpperCamelCase_							( _UpperCAmelCase  :				Dict   )   ->   Any:


       """simple docstring"""







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

def      UpperCamelCase_							( )   ->   Union[str, Any]:


       """simple docstring"""







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


__SCREAMING_SNAKE_CASE	:					Dict					=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


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

      @skip("Skipping until shape inference for to_tf_dataset PR is merged."	)
      def 					_A     (	self						:    Tuple	):
             _UpperCAmelCase					:  Any											=   self.get_auto_remove_tmp_dir()
             _UpperCAmelCase					:  int											=   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"	)
                    _UpperCAmelCase					:  Any											=   get_results(A	)
                    self.assertGreaterEqual(result["eval_accuracy"]						, 0.75	)




      def 					_A     (	self						:    Optional[Any]	):
             _UpperCAmelCase					:  List[Any]											=   self.get_auto_remove_tmp_dir()
             _UpperCAmelCase					:  Any											=   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()
                    _UpperCAmelCase					:  List[Any]											=   get_results(A	)
                    self.assertLess(result["eval_perplexity"]						, 100	)




      def 					_A     (	self						:    int	):
             _UpperCAmelCase					:  Union[str, Any]											=   self.get_auto_remove_tmp_dir()
             _UpperCAmelCase					:  Optional[int]											=   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()
                    _UpperCAmelCase					:  List[Any]											=   get_results(A	)
                    self.assertLess(result["eval_perplexity"]						, 42	)




      def 					_A     (	self						:    str	):
             # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
             _UpperCAmelCase					:  Optional[int]											=   7 if get_gpu_count() > 1 else 2

             _UpperCAmelCase					:  List[str]											=   self.get_auto_remove_tmp_dir()
             _UpperCAmelCase					:  str											=   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()
                    _UpperCAmelCase					:  Optional[int]											=   get_results(A	)
                    self.assertGreaterEqual(result["accuracy"]						, 0.75	)




      def 					_A     (	self						:    Any	):
             _UpperCAmelCase					:  Optional[Any]											=   self.get_auto_remove_tmp_dir()
             _UpperCAmelCase					:  Optional[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()
                    _UpperCAmelCase					:  Union[str, Any]											=   get_results(A	)
                    self.assertGreaterEqual(result["f1"]						, 30	)
                    self.assertGreaterEqual(result["exact"]						, 30	)




      def 					_A     (	self						:    Union[str, Any]	):
             _UpperCAmelCase					:  str											=   self.get_auto_remove_tmp_dir()
             _UpperCAmelCase					:  List[str]											=   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()
                    _UpperCAmelCase					:  Union[str, Any]											=   get_results(A	)
                    self.assertGreaterEqual(result["val_accuracy"]						, 0.8	)




      @slow
      def 					_A     (	self						:    List[str]	):
             _UpperCAmelCase					:  Union[str, Any]											=   self.get_auto_remove_tmp_dir()
             _UpperCAmelCase					:  Optional[int]											=   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()
                    _UpperCAmelCase					:  str											=   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 					_A     (	self						:    Optional[Any]	):
             _UpperCAmelCase					:  int											=   self.get_auto_remove_tmp_dir()
             _UpperCAmelCase					:  List[str]											=   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()
                    _UpperCAmelCase					:  Union[str, Any]											=   get_results(A	)
                    self.assertGreaterEqual(result["bleu"]						, 30	)





      def 					_A     (	self						:    Optional[Any]	):
             _UpperCAmelCase					:  Tuple											=   self.get_auto_remove_tmp_dir()
             _UpperCAmelCase					:  Optional[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()
                    _UpperCAmelCase					:  Any											=   get_results(A	)
                    self.assertGreaterEqual(result["accuracy"]						, 0.7	)


'''simple docstring'''




from itertools import compress, repeat
from math import ceil, sqrt

def      UpperCamelCase_							( _UpperCAmelCase  :				int   )   ->   list[int]:


       """simple docstring"""







       if num <= 2:
              return []
       if num == 3:
              return [2]

       # Odd sieve for numbers in range [3, num - 1]
       _UpperCAmelCase					:  Optional[int]											=   bytearray(b"\x01"   ) * ((num >> 1) - 1)

       for i in range(3      ,     int(sqrt(_UpperCAmelCase   )   ) + 1      ,     2   ):
              if sieve[(i >> 1) - 1]:
                     _UpperCAmelCase					:  List[Any]											=   i**2
                     _UpperCAmelCase					:  Optional[int]											=   repeat(
                         0      ,     ceil((num - i_squared) / (i << 1)   )   )

       return [2] + list(compress(range(3      ,     _UpperCAmelCase      ,     2   )      ,     _UpperCAmelCase   )   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

