
'''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


a_           =    [
    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_           =    logging.getLogger()

def     UpperCamelCase_  (						):
    """simple docstring"""






    snake_case_			:     int        =    argparse.ArgumentParser()
    parser.add_argument("-f"							)
    snake_case_			:     List[Any]        =    parser.parse_args()
    return args.f

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     Union[str, Any]        =    {}
    snake_case_			:     List[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:
            snake_case_			:     List[Any]        =    json.load(__SCREAMING_SNAKE_CASE							)
    else:
        raise ValueError(f'can\'t find {path}'							)
    return results

def     UpperCamelCase_  (						):
    """simple docstring"""






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


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






class  UpperCAmelCase_					(						snake_case__       ):





 @skip("Skipping until shape inference for to_tf_dataset PR is merged.")
 def 							snake_case__		(     self):
     snake_case_			:     int        =    self.get_auto_remove_tmp_dir()
     snake_case_			:     Dict        =    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(lowercase_			,							"argv"			,							lowercase_):
         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_			:     str        =    get_results(lowercase_)
         self.assertGreaterEqual(result["eval_accuracy"]			,							0.75)





 def 							snake_case__		(     self):
     snake_case_			:     Union[str, Any]        =    self.get_auto_remove_tmp_dir()
     snake_case_			:     Any        =    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(lowercase_			,							"argv"			,							lowercase_):
         run_clm.main()
         snake_case_			:     Union[str, Any]        =    get_results(lowercase_)
         self.assertLess(result["eval_perplexity"]			,							1_00)





 def 							snake_case__		(     self):
     snake_case_			:     Tuple        =    self.get_auto_remove_tmp_dir()
     snake_case_			:     Union[str, Any]        =    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(lowercase_			,							"argv"			,							lowercase_):
         run_mlm.main()
         snake_case_			:     Optional[Any]        =    get_results(lowercase_)
         self.assertLess(result["eval_perplexity"]			,							42)





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

     snake_case_			:     Tuple        =    self.get_auto_remove_tmp_dir()
     snake_case_			:     Dict        =    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(lowercase_			,							"argv"			,							lowercase_):
         run_ner.main()
         snake_case_			:     Optional[int]        =    get_results(lowercase_)
         self.assertGreaterEqual(result["accuracy"]			,							0.75)





 def 							snake_case__		(     self):
     snake_case_			:     int        =    self.get_auto_remove_tmp_dir()
     snake_case_			:     Union[str, 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(lowercase_			,							"argv"			,							lowercase_):
         run_squad.main()
         snake_case_			:     str        =    get_results(lowercase_)
         self.assertGreaterEqual(result["f1"]			,							30)
         self.assertGreaterEqual(result["exact"]			,							30)





 def 							snake_case__		(     self):
     snake_case_			:     List[str]        =    self.get_auto_remove_tmp_dir()
     snake_case_			:     Optional[int]        =    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(lowercase_			,							"argv"			,							lowercase_):
         run_swag.main()
         snake_case_			:     Union[str, Any]        =    get_results(lowercase_)
         self.assertGreaterEqual(result["val_accuracy"]			,							0.8)





 @slow
 def 							snake_case__		(     self):
     snake_case_			:     List[Any]        =    self.get_auto_remove_tmp_dir()
     snake_case_			:     Dict        =    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(lowercase_			,							"argv"			,							lowercase_):
         run_summarization.main()
         snake_case_			:     List[str]        =    get_results(lowercase_)
         self.assertGreaterEqual(result["rouge1"]			,							10)
         self.assertGreaterEqual(result["rouge2"]			,							2)
         self.assertGreaterEqual(result["rougeL"]			,							7)
         self.assertGreaterEqual(result["rougeLsum"]			,							7)





 @slow
 def 							snake_case__		(     self):
     snake_case_			:     Any        =    self.get_auto_remove_tmp_dir()
     snake_case_			:     List[str]        =    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(lowercase_			,							"argv"			,							lowercase_):
         run_translation.main()
         snake_case_			:     List[Any]        =    get_results(lowercase_)
         self.assertGreaterEqual(result["bleu"]			,							30)





 def 							snake_case__		(     self):
     snake_case_			:     int        =    self.get_auto_remove_tmp_dir()
     snake_case_			:     Optional[int]        =    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(lowercase_			,							"argv"			,							lowercase_):
         run_image_classification.main()
         snake_case_			:     List[str]        =    get_results(lowercase_)
         self.assertGreaterEqual(result["accuracy"]			,							0.7)






'''simple docstring'''
import itertools

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE = 1_0_0_0_0_0_0							):
    """simple docstring"""






    snake_case_			:     Optional[int]        =    [1]

    for i in itertools.count(len(__SCREAMING_SNAKE_CASE							)							):
        snake_case_			:     str        =    0
        for j in itertools.count(1							):
            snake_case_			:     List[str]        =    -1 if j % 2 == 0 else +1
            snake_case_			:     str        =    (j * j * 3 - j) // 2
            if index > i:
                break
            item += partitions[i - index] * sign
            item %= number
            index += j
            if index > i:
                break
            item += partitions[i - index] * sign
            item %= number

        if item == 0:
            return i
        partitions.append(__SCREAMING_SNAKE_CASE							)

    return 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(f"""{solution() = }""")




