






"""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					=     [
    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(    ):
       """simple docstring"""





       lowercase__:							Optional[Any]         =     argparse.ArgumentParser()
       parser.add_argument("-f"						)
       lowercase__:							Any         =     parser.parse_args()
       return args.f






def 							A(    snake_case_						):
       """simple docstring"""





       lowercase__:							List[Any]         =     {}
       lowercase__:							List[Any]         =     os.path.join(snake_case_		,  "all_results.json"						)
       if os.path.exists(snake_case_						):
              with open(snake_case_		,  "r"						) as f:
                     lowercase__:							Tuple         =     json.load(snake_case_						)
       else:
              raise ValueError(F"""can't find {path}"""						)
       return results






def 							A(    ):
       """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 __lowercase							(       self)			->   List[Any]:




          '''simple docstring'''




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





   def __lowercase							(       self)			->   int:




          '''simple docstring'''




          lowercase__:							Union[str, Any]         =     self.get_auto_remove_tmp_dir()
          lowercase__:							Union[str, 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(UpperCAmelCase_					, "argv"					, UpperCAmelCase_):
                 run_clm.main()
                 lowercase__:							List[Any]         =     get_results(UpperCAmelCase_)
                 self.assertLess(result["eval_perplexity"]					, 100)





   def __lowercase							(       self)			->   Any:




          '''simple docstring'''




          lowercase__:							Tuple         =     self.get_auto_remove_tmp_dir()
          lowercase__:							List[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(UpperCAmelCase_					, "argv"					, UpperCAmelCase_):
                 run_mlm.main()
                 lowercase__:							Optional[Any]         =     get_results(UpperCAmelCase_)
                 self.assertLess(result["eval_perplexity"]					, 42)





   def __lowercase							(       self)			->   Tuple:




          '''simple docstring'''




          lowercase__:							List[Any]         =     7 if get_gpu_count() > 1 else 2

          lowercase__:							Union[str, Any]         =     self.get_auto_remove_tmp_dir()
          lowercase__:							Tuple         =     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(UpperCAmelCase_					, "argv"					, UpperCAmelCase_):
                 run_ner.main()
                 lowercase__:							Optional[int]         =     get_results(UpperCAmelCase_)
                 self.assertGreaterEqual(result["accuracy"]					, 0.75)





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




          '''simple docstring'''




          lowercase__:							List[Any]         =     self.get_auto_remove_tmp_dir()
          lowercase__:							List[str]         =     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(UpperCAmelCase_					, "argv"					, UpperCAmelCase_):
                 run_squad.main()
                 lowercase__:							Union[str, Any]         =     get_results(UpperCAmelCase_)
                 self.assertGreaterEqual(result["f1"]					, 30)
                 self.assertGreaterEqual(result["exact"]					, 30)





   def __lowercase							(       self)			->   Dict:




          '''simple docstring'''




          lowercase__:							Tuple         =     self.get_auto_remove_tmp_dir()
          lowercase__:							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(UpperCAmelCase_					, "argv"					, UpperCAmelCase_):
                 run_swag.main()
                 lowercase__:							Optional[int]         =     get_results(UpperCAmelCase_)
                 self.assertGreaterEqual(result["val_accuracy"]					, 0.8)





   @slow
   def __lowercase							(       self)			->   str:




          '''simple docstring'''




          lowercase__:							Optional[Any]         =     self.get_auto_remove_tmp_dir()
          lowercase__:							List[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(UpperCAmelCase_					, "argv"					, UpperCAmelCase_):
                 run_summarization.main()
                 lowercase__:							int         =     get_results(UpperCAmelCase_)
                 self.assertGreaterEqual(result["rouge1"]					, 10)
                 self.assertGreaterEqual(result["rouge2"]					, 2)
                 self.assertGreaterEqual(result["rougeL"]					, 7)
                 self.assertGreaterEqual(result["rougeLsum"]					, 7)





   @slow
   def __lowercase							(       self)			->   Optional[Any]:




          '''simple docstring'''




          lowercase__:							Dict         =     self.get_auto_remove_tmp_dir()
          lowercase__:							Optional[Any]         =     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(UpperCAmelCase_					, "argv"					, UpperCAmelCase_):
                 run_translation.main()
                 lowercase__:							List[str]         =     get_results(UpperCAmelCase_)
                 self.assertGreaterEqual(result["bleu"]					, 30)





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




          '''simple docstring'''




          lowercase__:							int         =     self.get_auto_remove_tmp_dir()
          lowercase__:							Optional[Any]         =     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(UpperCAmelCase_					, "argv"					, UpperCAmelCase_):
                 run_image_classification.main()
                 lowercase__:							str         =     get_results(UpperCAmelCase_)
                 self.assertGreaterEqual(result["accuracy"]					, 0.7)







from __future__ import annotations

import sys







def     A__	(       __A						):







    '''simple docstring'''
    if n < 0:
        raise ValueError("""Negative arguments are not supported"""						)
    return _fib(__A						)[0]







def     A__	(       __A						):







    '''simple docstring'''
    if n == 0:  # (F(0), F(1))
        return (0, 1)

    # F(2n) = F(n)[2F(n+1) − F(n)]
    # F(2n+1) = F(n+1)^2+F(n)^2
    _lowerCamelCase    ,				_lowerCamelCase  :		List[Any]			    =    _fib(n // 2						)
    _lowerCamelCase  :		List[Any]			    =    a * (b * 2 - a)
    _lowerCamelCase  :		Optional[int]			    =    a * a + b * b
    return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
    lowerCAmelCase      :				Union[str, Any]     			=int(sys.argv[1])
    print(F"""fibonacci({n}) is {fibonacci(n)}""")




