

"""simple docstring"""




def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   []
					A     =   len(lowercase__ )
					for i in range(lowercase__ ):
										A     =   []
										for j in range(lowercase__ ):
															A     =   0
															for k in range(lowercase__ ):
																				A     =   val + matrix_a[i][k] * matrix_b[k][j]
															list_a.append(lowercase__ )
										matrix_c.append(lowercase__ )
					return matrix_c
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					return [[int(row == column ) for column in range(lowercase__ )] for row in range(lowercase__ )]
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					if n <= 1:
										return n
					A     =   identity(2 )
					A     =   [[1, 1], [1, 0]]
					A     =   n - 1
					while n > 0:
										if n % 2 == 1:
															A     =   multiply(lowercase__     ,				lowercase__ )
										A     =   multiply(lowercase__     ,				lowercase__ )
										A     =   int(n / 2 )
					return res_matrix[0][0]
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					if n <= 1:
										return n
					A     =   0
					A     =   1
					for _ in range(2     ,				n + 1 ):
										A     ,       A     =   fiba, fiba + fiba
					return fiba
def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
										A     =   int("".join(c for c in ordinal if c in "0123456789" ) )  # 1000th --> 1000
										print(
										    F"""{ordinal} fibonacci number using matrix exponentiation is """
										    F"""{nth_fibonacci_matrix(lowercase__ )} and using bruteforce is """
										    F"""{nth_fibonacci_bruteforce(lowercase__ )}\n""" )
					# from timeit import timeit
					# print(timeit("nth_fibonacci_matrix(1000000)",
					#              "from main import nth_fibonacci_matrix", number=5))
					# print(timeit("nth_fibonacci_bruteforce(1000000)",
					#              "from main import nth_fibonacci_bruteforce", number=5))
					# 2.3342058970001744
					# 57.256506615000035


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()











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__    :							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)

UpperCAmelCase__    :							Union[str, Any]       		=     logging.getLogger()


def 		_lowerCamelCase					(       )      ->		str:





  """simple docstring"""





  _lowercase:			int      =     argparse.ArgumentParser()
  parser.add_argument('-f'				)
  _lowercase:			Optional[Any]      =     parser.parse_args()
  return args.f


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		Dict:





  """simple docstring"""





  _lowercase:			str      =     {}
  _lowercase:			Union[str, Any]      =     os.path.join(UpperCamelCase       ,  'all_results.json'				)
  if os.path.exists(UpperCamelCase				):
    with open(UpperCamelCase       ,  'r'				) as f:
      _lowercase:			Dict      =     json.load(UpperCamelCase				)
  else:
    raise ValueError(F'can\'t find {path}'				)
  return results


def 		_lowerCamelCase					(       )      ->		Any:





  """simple docstring"""





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


UpperCAmelCase__    :							List[Any]       		=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class   UpperCAmelCase    (      A ):






   @skip('Skipping until shape inference for to_tf_dataset PR is merged.'  )
   def 			_a    (						self :							Union[str, Any]  )->      Optional[Any]:
     _lowercase:			List[str]      =     self.get_auto_remove_tmp_dir()
     _lowercase:			List[str]      =     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(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:			List[str]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['eval_accuracy']    ,       0.75  )






   def 			_a    (						self :							Optional[Any]  )->      int:
     _lowercase:			Dict      =     self.get_auto_remove_tmp_dir()
     _lowercase:			str      =     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(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_clm.main()
       _lowercase:			str      =     get_results(UpperCamelCase_  )
       self.assertLess(result['eval_perplexity']    ,       1_00  )






   def 			_a    (						self :							Union[str, Any]  )->      Optional[int]:
     _lowercase:			int      =     self.get_auto_remove_tmp_dir()
     _lowercase:			int      =     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(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_mlm.main()
       _lowercase:			str      =     get_results(UpperCamelCase_  )
       self.assertLess(result['eval_perplexity']    ,       42  )






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

     _lowercase:			Dict      =     self.get_auto_remove_tmp_dir()
     _lowercase:			Optional[int]      =     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(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_ner.main()
       _lowercase:			Optional[Any]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['accuracy']    ,       0.75  )






   def 			_a    (						self :							Tuple  )->      Optional[int]:
     _lowercase:			List[Any]      =     self.get_auto_remove_tmp_dir()
     _lowercase:			int      =     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(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_squad.main()
       _lowercase:			Union[str, Any]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['f1']    ,       30  )
       self.assertGreaterEqual(result['exact']    ,       30  )






   def 			_a    (						self :							List[str]  )->      Tuple:
     _lowercase:			str      =     self.get_auto_remove_tmp_dir()
     _lowercase:			Any      =     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(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_swag.main()
       _lowercase:			List[str]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['val_accuracy']    ,       0.8  )






   @slow
   def 			_a    (						self :							Dict  )->      Tuple:
     _lowercase:			Dict      =     self.get_auto_remove_tmp_dir()
     _lowercase:			str      =     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(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_summarization.main()
       _lowercase:			List[str]      =     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 			_a    (						self :							Tuple  )->      str:
     _lowercase:			List[Any]      =     self.get_auto_remove_tmp_dir()
     _lowercase:			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(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_translation.main()
       _lowercase:			Optional[int]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['bleu']    ,       30  )






   def 			_a    (						self :							int  )->      List[Any]:
     _lowercase:			str      =     self.get_auto_remove_tmp_dir()
     _lowercase:			Dict      =     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(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_image_classification.main()
       _lowercase:			Union[str, Any]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['accuracy']    ,       0.7  )
