






'''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


__lowerCAmelCase					   =       [
    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)

__lowerCAmelCase					   =       logging.getLogger()


def 			__UpperCamelCase				(			):
       """simple docstring"""




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


def 			__UpperCamelCase				(			lowercase_					:     Tuple      ):
       """simple docstring"""




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


def 			__UpperCamelCase				(			):
       """simple docstring"""




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


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




class      __UpperCAmelCase					(__lowerCamelCase					):


  """simple docstring"""


  @skip('Skipping until shape inference for to_tf_dataset PR is merged.'  )
  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_auto_remove_tmp_dir()
         a_	    = 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'  )
                a_	    = get_results(UpperCamelCase__  )
                self.assertGreaterEqual(result['eval_accuracy']      ,  0.75  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_auto_remove_tmp_dir()
         a_	    = 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()
                a_	    = get_results(UpperCamelCase__  )
                self.assertLess(result['eval_perplexity']      ,  100  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_auto_remove_tmp_dir()
         a_	    = 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()
                a_	    = get_results(UpperCamelCase__  )
                self.assertLess(result['eval_perplexity']      ,  42  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = 7 if get_gpu_count() > 1 else 2

         a_	    = self.get_auto_remove_tmp_dir()
         a_	    = 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()
                a_	    = get_results(UpperCamelCase__  )
                self.assertGreaterEqual(result['accuracy']      ,  0.75  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_auto_remove_tmp_dir()
         a_	    = 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()
                a_	    = get_results(UpperCamelCase__  )
                self.assertGreaterEqual(result['f1']      ,  30  )
                self.assertGreaterEqual(result['exact']      ,  30  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_auto_remove_tmp_dir()
         a_	    = 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()
                a_	    = get_results(UpperCamelCase__  )
                self.assertGreaterEqual(result['val_accuracy']      ,  0.8  )







  @slow
  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_auto_remove_tmp_dir()
         a_	    = 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()
                a_	    = 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  ):






         """simple docstring"""


         a_	    = self.get_auto_remove_tmp_dir()
         a_	    = 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()
                a_	    = get_results(UpperCamelCase__  )
                self.assertGreaterEqual(result['bleu']      ,  30  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_auto_remove_tmp_dir()
         a_	    = 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()
                a_	    = get_results(UpperCamelCase__  )
                self.assertGreaterEqual(result['accuracy']      ,  0.7  )












'''simple docstring'''







def 			__UpperCamelCase				(			lowercase_					:     int      ,					lowercase_					:     int      ):
       """simple docstring"""




       return (input_a, input_a).count(0      ) % 2


def 			__UpperCamelCase				(			):
       """simple docstring"""




       assert xor_gate(0      ,					0      ) == 0
       assert xor_gate(0      ,					1      ) == 1
       assert xor_gate(1      ,					0      ) == 1
       assert xor_gate(1      ,					1      ) == 0


if __name__ == "__main__":
  print(xor_gate(0, 0))
  print(xor_gate(0, 1))




