

'''simple docstring'''






import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


lowerCAmelCase__											=	[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        '''text-classification''',
        '''language-modeling''',
        '''summarization''',
        '''token-classification''',
        '''question-answering''',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
 import run_clm_flax
 import run_flax_glue
 import run_flax_ner
 import run_mlm_flax
 import run_qa
 import run_summarization_flax
 import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase__											=	logging.getLogger()


def 		_A     (    ):






 """simple docstring"""



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


def 		_A     (    A__     ,				A__="eval"      ):






 """simple docstring"""



 __lowercase							  = os.path.join(A__     ,				F"{split}_results.json"      )
 if os.path.exists(A__      ):
  with open(A__     ,				'''r'''      ) as f:
   return json.load(A__      )
 raise ValueError(F"can't find {path}"      )


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






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

   with patch.object(lowercase__					,'''argv'''					,lowercase__			):
    run_flax_glue.main()
    __lowercase							  = get_results(lowercase__			)
    self.assertGreaterEqual(result['''eval_accuracy''']					,0.7_5			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            run_clm_flax.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 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

   with patch.object(lowercase__					,'''argv'''					,lowercase__			):
    run_clm_flax.main()
    __lowercase							  = get_results(lowercase__			)
    self.assertLess(result['''eval_perplexity''']					,1_0_0			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = 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            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        ".split()

   with patch.object(lowercase__					,'''argv'''					,lowercase__			):
    run_summarization_flax.main()
    __lowercase							  = get_results(lowercase__					,split='''test'''			)
    self.assertGreaterEqual(result['''test_rouge1''']					,1_0			)
    self.assertGreaterEqual(result['''test_rouge2''']					,2			)
    self.assertGreaterEqual(result['''test_rougeL''']					,7			)
    self.assertGreaterEqual(result['''test_rougeLsum''']					,7			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = 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            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        ".split()

   with patch.object(lowercase__					,'''argv'''					,lowercase__			):
    run_mlm_flax.main()
    __lowercase							  = get_results(lowercase__			)
    self.assertLess(result['''eval_perplexity''']					,4_2			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

   with patch.object(lowercase__					,'''argv'''					,lowercase__			):
    run_ta_mlm_flax.main()
    __lowercase							  = get_results(lowercase__			)
    self.assertGreaterEqual(result['''eval_accuracy''']					,0.4_2			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
   __lowercase							  = 7 if get_gpu_count() > 1 else 2

   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            run_flax_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            --logging_steps 2 --eval_steps 2\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_flax_ner.main()
    __lowercase							  = get_results(lowercase__			)
    self.assertGreaterEqual(result['''eval_accuracy''']					,0.7_5			)
    self.assertGreaterEqual(result['''eval_f1''']					,0.3			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = 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            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\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_qa.main()
    __lowercase							  = get_results(lowercase__			)
    self.assertGreaterEqual(result['''eval_f1''']					,3_0			)
    self.assertGreaterEqual(result['''eval_exact''']					,3_0			)








'''simple docstring'''




import math

import qiskit






def        _lowerCAmelCase			(  __snake_case  : int = 1 ,							__snake_case  : int = 1 ,							__snake_case  : int = 1			)     ->     qiskit.result.counts.Counts:
     if (
         isinstance(__snake_case ,							__snake_case			)
         or isinstance(__snake_case ,							__snake_case			)
         or isinstance(__snake_case ,							__snake_case			)
     ):
          raise TypeError('inputs must be integers.'			)

     if (input_a < 0) or (input_a < 0) or (carry_in < 0):
          raise ValueError('inputs must be positive.'			)

     if (
         (math.floor(__snake_case			) != input_a)
         or (math.floor(__snake_case			) != input_a)
         or (math.floor(__snake_case			) != carry_in)
     ):
          raise ValueError('inputs must be exact integers.'			)

     if (input_a > 2) or (input_a > 2) or (carry_in > 2):
          raise ValueError('inputs must be less or equal to 2.'			)

     # build registers
     __A						: int      =       qiskit.QuantumRegister(4 ,							'qr'			)
     __A						: Optional[int]      =       qiskit.ClassicalRegister(2 ,							'cr'			)
     # list the entries
     __A						: Union[str, Any]      =       [input_a, input_a, carry_in]

     __A						: Dict      =       qiskit.QuantumCircuit(__snake_case ,							__snake_case			)

     for i in range(0 ,							3			):
          if entry[i] == 2:
               quantum_circuit.h(__snake_case			)  # for hadamard entries
          elif entry[i] == 1:
               quantum_circuit.x(__snake_case			)  # for 1 entries
          elif entry[i] == 0:
               quantum_circuit.i(__snake_case			)  # for 0 entries

    # build the circuit
     quantum_circuit.ccx(0 ,							1 ,							3			)  # ccx = toffoli gate
     quantum_circuit.cx(0 ,							1			)
     quantum_circuit.ccx(1 ,							2 ,							3			)
     quantum_circuit.cx(1 ,							2			)
     quantum_circuit.cx(0 ,							1			)

     quantum_circuit.measure([2, 3] ,							__snake_case			)  # measure the last two qbits

     __A						: str      =       qiskit.Aer.get_backend('aer_simulator'			)
     __A						: Any      =       qiskit.execute(__snake_case ,							__snake_case ,							shots=10_00			)

     return job.result().get_counts(__snake_case			)


if __name__ == "__main__":
  print(f"""Total sum count for state is: {quantum_full_adder(1, 1, 1)}""")