
'''simple docstring'''

import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
   from comet_ml import OfflineExperiment

if is_tensorboard_available():
   import struct

   import tensorboard.compat.proto.event_pba as event_pba

__UpperCAmelCase												=   logging.getLogger(__name__)


@require_tensorboard
class 		_a     (							unittest.TestCase  ):







   """simple docstring"""




   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		'''test_project_with_config'''
    with tempfile.TemporaryDirectory() as dirpath:
     SCREAMING_SNAKE_CASE  :  Tuple								=		Accelerator(log_with='tensorboard'    ,project_dir=lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE  :  Any								=		{'''num_iterations''': 12, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
     accelerator.init_trackers(lowerCAmelCase_    ,lowerCAmelCase_      )
     accelerator.end_training()
     for child in Path(f"""{dirpath}/{project_name}"""      ).glob('*/**'      ):
      SCREAMING_SNAKE_CASE  :  int								=		list(filter(lambda __SCREAMING_SNAKE_CASE      : x.is_file()    ,child.iterdir()      )      )[0]
     self.assertNotEqual(str(lowerCAmelCase_      )    ,''      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Any								=		'''test_project_with_log'''
    with tempfile.TemporaryDirectory() as dirpath:
     SCREAMING_SNAKE_CASE  :  Tuple								=		Accelerator(log_with='tensorboard'    ,project_dir=lowerCAmelCase_      )
     accelerator.init_trackers(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE  :  str								=		{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
     accelerator.log(lowerCAmelCase_    ,step=0      )
     accelerator.end_training()
     # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
     # Names are randomly generated each time
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		list(filter(lambda __SCREAMING_SNAKE_CASE      : x.is_file()    ,Path(f"""{dirpath}/{project_name}"""      ).iterdir()      )      )[0]
     self.assertNotEqual(str(lowerCAmelCase_      )    ,''      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		'''test_project_with_log'''
    with tempfile.TemporaryDirectory() as dirpath:
     SCREAMING_SNAKE_CASE  :  int								=		Accelerator(log_with='tensorboard'    ,project_dir=lowerCAmelCase_      )
     accelerator.init_trackers(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		{'''tensor''': torch.tensor(1      )}
     accelerator.log(lowerCAmelCase_    ,step=0      )
     accelerator.end_training()
     # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
     # Names are randomly generated each time
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		list(filter(lambda __SCREAMING_SNAKE_CASE      : x.is_file()    ,Path(f"""{dirpath}/{project_name}"""      ).iterdir()      )      )[0]
     # Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
     with open(lowerCAmelCase_    ,'rb'      ) as f:
      SCREAMING_SNAKE_CASE  :  Tuple								=		f.read()
     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		False
     while data:
      SCREAMING_SNAKE_CASE  :  str								=		struct.unpack('Q'    ,data[:8]      )

      SCREAMING_SNAKE_CASE  :  List[Any]								=		data[12 : 12 + int(header[0]      )]  # 8+4
      SCREAMING_SNAKE_CASE  :  Any								=		data[12 + int(header[0]      ) + 4 :]
      SCREAMING_SNAKE_CASE  :  Any								=		event_pba.Event()

      event.ParseFromString(lowerCAmelCase_      )
      if event.HasField('summary'      ):
       for value in event.summary.value:
        if value.simple_value == 1.0 and value.tag == "tensor":
         SCREAMING_SNAKE_CASE  :  int								=		True
     self.assertTrue(lowerCAmelCase_    ,'Converted tensor was not found in the log file!'      )






   def 					__a			(       self      ):
    with self.assertRaisesRegex(lowerCAmelCase_    ,'Logging with `tensorboard` requires a `logging_dir`'      ):
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		Accelerator(log_with='tensorboard'      )
    with tempfile.TemporaryDirectory() as dirpath:
     SCREAMING_SNAKE_CASE  :  Tuple								=		Accelerator(log_with='tensorboard'    ,project_dir=lowerCAmelCase_      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Dict								=		ProjectConfiguration(total_limit=30      )
    with tempfile.TemporaryDirectory() as dirpath:
     SCREAMING_SNAKE_CASE  :  str								=		Accelerator(log_with='tensorboard'    ,project_dir=lowerCAmelCase_    ,project_config=lowerCAmelCase_      )



@require_wandb
@mock.patch.dict(os.environ			,    {'WANDB_MODE': 'offline'}  )
class 		_a     (							SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__  ):







   """simple docstring"""




   def 					__a			(       self      ):
    super().setUp()
    # wandb let's us override where logs are stored to via the WANDB_DIR env var
    self.add_mocks(mock.patch.dict(os.environ    ,{'WANDB_DIR': self.tmpdir}      )      )






   @staticmethod
   def 					__a			(       __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = True      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		Rf"""{section} ([\S\s]*?)\n\n"""
    if record:
     SCREAMING_SNAKE_CASE  :  Tuple								=		Rf"""Record: {pattern}"""
    SCREAMING_SNAKE_CASE  :  int								=		re.findall(lowerCAmelCase_    ,lowerCAmelCase_      )[0]
    # A config
    if section == "config" or section == "history":
     SCREAMING_SNAKE_CASE  :  Dict								=		re.findall(R'"([a-zA-Z0-9_.,]+)'    ,lowerCAmelCase_      )
     return {key: val for key, val in zip(cleaned_record[0::2]    ,cleaned_record[1::2]      )}
    # Everything else
    else:
     return dict(re.findall(R'(\w+): "([^\s]+)"'    ,lowerCAmelCase_      )      )






   @skip
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		'''test_project_with_config'''
    SCREAMING_SNAKE_CASE  :  List[str]								=		Accelerator(log_with='wandb'      )
    SCREAMING_SNAKE_CASE  :  List[str]								=		{'''num_iterations''': 12, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		{'''wandb''': {'''tags''': ['''my_tag''']}}
    accelerator.init_trackers(lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
    accelerator.log(lowerCAmelCase_    ,step=0      )
    accelerator.end_training()
    # The latest offline log is stored at wandb/latest-run/*.wandb
    for child in Path(f"""{self.tmpdir}/wandb/latest-run"""      ).glob('*'      ):
     if child.is_file() and child.suffix == ".wandb":
      SCREAMING_SNAKE_CASE  :  Optional[Any]								=		subprocess.check_output(
          ['wandb', 'sync', '--view', '--verbose', str(lowerCAmelCase_      )]    ,env=os.environ.copy()      ).decode('utf8'    ,'ignore'      )
      break

        # Check HPS through careful parsing and cleaning
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.parse_log(lowerCAmelCase_    ,'config'      )
    self.assertEqual(logged_items['num_iterations']    ,'12'      )
    self.assertEqual(logged_items['learning_rate']    ,'0.01'      )
    self.assertEqual(logged_items['some_boolean']    ,'false'      )
    self.assertEqual(logged_items['some_string']    ,'some_value'      )
    self.assertEqual(logged_items['some_string']    ,'some_value'      )

    # Run tags
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.parse_log(lowerCAmelCase_    ,'run'    ,lowerCAmelCase_      )
    self.assertEqual(logged_items['tags']    ,'my_tag'      )

    # Actual logging
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.parse_log(lowerCAmelCase_    ,'history'      )
    self.assertEqual(logged_items['total_loss']    ,'0.1'      )
    self.assertEqual(logged_items['iteration']    ,'1'      )
    self.assertEqual(logged_items['my_text']    ,'some_value'      )
    self.assertEqual(logged_items['_step']    ,'0'      )



def        SCREAMING_SNAKE_CASE_  (	self				:	int   ,     snake_case_				:	Optional[Any]   ,     snake_case_				:	Tuple							)       ->	int:
 SCREAMING_SNAKE_CASE  :  List[Any]								=		run_name
 SCREAMING_SNAKE_CASE  :  Any								=		OfflineExperiment(project_name=snake_case_   ,     offline_directory=snake_case_							)
 logger.info(f"""Initialized offline CometML project {self.run_name}"""							)
 logger.info('Make sure to log any initial configurations with `self.store_init_configuration` before training!'							)


@require_comet_ml
@mock.patch.object(SCREAMING_SNAKE_CASE__			,    '__init__'			,    SCREAMING_SNAKE_CASE__  )
class 		_a     (							unittest.TestCase  ):







   """simple docstring"""




   @staticmethod
   def 					__a			(       __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = False      ):
    for log in log_list:
     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		json.loads(lowerCAmelCase_      )['''payload''']
     if is_param and "param" in j.keys():
      if j["param"]["paramName"] == key:
       return j["param"]["paramValue"]
     if "log_other" in j.keys():
      if j["log_other"]["key"] == key:
       return j["log_other"]["val"]
     if "metric" in j.keys():
      if j["metric"]["metricName"] == key:
       return j["metric"]["metricValue"]






   def 					__a			(       self      ):
    with tempfile.TemporaryDirectory() as d:
     SCREAMING_SNAKE_CASE  :  int								=		CometMLTracker('test_project_with_config'    ,lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE  :  List[Any]								=		Accelerator(log_with=lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE  :  Any								=		{'''num_iterations''': 12, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
     accelerator.init_trackers(lowerCAmelCase_    ,lowerCAmelCase_      )
     accelerator.end_training()
     SCREAMING_SNAKE_CASE  :  List[Any]								=		os.listdir(lowerCAmelCase_      )[0]  # Comet is nice, it's just a zip file here
     # We parse the raw logs
     SCREAMING_SNAKE_CASE  :  int								=		os.path.join(lowerCAmelCase_    ,lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE  :  Tuple								=		zipfile.ZipFile(lowerCAmelCase_    ,'r'      )
     SCREAMING_SNAKE_CASE  :  Tuple								=		archive.open('messages.json'      ).read().decode('utf-8'      )
    SCREAMING_SNAKE_CASE  :  Tuple								=		log.split('\n'      )[:-1]
    self.assertEqual(self.get_value_from_key(lowerCAmelCase_    ,'num_iterations'    ,lowerCAmelCase_      )    ,12      )
    self.assertEqual(self.get_value_from_key(lowerCAmelCase_    ,'learning_rate'    ,lowerCAmelCase_      )    ,0.01      )
    self.assertEqual(self.get_value_from_key(lowerCAmelCase_    ,'some_boolean'    ,lowerCAmelCase_      )    ,lowerCAmelCase_      )
    self.assertEqual(self.get_value_from_key(lowerCAmelCase_    ,'some_string'    ,lowerCAmelCase_      )    ,'some_value'      )






   def 					__a			(       self      ):
    with tempfile.TemporaryDirectory() as d:
     SCREAMING_SNAKE_CASE  :  str								=		CometMLTracker('test_project_with_config'    ,lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		Accelerator(log_with=lowerCAmelCase_      )
     accelerator.init_trackers(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
     accelerator.log(lowerCAmelCase_    ,step=0      )
     accelerator.end_training()
     SCREAMING_SNAKE_CASE  :  List[str]								=		os.listdir(lowerCAmelCase_      )[0]  # Comet is nice, it's just a zip file here
     # We parse the raw logs
     SCREAMING_SNAKE_CASE  :  str								=		os.path.join(lowerCAmelCase_    ,lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE  :  List[Any]								=		zipfile.ZipFile(lowerCAmelCase_    ,'r'      )
     SCREAMING_SNAKE_CASE  :  Dict								=		archive.open('messages.json'      ).read().decode('utf-8'      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		log.split('\n'      )[:-1]
    self.assertEqual(self.get_value_from_key(lowerCAmelCase_    ,'curr_step'    ,lowerCAmelCase_      )    ,0      )
    self.assertEqual(self.get_value_from_key(lowerCAmelCase_    ,'total_loss'      )    ,0.1      )
    self.assertEqual(self.get_value_from_key(lowerCAmelCase_    ,'iteration'      )    ,1      )
    self.assertEqual(self.get_value_from_key(lowerCAmelCase_    ,'my_text'      )    ,'some_value'      )



class 		_a     (							SCREAMING_SNAKE_CASE__  ):







   """simple docstring"""




   A		= [
       '''total_loss''',
       '''iteration''',
       '''my_text''',
       '''learning_rate''',
       '''num_iterations''',
       '''some_boolean''',
       '''some_string''',
   ]

   A		= '''my_custom_tracker'''
   A		= False






   def __init__(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		open(f"""{dir}/log.csv"""    ,'w+'      )
    SCREAMING_SNAKE_CASE  :  str								=		csv.DictWriter(self.f    ,fieldnames=self._col_names      )
    self.writer.writeheader()






   @property
   def 					__a			(       self      ):
    return self.writer






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    logger.info('Call init'      )
    self.writer.writerow(lowerCAmelCase_      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    logger.info('Call log'      )
    self.writer.writerow(lowerCAmelCase_      )






   def 					__a			(       self      ):
    self.f.close()



class 		_a     (							unittest.TestCase  ):







   """simple docstring"""




   def 					__a			(       self      ):
    with tempfile.TemporaryDirectory() as d:
     SCREAMING_SNAKE_CASE  :  str								=		MyCustomTracker(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		Accelerator(log_with=lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE  :  List[str]								=		{'''num_iterations''': 12, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
     accelerator.init_trackers('Some name'    ,lowerCAmelCase_      )
     accelerator.end_training()
     with open(f"""{d}/log.csv"""    ,'r'      ) as f:
      SCREAMING_SNAKE_CASE  :  str								=		csv.DictReader(lowerCAmelCase_      )
      SCREAMING_SNAKE_CASE  :  int								=		next(lowerCAmelCase_      )
      SCREAMING_SNAKE_CASE  :  int								=		{
          '''total_loss''': '''''',
          '''iteration''': '''''',
          '''my_text''': '''''',
          '''learning_rate''': '''0.01''',
          '''num_iterations''': '''12''',
          '''some_boolean''': '''False''',
          '''some_string''': '''some_value''',
      }
      self.assertDictEqual(lowerCAmelCase_    ,lowerCAmelCase_      )






   def 					__a			(       self      ):
    with tempfile.TemporaryDirectory() as d:
     SCREAMING_SNAKE_CASE  :  Any								=		MyCustomTracker(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE  :  Any								=		Accelerator(log_with=lowerCAmelCase_      )
     accelerator.init_trackers('Some name'      )
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
     accelerator.log(lowerCAmelCase_    ,step=0      )
     accelerator.end_training()
     with open(f"""{d}/log.csv"""    ,'r'      ) as f:
      SCREAMING_SNAKE_CASE  :  List[str]								=		csv.DictReader(lowerCAmelCase_      )
      SCREAMING_SNAKE_CASE  :  List[str]								=		next(lowerCAmelCase_      )
      SCREAMING_SNAKE_CASE  :  List[Any]								=		{
          '''total_loss''': '''0.1''',
          '''iteration''': '''1''',
          '''my_text''': '''some_value''',
          '''learning_rate''': '''''',
          '''num_iterations''': '''''',
          '''some_boolean''': '''''',
          '''some_string''': '''''',
      }
      self.assertDictEqual(lowerCAmelCase_    ,lowerCAmelCase_      )




import numpy as np
import qiskit






def     lowerCamelCase__				(   a						,						a ) ->  qiskit.QuantumCircuit:
   _A:    List[Any]						     =			qiskit.QuantumCircuit(num_qubits + 1 )

   # First, let's deal with the case in which oracle is balanced
   if case == "balanced":
      # First generate a random number that tells us which CNOTs to
      # wrap in X-gates:
      _A:    Any						     =			np.random.randint(1						,						2**num_qubits )
      # Next, format 'b' as a binary string of length 'n', padded with zeros:
      _A:    int						     =			format(a						,						f"""0{num_qubits}b""" )
      # Next, we place the first X-gates. Each digit in our binary string
      # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
      # we apply an X-gate to that qubit:
      for index, bit in enumerate(a ):
         if bit == "1":
            oracle_qc.x(a )
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
      for index in range(a ):
         oracle_qc.cx(a						,						a )
      # Next, place the final X-gates
      for index, bit in enumerate(a ):
         if bit == "1":
            oracle_qc.x(a )

    # Case in which oracle is constant
   if case == "constant":
      # First decide what the fixed output of the oracle will be
      # (either always 0 or always 1)
      _A:    int						     =			np.random.randint(2 )
      if output == 1:
         oracle_qc.x(a )

   _A:    int						     =			oracle_qc.to_gate()
   _A:    str						     =			'''Oracle'''  # To show when we display the circuit
   return oracle_gate






def     lowerCamelCase__				(   a						,						a ) ->  qiskit.QuantumCircuit:
   _A:    Optional[int]						     =			qiskit.QuantumCircuit(num_qubits + 1						,						a )
   # Set up the output qubit:
   dj_circuit.x(a )
   dj_circuit.h(a )
   # And set up the input register:
   for qubit in range(a ):
      dj_circuit.h(a )
   # Let's append the oracle gate to our circuit:
   dj_circuit.append(a						,						range(num_qubits + 1 ) )
   # Finally, perform the H-gates again and measure:
   for qubit in range(a ):
      dj_circuit.h(a )

   for i in range(a ):
      dj_circuit.measure(a						,						a )

   return dj_circuit






def     lowerCamelCase__				(   a						,						a ) ->  qiskit.result.counts.Counts:
   _A:    int						     =			qiskit.Aer.get_backend('''aer_simulator''' )

   _A:    Optional[Any]						     =			dj_oracle(a						,						a )
   _A:    int						     =			dj_algorithm(a						,						a )

   # Execute the circuit on the simulator
   _A:    int						     =			qiskit.execute(a						,						a						,						shots=10_00 )

   # Return the histogram data of the results of the experiment.
   return job.result().get_counts(a )


if __name__ == "__main__":
   print(F"""Deutsch Jozsa - Constant Oracle: {deutsch_jozsa("constant", 3)}""")
   print(F"""Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa("balanced", 3)}""")



