






"""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		:  List[str]						=		logging.getLogger(__name__)

@require_tensorboard
class 	a					(       unittest.TestCase     ):







    def       UpperCamelCase_				(      self	):
      lowercase							   =	"test_project_with_config"
      with tempfile.TemporaryDirectory() as dirpath:
        lowercase							   =	Accelerator(log_with='tensorboard'							,		project_dir=_A	)
        lowercase							   =	{"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
        accelerator.init_trackers(_A							,		_A	)
        accelerator.end_training()
        for child in Path(F'{dirpath}/{project_name}'	).glob('*/**'	):
          lowercase							   =	list(filter(lambda _lowerCamelCase	: x.is_file()							,		child.iterdir()	)	)[0]
        self.assertNotEqual(str(_A	)							,		''	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	"test_project_with_log"
      with tempfile.TemporaryDirectory() as dirpath:
        lowercase							   =	Accelerator(log_with='tensorboard'							,		project_dir=_A	)
        accelerator.init_trackers(_A	)
        lowercase							   =	{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
        accelerator.log(_A							,		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
        lowercase							   =	list(filter(lambda _lowerCamelCase	: x.is_file()							,		Path(F'{dirpath}/{project_name}'	).iterdir()	)	)[0]
        self.assertNotEqual(str(_A	)							,		''	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	"test_project_with_log"
      with tempfile.TemporaryDirectory() as dirpath:
        lowercase							   =	Accelerator(log_with='tensorboard'							,		project_dir=_A	)
        accelerator.init_trackers(_A	)
        lowercase							   =	{"tensor": torch.tensor(1	)}
        accelerator.log(_A							,		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
        lowercase							   =	list(filter(lambda _lowerCamelCase	: 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(_A							,		'rb'	) as f:
          lowercase							   =	f.read()
        lowercase							   =	False
        while data:
          lowercase							   =	struct.unpack('Q'							,		data[:8]	)

          lowercase							   =	data[1_2 : 1_2 + int(header[0]	)]  # 8+4
          lowercase							   =	data[1_2 + int(header[0]	) + 4 :]
          lowercase							   =	event_pba.Event()

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







    def       UpperCamelCase_				(      self	):






      with self.assertRaisesRegex(_A							,		'Logging with `tensorboard` requires a `logging_dir`'	):
        lowercase							   =	Accelerator(log_with='tensorboard'	)
      with tempfile.TemporaryDirectory() as dirpath:
        lowercase							   =	Accelerator(log_with='tensorboard'							,		project_dir=_A	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	ProjectConfiguration(total_limit=3_0	)
      with tempfile.TemporaryDirectory() as dirpath:
        lowercase							   =	Accelerator(log_with='tensorboard'							,		project_dir=_A							,		project_config=_A	)


@require_wandb
@mock.patch.dict(os.environ,    {"WANDB_MODE": "offline"}     )
class 	a					(       A__,    A__     ):







    def       UpperCamelCase_				(      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       UpperCamelCase_				(      _lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = True	):
      lowercase							   =	RF'{section} ([\S\s]*?)\n\n'
      if record:
        lowercase							   =	RF'Record: {pattern}'
      lowercase							   =	re.findall(_A							,		_A	)[0]
      # A config
      if section == "config" or section == "history":
        lowercase							   =	re.findall(R'\"([a-zA-Z0-9_.,]+)'							,		_A	)
        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]+)\"'							,		_A	)	)







    @skip
    def       UpperCamelCase_				(      self	):
      lowercase							   =	"test_project_with_config"
      lowercase							   =	Accelerator(log_with='wandb'	)
      lowercase							   =	{"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
      lowercase							   =	{"wandb": {"tags": ["my_tag"]}}
      accelerator.init_trackers(_A							,		_A							,		_A	)
      lowercase							   =	{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
      accelerator.log(_A							,		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":
          lowercase							   =	subprocess.check_output(
              ['wandb', 'sync', '--view', '--verbose', str(_A	)]							,		env=os.environ.copy()	).decode('utf8'							,		'ignore'	)
          break

        # Check HPS through careful parsing and cleaning
      lowercase							   =	self.parse_log(_A							,		'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
      lowercase							   =	self.parse_log(_A							,		'run'							,		_A	)
      self.assertEqual(logged_items['tags']							,		'my_tag'	)

      # Actual logging
      lowercase							   =	self.parse_log(_A							,		'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      :  Union[str, Any]		, __snake_case      :  str		, __snake_case      :  str   ):





  '''simple docstring'''





  lowercase							   =	run_name
  lowercase							   =	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(A__,    "__init__",    A__     )
class 	a					(       unittest.TestCase     ):







    @staticmethod
    def       UpperCamelCase_				(      _lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = False	):






      for log in log_list:
        lowercase							   =	json.loads(_A	)["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       UpperCamelCase_				(      self	):






      with tempfile.TemporaryDirectory() as d:
        lowercase							   =	CometMLTracker('test_project_with_config'							,		_A	)
        lowercase							   =	Accelerator(log_with=_A	)
        lowercase							   =	{"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
        accelerator.init_trackers(_A							,		_A	)
        accelerator.end_training()
        lowercase							   =	os.listdir(_A	)[0]  # Comet is nice, it's just a zip file here
        # We parse the raw logs
        lowercase							   =	os.path.join(_A							,		_A	)
        lowercase							   =	zipfile.ZipFile(_A							,		'r'	)
        lowercase							   =	archive.open('messages.json'	).read().decode('utf-8'	)
      lowercase							   =	log.split('\n'	)[:-1]
      self.assertEqual(self.get_value_from_key(_A							,		'num_iterations'							,		_A	)							,		1_2	)
      self.assertEqual(self.get_value_from_key(_A							,		'learning_rate'							,		_A	)							,		0.0_1	)
      self.assertEqual(self.get_value_from_key(_A							,		'some_boolean'							,		_A	)							,		_A	)
      self.assertEqual(self.get_value_from_key(_A							,		'some_string'							,		_A	)							,		'some_value'	)







    def       UpperCamelCase_				(      self	):






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


class 	a					(       A__     ):
    UpperCAmelCase_			:					List[str]     					=[
        "total_loss",
        "iteration",
        "my_text",
        "learning_rate",
        "num_iterations",
        "some_boolean",
        "some_string",
    ]

    UpperCAmelCase_			:					Tuple     					="my_custom_tracker"
    UpperCAmelCase_			:					Any     					=False







    def __init__(      self							,		_lowerCamelCase	):
      lowercase							   =	open(F'{dir}/log.csv'							,		'w+'	)
      lowercase							   =	csv.DictWriter(self.f							,		fieldnames=self._col_names	)
      self.writer.writeheader()







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







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      logger.info('Call init'	)
      self.writer.writerow(_A	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase	):
      logger.info('Call log'	)
      self.writer.writerow(_A	)







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


class 	a					(       unittest.TestCase     ):







    def       UpperCamelCase_				(      self	):






      with tempfile.TemporaryDirectory() as d:
        lowercase							   =	MyCustomTracker(_A	)
        lowercase							   =	Accelerator(log_with=_A	)
        lowercase							   =	{"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
        accelerator.init_trackers('Some name'							,		_A	)
        accelerator.end_training()
        with open(F'{d}/log.csv'							,		'r'	) as f:
          lowercase							   =	csv.DictReader(_A	)
          lowercase							   =	next(_A	)
          lowercase							   =	{
              "total_loss": "",
              "iteration": "",
              "my_text": "",
              "learning_rate": "0.01",
              "num_iterations": "12",
              "some_boolean": "False",
              "some_string": "some_value",
          }
          self.assertDictEqual(_A							,		_A	)







    def       UpperCamelCase_				(      self	):






      with tempfile.TemporaryDirectory() as d:
        lowercase							   =	MyCustomTracker(_A	)
        lowercase							   =	Accelerator(log_with=_A	)
        accelerator.init_trackers('Some name'	)
        lowercase							   =	{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
        accelerator.log(_A							,		step=0	)
        accelerator.end_training()
        with open(F'{d}/log.csv'							,		'r'	) as f:
          lowercase							   =	csv.DictReader(_A	)
          lowercase							   =	next(_A	)
          lowercase							   =	{
              "total_loss": "0.1",
              "iteration": "1",
              "my_text": "some_value",
              "learning_rate": "",
              "num_iterations": "",
              "some_boolean": "",
              "some_string": "",
          }
          self.assertDictEqual(_A							,		_A	)










"""simple docstring"""



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor

class 	a					(       unittest.TestCase     ):







    def __init__(      self							,		_lowerCamelCase							,		_lowerCamelCase=7							,		_lowerCamelCase=3							,		_lowerCamelCase=1_8							,		_lowerCamelCase=3_0							,		_lowerCamelCase=4_0_0							,		_lowerCamelCase=True							,		_lowerCamelCase=None							,		_lowerCamelCase=True							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		):
      lowercase							   =	size if size is not None else {'height': 1_8, 'width': 1_8}
      lowercase							   =	parent
      lowercase							   =	batch_size
      lowercase							   =	num_channels
      lowercase							   =	image_size
      lowercase							   =	min_resolution
      lowercase							   =	max_resolution
      lowercase							   =	do_resize
      lowercase							   =	size
      lowercase							   =	do_normalize
      lowercase							   =	image_mean
      lowercase							   =	image_std







    def       UpperCamelCase_				(      self	):
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
      }


@require_torch
@require_vision
class 	a					(       a_,    unittest.TestCase     ):
    UpperCAmelCase_			:					int     					=ViTImageProcessor if is_vision_available() else None







    def       UpperCamelCase_				(      self	):
      lowercase							   =	ViTImageProcessingTester(self	)







    @property
    def       UpperCamelCase_				(      self	):
      return self.image_processor_tester.prepare_image_processor_dict()







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'image_mean'	)	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'image_std'	)	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_normalize'	)	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_resize'	)	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'size'	)	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.image_processing_class.from_dict(self.image_processor_dict	)
      self.assertEqual(image_processor.size							,		{'height': 1_8, 'width': 1_8}	)

      lowercase							   =	self.image_processing_class.from_dict(self.image_processor_dict							,		size=4_2	)
      self.assertEqual(image_processor.size							,		{'height': 4_2, 'width': 4_2}	)







    def       UpperCamelCase_				(      self	):
      pass







    def       UpperCamelCase_				(      self	):
      # Initialize image_processing
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      # create random PIL images
      lowercase							   =	prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase	)
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		Image.Image	)

      # Test not batched input
      lowercase							   =	image_processing(image_inputs[0]							,		return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							   =	image_processing(_lowerCamelCase							,		return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def       UpperCamelCase_				(      self	):
      # Initialize image_processing
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      # create random numpy tensors
      lowercase							   =	prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase							,		numpify=_lowerCamelCase	)
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		np.ndarray	)

      # Test not batched input
      lowercase							   =	image_processing(image_inputs[0]							,		return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							   =	image_processing(_lowerCamelCase							,		return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def       UpperCamelCase_				(      self	):
      # Initialize image_processing
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      # create random PyTorch tensors
      lowercase							   =	prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase							,		torchify=_lowerCamelCase	)
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		torch.Tensor	)

      # Test not batched input
      lowercase							   =	image_processing(image_inputs[0]							,		return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							   =	image_processing(_lowerCamelCase							,		return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)


