




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

a_ 		=  logging.getLogger(__name__)

@require_tensorboard
class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase			=    '''test_project_with_config'''
          with tempfile.TemporaryDirectory() as dirpath:
              __UpperCamelCase			=    Accelerator(log_with='''tensorboard'''			,							project_dir=snake_case					)
              __UpperCamelCase			=    {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
              accelerator.init_trackers(snake_case			,							snake_case					)
              accelerator.end_training()
              for child in Path(F"{dirpath}/{project_name}"					).glob('''*/**'''					):
                  __UpperCamelCase			=    list(filter(lambda snake_case					: x.is_file()			,							child.iterdir()					)					)[0]
              self.assertNotEqual(str(snake_case					)			,							''''''					)







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







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

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

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







      def    snake_case						(  self       :			Union[str, Any]					):
          with self.assertRaisesRegex(snake_case			,							'''Logging with `tensorboard` requires a `logging_dir`'''					):
              __UpperCamelCase			=    Accelerator(log_with='''tensorboard'''					)
          with tempfile.TemporaryDirectory() as dirpath:
              __UpperCamelCase			=    Accelerator(log_with='''tensorboard'''			,							project_dir=snake_case					)







      def    snake_case						(  self       :			str					):
          __UpperCamelCase			=    ProjectConfiguration(total_limit=30					)
          with tempfile.TemporaryDirectory() as dirpath:
              __UpperCamelCase			=    Accelerator(log_with='''tensorboard'''			,							project_dir=snake_case			,							project_config=snake_case					)







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

      """simple docstring"""

      def    snake_case						(  self       :			Union[str, Any]					):
          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    snake_case						(  snake_case       :			str			,							snake_case       :			str			,							snake_case       :			bool = True					):
          __UpperCamelCase			=    RF"{section} ([\S\s]*?)\n\n"
          if record:
              __UpperCamelCase			=    RF"Record: {pattern}"
          __UpperCamelCase			=    re.findall(snake_case			,							snake_case					)[0]
          # A config
          if section == "config" or section == "history":
              __UpperCamelCase			=    re.findall(R'''"([a-zA-Z0-9_.,]+)'''			,							snake_case					)
              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]+)"'''			,							snake_case					)					)







      @skip
      def    snake_case						(  self       :			int					):
          __UpperCamelCase			=    '''test_project_with_config'''
          __UpperCamelCase			=    Accelerator(log_with='''wandb'''					)
          __UpperCamelCase			=    {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
          __UpperCamelCase			=    {'''wandb''': {'''tags''': ['''my_tag''']}}
          accelerator.init_trackers(snake_case			,							snake_case			,							snake_case					)
          __UpperCamelCase			=    {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
          accelerator.log(snake_case			,							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":
                  __UpperCamelCase			=    subprocess.check_output(
                      ['''wandb''', '''sync''', '''--view''', '''--verbose''', str(snake_case					)]			,							env=os.environ.copy()					).decode('''utf8'''			,							'''ignore'''					)
                  break

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

          # Actual logging
          __UpperCamelCase			=    self.parse_log(snake_case			,							'''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       ,  lowercase_       ,  lowercase_   )							->						Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase			=    run_name
    __UpperCamelCase			=    OfflineExperiment(project_name=lowercase_       ,  offline_directory=lowercase_   )
    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 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

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







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







class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			Optional[int]     =       [
          "total_loss",
          "iteration",
          "my_text",
          "learning_rate",
          "num_iterations",
          "some_boolean",
          "some_string",
      ]

      lowerCAmelCase__      :			Any     =       "my_custom_tracker"
      lowerCAmelCase__      :			Optional[int]     =       False







      def __init__(  self       :			Dict			,							snake_case       :			str					):
          __UpperCamelCase			=    open(F"{dir}/log.csv"			,							'''w+'''					)
          __UpperCamelCase			=    csv.DictWriter(self.f			,							fieldnames=self._col_names					)
          self.writer.writeheader()







      @property
      def    snake_case						(  self       :			Optional[Any]					):
          return self.writer







      def    snake_case						(  self       :			Optional[int]			,							snake_case       :			dict					):
          logger.info('''Call init'''					)
          self.writer.writerow(snake_case					)







      def    snake_case						(  self       :			int			,							snake_case       :			dict			,							snake_case       :			Optional[int]					):
          logger.info('''Call log'''					)
          self.writer.writerow(snake_case					)







      def    snake_case						(  self       :			int					):
          self.f.close()







class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

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







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









"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a__			:						Tuple							=			{
    """configuration_segformer""": ["""SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SegformerConfig""", """SegformerOnnxConfig"""]
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						List[str]							=			["""SegformerFeatureExtractor"""]
							a__			:						str							=			["""SegformerImageProcessor"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						Optional[Any]							=			[
							    """SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """SegformerDecodeHead""",
							    """SegformerForImageClassification""",
							    """SegformerForSemanticSegmentation""",
							    """SegformerLayer""",
							    """SegformerModel""",
							    """SegformerPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						List[Any]							=			[
							    """TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFSegformerDecodeHead""",
							    """TFSegformerForImageClassification""",
							    """TFSegformerForSemanticSegmentation""",
							    """TFSegformerModel""",
							    """TFSegformerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_segformer import SegformerFeatureExtractor
														from .image_processing_segformer import SegformerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_segformer import (
														    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SegformerDecodeHead,
														    SegformerForImageClassification,
														    SegformerForSemanticSegmentation,
														    SegformerLayer,
														    SegformerModel,
														    SegformerPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_segformer import (
														    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFSegformerDecodeHead,
														    TFSegformerForImageClassification,
														    TFSegformerForSemanticSegmentation,
														    TFSegformerModel,
														    TFSegformerPreTrainedModel,
														)

else:
							import sys

							a__			:						Any							=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


