






import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_     =			logging.get_logger(__name__)


lowerCAmelCase_     =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCAmelCase_     =			{
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
}

lowerCAmelCase_     =			{
    'RUCAIBox/mvp': 1_024,
}







@lru_cache()
def   snake_case		(				):
				A									=   (
				    list(range(ord('!' ),    ord('~' ) + 1 ) ) + list(range(ord('¡' ),    ord('¬' ) + 1 ) ) + list(range(ord('®' ),    ord('ÿ' ) + 1 ) )
				)
				A									=   bs[:]
				A									=   0
				for b in range(2**8 ):
								if b not in bs:
												bs.append(UpperCAmelCase )
												cs.append(2**8 + n )
												n += 1
				A									=   [chr(UpperCAmelCase ) for n in cs]
				return dict(zip(UpperCAmelCase,    UpperCAmelCase ) )







def   snake_case		(				UpperCAmelCase						:							List[str] ):
				A									=   set()
				A									=   word[0]
				for char in word[1:]:
								pairs.add((prev_char, char) )
								A									=   char
				return pairs



class   UpperCamelCase			(					snake_case__			):







		"""simple docstring"""



		snake_case			 = VOCAB_FILES_NAMES
		snake_case			 = PRETRAINED_VOCAB_FILES_MAP
		snake_case			 = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		snake_case			 = ["input_ids", "attention_mask"]
		def __init__(	self		:							Any    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any]    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any]    ,_SCREAMING_SNAKE_CASE		:							List[str]="replace"    ,_SCREAMING_SNAKE_CASE		:							Dict="<s>"    ,_SCREAMING_SNAKE_CASE		:							List[str]="</s>"    ,_SCREAMING_SNAKE_CASE		:							int="</s>"    ,_SCREAMING_SNAKE_CASE		:							Dict="<s>"    ,_SCREAMING_SNAKE_CASE		:							Dict="<unk>"    ,_SCREAMING_SNAKE_CASE		:							Optional[Any]="<pad>"    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any]="<mask>"    ,_SCREAMING_SNAKE_CASE		:							int=False    ,**_SCREAMING_SNAKE_CASE		:							str    ,)     -> Any:


						'''simple docstring'''
						A									=   AddedToken(_SCREAMING_SNAKE_CASE    ,lstrip=_SCREAMING_SNAKE_CASE    ,rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		) else bos_token
						A									=   AddedToken(_SCREAMING_SNAKE_CASE    ,lstrip=_SCREAMING_SNAKE_CASE    ,rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		) else eos_token
						A									=   AddedToken(_SCREAMING_SNAKE_CASE    ,lstrip=_SCREAMING_SNAKE_CASE    ,rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		) else sep_token
						A									=   AddedToken(_SCREAMING_SNAKE_CASE    ,lstrip=_SCREAMING_SNAKE_CASE    ,rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		) else cls_token
						A									=   AddedToken(_SCREAMING_SNAKE_CASE    ,lstrip=_SCREAMING_SNAKE_CASE    ,rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		) else unk_token
						A									=   AddedToken(_SCREAMING_SNAKE_CASE    ,lstrip=_SCREAMING_SNAKE_CASE    ,rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		) else pad_token

						# Mask token behave like a normal word, i.e. include the space before it
						A									=   AddedToken(_SCREAMING_SNAKE_CASE    ,lstrip=_SCREAMING_SNAKE_CASE    ,rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		) else mask_token

						super().__init__(
						    errors=_SCREAMING_SNAKE_CASE    ,bos_token=_SCREAMING_SNAKE_CASE    ,eos_token=_SCREAMING_SNAKE_CASE    ,unk_token=_SCREAMING_SNAKE_CASE    ,sep_token=_SCREAMING_SNAKE_CASE    ,cls_token=_SCREAMING_SNAKE_CASE    ,pad_token=_SCREAMING_SNAKE_CASE    ,mask_token=_SCREAMING_SNAKE_CASE    ,add_prefix_space=_SCREAMING_SNAKE_CASE    ,**_SCREAMING_SNAKE_CASE    ,)
						with open(_SCREAMING_SNAKE_CASE    ,encoding='utf-8'		) as vocab_handle:
										A									=   json.load(_SCREAMING_SNAKE_CASE		)
						A									=   {v: k for k, v in self.encoder.items()}
						A									=   errors  # how to handle errors in decoding
						A									=   bytes_to_unicode()
						A									=   {v: k for k, v in self.byte_encoder.items()}
						with open(_SCREAMING_SNAKE_CASE    ,encoding='utf-8'		) as merges_handle:
										A									=   merges_handle.read().split('\n'		)[1:-1]
						A									=   [tuple(merge.split()		) for merge in bpe_merges]
						A									=   dict(zip(_SCREAMING_SNAKE_CASE    ,range(len(_SCREAMING_SNAKE_CASE		)		)		)		)
						A									=   {}
						A									=   add_prefix_space

						# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
						A									=   re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'		)
		@property
		def    A(	self		:							str		)     -> Optional[Any]:


						'''simple docstring'''
						return len(self.encoder		)
		def    A(	self		:							int		)     -> List[str]:


						'''simple docstring'''
						return dict(self.encoder    ,**self.added_tokens_encoder		)
		def    A(	self		:							Any    ,_SCREAMING_SNAKE_CASE		:							Dict		)     -> Union[str, Any]:


						'''simple docstring'''
						if token in self.cache:
										return self.cache[token]
						A									=   tuple(_SCREAMING_SNAKE_CASE		)
						A									=   get_pairs(_SCREAMING_SNAKE_CASE		)

						if not pairs:
										return token

						while True:
										A									=   min(_SCREAMING_SNAKE_CASE    ,key=lambda _SCREAMING_SNAKE_CASE		: self.bpe_ranks.get(_SCREAMING_SNAKE_CASE    ,float('inf'		)		)		)
										if bigram not in self.bpe_ranks:
														break
										A			,		A									=   bigram
										A									=   []
										A									=   0
										while i < len(_SCREAMING_SNAKE_CASE		):
														try:
																		A									=   word.index(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)
														except ValueError:
																		new_word.extend(word[i:]		)
																		break
														else:
																		new_word.extend(word[i:j]		)
																		A									=   j

														if word[i] == first and i < len(_SCREAMING_SNAKE_CASE		) - 1 and word[i + 1] == second:
																		new_word.append(first + second		)
																		i += 2
														else:
																		new_word.append(word[i]		)
																		i += 1
										A									=   tuple(_SCREAMING_SNAKE_CASE		)
										A									=   new_word
										if len(_SCREAMING_SNAKE_CASE		) == 1:
														break
										else:
														A									=   get_pairs(_SCREAMING_SNAKE_CASE		)
						A									=   ' '.join(_SCREAMING_SNAKE_CASE		)
						A									=   word
						return word
		def    A(	self		:							Union[str, Any]    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any]		)     -> Dict:


						'''simple docstring'''
						A									=   []
						for token in re.findall(self.pat    ,_SCREAMING_SNAKE_CASE		):
										A									=   ''.join(
										    self.byte_encoder[b] for b in token.encode('utf-8'		)		)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
										bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_SCREAMING_SNAKE_CASE		).split(' '		)		)
						return bpe_tokens
		def    A(	self		:							Tuple    ,_SCREAMING_SNAKE_CASE		:							Optional[int]		)     -> Optional[Any]:


						'''simple docstring'''
						return self.encoder.get(_SCREAMING_SNAKE_CASE    ,self.encoder.get(self.unk_token		)		)
		def    A(	self		:							Dict    ,_SCREAMING_SNAKE_CASE		:							Any		)     -> Tuple:


						'''simple docstring'''
						return self.decoder.get(_SCREAMING_SNAKE_CASE		)
		def    A(	self		:							str    ,_SCREAMING_SNAKE_CASE		:							List[str]		)     -> Union[str, Any]:


						'''simple docstring'''
						A									=   ''.join(_SCREAMING_SNAKE_CASE		)
						A									=   bytearray([self.byte_decoder[c] for c in text]		).decode('utf-8'    ,errors=self.errors		)
						return text
		def    A(	self		:							List[str]    ,_SCREAMING_SNAKE_CASE		:							str    ,_SCREAMING_SNAKE_CASE		:							Optional[str] = None		)     -> Tuple[str]:


						'''simple docstring'''
						if not os.path.isdir(_SCREAMING_SNAKE_CASE		):
										logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
										return
						A									=   os.path.join(
						    _SCREAMING_SNAKE_CASE    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)
						A									=   os.path.join(
						    _SCREAMING_SNAKE_CASE    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']		)

						with open(_SCREAMING_SNAKE_CASE    ,'w'    ,encoding='utf-8'		) as f:
										f.write(json.dumps(self.encoder    ,indent=2    ,sort_keys=_SCREAMING_SNAKE_CASE    ,ensure_ascii=_SCREAMING_SNAKE_CASE		) + '\n'		)

						A									=   0
						with open(_SCREAMING_SNAKE_CASE    ,'w'    ,encoding='utf-8'		) as writer:
										writer.write('#version: 0.2\n'		)
										for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,key=lambda _SCREAMING_SNAKE_CASE		: kv[1]		):
														if index != token_index:
																		logger.warning(
																		    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																		    ' Please check that the tokenizer is not corrupted!'		)
																		A									=   token_index
														writer.write(' '.join(_SCREAMING_SNAKE_CASE		) + '\n'		)
														index += 1

						return vocab_file, merge_file
		def    A(	self		:							List[str]    ,_SCREAMING_SNAKE_CASE		:							List[int]    ,_SCREAMING_SNAKE_CASE		:							Optional[List[int]] = None		)     -> List[int]:


						'''simple docstring'''
						if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						A									=   [self.cls_token_id]
						A									=   [self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep
		def    A(	self		:							int    ,_SCREAMING_SNAKE_CASE		:							List[int]    ,_SCREAMING_SNAKE_CASE		:							Optional[List[int]] = None    ,_SCREAMING_SNAKE_CASE		:							bool = False		)     -> List[int]:


						'''simple docstring'''
						if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=_SCREAMING_SNAKE_CASE    ,token_ids_a=_SCREAMING_SNAKE_CASE    ,already_has_special_tokens=_SCREAMING_SNAKE_CASE		)

						if token_ids_a is None:
										return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
						return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1, 1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
		def    A(	self		:							Union[str, Any]    ,_SCREAMING_SNAKE_CASE		:							List[int]    ,_SCREAMING_SNAKE_CASE		:							Optional[List[int]] = None		)     -> List[int]:


						'''simple docstring'''
						A									=   [self.sep_token_id]
						A									=   [self.cls_token_id]

						if token_ids_a is None:
										return len(cls + token_ids_a + sep		) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]







		def    A(	self		:							Optional[int]    ,_SCREAMING_SNAKE_CASE		:							Dict    ,_SCREAMING_SNAKE_CASE		:							str=False    ,**_SCREAMING_SNAKE_CASE		:							Optional[int]		)     -> List[Any]:


						'''simple docstring'''
						A									=   kwargs.pop('add_prefix_space'    ,self.add_prefix_space		)
						if (is_split_into_words or add_prefix_space) and (len(_SCREAMING_SNAKE_CASE		) > 0 and not text[0].isspace()):
										A									=   ' ' + text
						return (text, kwargs)













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

lowerCAmelCase_     =			logging.getLogger(__name__)
@require_tensorboard
class   UpperCamelCase			(					unittest.TestCase			):







		"""simple docstring"""



		def    A(	self		:							Dict		)     -> int:


						'''simple docstring'''
						A									=   'test_project_with_config'
						with tempfile.TemporaryDirectory() as dirpath:
										A									=   Accelerator(log_with='tensorboard'    ,project_dir=_SCREAMING_SNAKE_CASE		)
										A									=   {'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
										accelerator.init_trackers(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)
										accelerator.end_training()
										for child in Path(f'{dirpath}/{project_name}'		).glob('*/**'		):
														A									=   list(filter(lambda _SCREAMING_SNAKE_CASE		: x.is_file()    ,child.iterdir()		)		)[0]
										self.assertNotEqual(str(_SCREAMING_SNAKE_CASE		)    ,''		)
		def    A(	self		:							Any		)     -> Dict:


						'''simple docstring'''
						A									=   'test_project_with_log'
						with tempfile.TemporaryDirectory() as dirpath:
										A									=   Accelerator(log_with='tensorboard'    ,project_dir=_SCREAMING_SNAKE_CASE		)
										accelerator.init_trackers(_SCREAMING_SNAKE_CASE		)
										A									=   {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
										accelerator.log(_SCREAMING_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
										A									=   list(filter(lambda _SCREAMING_SNAKE_CASE		: x.is_file()    ,Path(f'{dirpath}/{project_name}'		).iterdir()		)		)[0]
										self.assertNotEqual(str(_SCREAMING_SNAKE_CASE		)    ,''		)
		def    A(	self		:							int		)     -> Any:


						'''simple docstring'''
						A									=   'test_project_with_log'
						with tempfile.TemporaryDirectory() as dirpath:
										A									=   Accelerator(log_with='tensorboard'    ,project_dir=_SCREAMING_SNAKE_CASE		)
										accelerator.init_trackers(_SCREAMING_SNAKE_CASE		)
										A									=   {'tensor': torch.tensor(1		)}
										accelerator.log(_SCREAMING_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
										A									=   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(_SCREAMING_SNAKE_CASE    ,'rb'		) as f:
														A									=   f.read()
										A									=   False
										while data:
														A									=   struct.unpack('Q'    ,data[:8]		)

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

														event.ParseFromString(_SCREAMING_SNAKE_CASE		)
														if event.HasField('summary'		):
																		for value in event.summary.value:
																						if value.simple_value == 1.0 and value.tag == "tensor":
																										A									=   True
										self.assertTrue(_SCREAMING_SNAKE_CASE    ,'Converted tensor was not found in the log file!'		)
		def    A(	self		:							List[str]		)     -> Union[str, Any]:


						'''simple docstring'''
						with self.assertRaisesRegex(_SCREAMING_SNAKE_CASE    ,'Logging with `tensorboard` requires a `logging_dir`'		):
										A									=   Accelerator(log_with='tensorboard'		)
						with tempfile.TemporaryDirectory() as dirpath:
										A									=   Accelerator(log_with='tensorboard'    ,project_dir=_SCREAMING_SNAKE_CASE		)







		def    A(	self		:							Optional[Any]		)     -> Tuple:


						'''simple docstring'''
						A									=   ProjectConfiguration(total_limit=3_0		)
						with tempfile.TemporaryDirectory() as dirpath:
										A									=   Accelerator(log_with='tensorboard'    ,project_dir=_SCREAMING_SNAKE_CASE    ,project_config=_SCREAMING_SNAKE_CASE		)


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







		"""simple docstring"""



		def    A(	self		:							Dict		)     -> Optional[int]:


						'''simple docstring'''
						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		:							str    ,_SCREAMING_SNAKE_CASE		:							str    ,_SCREAMING_SNAKE_CASE		:							bool = True		)     -> str:


						'''simple docstring'''
						A									=   Rf'{section} ([\S\s]*?)\n\n'
						if record:
										A									=   Rf'Record: {pattern}'
						A									=   re.findall(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)[0]
						# A config
						if section == "config" or section == "history":
										A									=   re.findall(R'"([a-zA-Z0-9_.,]+)'    ,_SCREAMING_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]+)"'    ,_SCREAMING_SNAKE_CASE		)		)







		@skip
		def    A(	self		:							List[Any]		)     -> Optional[Any]:


						'''simple docstring'''
						A									=   'test_project_with_config'
						A									=   Accelerator(log_with='wandb'		)
						A									=   {'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
						A									=   {'wandb': {'tags': ['my_tag']}}
						accelerator.init_trackers(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)
						A									=   {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
						accelerator.log(_SCREAMING_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":
														A									=   subprocess.check_output(
														    ['wandb', 'sync', '--view', '--verbose', str(_SCREAMING_SNAKE_CASE		)]    ,env=os.environ.copy()		).decode('utf8'    ,'ignore'		)
														break

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

						# Actual logging
						A									=   self.parse_log(_SCREAMING_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   snake_case		(				self						:							Union[str, Any],    UpperCAmelCase						:							str,    UpperCAmelCase						:							str ):
				A									=   run_name
				A									=   OfflineExperiment(project_name=UpperCAmelCase,    offline_directory=UpperCAmelCase )
				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(snake_case__      ,							"__init__"      ,							snake_case__			)
class   UpperCamelCase			(					unittest.TestCase			):







		"""simple docstring"""



		@staticmethod
		def    A(	_SCREAMING_SNAKE_CASE		:							Tuple    ,_SCREAMING_SNAKE_CASE		:							str    ,_SCREAMING_SNAKE_CASE		:							bool = False		)     -> List[Any]:


						'''simple docstring'''
						for log in log_list:
										A									=   json.loads(_SCREAMING_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    A(	self		:							int		)     -> int:


						'''simple docstring'''
						with tempfile.TemporaryDirectory() as d:
										A									=   CometMLTracker('test_project_with_config'    ,_SCREAMING_SNAKE_CASE		)
										A									=   Accelerator(log_with=_SCREAMING_SNAKE_CASE		)
										A									=   {'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
										accelerator.init_trackers(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)
										accelerator.end_training()
										A									=   os.listdir(_SCREAMING_SNAKE_CASE		)[0]  # Comet is nice, it's just a zip file here
										# We parse the raw logs
										A									=   os.path.join(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)
										A									=   zipfile.ZipFile(_SCREAMING_SNAKE_CASE    ,'r'		)
										A									=   archive.open('messages.json'		).read().decode('utf-8'		)
						A									=   log.split('\n'		)[:-1]
						self.assertEqual(self.get_value_from_key(_SCREAMING_SNAKE_CASE    ,'num_iterations'    ,_SCREAMING_SNAKE_CASE		)    ,1_2		)
						self.assertEqual(self.get_value_from_key(_SCREAMING_SNAKE_CASE    ,'learning_rate'    ,_SCREAMING_SNAKE_CASE		)    ,0.01		)
						self.assertEqual(self.get_value_from_key(_SCREAMING_SNAKE_CASE    ,'some_boolean'    ,_SCREAMING_SNAKE_CASE		)    ,_SCREAMING_SNAKE_CASE		)
						self.assertEqual(self.get_value_from_key(_SCREAMING_SNAKE_CASE    ,'some_string'    ,_SCREAMING_SNAKE_CASE		)    ,'some_value'		)







		def    A(	self		:							Optional[int]		)     -> Optional[int]:


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


class   UpperCamelCase			(					snake_case__			):







		"""simple docstring"""



		snake_case			 = [
		    "total_loss",
		    "iteration",
		    "my_text",
		    "learning_rate",
		    "num_iterations",
		    "some_boolean",
		    "some_string",
		]

		snake_case			 = "my_custom_tracker"
		snake_case			 = False
		def __init__(	self		:							List[Any]    ,_SCREAMING_SNAKE_CASE		:							str		)     -> List[str]:


						'''simple docstring'''
						A									=   open(f'{dir}/log.csv'    ,'w+'		)
						A									=   csv.DictWriter(self.f    ,fieldnames=self._col_names		)
						self.writer.writeheader()
		@property
		def    A(	self		:							int		)     -> Union[str, Any]:


						'''simple docstring'''
						return self.writer
		def    A(	self		:							Union[str, Any]    ,_SCREAMING_SNAKE_CASE		:							dict		)     -> Any:


						'''simple docstring'''
						logger.info('Call init'		)
						self.writer.writerow(_SCREAMING_SNAKE_CASE		)
		def    A(	self		:							Tuple    ,_SCREAMING_SNAKE_CASE		:							dict    ,_SCREAMING_SNAKE_CASE		:							Optional[int]		)     -> List[Any]:


						'''simple docstring'''
						logger.info('Call log'		)
						self.writer.writerow(_SCREAMING_SNAKE_CASE		)







		def    A(	self		:							Union[str, Any]		)     -> Tuple:


						'''simple docstring'''
						self.f.close()


class   UpperCamelCase			(					unittest.TestCase			):







		"""simple docstring"""



		def    A(	self		:							Union[str, Any]		)     -> int:


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







		def    A(	self		:							List[Any]		)     -> Tuple:


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





