
'''simple docstring'''





import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
 from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
 raise ImportError("You need to install the version of fairseq from the Mega repo!")


class 			UpperCamelCase__	(     nn.Module):






 def __init__(      self	:Union[str, Any]						,					_A	:List[Any]						,					_A	:Tuple						,					_A	:List[str]      )					->      int:







       '''simple docstring'''



       super().__init__()
       __A													=      mega_args
       __A													=      nn.Embedding(_A						,					self.mega_args.encoder_embed_dim      )
       __A													=      nn.ModuleList([MegaEncoderLayer(self.mega_args      ) for _ in range(_A      )]      )
       __A													=      depth






 def 						lowercase_    (      self	:Optional[Any]						,					_A	:int						,					_A	:Dict						,					_A	:int=True						,					_A	:Tuple=0      )					->      Tuple:







       '''simple docstring'''



       if batch_first:
             __A													=      input_ids.T

       # to make things more confusing, Mega expects the attention mask to
       # be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
       # which is the opposite of what HF returns
       if ignore_mask_value == 0:
             __A													=      1 - attention_mask

       # get token embeddings from IDs
       __A													=      self.embedding_layer(_A      )

       # pass through the Mega layers
       # input is (time, batch, encoder dim) and output is the same
       for encoder in self.encoders:
             __A													=      encoder(_A						,					_A      )

       # return according to the shape specified
       if batch_first:
             # (T, B, H) --> (B, T, H)
             return torch.transpose(_A						,					0						,					1      )
       else:
             return embeds


class 			UpperCamelCase__	(     nn.Module):






 def __init__(      self	:List[str]						,					_A	:Optional[Any]						,					_A	:Tuple						,					_A	:str      )					->      Optional[Any]:







       '''simple docstring'''



       super().__init__()
       __A													=      MegaLM(_A						,					_A						,					_A      )
       __A													=      nn.Linear(mega_args.encoder_embed_dim						,					_A      )
       __A													=      nn.Dropout(p=0.1      )






 def 						lowercase_    (      self	:Optional[int]						,					_A	:Union[str, Any]						,					_A	:Any						,					_A	:Union[str, Any]=True						,					_A	:List[str]=0      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      self.mega(_A						,					_A						,					_A						,					_A      )
       return self.mlm_head(self.dropout(_A      )      )





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Dict:







      """simple docstring"""
      with open(os.path.join(UpperCAmelCase   ,	'model_args.pkl'						)   ,	'rb'						) as f:
            __A													=      pkl.load(UpperCAmelCase						)

      # load the original encoder
      __A													=      OriginalMegaForMaskedLM(**UpperCAmelCase						).eval()

      # load its weights
      print(
          'Original Mega encoder:'   ,	original_mlm.mega.load_state_dict(
              torch.load(os.path.join(UpperCAmelCase   ,	'encoder_weights.pt'						)   ,	map_location='cpu'						)						)   ,	)
      print(
          'Original Mega MLM layer:'   ,	original_mlm.mlm_head.load_state_dict(
              torch.load(os.path.join(UpperCAmelCase   ,	'mlm_head_weights.pt'						)   ,	map_location='cpu'						)						)   ,	)

      # create a new config from the old one
      __A													=      MegaConfig(
          num_hidden_layers=mega_original_args['depth']   ,	vocab_size=mega_original_args['vocab_size']   ,	hidden_size=mega_original_args['mega_args'].encoder_embed_dim   ,	shared_representation_size=mega_original_args['mega_args'].encoder_z_dim   ,	intermediate_size=mega_original_args['mega_args'].encoder_hidden_dim   ,	ema_projection_size=mega_original_args['mega_args'].encoder_n_dim   ,	dropout_prob=mega_original_args['mega_args'].dropout   ,	attention_probs_dropout_prob=mega_original_args['mega_args'].attention_dropout   ,	hidden_dropout_prob=mega_original_args['mega_args'].hidden_dropout   ,	activation=mega_original_args['mega_args'].activation_fn   ,	attention_activation=mega_original_args['mega_args'].attention_activation_fn   ,	bidirectional=mega_original_args['mega_args'].bidirectional   ,	use_chunking=mega_original_args['mega_args'].encoder_chunk_size > 0   ,	chunk_size=mega_original_args['mega_args'].encoder_chunk_size   ,	truncation=mega_original_args['mega_args'].truncation_length   ,	normalization_type=mega_original_args['mega_args'].normalization_type   ,	normalize_before_mega=UpperCAmelCase   ,	norm_affine=UpperCAmelCase   ,	use_feature_dropout=mega_original_args['mega_args'].feature_dropout   ,	relative_positional_bias=mega_original_args['mega_args'].rel_pos_bias   ,	max_positions=mega_original_args['mega_args'].max_source_positions   ,	nffn_hidden_size=mega_original_args['mega_args'].encoder_ffn_embed_dim   ,	normalize_before_ffn=mega_original_args['mega_args'].normalize_before   ,	nffn_activation_dropout_prob=0.0   ,	add_token_type_embeddings=UpperCAmelCase   ,	add_lm_hidden_dense_layer=UpperCAmelCase   ,	)

      __A													=      MegaForMaskedLM(UpperCAmelCase						).eval()

      # the originl checkpoint just uses nn.Embedding for the word embeddings
      # we use a wrapper module for embeddings to add support for positional embeddings
      __A													=      original_mlm.mega.embedding_layer.weight

      # modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
      # ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
      # also renaming previously confusing parameter names
      __A													=      original_mlm.mega.encoders.state_dict()
      __A													=      {}
      for module_name in original_state_dict.keys():
            __A													=      None
            # have to handle gamma, beta, and alpha differently due to their use
            # in multiple modules within the original repository;
            # beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
            # the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
            if "beta" in module_name:
                  # EMA sub-layers were always called "move" in the original repo
                  if "move.beta" in module_name:
                        __A													=      module_name.replace('move.beta'   ,	'ema_gate.ema_expansion_matrix'						)
                  elif "mega_layer.beta" in module_name:
                        __A													=      module_name.replace('beta'   ,	'qk_bias'						)
                  else:
                        __A													=      module_name.replace('beta'   ,	'b_param'						)
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
            elif "gamma" in module_name:
                  if "move.gamma" in module_name:
                        __A													=      module_name.replace('move.gamma'   ,	'ema_gate.kernel_projection_matrix'						)
                  elif "mega_layer.gamma" in module_name:
                        __A													=      module_name.replace('gamma'   ,	'qk_weight'						)
                  else:
                        __A													=      module_name.replace('gamma'   ,	'g_param'						)
        # alpha is used in EMA and positional bias; renaming to improve readability
            elif "move.alpha" in module_name:
                  __A													=      module_name.replace('move.alpha'   ,	'ema_gate.decay_factor'						)
            # delta is only used in EMA; renaming to improve readability
            elif "move.delta" in module_name:
                  __A													=      module_name.replace('move.delta'   ,	'ema_gate.damping_factor'						)
            # omega is only used in EMA; renaming to improve readability
            elif "omega" in module_name:
                  __A													=      module_name.replace('move.omega'   ,	'ema_gate.residual_weight'						)

            if new_module_name:
                  __A													=      new_module_name

      if len(UpperCAmelCase						) != 0:
            print(f'Renaming these keys: {updated_keys.keys()}'						)
      else:
            print('No need to rename state dict entries'						)
      for old, new in updated_keys.items():
            __A													=      original_state_dict.pop(UpperCAmelCase						)

      # now attempt to load the state dictionary with updated names
      # note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
      print('HF Mega encoder:'   ,	hf_mlm.mega.layers.load_state_dict(UpperCAmelCase						)						)

      # load the MLM head weights directly
      print(
          'HF Mega MLM layer:'   ,	hf_mlm.mlm_head.load_state_dict(
              torch.load(os.path.join(UpperCAmelCase   ,	'mlm_head_weights.pt'						)   ,	map_location='cpu'						)						)   ,	)

      # test on a randomly generated input sequence
      __A													=      torch.randint(0   ,	hf_config.vocab_size   ,	size=(4, 2_5_6)						)
      __A													=      torch.ones_like(UpperCAmelCase						)
      # mask a few tokens to make sure masking is applied appropriately :)
      __A													=      0

      # run forward passes
      __A													=      original_mlm(UpperCAmelCase   ,	UpperCAmelCase   ,	batch_first=UpperCAmelCase   ,	ignore_mask_value=0						)
      __A													=      hf_mlm(UpperCAmelCase   ,	UpperCAmelCase						)[0]

      # print shapes and diff
      print(f'original output {original_output.shape}'						)
      print(f'hf output {hf_output.shape}'						)
      print(f'max diff: {(original_output - hf_output).max()}'						)  # 0.0
      __A													=      torch.allclose(UpperCAmelCase   ,	UpperCAmelCase   ,	atol=1e-3						)

      if success:
            print('Yay!'						)
            hf_mlm.save_pretrained(UpperCAmelCase						)
      else:
            raise RuntimeError(f'Something\'s broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}'						)

      if includes_tokenizer:
            print('Transferring tokenizer'						)
            __A													=      AutoTokenizer.from_pretrained(UpperCAmelCase						)
            tokenizer.save_pretrained(UpperCAmelCase						)


if __name__ == "__main__":
 a__		: str  							=							argparse.ArgumentParser()

 parser.add_argument(
     "--pretrained_checkpoint_path",
     default=None,
     type=str,
     required=True,
     help="Point to the directory containing your model weights using the official Mega repo",
 )

 parser.add_argument(
     "--output_path", default=None, type=str, required=True, help="Location to save the Hugging Face version"
 )

 parser.add_argument(
     "--includes_tokenizer",
     action="store_true",
     help="Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo",
 )

 a__		: Tuple  							=							parser.parse_args()

 convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)













'''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

lowerCAmelCase__      :   Tuple      	=			logging.getLogger(__name__)


@require_tensorboard
class      SCREAMING_SNAKE_CASE__      (   unittest.TestCase		):







					"""simple docstring"""






					def 		lowerCamelCase_   (   self       :      List[str]		):







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







							"""simple docstring"""
							__UpperCAmelCase	:							Optional[int]     =     "test_project_with_log"
							with tempfile.TemporaryDirectory() as dirpath:
									__UpperCAmelCase	:							Any     =     Accelerator(log_with="tensorboard"		,	project_dir=UpperCAmelCase_		)
									accelerator.init_trackers(UpperCAmelCase_		)
									__UpperCAmelCase	:							Optional[Any]     =     {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
									accelerator.log(UpperCAmelCase_		,	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	:							Union[str, Any]     =     list(filter(lambda UpperCAmelCase_		: x.is_file()		,	Path(f"{dirpath}/{project_name}"		).iterdir()		)		)[0]
									self.assertNotEqual(str(UpperCAmelCase_		)		,	""		)
					def 		lowerCamelCase_   (   self       :      Tuple		):







							"""simple docstring"""
							__UpperCAmelCase	:							List[str]     =     "test_project_with_log"
							with tempfile.TemporaryDirectory() as dirpath:
									__UpperCAmelCase	:							List[Any]     =     Accelerator(log_with="tensorboard"		,	project_dir=UpperCAmelCase_		)
									accelerator.init_trackers(UpperCAmelCase_		)
									__UpperCAmelCase	:							int     =     {"tensor": torch.tensor(1		)}
									accelerator.log(UpperCAmelCase_		,	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	:							Dict     =     list(filter(lambda UpperCAmelCase_		: 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(UpperCAmelCase_		,	"rb"		) as f:
											__UpperCAmelCase	:							Dict     =     f.read()
									__UpperCAmelCase	:							Dict     =     False
									while data:
											__UpperCAmelCase	:							str     =     struct.unpack("Q"		,	data[:8]		)

											__UpperCAmelCase	:							List[str]     =     data[12 : 12 + int(header[0]		)]  # 8+4
											__UpperCAmelCase	:							Tuple     =     data[12 + int(header[0]		) + 4 :]
											__UpperCAmelCase	:							Optional[int]     =     event_pba.Event()

											event.ParseFromString(UpperCAmelCase_		)
											if event.HasField("summary"		):
													for value in event.summary.value:
															if value.simple_value == 1.0 and value.tag == "tensor":
																	__UpperCAmelCase	:							List[str]     =     True
									self.assertTrue(UpperCAmelCase_		,	"Converted tensor was not found in the log file!"		)
					def 		lowerCamelCase_   (   self       :      Dict		):







							"""simple docstring"""
							with self.assertRaisesRegex(UpperCAmelCase_		,	"Logging with `tensorboard` requires a `logging_dir`"		):
									__UpperCAmelCase	:							Union[str, Any]     =     Accelerator(log_with="tensorboard"		)
							with tempfile.TemporaryDirectory() as dirpath:
									__UpperCAmelCase	:							str     =     Accelerator(log_with="tensorboard"		,	project_dir=UpperCAmelCase_		)






					def 		lowerCamelCase_   (   self       :      Optional[Any]		):







							"""simple docstring"""
							__UpperCAmelCase	:							int     =     ProjectConfiguration(total_limit=30		)
							with tempfile.TemporaryDirectory() as dirpath:
									__UpperCAmelCase	:							Optional[Any]     =     Accelerator(log_with="tensorboard"		,	project_dir=UpperCAmelCase_		,	project_config=UpperCAmelCase_		)






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







					"""simple docstring"""






					def 		lowerCamelCase_   (   self       :      List[str]		):







							"""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 		lowerCamelCase_   (   UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      bool = True		):







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






					@skip
					def 		lowerCamelCase_   (   self       :      str		):







							"""simple docstring"""
							__UpperCAmelCase	:							Optional[int]     =     "test_project_with_config"
							__UpperCAmelCase	:							List[Any]     =     Accelerator(log_with="wandb"		)
							__UpperCAmelCase	:							Any     =     {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
							__UpperCAmelCase	:							Any     =     {"wandb": {"tags": ["my_tag"]}}
							accelerator.init_trackers(UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		)
							__UpperCAmelCase	:							int     =     {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
							accelerator.log(UpperCAmelCase_		,	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	:							List[str]     =     subprocess.check_output(
											    ["wandb", "sync", "--view", "--verbose", str(UpperCAmelCase_		)]		,	env=os.environ.copy()		).decode("utf8"		,	"ignore"		)
											break

        # Check HPS through careful parsing and cleaning
							__UpperCAmelCase	:							Dict     =     self.parse_log(UpperCAmelCase_		,	"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	:							Union[str, Any]     =     self.parse_log(UpperCAmelCase_		,	"run"		,	UpperCAmelCase_		)
							self.assertEqual(logged_items["tags"]		,	"my_tag"		)

							# Actual logging
							__UpperCAmelCase	:							Optional[Any]     =     self.parse_log(UpperCAmelCase_		,	"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 						__UpperCamelCase						(			self,       _UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase	:							str     =     run_name
		__UpperCAmelCase	:							Optional[int]     =     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      SCREAMING_SNAKE_CASE__      (   unittest.TestCase		):







					"""simple docstring"""






					@staticmethod
					def 		lowerCamelCase_   (   UpperCAmelCase_       :      Optional[Any]		,	UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      bool = False		):







							"""simple docstring"""
							for log in log_list:
									__UpperCAmelCase	:							List[Any]     =     json.loads(UpperCAmelCase_		)["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 		lowerCamelCase_   (   self       :      int		):







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






					def 		lowerCamelCase_   (   self       :      Dict		):







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






class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






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

					SCREAMING_SNAKE_CASE							=	'''my_custom_tracker'''
					SCREAMING_SNAKE_CASE							=	False
					def __init__(   self       :      List[Any]		,	UpperCAmelCase_       :      str		):







							"""simple docstring"""
							__UpperCAmelCase	:							int     =     open(f"{dir}/log.csv"		,	"w+"		)
							__UpperCAmelCase	:							Optional[int]     =     csv.DictWriter(self.f		,	fieldnames=self._col_names		)
							self.writer.writeheader()
					@property
					def 		lowerCamelCase_   (   self       :      Tuple		):







							"""simple docstring"""
							return self.writer
					def 		lowerCamelCase_   (   self       :      Tuple		,	UpperCAmelCase_       :      dict		):







							"""simple docstring"""
							logger.info("Call init"		)
							self.writer.writerow(UpperCAmelCase_		)
					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      dict		,	UpperCAmelCase_       :      Optional[int]		):







							"""simple docstring"""
							logger.info("Call log"		)
							self.writer.writerow(UpperCAmelCase_		)






					def 		lowerCamelCase_   (   self       :      str		):







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






class      SCREAMING_SNAKE_CASE__      (   unittest.TestCase		):







					"""simple docstring"""






					def 		lowerCamelCase_   (   self       :      Tuple		):







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






					def 		lowerCamelCase_   (   self       :      Dict		):







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




