

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLMForMultipleChoice,
          TFXLMForQuestionAnsweringSimple,
          TFXLMForSequenceClassification,
          TFXLMForTokenClassification,
          TFXLMModel,
          TFXLMWithLMHeadModel,
          XLMConfig,
      )






class     __magic_name__     :


 def __init__(    self		:			List[Any]      ,    lowerCAmelCase__		:			Union[str, Any]      ,    )     ->   str:
   UpperCAmelCase          =	parent
   UpperCAmelCase          =	1_3
   UpperCAmelCase          =	7
   UpperCAmelCase          =	True
   UpperCAmelCase          =	True
   UpperCAmelCase          =	True
   UpperCAmelCase          =	True
   UpperCAmelCase          =	True
   UpperCAmelCase          =	False
   UpperCAmelCase          =	False
   UpperCAmelCase          =	False
   UpperCAmelCase          =	2
   UpperCAmelCase          =	9_9
   UpperCAmelCase          =	0
   UpperCAmelCase          =	3_2
   UpperCAmelCase          =	2
   UpperCAmelCase          =	4
   UpperCAmelCase          =	0.1
   UpperCAmelCase          =	0.1
   UpperCAmelCase          =	5_1_2
   UpperCAmelCase          =	1_6
   UpperCAmelCase          =	2
   UpperCAmelCase          =	0.02
   UpperCAmelCase          =	3
   UpperCAmelCase          =	4
   UpperCAmelCase          =	"last"
   UpperCAmelCase          =	True
   UpperCAmelCase          =	None
   UpperCAmelCase          =	0


 def _UpperCamelCase						(    self		:			Dict     )     ->   Union[str, Any]:
   UpperCAmelCase          =	ids_tensor([self.batch_size, self.seq_length]      ,    self.vocab_size     )
   UpperCAmelCase          =	random_attention_mask([self.batch_size, self.seq_length]      ,    dtype=tf.floataa     )

   UpperCAmelCase          =	None
   if self.use_input_lengths:
     UpperCAmelCase          =	(
         ids_tensor([self.batch_size]      ,    vocab_size=2     ) + self.seq_length - 2
     )  # small variation of seq_length

   UpperCAmelCase          =	None
   if self.use_token_type_ids:
     UpperCAmelCase          =	ids_tensor([self.batch_size, self.seq_length]      ,    self.n_langs     )

   UpperCAmelCase          =	None
   UpperCAmelCase          =	None
   UpperCAmelCase          =	None
   if self.use_labels:
     UpperCAmelCase          =	ids_tensor([self.batch_size]      ,    self.type_sequence_label_size     )
     UpperCAmelCase          =	ids_tensor([self.batch_size, self.seq_length]      ,    self.num_labels     )
     UpperCAmelCase          =	ids_tensor([self.batch_size]      ,    2      ,    dtype=tf.floataa     )
     UpperCAmelCase          =	ids_tensor([self.batch_size]      ,    self.num_choices     )

   UpperCAmelCase          =	XLMConfig(
       vocab_size=self.vocab_size      ,    n_special=self.n_special      ,    emb_dim=self.hidden_size      ,    n_layers=self.num_hidden_layers      ,    n_heads=self.num_attention_heads      ,    dropout=self.hidden_dropout_prob      ,    attention_dropout=self.attention_probs_dropout_prob      ,    gelu_activation=self.gelu_activation      ,    sinusoidal_embeddings=self.sinusoidal_embeddings      ,    asm=self.asm      ,    causal=self.causal      ,    n_langs=self.n_langs      ,    max_position_embeddings=self.max_position_embeddings      ,    initializer_range=self.initializer_range      ,    summary_type=self.summary_type      ,    use_proj=self.use_proj      ,    bos_token_id=self.bos_token_id      ,    )

   return (
       config,
       input_ids,
       token_type_ids,
       input_lengths,
       sequence_labels,
       token_labels,
       is_impossible_labels,
       choice_labels,
       input_mask,
   )


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    lowerCAmelCase__		:			Dict      ,    lowerCAmelCase__		:			Any      ,    lowerCAmelCase__		:			List[str]      ,    lowerCAmelCase__		:			Any      ,    lowerCAmelCase__		:			int      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			Optional[Any]      ,    lowerCAmelCase__		:			int      ,    lowerCAmelCase__		:			Any      ,    )     ->   Optional[int]:
   UpperCAmelCase          =	TFXLMModel(config=_SCREAMING_SNAKE_CASE     )
   UpperCAmelCase          =	{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
   UpperCAmelCase          =	model(_SCREAMING_SNAKE_CASE     )

   UpperCAmelCase          =	[input_ids, input_mask]
   UpperCAmelCase          =	model(_SCREAMING_SNAKE_CASE     )
   self.parent.assertEqual(result.last_hidden_state.shape      ,    (self.batch_size, self.seq_length, self.hidden_size)     )


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    lowerCAmelCase__		:			int      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			Any      ,    lowerCAmelCase__		:			List[Any]      ,    lowerCAmelCase__		:			Optional[int]      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			int      ,    lowerCAmelCase__		:			Union[str, Any]      ,    lowerCAmelCase__		:			Union[str, Any]      ,    )     ->   int:
   UpperCAmelCase          =	TFXLMWithLMHeadModel(_SCREAMING_SNAKE_CASE     )

   UpperCAmelCase          =	{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
   UpperCAmelCase          =	model(_SCREAMING_SNAKE_CASE     )

   UpperCAmelCase          =	outputs

   self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.seq_length, self.vocab_size)     )


 def _UpperCamelCase						(    self		:			List[str]      ,    lowerCAmelCase__		:			Optional[Any]      ,    lowerCAmelCase__		:			List[Any]      ,    lowerCAmelCase__		:			Optional[int]      ,    lowerCAmelCase__		:			Optional[Any]      ,    lowerCAmelCase__		:			int      ,    lowerCAmelCase__		:			Optional[Any]      ,    lowerCAmelCase__		:			List[Any]      ,    lowerCAmelCase__		:			str      ,    lowerCAmelCase__		:			Any      ,    )     ->   List[str]:
   UpperCAmelCase          =	TFXLMForQuestionAnsweringSimple(_SCREAMING_SNAKE_CASE     )

   UpperCAmelCase          =	{"input_ids": input_ids, "lengths": input_lengths}

   UpperCAmelCase          =	model(_SCREAMING_SNAKE_CASE     )

   self.parent.assertEqual(result.start_logits.shape      ,    (self.batch_size, self.seq_length)     )
   self.parent.assertEqual(result.end_logits.shape      ,    (self.batch_size, self.seq_length)     )


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    lowerCAmelCase__		:			List[Any]      ,    lowerCAmelCase__		:			Any      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			Union[str, Any]      ,    lowerCAmelCase__		:			Dict      ,    lowerCAmelCase__		:			Optional[int]      ,    lowerCAmelCase__		:			Optional[Any]      ,    lowerCAmelCase__		:			Dict      ,    lowerCAmelCase__		:			int      ,    )     ->   Optional[int]:
   UpperCAmelCase          =	TFXLMForSequenceClassification(_SCREAMING_SNAKE_CASE     )

   UpperCAmelCase          =	{"input_ids": input_ids, "lengths": input_lengths}

   UpperCAmelCase          =	model(_SCREAMING_SNAKE_CASE     )

   self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.type_sequence_label_size)     )


 def _UpperCamelCase						(    self		:			int      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			int      ,    lowerCAmelCase__		:			str      ,    lowerCAmelCase__		:			Union[str, Any]      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			Dict      ,    lowerCAmelCase__		:			Optional[int]      ,    lowerCAmelCase__		:			Union[str, Any]      ,    lowerCAmelCase__		:			Optional[Any]      ,    )     ->   Union[str, Any]:
   UpperCAmelCase          =	self.num_labels
   UpperCAmelCase          =	TFXLMForTokenClassification(config=_SCREAMING_SNAKE_CASE     )
   UpperCAmelCase          =	{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
   UpperCAmelCase          =	model(_SCREAMING_SNAKE_CASE     )
   self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.seq_length, self.num_labels)     )


 def _UpperCamelCase						(    self		:			int      ,    lowerCAmelCase__		:			List[str]      ,    lowerCAmelCase__		:			Union[str, Any]      ,    lowerCAmelCase__		:			Optional[Any]      ,    lowerCAmelCase__		:			Optional[int]      ,    lowerCAmelCase__		:			List[Any]      ,    lowerCAmelCase__		:			Union[str, Any]      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			Dict      ,    )     ->   Tuple:
   UpperCAmelCase          =	self.num_choices
   UpperCAmelCase          =	TFXLMForMultipleChoice(config=_SCREAMING_SNAKE_CASE     )
   UpperCAmelCase          =	tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE      ,    1     )      ,    (1, self.num_choices, 1)     )
   UpperCAmelCase          =	tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE      ,    1     )      ,    (1, self.num_choices, 1)     )
   UpperCAmelCase          =	tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE      ,    1     )      ,    (1, self.num_choices, 1)     )
   UpperCAmelCase          =	{
       "input_ids": multiple_choice_inputs_ids,
       "attention_mask": multiple_choice_input_mask,
       "token_type_ids": multiple_choice_token_type_ids,
   }
   UpperCAmelCase          =	model(_SCREAMING_SNAKE_CASE     )
   self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.num_choices)     )


 def _UpperCamelCase						(    self		:			Optional[int]     )     ->   Tuple:
   UpperCAmelCase          =	self.prepare_config_and_inputs()
   (
       (
       UpperCAmelCase		
   )   ,						(
       UpperCAmelCase		
   )   ,						(
       UpperCAmelCase		
   )   ,						(
       UpperCAmelCase		
   )   ,						(
       UpperCAmelCase		
   )   ,						(
       UpperCAmelCase		
   )   ,						(
       UpperCAmelCase		
   )   ,						(
       UpperCAmelCase		
   )   ,						(
       UpperCAmelCase		
   )   ,								
   )          =	config_and_inputs
   UpperCAmelCase          =	{
       "input_ids": input_ids,
       "token_type_ids": token_type_ids,
       "langs": token_type_ids,
       "lengths": input_lengths,
   }
   return config, inputs_dict






@require_tf
class     __magic_name__     (							snake_case__						,    snake_case__						,    unittest.TestCase     ):
 UpperCAmelCase				     =    (
     (
         TFXLMModel,
         TFXLMWithLMHeadModel,
         TFXLMForSequenceClassification,
         TFXLMForQuestionAnsweringSimple,
         TFXLMForTokenClassification,
         TFXLMForMultipleChoice,
     )
     if is_tf_available()
     else ()
 )
 UpperCAmelCase				     =    (
     (TFXLMWithLMHeadModel,) if is_tf_available() else ()
 )  # TODO (PVP): Check other models whether language generation is also applicable
 UpperCAmelCase				     =    (
     {
         """feature-extraction""": TFXLMModel,
         """fill-mask""": TFXLMWithLMHeadModel,
         """question-answering""": TFXLMForQuestionAnsweringSimple,
         """text-classification""": TFXLMForSequenceClassification,
         """text-generation""": TFXLMWithLMHeadModel,
         """token-classification""": TFXLMForTokenClassification,
         """zero-shot""": TFXLMForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 UpperCAmelCase				     =    False
 UpperCAmelCase				     =    False


 def _UpperCamelCase						(    self		:			List[str]      ,    lowerCAmelCase__		:			Optional[int]      ,    lowerCAmelCase__		:			List[Any]      ,    lowerCAmelCase__		:			List[Any]      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			str     )     ->   str:
   if (
       pipeline_test_casse_name == "QAPipelineTests"
       and tokenizer_name is not None
       and not tokenizer_name.endswith("Fast"     )
   ):
     # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
     # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
     # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
     return True

   return False


 def _UpperCamelCase						(    self		:			Optional[int]     )     ->   List[Any]:
   UpperCAmelCase          =	TFXLMModelTester(self     )
   UpperCAmelCase          =	ConfigTester(self      ,    config_class=_SCREAMING_SNAKE_CASE      ,    emb_dim=3_7     )


 def _UpperCamelCase						(    self		:			Optional[Any]     )     ->   Union[str, Any]:
   self.config_tester.run_common_tests()


 def _UpperCamelCase						(    self		:			List[str]     )     ->   Optional[int]:
   UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_xlm_model(*_SCREAMING_SNAKE_CASE     )


 def _UpperCamelCase						(    self		:			Optional[int]     )     ->   str:
   UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_xlm_lm_head(*_SCREAMING_SNAKE_CASE     )


 def _UpperCamelCase						(    self		:			Optional[Any]     )     ->   str:
   UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_xlm_qa(*_SCREAMING_SNAKE_CASE     )


 def _UpperCamelCase						(    self		:			List[Any]     )     ->   str:
   UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_xlm_sequence_classif(*_SCREAMING_SNAKE_CASE     )


 def _UpperCamelCase						(    self		:			int     )     ->   Optional[int]:
   UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_xlm_for_token_classification(*_SCREAMING_SNAKE_CASE     )


 def _UpperCamelCase						(    self		:			List[Any]     )     ->   Optional[Any]:
   UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_xlm_for_multiple_choice(*_SCREAMING_SNAKE_CASE     )


 @slow
 def _UpperCamelCase						(    self		:			Union[str, Any]     )     ->   int:
   for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     UpperCAmelCase          =	TFXLMModel.from_pretrained(_SCREAMING_SNAKE_CASE     )
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE     )






@require_tf
class     __magic_name__     (							unittest.TestCase     ):


 @slow
 def _UpperCamelCase						(    self		:			str     )     ->   str:
   UpperCAmelCase          =	TFXLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048"     )
   UpperCAmelCase          =	tf.convert_to_tensor([[1_4, 4_4_7]]      ,    dtype=tf.intaa     )  # the president
   UpperCAmelCase          =	[
       1_4,
       4_4_7,
       1_4,
       4_4_7,
       1_4,
       4_4_7,
       1_4,
       4_4_7,
       1_4,
       4_4_7,
       1_4,
       4_4_7,
       1_4,
       4_4_7,
       1_4,
       4_4_7,
       1_4,
       4_4_7,
       1_4,
       4_4_7,
   ]  # the president the president the president the president the president the president the president the president the president the president
   # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
   UpperCAmelCase          =	model.generate(_SCREAMING_SNAKE_CASE      ,    do_sample=_SCREAMING_SNAKE_CASE     )
   self.assertListEqual(output_ids[0].numpy().tolist()      ,    _SCREAMING_SNAKE_CASE     )











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		)





