


'''simple docstring'''

from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import (
       TF_MODEL_FOR_CAUSAL_LM_MAPPING,
       TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
       TF_MODEL_FOR_MASKED_LM_MAPPING,
       TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
       TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
       TF_MODEL_FOR_PRETRAINING_MAPPING,
       TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
       TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
       TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
       TFSharedEmbeddings,
   )

   if _tf_gpu_memory_limit is not None:
      lowercase__					  =		tf.config.list_physical_devices("GPU")
      for gpu in gpus:
         # Restrict TensorFlow to only allocate x GB of memory on the GPUs
         try:
            tf.config.set_logical_device_configuration(
                gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
            )
            lowercase__					  =		tf.config.list_logical_devices("GPU")
            print("Logical GPUs", logical_gpus)
         except RuntimeError as e:
            # Virtual devices must be set before GPUs have been initialized
            print(e)





@require_tf
class     __SCREAMING_SNAKE_CASE       :
     UpperCAmelCase			       =					None
     UpperCAmelCase			       =					()
     UpperCAmelCase			       =					()
     UpperCAmelCase			       =					True
     UpperCAmelCase			       =					True
     UpperCAmelCase			       =					True
     UpperCAmelCase			       =					False






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase=False		)							->     dict:
          _a            =		copy.deepcopy(__UpperCamelCase		)

          if model_class in get_values(__UpperCamelCase		):
               _a            =		{
                   k: tf.tile(tf.expand_dims(__UpperCamelCase  ,				1		)  ,				(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)		)
                   if isinstance(__UpperCamelCase  ,				tf.Tensor		) and v.ndim > 0
                   else v
                   for k, v in inputs_dict.items()
               }

          if return_labels:
               if model_class in get_values(__UpperCamelCase		):
                    _a            =		tf.ones(self.model_tester.batch_size  ,				dtype=tf.intaa		)
               elif model_class in get_values(__UpperCamelCase		):
                    _a            =		tf.zeros(self.model_tester.batch_size  ,				dtype=tf.intaa		)
                    _a            =		tf.zeros(self.model_tester.batch_size  ,				dtype=tf.intaa		)
               elif model_class in [
                   *get_values(__UpperCamelCase		),
                   *get_values(__UpperCamelCase		),
               ]:
                    _a            =		tf.zeros(self.model_tester.batch_size  ,				dtype=tf.intaa		)
               elif model_class in get_values(__UpperCamelCase		):
                    _a            =		tf.zeros(self.model_tester.batch_size  ,				dtype=tf.intaa		)
               elif model_class in [
                   *get_values(__UpperCamelCase		),
                   *get_values(__UpperCamelCase		),
                   *get_values(__UpperCamelCase		),
                   *get_values(__UpperCamelCase		),
                   *get_values(__UpperCamelCase		),
               ]:
                    _a            =		tf.zeros(
                        (self.model_tester.batch_size, self.model_tester.seq_length)  ,				dtype=tf.intaa		)
          return inputs_dict






     @slow
     def 			a_       (      self		)							->     Optional[Any]:
          _a      ,      _a            =		self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes[:2]:
               _a            =		self._prepare_for_class(__UpperCamelCase  ,				__UpperCamelCase		)
               _a            =		model_class(__UpperCamelCase		)

               @tf.function
               def run_in_graph_mode():
                    return model(__UpperCamelCase		)

               _a            =		run_in_graph_mode()
               self.assertIsNotNone(__UpperCamelCase		)






     @slow
     def 			a_       (      self		)							->     Optional[Any]:
          _a      ,      _a            =		self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes[:2]:
               _a            =		self._prepare_for_class(__UpperCamelCase  ,				__UpperCamelCase		)
               _a            =		model_class(__UpperCamelCase		)

               @tf.function(experimental_compile=__UpperCamelCase		)
               def run_in_graph_mode():
                    return model(__UpperCamelCase		)

               _a            =		run_in_graph_mode()
               self.assertIsNotNone(__UpperCamelCase		)






     @slow
     def 			a_       (      self		)							->     Tuple:
          # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
          _a      ,      _a            =		self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes[:2]:
               _a            =		model_class(__UpperCamelCase		)
               if getattr(__UpperCamelCase  ,				"hf_compute_loss"  ,				__UpperCamelCase		):
                    # Test that model correctly compute the loss with kwargs
                    _a            =		self._prepare_for_class(inputs_dict.copy()  ,				__UpperCamelCase  ,				return_labels=__UpperCamelCase		)
                    # Is there a better way to remove these decoder inputs?
                    _a            =		{
                        key: val
                        for key, val in prepared_for_class.items()
                        if key not in ("head_mask", "decoder_head_mask", "cross_attn_head_mask", "decoder_input_ids")
                    }

                    _a            =		{
                        "labels",
                        "label",
                        "label_ids",
                        "start_positions",
                        "start_position",
                        "end_positions",
                        "end_position",
                        "next_sentence_label",
                    }
                    _a            =		possible_label_cols.intersection(set(__UpperCamelCase		)		)
                    self.assertGreater(len(__UpperCamelCase		)  ,				0  ,				msg="No matching label names found!"		)
                    _a            =		{key: val for key, val in prepared_for_class.items() if key in label_names}
                    _a            =		{key: val for key, val in prepared_for_class.items() if key not in label_names}
                    self.assertGreater(len(__UpperCamelCase		)  ,				0		)

                    # Make sure it works with XLA!
                    model.compile(optimizer=tf.keras.optimizers.SGD(0.0		)  ,				jit_compile=__UpperCamelCase		)
                    # Make sure the model fits without crashing regardless of where we pass the labels
                    _a            =		model.fit(
                        __UpperCamelCase  ,				validation_data=__UpperCamelCase  ,				steps_per_epoch=1  ,				validation_steps=1  ,				shuffle=__UpperCamelCase  ,				verbose=0  ,				)
                    _a            =		history.history["loss"][0]
                    self.assertTrue(not isnan(__UpperCamelCase		)		)
                    _a            =		history.history["val_loss"][0]
                    self.assertTrue(not isnan(__UpperCamelCase		)		)

                    # Now test it with separate labels, to make sure that path works in XLA too.
                    _a            =		model_class(__UpperCamelCase		)
                    model.compile(optimizer=tf.keras.optimizers.SGD(0.0		)  ,				jit_compile=__UpperCamelCase		)
                    _a            =		model.fit(
                        __UpperCamelCase  ,				__UpperCamelCase  ,				validation_data=(inputs_minus_labels, labels)  ,				steps_per_epoch=1  ,				validation_steps=1  ,				shuffle=__UpperCamelCase  ,				verbose=0  ,				)

                    _a            =		history.history["loss"][0]
                    self.assertTrue(not isnan(__UpperCamelCase		)		)
                    _a            =		history.history["val_loss"][0]
                    self.assertTrue(not isnan(__UpperCamelCase		)		)






     @slow
     def 			a_       (      self		)							->     Tuple:
          _a      ,      _a            =		self.model_tester.prepare_config_and_inputs_for_common()
          _a            =		True
          _a            =		True

          if hasattr(__UpperCamelCase  ,				"use_cache"		):
               _a            =		True

          _a            =		getattr(self.model_tester  ,				"encoder_seq_length"  ,				self.model_tester.seq_length		)
          _a            =		getattr(self.model_tester  ,				"key_length"  ,				__UpperCamelCase		)

          for model_class in self.all_model_classes[:2]:
               _a            =		self._prepare_for_class(__UpperCamelCase  ,				__UpperCamelCase		)
               _a            =		model_class(__UpperCamelCase		)
               model.build()
               _a            =		len(model(__UpperCamelCase		)		)

               for key in list(class_inputs_dict.keys()		):
                    # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
                    if key not in model.input_signature:
                         del class_inputs_dict[key]
                    # Check it's a tensor, in case the inputs dict has some bools in it too
                    elif isinstance(class_inputs_dict[key]  ,				tf.Tensor		) and class_inputs_dict[key].dtype.is_integer:
                         _a            =		tf.cast(class_inputs_dict[key]  ,				tf.intaa		)

               if set(class_inputs_dict.keys()		) != set(model.input_signature.keys()		):
                    continue  # Some models have inputs that the preparation functions don't create, we skip those

               with tempfile.TemporaryDirectory() as tmpdirname:
                    model.save_pretrained(__UpperCamelCase  ,				saved_model=__UpperCamelCase		)
                    _a            =		os.path.join(__UpperCamelCase  ,				"saved_model"  ,				"1"		)
                    _a            =		tf.keras.models.load_model(__UpperCamelCase		)
                    _a            =		model(__UpperCamelCase		)

                    if self.is_encoder_decoder:
                         _a            =		outputs["encoder_hidden_states"]
                         _a            =		outputs["encoder_attentions"]
                    else:
                         _a            =		outputs["hidden_states"]
                         _a            =		outputs["attentions"]

                    self.assertEqual(len(__UpperCamelCase		)  ,				__UpperCamelCase		)

                    _a            =		getattr(
                        self.model_tester  ,				"expected_num_hidden_layers"  ,				self.model_tester.num_hidden_layers + 1		)

                    self.assertEqual(len(__UpperCamelCase		)  ,				__UpperCamelCase		)
                    self.assertListEqual(
                        list(output_hidden_states[0].shape[-2:]		)  ,				[self.model_tester.seq_length, self.model_tester.hidden_size]  ,				)

                    self.assertEqual(len(__UpperCamelCase		)  ,				self.model_tester.num_hidden_layers		)
                    self.assertListEqual(
                        list(output_attentions[0].shape[-3:]		)  ,				[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]  ,				)






     @slow
     def 			a_       (      self		)							->     int:
          tf.keras.mixed_precision.set_global_policy("mixed_float16"		)

          # try/finally block to ensure subsequent tests run in float32
          try:
               _a      ,      _a            =		self.model_tester.prepare_config_and_inputs_for_common()
               for model_class in self.all_model_classes[:2]:
                    _a            =		self._prepare_for_class(__UpperCamelCase  ,				__UpperCamelCase		)
                    _a            =		model_class(__UpperCamelCase		)
                    _a            =		model(__UpperCamelCase		)

                    self.assertIsNotNone(__UpperCamelCase		)
          finally:
               tf.keras.mixed_precision.set_global_policy("float32"		)






     @slow
     def 			a_       (      self		)							->     Optional[Any]:
          _a      ,      _a            =		self.model_tester.prepare_config_and_inputs_for_common()
          # head_mask and decoder_head_mask has different shapes than other input args
          if "head_mask" in inputs_dict:
               del inputs_dict["head_mask"]
          if "decoder_head_mask" in inputs_dict:
               del inputs_dict["decoder_head_mask"]
          if "cross_attn_head_mask" in inputs_dict:
               del inputs_dict["cross_attn_head_mask"]
          _a            =		{
              module_member
              for model_class in self.all_model_classes
              for module in (import_module(model_class.__module__		),)
              for module_member_name in dir(__UpperCamelCase		)
              if module_member_name.endswith("MainLayer"		)
              for module_member in (getattr(__UpperCamelCase  ,				__UpperCamelCase		),)
              if isinstance(__UpperCamelCase  ,				__UpperCamelCase		)
              and tf.keras.layers.Layer in module_member.__bases__
              and getattr(__UpperCamelCase  ,				"_keras_serializable"  ,				__UpperCamelCase		)
          }

          for main_layer_class in tf_main_layer_classes:
               # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
               if "T5" in main_layer_class.__name__:
                    # Take the same values than in TFT5ModelTester for this shared layer
                    _a            =		TFSharedEmbeddings(self.model_tester.vocab_size  ,				self.model_tester.hidden_size  ,				name="shared"		)
                    _a            =		False
                    _a            =		main_layer_class(__UpperCamelCase  ,				embed_tokens=__UpperCamelCase		)
               else:
                    _a            =		main_layer_class(__UpperCamelCase		)

               _a            =		{
                   name: tf.keras.Input(tensor.shape[1:]  ,				dtype=tensor.dtype		) for name, tensor in inputs_dict.items()
               }

               if hasattr(self.model_tester  ,				"num_labels"		):
                    _a            =		self.model_tester.num_labels
               else:
                    _a            =		2

               _a            =		tf.data.Dataset.from_tensor_slices(
                   (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)		))		).batch(1		)

               _a            =		main_layer(__UpperCamelCase		)[0]
               _a            =		tf.keras.layers.Dense(__UpperCamelCase  ,				activation="softmax"  ,				name="outputs"		)(__UpperCamelCase		)
               _a            =		tf.keras.models.Model(inputs=__UpperCamelCase  ,				outputs=[outputs]		)

               model.compile(loss="binary_crossentropy"  ,				optimizer="adam"  ,				metrics=["binary_accuracy"]		)
               model.fit(__UpperCamelCase  ,				epochs=1		)

               with tempfile.TemporaryDirectory() as tmpdirname:
                    _a            =		os.path.join(__UpperCamelCase  ,				"keras_model.h5"		)
                    model.save(__UpperCamelCase		)
                    if "T5" in main_layer_class.__name__:
                         _a            =		tf.keras.models.load_model(
                             __UpperCamelCase  ,				custom_objects={
                                 main_layer_class.__name__: main_layer_class,
                                 "TFSharedEmbeddings": TFSharedEmbeddings,
                             }  ,				)
                    else:
                         _a            =		tf.keras.models.load_model(
                             __UpperCamelCase  ,				custom_objects={main_layer_class.__name__: main_layer_class}		)
                    assert isinstance(__UpperCamelCase  ,				tf.keras.Model		)
                    model(__UpperCamelCase		)






     @slow
     def 			a_       (      self		)							->     Dict:
          _a      ,      _a            =		self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes[:2]:
               _a            =		model_class(__UpperCamelCase		)

               _a            =		copy.deepcopy(__UpperCamelCase		)

               if not self.is_encoder_decoder:
                    _a            =		inputs["input_ids"]
                    del inputs["input_ids"]
               else:
                    _a            =		inputs["input_ids"]
                    _a            =		inputs.get("decoder_input_ids"  ,				__UpperCamelCase		)
                    del inputs["input_ids"]
                    inputs.pop("decoder_input_ids"  ,				__UpperCamelCase		)

               if not self.is_encoder_decoder:
                    _a            =		model.get_input_embeddings()(__UpperCamelCase		)
               else:
                    _a            =		model.get_input_embeddings()(__UpperCamelCase		)
                    _a            =		model.get_input_embeddings()(__UpperCamelCase		)

               _a            =		self._prepare_for_class(__UpperCamelCase  ,				__UpperCamelCase		)

               @tf.function
               def run_in_graph_mode():
                    return model(__UpperCamelCase		)

               _a            =		run_in_graph_mode()
               self.assertIsNotNone(__UpperCamelCase		)






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase		)							->     List[str]:
          # special tokens cannot be bad tokens
          _a            =		[]
          if model.config.bos_token_id is not None:
               special_tokens.append(model.config.bos_token_id		)
          if model.config.pad_token_id is not None:
               special_tokens.append(model.config.pad_token_id		)
          if model.config.eos_token_id is not None:
               special_tokens.append(model.config.eos_token_id		)

          # create random bad tokens that are not special tokens
          _a            =		[]
          while len(__UpperCamelCase		) < num_bad_tokens:
               _a            =		tf.squeeze(ids_tensor((1, 1)  ,				self.model_tester.vocab_size		)  ,				0		).numpy()[0]
               if token not in special_tokens:
                    bad_tokens.append(__UpperCamelCase		)
          return bad_tokens






     def 			a_       (      self  ,				__UpperCamelCase		)							->     Optional[Any]:
          for token_id in output_ids[0].numpy().tolist():
               self.assertGreaterEqual(__UpperCamelCase  ,				0		)
               self.assertLess(__UpperCamelCase  ,				self.model_tester.vocab_size		)






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase		)							->     Dict:
          # for all bad word tokens
          for bad_word_ids in bad_words_ids:
               # for all slices in batch
               for generated_ids_slice in generated_ids:
                    # for all word idx
                    for i in range(len(__UpperCamelCase		)  ,				len(__UpperCamelCase		)		):
                         # if tokens match
                         if generated_ids_slice[i - len(__UpperCamelCase		) : i] == bad_word_ids:
                              return True
          return False





'''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

lowercase__					  =		logging.getLogger(__name__)





@require_tensorboard
class     __SCREAMING_SNAKE_CASE       (   unittest.TestCase			):






     def 			a_       (      self		)							->     Tuple:
          _a            =		"test_project_with_config"
          with tempfile.TemporaryDirectory() as dirpath:
               _a            =		Accelerator(log_with="tensorboard"  ,				project_dir=__UpperCamelCase		)
               _a            =		{"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("*/**"		):
                    _a            =		list(filter(lambda __UpperCamelCase		: x.is_file()  ,				child.iterdir()		)		)[0]
               self.assertNotEqual(str(__UpperCamelCase		)  ,				""		)






     def 			a_       (      self		)							->     List[Any]:
          _a            =		"test_project_with_log"
          with tempfile.TemporaryDirectory() as dirpath:
               _a            =		Accelerator(log_with="tensorboard"  ,				project_dir=__UpperCamelCase		)
               accelerator.init_trackers(__UpperCamelCase		)
               _a            =		{"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
               _a            =		list(filter(lambda __UpperCamelCase		: x.is_file()  ,				Path(f"{dirpath}/{project_name}"		).iterdir()		)		)[0]
               self.assertNotEqual(str(__UpperCamelCase		)  ,				""		)






     def 			a_       (      self		)							->     str:
          _a            =		"test_project_with_log"
          with tempfile.TemporaryDirectory() as dirpath:
               _a            =		Accelerator(log_with="tensorboard"  ,				project_dir=__UpperCamelCase		)
               accelerator.init_trackers(__UpperCamelCase		)
               _a            =		{"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
               _a            =		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:
                    _a            =		f.read()
               _a            =		False
               while data:
                    _a            =		struct.unpack("Q"  ,				data[:8]		)

                    _a            =		data[12 : 12 + int(header[0]		)]  # 8+4
                    _a            =		data[12 + int(header[0]		) + 4 :]
                    _a            =		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":
                                   _a            =		True
               self.assertTrue(__UpperCamelCase  ,				"Converted tensor was not found in the log file!"		)






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






     def 			a_       (      self		)							->     Any:
          _a            =		ProjectConfiguration(total_limit=30		)
          with tempfile.TemporaryDirectory() as dirpath:
               _a            =		Accelerator(log_with="tensorboard"  ,				project_dir=__UpperCamelCase  ,				project_config=__UpperCamelCase		)







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






     def 			a_       (      self		)							->     List[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 			a_       (      __UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase = True		)							->     Optional[int]:
          _a            =		Rf"{section} ([\S\s]*?)\n\n"
          if record:
               _a            =		Rf"Record: {pattern}"
          _a            =		re.findall(__UpperCamelCase  ,				__UpperCamelCase		)[0]
          # A config
          if section == "config" or section == "history":
               _a            =		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 			a_       (      self		)							->     Dict:
          _a            =		"test_project_with_config"
          _a            =		Accelerator(log_with="wandb"		)
          _a            =		{"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
          _a            =		{"wandb": {"tags": ["my_tag"]}}
          accelerator.init_trackers(__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase		)
          _a            =		{"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":
                    _a            =		subprocess.check_output(
                        ["wandb", "sync", "--view", "--verbose", str(__UpperCamelCase		)]  ,				env=os.environ.copy()		).decode("utf8"  ,				"ignore"		)
                    break

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

          # Actual logging
          _a            =		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		:			List[Any]   ,	__lowerCamelCase		:			str   ,	__lowerCamelCase		:			str					)   ->  List[Any]:







     '''simple docstring'''






     _a            =		run_name
     _a            =		OfflineExperiment(project_name=__lowerCamelCase   ,	offline_directory=__lowerCamelCase					)
     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(lowerCamelCase__			,      '''__init__'''			,      lowerCamelCase__			)
class     __SCREAMING_SNAKE_CASE       (   unittest.TestCase			):






     @staticmethod
     def 			a_       (      __UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase = False		)							->     Optional[int]:
          for log in log_list:
               _a            =		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 			a_       (      self		)							->     Tuple:
          with tempfile.TemporaryDirectory() as d:
               _a            =		CometMLTracker("test_project_with_config"  ,				__UpperCamelCase		)
               _a            =		Accelerator(log_with=__UpperCamelCase		)
               _a            =		{"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
               accelerator.init_trackers(__UpperCamelCase  ,				__UpperCamelCase		)
               accelerator.end_training()
               _a            =		os.listdir(__UpperCamelCase		)[0]  # Comet is nice, it's just a zip file here
               # We parse the raw logs
               _a            =		os.path.join(__UpperCamelCase  ,				__UpperCamelCase		)
               _a            =		zipfile.ZipFile(__UpperCamelCase  ,				"r"		)
               _a            =		archive.open("messages.json"		).read().decode("utf-8"		)
          _a            =		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 			a_       (      self		)							->     List[Any]:
          with tempfile.TemporaryDirectory() as d:
               _a            =		CometMLTracker("test_project_with_config"  ,				__UpperCamelCase		)
               _a            =		Accelerator(log_with=__UpperCamelCase		)
               accelerator.init_trackers(__UpperCamelCase		)
               _a            =		{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
               accelerator.log(__UpperCamelCase  ,				step=0		)
               accelerator.end_training()
               _a            =		os.listdir(__UpperCamelCase		)[0]  # Comet is nice, it's just a zip file here
               # We parse the raw logs
               _a            =		os.path.join(__UpperCamelCase  ,				__UpperCamelCase		)
               _a            =		zipfile.ZipFile(__UpperCamelCase  ,				"r"		)
               _a            =		archive.open("messages.json"		).read().decode("utf-8"		)
          _a            =		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       (   lowerCamelCase__			):
     UpperCAmelCase			       =					[
         '''total_loss''',
         '''iteration''',
         '''my_text''',
         '''learning_rate''',
         '''num_iterations''',
         '''some_boolean''',
         '''some_string''',
     ]

     UpperCAmelCase			       =					'''my_custom_tracker'''
     UpperCAmelCase			       =					False






     def __init__(      self  ,				__UpperCamelCase		)							->     int:
          _a            =		open(f"{dir}/log.csv"  ,				"w+"		)
          _a            =		csv.DictWriter(self.f  ,				fieldnames=self._col_names		)
          self.writer.writeheader()






     @property
     def 			a_       (      self		)							->     Optional[int]:
          return self.writer






     def 			a_       (      self  ,				__UpperCamelCase		)							->     List[str]:
          logger.info("Call init"		)
          self.writer.writerow(__UpperCamelCase		)






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase		)							->     Tuple:
          logger.info("Call log"		)
          self.writer.writerow(__UpperCamelCase		)






     def 			a_       (      self		)							->     Union[str, Any]:
          self.f.close()







class     __SCREAMING_SNAKE_CASE       (   unittest.TestCase			):






     def 			a_       (      self		)							->     Optional[int]:
          with tempfile.TemporaryDirectory() as d:
               _a            =		MyCustomTracker(__UpperCamelCase		)
               _a            =		Accelerator(log_with=__UpperCamelCase		)
               _a            =		{"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:
                    _a            =		csv.DictReader(__UpperCamelCase		)
                    _a            =		next(__UpperCamelCase		)
                    _a            =		{
                        "total_loss": "",
                        "iteration": "",
                        "my_text": "",
                        "learning_rate": "0.01",
                        "num_iterations": "12",
                        "some_boolean": "False",
                        "some_string": "some_value",
                    }
                    self.assertDictEqual(__UpperCamelCase  ,				__UpperCamelCase		)






     def 			a_       (      self		)							->     List[Any]:
          with tempfile.TemporaryDirectory() as d:
               _a            =		MyCustomTracker(__UpperCamelCase		)
               _a            =		Accelerator(log_with=__UpperCamelCase		)
               accelerator.init_trackers("Some name"		)
               _a            =		{"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:
                    _a            =		csv.DictReader(__UpperCamelCase		)
                    _a            =		next(__UpperCamelCase		)
                    _a            =		{
                        "total_loss": "0.1",
                        "iteration": "1",
                        "my_text": "some_value",
                        "learning_rate": "",
                        "num_iterations": "",
                        "some_boolean": "",
                        "some_string": "",
                    }
                    self.assertDictEqual(__UpperCamelCase  ,				__UpperCamelCase		)

