
import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
   import torch

   from ..models.auto.modeling_auto import (
       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
       MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
   )

if is_tf_available() and is_tensorflow_probability_available():
   import tensorflow as tf
   import tensorflow_probability as tfp

   from ..models.auto.modeling_tf_auto import (
       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
       TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
   )






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''


   def __call__(   self	:				List[Any]     ,     lowerCAmelCase_	:				int=None     ,     lowerCAmelCase_	:				List[Any]=None     ,     **lowerCAmelCase_	:				Dict	):

      """simple docstring"""
      # Returns tqa_pipeline_inputs of shape:
      # [
      #   {"table": pd.DataFrame, "query": List[str]},
      #   ...,
      #   {"table": pd.DataFrame, "query" : List[str]}
      # ]
      requires_backends(self     ,     '''pandas'''	)
      import pandas as pd

      if table is None:
         raise ValueError('''Keyword argument `table` cannot be None.'''	)
      elif query is None:
         if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_	) and table.get('''query'''	) is not None and table.get('''table'''	) is not None:
            _A:    int						     =			[table]
         elif isinstance(lowerCAmelCase_     ,     lowerCAmelCase_	) and len(lowerCAmelCase_	) > 0:
            if not all(isinstance(lowerCAmelCase_     ,     lowerCAmelCase_	) for d in table	):
               raise ValueError(
                   F"""Keyword argument `table` should be a list of dict, but is {(type(lowerCAmelCase_	) for d in table)}"""	)

            if table[0].get('''query'''	) is not None and table[0].get('''table'''	) is not None:
               _A:    Tuple						     =			table
            else:
               raise ValueError(
                   '''If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`'''
                   F""" and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys."""	)
         elif Dataset is not None and isinstance(lowerCAmelCase_     ,     lowerCAmelCase_	) or isinstance(lowerCAmelCase_     ,     types.GeneratorType	):
            return table
         else:
            raise ValueError(
                '''Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but '''
                F"""is {type(lowerCAmelCase_	)})"""	)
      else:
         _A:    List[Any]						     =			[{'''table''': table, '''query''': query}]

      for tqa_pipeline_input in tqa_pipeline_inputs:
         if not isinstance(tqa_pipeline_input['''table''']     ,     pd.DataFrame	):
            if tqa_pipeline_input["table"] is None:
               raise ValueError('''Table cannot be None.'''	)

            _A:    Any						     =			pd.DataFrame(tqa_pipeline_input['''table''']	)

      return tqa_pipeline_inputs






@add_end_docstrings(SCREAMING_SNAKE_CASE__  )
class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Any												=       '''table,query'''


   def __init__(   self	:				Tuple     ,     lowerCAmelCase_	:				List[str]=TableQuestionAnsweringArgumentHandler()     ,     *lowerCAmelCase_	:				Dict     ,     **lowerCAmelCase_	:				Dict	):

      """simple docstring"""
      super().__init__(*lowerCAmelCase_     ,     **lowerCAmelCase_	)
      _A:    List[str]						     =			args_parser

      self.check_model_type(
          dict(
              TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
              + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()	)
          if self.framework == '''tf'''
          else dict(
              MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()	)	)

      _A:    Tuple						     =			bool(getattr(self.model.config     ,     '''aggregation_labels'''     ,     lowerCAmelCase_	)	) and bool(
          getattr(self.model.config     ,     '''num_aggregation_labels'''     ,     lowerCAmelCase_	)	)
      _A:    Optional[int]						     =			'''tapas''' if hasattr(self.model.config     ,     '''aggregation_labels'''	) else None


   def 					__magic_name__							(   self	:				Dict     ,     **lowerCAmelCase_	:				int	):

      """simple docstring"""
      return self.model(**lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Any     ,     **lowerCAmelCase_	:				List[str]	):

      """simple docstring"""
      if self.framework == "pt":
         _A:    Any						     =			[]
         _A:    int						     =			[]
         _A:    Union[str, Any]						     =			None
         _A:    Optional[int]						     =			inputs['''input_ids'''].shape[0]

         _A:    Optional[Any]						     =			inputs['''input_ids'''].to(self.device	)
         _A:    int						     =			inputs['''attention_mask'''].to(self.device	)
         _A:    List[str]						     =			inputs['''token_type_ids'''].to(self.device	)
         _A:    Any						     =			None

         for index in range(lowerCAmelCase_	):
            # If sequences have already been processed, the token type IDs will be created according to the previous
            # answer.
            if prev_answers is not None:
               _A:    Any						     =			token_type_ids_example[:, 3]  # shape (seq_len,)
               _A:    Any						     =			np.zeros_like(prev_labels_example.cpu().numpy()	)  # shape (seq_len,)

               _A:    Dict						     =			token_type_ids[index]  # shape (seq_len, 7)
               for i in range(model_labels.shape[0]	):
                  _A:    Optional[Any]						     =			token_type_ids_example[:, 0].tolist()[i]
                  _A:    Tuple						     =			token_type_ids_example[:, 1].tolist()[i] - 1
                  _A:    List[str]						     =			token_type_ids_example[:, 2].tolist()[i] - 1

                  if row_id >= 0 and col_id >= 0 and segment_id == 1:
                     _A:    Tuple						     =			int(prev_answers[(col_id, row_id)]	)

               _A:    Union[str, Any]						     =			torch.from_numpy(lowerCAmelCase_	).type(torch.long	).to(self.device	)

            _A:    Any						     =			input_ids[index]
            _A:    List[str]						     =			attention_mask[index]  # shape (seq_len,)
            _A:    Any						     =			token_type_ids[index]  # shape (seq_len, 7)
            _A:    Tuple						     =			self.model(
                input_ids=input_ids_example.unsqueeze(0	)     ,     attention_mask=attention_mask_example.unsqueeze(0	)     ,     token_type_ids=token_type_ids_example.unsqueeze(0	)     ,     )
            _A:    Union[str, Any]						     =			outputs.logits

            if self.aggregate:
               all_aggregations.append(outputs.logits_aggregation	)

            all_logits.append(lowerCAmelCase_	)

            _A:    Optional[int]						     =			torch.distributions.Bernoulli(logits=lowerCAmelCase_	)
            _A:    Any						     =			dist_per_token.probs * attention_mask_example.type(torch.floataa	).to(
                dist_per_token.probs.device	)

            _A:    Tuple						     =			collections.defaultdict(lowerCAmelCase_	)
            for i, p in enumerate(probabilities.squeeze().tolist()	):
               _A:    Dict						     =			token_type_ids_example[:, 0].tolist()[i]
               _A:    List[str]						     =			token_type_ids_example[:, 1].tolist()[i] - 1
               _A:    str						     =			token_type_ids_example[:, 2].tolist()[i] - 1
               if col >= 0 and row >= 0 and segment_id == 1:
                  coords_to_probs[(col, row)].append(lowerCAmelCase_	)

            _A:    Union[str, Any]						     =			{key: np.array(coords_to_probs[key]	).mean() > 0.5 for key in coords_to_probs}

         _A:    List[Any]						     =			torch.cat(tuple(lowerCAmelCase_	)     ,     0	)

         return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(lowerCAmelCase_	)     ,     0	))
      else:
         _A:    List[Any]						     =			[]
         _A:    str						     =			[]
         _A:    int						     =			None
         _A:    Union[str, Any]						     =			inputs['''input_ids'''].shape[0]

         _A:    Union[str, Any]						     =			inputs['''input_ids''']
         _A:    Dict						     =			inputs['''attention_mask''']
         _A:    Any						     =			inputs['''token_type_ids'''].numpy()
         _A:    Tuple						     =			None

         for index in range(lowerCAmelCase_	):
            # If sequences have already been processed, the token type IDs will be created according to the previous
            # answer.
            if prev_answers is not None:
               _A:    Tuple						     =			token_type_ids_example[:, 3]  # shape (seq_len,)
               _A:    Any						     =			np.zeros_like(lowerCAmelCase_     ,     dtype=np.intaa	)  # shape (seq_len,)

               _A:    Optional[Any]						     =			token_type_ids[index]  # shape (seq_len, 7)
               for i in range(model_labels.shape[0]	):
                  _A:    List[Any]						     =			token_type_ids_example[:, 0].tolist()[i]
                  _A:    int						     =			token_type_ids_example[:, 1].tolist()[i] - 1
                  _A:    Dict						     =			token_type_ids_example[:, 2].tolist()[i] - 1

                  if row_id >= 0 and col_id >= 0 and segment_id == 1:
                     _A:    List[Any]						     =			int(prev_answers[(col_id, row_id)]	)

               _A:    List[Any]						     =			model_labels

            _A:    Tuple						     =			input_ids[index]
            _A:    Union[str, Any]						     =			attention_mask[index]  # shape (seq_len,)
            _A:    Dict						     =			token_type_ids[index]  # shape (seq_len, 7)
            _A:    List[Any]						     =			self.model(
                input_ids=np.expand_dims(lowerCAmelCase_     ,     axis=0	)     ,     attention_mask=np.expand_dims(lowerCAmelCase_     ,     axis=0	)     ,     token_type_ids=np.expand_dims(lowerCAmelCase_     ,     axis=0	)     ,     )
            _A:    str						     =			outputs.logits

            if self.aggregate:
               all_aggregations.append(outputs.logits_aggregation	)

            all_logits.append(lowerCAmelCase_	)

            _A:    int						     =			tfp.distributions.Bernoulli(logits=lowerCAmelCase_	)
            _A:    Tuple						     =			dist_per_token.probs_parameter() * tf.cast(lowerCAmelCase_     ,     tf.floataa	)

            _A:    List[Any]						     =			collections.defaultdict(lowerCAmelCase_	)
            _A:    List[str]						     =			token_type_ids_example
            for i, p in enumerate(tf.squeeze(lowerCAmelCase_	).numpy().tolist()	):
               _A:    List[Any]						     =			token_type_ids_example[:, 0].tolist()[i]
               _A:    Tuple						     =			token_type_ids_example[:, 1].tolist()[i] - 1
               _A:    int						     =			token_type_ids_example[:, 2].tolist()[i] - 1
               if col >= 0 and row >= 0 and segment_id == 1:
                  coords_to_probs[(col, row)].append(lowerCAmelCase_	)

            _A:    List[Any]						     =			{key: np.array(coords_to_probs[key]	).mean() > 0.5 for key in coords_to_probs}

         _A:    str						     =			tf.concat(tuple(lowerCAmelCase_	)     ,     0	)

         return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(lowerCAmelCase_	)     ,     0	))


   def __call__(   self	:				Optional[int]     ,     *lowerCAmelCase_	:				Dict     ,     **lowerCAmelCase_	:				int	):

      """simple docstring"""
      _A:    List[str]						     =			self._args_parser(*lowerCAmelCase_     ,     **lowerCAmelCase_	)

      _A:    List[str]						     =			super().__call__(lowerCAmelCase_     ,     **lowerCAmelCase_	)
      if len(lowerCAmelCase_	) == 1:
         return results[0]
      return results


   def 					__magic_name__							(   self	:				Optional[Any]     ,     lowerCAmelCase_	:				Optional[Any]=None     ,     lowerCAmelCase_	:				Optional[int]=None     ,     lowerCAmelCase_	:				Any=None     ,     **lowerCAmelCase_	:				Optional[int]	):

      """simple docstring"""
      _A:    Dict						     =			{}
      if padding is not None:
         _A:    str						     =			padding
      if truncation is not None:
         _A:    int						     =			truncation

      _A:    List[str]						     =			{}
      if sequential is not None:
         _A:    str						     =			sequential
      return preprocess_params, forward_params, {}


   def 					__magic_name__							(   self	:				str     ,     lowerCAmelCase_	:				List[Any]     ,     lowerCAmelCase_	:				List[Any]=None     ,     lowerCAmelCase_	:				List[Any]=True     ,     lowerCAmelCase_	:				Any=None	):

      """simple docstring"""
      if truncation is None:
         if self.type == "tapas":
            _A:    Dict						     =			'''drop_rows_to_fit'''
         else:
            _A:    Optional[int]						     =			'''do_not_truncate'''

      _A     ,    _A:    Optional[int]						     =			pipeline_input['''table'''], pipeline_input['''query''']
      if table.empty:
         raise ValueError('''table is empty'''	)
      if query is None or query == "":
         raise ValueError('''query is empty'''	)
      _A:    int						     =			self.tokenizer(lowerCAmelCase_     ,     lowerCAmelCase_     ,     return_tensors=self.framework     ,     truncation=lowerCAmelCase_     ,     padding=lowerCAmelCase_	)
      _A:    Optional[Any]						     =			table
      return inputs


   def 					__magic_name__							(   self	:				Dict     ,     lowerCAmelCase_	:				Dict     ,     lowerCAmelCase_	:				Union[str, Any]=False	):

      """simple docstring"""
      _A:    Tuple						     =			model_inputs.pop('''table'''	)

      if self.type == "tapas":
         if sequential:
            _A:    Optional[Any]						     =			self.sequential_inference(**lowerCAmelCase_	)
         else:
            _A:    int						     =			self.batch_inference(**lowerCAmelCase_	)
      else:
         _A:    Optional[int]						     =			self.model.generate(**lowerCAmelCase_	)
      _A:    Tuple						     =			{'''model_inputs''': model_inputs, '''table''': table, '''outputs''': outputs}
      return model_outputs


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				Dict	):

      """simple docstring"""
      _A:    str						     =			model_outputs['''model_inputs''']
      _A:    List[Any]						     =			model_outputs['''table''']
      _A:    Any						     =			model_outputs['''outputs''']
      if self.type == "tapas":
         if self.aggregate:
            _A     ,    _A:    str						     =			outputs[:2]
            _A:    List[str]						     =			self.tokenizer.convert_logits_to_predictions(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)
            _A     ,    _A:    str						     =			predictions
            _A:    List[Any]						     =			{i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(lowerCAmelCase_	)}

            _A:    str						     =			self.model.config.no_aggregation_label_index
            _A:    Dict						     =			{
                i: aggregators[i] + ''' > ''' for i, pred in enumerate(lowerCAmelCase_	) if pred != no_agg_label_index
            }
         else:
            _A:    Union[str, Any]						     =			outputs[0]
            _A:    Optional[int]						     =			self.tokenizer.convert_logits_to_predictions(lowerCAmelCase_     ,     lowerCAmelCase_	)
            _A:    Union[str, Any]						     =			predictions[0]
            _A:    Any						     =			{}
            _A:    List[str]						     =			{}
         _A:    List[str]						     =			[]
         for index, coordinates in enumerate(lowerCAmelCase_	):
            _A:    str						     =			[table.iat[coordinate] for coordinate in coordinates]
            _A:    str						     =			aggregators.get(lowerCAmelCase_     ,     ''''''	)
            _A:    List[str]						     =			aggregators_prefix.get(lowerCAmelCase_     ,     ''''''	)
            _A:    List[str]						     =			{
                '''answer''': aggregator_prefix + ''', '''.join(lowerCAmelCase_	),
                '''coordinates''': coordinates,
                '''cells''': [table.iat[coordinate] for coordinate in coordinates],
            }
            if aggregator:
               _A:    int						     =			aggregator

            answers.append(lowerCAmelCase_	)
         if len(lowerCAmelCase_	) == 0:
            raise PipelineException('''Empty answer'''	)
      else:
         _A:    List[Any]						     =			[{'''answer''': answer} for answer in self.tokenizer.batch_decode(lowerCAmelCase_     ,     skip_special_tokens=lowerCAmelCase_	)]

      return answers if len(lowerCAmelCase_	) > 1 else answers[0]





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

UpperCAmelCase__						:	int	     = logging.getLogger(__name__)






@require_tensorboard
class UpperCAmelCase  (		unittest.TestCase  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Any	):

      """simple docstring"""
      _A:    int						     =			'''test_project_with_config'''
      with tempfile.TemporaryDirectory() as dirpath:
         _A:    Tuple						     =			Accelerator(log_with='''tensorboard'''     ,     project_dir=lowerCAmelCase_	)
         _A:    Any						     =			{'''num_iterations''': 1_2, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
         accelerator.init_trackers(lowerCAmelCase_     ,     lowerCAmelCase_	)
         accelerator.end_training()
         for child in Path(F"""{dirpath}/{project_name}"""	).glob('''*/**'''	):
            _A:    int						     =			list(filter(lambda lowerCAmelCase_	: x.is_file()     ,     child.iterdir()	)	)[0]
         self.assertNotEqual(str(lowerCAmelCase_	)     ,     ''''''	)


   def 					__magic_name__							(   self	:				Tuple	):

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


   def 					__magic_name__							(   self	:				Union[str, Any]	):

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

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

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


   def 					__magic_name__							(   self	:				Union[str, Any]	):

      """simple docstring"""
      with self.assertRaisesRegex(lowerCAmelCase_     ,     '''Logging with `tensorboard` requires a `logging_dir`'''	):
         _A:    Optional[int]						     =			Accelerator(log_with='''tensorboard'''	)
      with tempfile.TemporaryDirectory() as dirpath:
         _A:    Tuple						     =			Accelerator(log_with='''tensorboard'''     ,     project_dir=lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				str	):

      """simple docstring"""
      _A:    Dict						     =			ProjectConfiguration(total_limit=3_0	)
      with tempfile.TemporaryDirectory() as dirpath:
         _A:    str						     =			Accelerator(log_with='''tensorboard'''     ,     project_dir=lowerCAmelCase_     ,     project_config=lowerCAmelCase_	)






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





   '''simple docstring'''


   def 					__magic_name__							(   self	:				List[Any]	):

      """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 					__magic_name__							(   lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				bool = True	):

      """simple docstring"""
      _A:    List[Any]						     =			RF"""{section} ([\S\s]*?)\n\n"""
      if record:
         _A:    Tuple						     =			RF"""Record: {pattern}"""
      _A:    int						     =			re.findall(lowerCAmelCase_     ,     lowerCAmelCase_	)[0]
      # A config
      if section == "config" or section == "history":
         _A:    Dict						     =			re.findall(R'''"([a-zA-Z0-9_.,]+)'''     ,     lowerCAmelCase_	)
         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]+)"'''     ,     lowerCAmelCase_	)	)


   @skip
   def 					__magic_name__							(   self	:				Dict	):

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

        # Check HPS through careful parsing and cleaning
      _A:    Optional[int]						     =			self.parse_log(lowerCAmelCase_     ,     '''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:    Optional[int]						     =			self.parse_log(lowerCAmelCase_     ,     '''run'''     ,     lowerCAmelCase_	)
      self.assertEqual(logged_items['''tags''']     ,     '''my_tag'''	)

      # Actual logging
      _A:    Tuple						     =			self.parse_log(lowerCAmelCase_     ,     '''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     lowerCamelCase__				(   self						,						a						,						a ) ->  int:
   _A:    List[Any]						     =			run_name
   _A:    Any						     =			OfflineExperiment(project_name=a						,						offline_directory=a )
   logger.info(f"""Initialized offline CometML project {self.run_name}""" )
   logger.info('''Make sure to log any initial configurations with `self.store_init_configuration` before training!''' )






@require_comet_ml
@mock.patch.object(SCREAMING_SNAKE_CASE__				,				'''__init__'''				,				SCREAMING_SNAKE_CASE__  )
class UpperCAmelCase  (		unittest.TestCase  ):





   '''simple docstring'''


   @staticmethod
   def 					__magic_name__							(   lowerCAmelCase_	:				Any     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				bool = False	):

      """simple docstring"""
      for log in log_list:
         _A:    Optional[Any]						     =			json.loads(lowerCAmelCase_	)['''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 					__magic_name__							(   self	:				Optional[int]	):

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


   def 					__magic_name__							(   self	:				int	):

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






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       str												=       [
       '''total_loss''',
       '''iteration''',
       '''my_text''',
       '''learning_rate''',
       '''num_iterations''',
       '''some_boolean''',
       '''some_string''',
   ]

   __UpperCamelCase						:       Optional[int]												=       '''my_custom_tracker'''
   __UpperCamelCase						:       Optional[int]												=       False


   def __init__(   self	:				Dict     ,     lowerCAmelCase_	:				str	):

      """simple docstring"""
      _A:    Optional[int]						     =			open(F"""{dir}/log.csv"""     ,     '''w+'''	)
      _A:    str						     =			csv.DictWriter(self.f     ,     fieldnames=self._col_names	)
      self.writer.writeheader()


   @property
   def 					__magic_name__							(   self	:				int	):

      """simple docstring"""
      return self.writer


   def 					__magic_name__							(   self	:				List[Any]     ,     lowerCAmelCase_	:				dict	):

      """simple docstring"""
      logger.info('''Call init'''	)
      self.writer.writerow(lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				dict     ,     lowerCAmelCase_	:				Optional[int]	):

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


   def 					__magic_name__							(   self	:				Any	):

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






class UpperCAmelCase  (		unittest.TestCase  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Tuple	):

      """simple docstring"""
      with tempfile.TemporaryDirectory() as d:
         _A:    str						     =			MyCustomTracker(lowerCAmelCase_	)
         _A:    Optional[int]						     =			Accelerator(log_with=lowerCAmelCase_	)
         _A:    List[str]						     =			{'''num_iterations''': 1_2, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
         accelerator.init_trackers('''Some name'''     ,     lowerCAmelCase_	)
         accelerator.end_training()
         with open(F"""{d}/log.csv"""     ,     '''r'''	) as f:
            _A:    str						     =			csv.DictReader(lowerCAmelCase_	)
            _A:    int						     =			next(lowerCAmelCase_	)
            _A:    int						     =			{
                '''total_loss''': '''''',
                '''iteration''': '''''',
                '''my_text''': '''''',
                '''learning_rate''': '''0.01''',
                '''num_iterations''': '''12''',
                '''some_boolean''': '''False''',
                '''some_string''': '''some_value''',
            }
            self.assertDictEqual(lowerCAmelCase_     ,     lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Any	):

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



