
import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


UpperCAmelCase__						:	Dict	     = {
    'vit_b32': {
        'embed_dim': 512,
        'image_resolution': 768,
        'context_length': 16,
        'vocab_size': 49408,
        'vision_layers': 12,
        'vision_width': 768,
        'vision_patch_size': 32,
        'transformer_width': 512,
        'transformer_heads': 8,
        'transformer_layers': 12,
    },
    'vit_b16': {
        'embed_dim': 512,
        'image_resolution': 768,
        'context_length': 16,
        'vocab_size': 49408,
        'vision_layers': 12,
        'vision_width': 768,
        'vision_patch_size': 16,
        'transformer_width': 512,
        'transformer_heads': 8,
        'transformer_layers': 12,
    },
    'vit_l14': {
        'embed_dim': 768,
        'image_resolution': 840,
        'context_length': 16,
        'vocab_size': 49408,
        'vision_layers': 24,
        'vision_width': 1024,
        'vision_patch_size': 14,
        'transformer_width': 768,
        'transformer_heads': 12,
        'transformer_layers': 12,
    },
}






def     lowerCamelCase__				(   a						,						a=""						,						a="/" ) ->  Any:
   _A:    Optional[Any]						     =			[]

   for k, v in params.items():
      _A:    List[Any]						     =			parent_key + sep + k if parent_key else k

      if isinstance(a						,						collections.MutableMapping ):
         items.extend(flatten_nested_dict(a						,						a						,						sep=a ).items() )
      else:
         items.append((new_key, v) )
   return dict(a )






def     lowerCamelCase__				(   a ) ->  List[str]:
   return jax.tree_util.tree_map(lambda a : x.astype(jnp.floataa ) if x.dtype == jnp.bfloataa else x						,						a )






def     lowerCamelCase__				(   a						,						a ) ->  Union[str, Any]:
   _A     ,    _A     ,    _A:    Tuple						     =			pt_attn_layer.in_proj_weight.chunk(3						,						dim=0 )
   _A     ,    _A     ,    _A:    Tuple						     =			pt_attn_layer.in_proj_bias.chunk(3						,						dim=0 )

   _A:    Optional[Any]						     =			pt_attn_layer.out_proj.weight
   _A:    Any						     =			pt_attn_layer.out_proj.bias

   _A:    Optional[int]						     =			q_proj
   _A:    Union[str, Any]						     =			q_proj_bias

   _A:    Union[str, Any]						     =			k_proj
   _A:    Optional[Any]						     =			k_proj_bias

   _A:    Optional[Any]						     =			v_proj
   _A:    List[str]						     =			v_proj_bias

   _A:    List[str]						     =			out_proj_weights
   _A:    Tuple						     =			out_proj_bias






def     lowerCamelCase__				(   a						,						a ) ->  List[str]:
   copy_linear(hf_mlp.fca						,						pt_mlp.c_fc )
   copy_linear(hf_mlp.fca						,						pt_mlp.c_proj )






def     lowerCamelCase__				(   a						,						a ) ->  Tuple:
   _A:    str						     =			pt_linear.weight
   _A:    str						     =			pt_linear.bias






def     lowerCamelCase__				(   a						,						a ) ->  int:
   # copy layer norms
   copy_linear(hf_layer.layer_norma						,						pt_layer.ln_a )
   copy_linear(hf_layer.layer_norma						,						pt_layer.ln_a )

   # copy MLP
   copy_mlp(hf_layer.mlp						,						pt_layer.mlp )

   # copy attn
   copy_attn_layer(hf_layer.self_attn						,						pt_layer.attn )






def     lowerCamelCase__				(   a						,						a ) ->  str:
   for hf_layer, pt_layer in zip(a						,						a ):
      copy_layer(a						,						a )






def     lowerCamelCase__				(   a						,						a ) ->  Dict:
   # copy  embeds
   _A:    List[Any]						     =			pt_model.token_embedding.weight
   _A:    int						     =			pt_model.positional_embedding

   # copy layer norm
   copy_linear(hf_encoder.final_layer_norm						,						pt_model.ln_final )

   # copy hidden layers
   copy_layers(hf_encoder.encoder.layers						,						pt_model.transformer.resblocks )






def     lowerCamelCase__				(   a						,						a ) ->  Optional[int]:
   # copy projection
   _A:    Optional[int]						     =			pt_model.text_projection.data.T

   # copy text encoder
   copy_encoder(hf_model.text_model						,						a )






def     lowerCamelCase__				(   a						,						a ) ->  Any:
   # copy projection
   _A:    Any						     =			pt_model.visual.proj.data.T

   # copy layer norms
   copy_linear(hf_model.vision_model.pre_layernorm						,						pt_model.visual.ln_pre )
   copy_linear(hf_model.vision_model.post_layernorm						,						pt_model.visual.ln_post )

   # copy embeds
   _A:    Optional[int]						     =			pt_model.visual.conva.weight.data
   _A:    Union[str, Any]						     =			pt_model.visual.class_embedding
   _A:    Any						     =			pt_model.visual.positional_embedding.data

   # copy encoder
   copy_layers(hf_model.vision_model.encoder.layers						,						pt_model.visual.transformer.resblocks )






def     lowerCamelCase__				(   a						,						a ) ->  List[str]:
   _A:    int						     =			flatten_nested_dict(flax_params['''backbone''']['''merged_class_token'''] )

   _A:    Dict						     =			torch.from_numpy(flax_class_token_params['''scale'''] )
   _A:    int						     =			torch.from_numpy(flax_class_token_params['''bias'''] )
   _A:    Tuple						     =			nn.Parameter(a )
   _A:    Tuple						     =			nn.Parameter(a )






def     lowerCamelCase__				(   a						,						a ) ->  Optional[Any]:
   _A:    List[Any]						     =			hf_model.state_dict()
   _A:    Dict						     =			{}

   # Rename class prediction head flax params to pytorch HF
   _A:    str						     =			flatten_nested_dict(flax_params['''class_head'''] )

   for flax_key, v in flax_class_params.items():
      _A:    Tuple						     =			flax_key.replace('''/'''						,						'''.''' )
      _A:    Union[str, Any]						     =			torch_key.replace('''.kernel'''						,						'''.weight''' )
      _A:    Union[str, Any]						     =			torch_key.replace('''Dense_0'''						,						'''dense0''' )
      _A:    Optional[Any]						     =			'''class_head.''' + torch_key

      if "weight" in torch_key and v.ndim == 2:
         _A:    int						     =			v.T

      _A:    Tuple						     =			nn.Parameter(torch.from_numpy(a ) )

   # Rename box prediction box flax params to pytorch HF
   _A:    int						     =			flatten_nested_dict(flax_params['''obj_box_head'''] )

   for flax_key, v in flax_box_params.items():
      _A:    Dict						     =			flax_key.replace('''/'''						,						'''.''' )
      _A:    Any						     =			torch_key.replace('''.kernel'''						,						'''.weight''' )
      _A:    Any						     =			torch_key.replace('''_'''						,						'''''' ).lower()
      _A:    str						     =			'''box_head.''' + torch_key

      if "weight" in torch_key and v.ndim == 2:
         _A:    Any						     =			v.T

      _A:    Optional[Any]						     =			nn.Parameter(torch.from_numpy(a ) )

   # Copy flax params to PyTorch params
   for name, param in new_params.items():
      if name in pt_params.keys():
         pt_params[name].copy_(a )






def     lowerCamelCase__				(   a						,						a ) ->  List[str]:
   for k, v in flax_attn_params.items():
      if k.startswith('''transformer''' ):
         _A:    List[str]						     =			k.replace('''transformer.resblocks'''						,						'''text_model.encoder.layers''' )
      else:
         _A:    str						     =			k.replace('''visual.transformer.resblocks'''						,						'''vision_model.encoder.layers''' )

      _A:    Any						     =			torch_key.replace('''attn'''						,						'''self_attn''' )
      _A:    List[Any]						     =			torch_key.replace('''key'''						,						'''k_proj''' )
      _A:    Any						     =			torch_key.replace('''value'''						,						'''v_proj''' )
      _A:    List[str]						     =			torch_key.replace('''query'''						,						'''q_proj''' )
      _A:    List[str]						     =			torch_key.replace('''out'''						,						'''out_proj''' )

      if "bias" in torch_key and v.ndim == 2:
         _A:    str						     =			v.shape[0] * v.shape[1]
         _A:    Optional[int]						     =			v.reshape(a )

      if "weight" in torch_key and "out" in torch_key:
         _A:    int						     =			(v.shape[0] * v.shape[1], v.shape[2])
         _A:    int						     =			v.reshape(a ).T

      if "weight" in torch_key and "out" not in torch_key:
         _A:    Dict						     =			(v.shape[0], v.shape[1] * v.shape[2])
         _A:    Union[str, Any]						     =			v.reshape(a ).T

      # Copy flax CLIP attn params to HF PyTorch params
      _A:    List[Any]						     =			torch.from_numpy(a )
      hf_backbone.state_dict()[torch_key].copy_(a )






def     lowerCamelCase__				(   a ) ->  int:
   _A:    Optional[int]						     =			{}
   _A:    Dict						     =			[]

   for k, v in params.items():
      if "attn." in k:
         _A:    str						     =			k[: k.rindex('''attn.''' ) + 5]
         if base in processed_attn_layers:
            continue

         processed_attn_layers.append(a )
         _A:    Union[str, Any]						     =			params[base + '''out.weight'''].shape[-1]
         _A:    int						     =			params[base + '''out.weight'''].reshape(a						,						a ).T
         _A:    Union[str, Any]						     =			params[base + '''out.bias''']
      else:
         _A:    Dict						     =			v
   return new_params






def     lowerCamelCase__				(   a						,						a ) ->  Any:
   _A:    List[str]						     =			CLIP(**a )
   torch_model.eval()
   _A:    Tuple						     =			torch_model.state_dict()

   _A:    List[str]						     =			flatten_nested_dict(flax_params['''backbone''']['''clip'''] )
   _A:    Any						     =			{}

   for flax_key, v in flax_clip_params.items():
      _A:    Any						     =			flax_key.replace('''/'''						,						'''.''' )
      _A:    Any						     =			torch_key.replace('''text.token_embedding.embedding'''						,						'''token_embedding.kernel''' )

      if (
          torch_key.startswith('''text.transformer''' )
          or torch_key.startswith('''text.text_projection''' )
          or torch_key.startswith('''text.ln_final''' )
          or torch_key.startswith('''text.positional_embedding''' )
      ):
         _A:    Union[str, Any]						     =			torch_key[5:]

      _A:    List[Any]						     =			torch_key.replace('''text_projection.kernel'''						,						'''text_projection''' )
      _A:    Union[str, Any]						     =			torch_key.replace('''visual.proj.kernel'''						,						'''visual.proj''' )
      _A:    Optional[int]						     =			torch_key.replace('''.scale'''						,						'''.weight''' )
      _A:    int						     =			torch_key.replace('''.kernel'''						,						'''.weight''' )

      if "conv" in torch_key or "downsample.0.weight" in torch_key:
         _A:    List[str]						     =			v.transpose(3						,						2						,						0						,						1 )

      elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
         # Fully connected layers are transposed, embeddings are not
         _A:    Optional[Any]						     =			v.T

      _A:    Dict						     =			v

   _A:    List[str]						     =			_convert_attn_layers(a )
   new_torch_params.update(a )
   _A:    Tuple						     =			{}

   # Copy flax CLIP backbone params to PyTorch params
   for name, param in new_torch_params.items():
      if name in torch_clip_params.keys():
         _A:    Dict						     =			torch.from_numpy(new_torch_params[name] )
         torch_clip_params[name].copy_(a )
      else:
         _A:    Any						     =			param

   return torch_clip_params, torch_model, attn_params






@torch.no_grad()
def     lowerCamelCase__				(   a						,						a						,						a						,						a						,						a=None ) ->  List[Any]:
   _A:    Dict						     =			Repository(a						,						clone_from=f"""google/{pytorch_dump_folder_path}""" )
   repo.git_pull()

   if config_path is not None:
      _A:    Optional[Any]						     =			OwlViTConfig.from_pretrained(a )
   else:
      _A:    Optional[Any]						     =			OwlViTConfig()

   _A:    Union[str, Any]						     =			OwlViTModel(a ).eval()
   _A:    int						     =			OwlViTForObjectDetection(a ).eval()

   copy_text_model_and_projection(a						,						a )
   copy_vision_model_and_projection(a						,						a )
   _A:    Union[str, Any]						     =			pt_backbone.logit_scale
   copy_flax_attn_params(a						,						a )

   _A:    Optional[Any]						     =			hf_backbone
   copy_class_merge_token(a						,						a )
   copy_class_box_heads(a						,						a )

   # Save HF model
   hf_model.save_pretrained(repo.local_dir )

   # Initialize image processor
   _A:    List[str]						     =			OwlViTImageProcessor(
       size=config.vision_config.image_size						,						crop_size=config.vision_config.image_size )
   # Initialize tokenizer
   _A:    Optional[int]						     =			CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''						,						pad_token='''!'''						,						model_max_length=16 )

   # Initialize processor
   _A:    List[Any]						     =			OwlViTProcessor(image_processor=a						,						tokenizer=a )
   image_processor.save_pretrained(repo.local_dir )
   processor.save_pretrained(repo.local_dir )

   repo.git_add()
   repo.git_commit('''Upload model and processor''' )
   repo.git_push()


if __name__ == "__main__":
   UpperCAmelCase__						:	int	     = argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--owlvit_version',
       default=None,
       type=str,
       required=True,
       help='OWL-ViT model name [clip_b16, clip_b32, clip_l14].',
   )
   parser.add_argument(
       '--owlvit_checkpoint', default=None, type=str, required=True, help='Path to flax model checkpoint.'
   )
   parser.add_argument('--hf_config', default=None, type=str, required=True, help='Path to HF model config.')
   parser.add_argument(
       '--pytorch_dump_folder_path', default='hf_model', type=str, help='Path to the output PyTorch model.'
   )
   UpperCAmelCase__						:	Union[str, Any]	     = parser.parse_args()

   # Initialize PyToch clip model
   UpperCAmelCase__						:	Optional[int]	     = args.owlvit_version
   if model_name == "clip_b16":
      UpperCAmelCase__						:	int	     = CONFIGS['vit_b16']
   elif model_name == "clip_b32":
      UpperCAmelCase__						:	Tuple	     = CONFIGS['vit_b32']
   elif model_name == "clip_l14":
      UpperCAmelCase__						:	List[str]	     = CONFIGS['vit_l14']

   # Load from checkpoint and convert params to float-32
   UpperCAmelCase__						:	Tuple	     = checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['optimizer']['target']
   UpperCAmelCase__						:	Any	     = jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
   del variables

   # Convert CLIP backbone
   UpperCAmelCase__     ,UpperCAmelCase__     ,UpperCAmelCase__						:	Tuple	     = convert_clip_backbone(flax_params, torch_config)

   convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)





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_	)



