




import argparse

import torch

from diffusers import UNetaDConditionModel

def       lowerCAmelCase_      (	lowerCamelCase:							Union[str, Any]  ,					lowerCamelCase:							List[Any]  ,					lowerCamelCase:							int  ,					lowerCamelCase:							Tuple=None  ,					lowerCamelCase:							Tuple=None  ,					lowerCamelCase:							Any=None    )       ->      Optional[int]:


      '''simple docstring'''




      assert isinstance(_UpperCAmelCase  ,					_UpperCAmelCase    ), "Paths should be a list of dicts containing 'old' and 'new' keys."

      # Splits the attention layers into three variables.
      if attention_paths_to_split is not None:
            for path, path_map in attention_paths_to_split.items():
                  lowerCamelCase__   	=					old_checkpoint[path]
                  lowerCamelCase__   	=					old_tensor.shape[0] // 3

                  lowerCamelCase__   	=					(-1, channels) if len(old_tensor.shape    ) == 3 else (-1)

                  lowerCamelCase__   	=					old_tensor.shape[0] // config["num_head_channels"] // 3

                  lowerCamelCase__   	=					old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]    )
                  lowerCamelCase__   	=					old_tensor.split(channels // num_heads  ,					dim=1    )

                  lowerCamelCase__   	=					query.reshape(_UpperCAmelCase    )
                  lowerCamelCase__   	=					key.reshape(_UpperCAmelCase    )
                  lowerCamelCase__   	=					value.reshape(_UpperCAmelCase    )

      for path in paths:
            lowerCamelCase__   	=					path["new"]

            # These have already been assigned
            if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                  continue

            if additional_replacements is not None:
                  for replacement in additional_replacements:
                        lowerCamelCase__   	=					new_path.replace(replacement['old']  ,					replacement['new']    )

        # proj_attn.weight has to be converted from conv 1D to linear
            lowerCamelCase__   	=					old_checkpoint[path["old"]]
            lowerCamelCase__   	=					["proj_attn.weight"]
            lowerCamelCase__   	=					["proj_out.weight", "proj_in.weight"]
            if any(k in new_path for k in names    ):
                  lowerCamelCase__   	=					weight[:, :, 0]
            elif any(k in new_path for k in names_a    ) and len(weight.shape    ) > 2 and ".attentions." not in new_path:
                  lowerCamelCase__   	=					weight[:, :, 0]
            else:
                  lowerCamelCase__   	=					weight

def       lowerCAmelCase_      (	lowerCamelCase:							int  ,					lowerCamelCase:							List[Any]=0    )       ->      str:


      '''simple docstring'''




      lowerCamelCase__   	=					[]
      for old_item in old_list:
            lowerCamelCase__   	=					old_item

            #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
            #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

            #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
            #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

            #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

            mapping.append({'old': old_item, 'new': new_item}    )

      return mapping

def       lowerCAmelCase_      (	lowerCamelCase:							Optional[int]  ,					lowerCamelCase:							str=1    )       ->      List[Any]:


      '''simple docstring'''




      if n_shave_prefix_segments >= 0:
            return ".".join(path.split('.'    )[n_shave_prefix_segments:]    )
      else:
            return ".".join(path.split('.'    )[:n_shave_prefix_segments]    )

def       lowerCAmelCase_      (	lowerCamelCase:							Optional[Any]  ,					lowerCamelCase:							Any=0    )       ->      Any:


      '''simple docstring'''




      lowerCamelCase__   	=					[]
      for old_item in old_list:
            mapping.append({'old': old_item, 'new': old_item}    )

      return mapping

def       lowerCAmelCase_      (	lowerCamelCase:							Any  ,					lowerCamelCase:							Optional[int]=0    )       ->      Dict:


      '''simple docstring'''




      lowerCamelCase__   	=					[]
      for old_item in old_list:
            lowerCamelCase__   	=					old_item.replace('in_layers.0'  ,					'norm1'    )
            lowerCamelCase__   	=					new_item.replace('in_layers.2'  ,					'conv1'    )

            lowerCamelCase__   	=					new_item.replace('out_layers.0'  ,					'norm2'    )
            lowerCamelCase__   	=					new_item.replace('out_layers.3'  ,					'conv2'    )

            lowerCamelCase__   	=					new_item.replace('emb_layers.1'  ,					'time_emb_proj'    )
            lowerCamelCase__   	=					new_item.replace('skip_connection'  ,					'conv_shortcut'    )

            lowerCamelCase__   	=					shave_segments(_UpperCAmelCase  ,					n_shave_prefix_segments=_UpperCAmelCase    )

            if "temopral_conv" not in old_item:
                  mapping.append({'old': old_item, 'new': new_item}    )

      return mapping

def       lowerCAmelCase_      (	lowerCamelCase:							Union[str, Any]  ,					lowerCamelCase:							Optional[int]  ,					lowerCamelCase:							List[str]=None  ,					lowerCamelCase:							Optional[int]=False    )       ->      Any:


      '''simple docstring'''




      lowerCamelCase__   	=					{}
      lowerCamelCase__   	=					list(checkpoint.keys()    )

      lowerCamelCase__   	=					"model.diffusion_model."

      # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
      if sum(k.startswith('model_ema'    ) for k in keys    ) > 1_00 and extract_ema:
            print(f"""Checkpoint {path} has both EMA and non-EMA weights."""    )
            print(
                'In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'
                ' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.'    )
            for key in keys:
                  if key.startswith('model.diffusion_model'    ):
                        lowerCamelCase__   	=					"model_ema." + "".join(key.split('.'    )[1:]    )
                        lowerCamelCase__   	=					checkpoint.pop(_UpperCAmelCase    )
      else:
            if sum(k.startswith('model_ema'    ) for k in keys    ) > 1_00:
                  print(
                      'In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'
                      ' weights (usually better for inference), please make sure to add the `--extract_ema` flag.'    )

            for key in keys:
                  lowerCamelCase__   	=					checkpoint.pop(_UpperCAmelCase    )

      lowerCamelCase__   	=					{}

      lowerCamelCase__   	=					unet_state_dict["time_embed.0.weight"]
      lowerCamelCase__   	=					unet_state_dict["time_embed.0.bias"]
      lowerCamelCase__   	=					unet_state_dict["time_embed.2.weight"]
      lowerCamelCase__   	=					unet_state_dict["time_embed.2.bias"]

      if config["class_embed_type"] is None:
            # No parameters to port
            ...
      elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
            lowerCamelCase__   	=					unet_state_dict["label_emb.0.0.weight"]
            lowerCamelCase__   	=					unet_state_dict["label_emb.0.0.bias"]
            lowerCamelCase__   	=					unet_state_dict["label_emb.0.2.weight"]
            lowerCamelCase__   	=					unet_state_dict["label_emb.0.2.bias"]
      else:
            raise NotImplementedError(f"""Not implemented `class_embed_type`: {config["class_embed_type"]}"""    )

      lowerCamelCase__   	=					unet_state_dict["input_blocks.0.0.weight"]
      lowerCamelCase__   	=					unet_state_dict["input_blocks.0.0.bias"]

      lowerCamelCase__   	=					[v for v in unet_state_dict if v.startswith('input_blocks.0.1'    )]
      lowerCamelCase__   	=					renew_attention_paths(_UpperCAmelCase    )
      lowerCamelCase__   	=					{"old": "input_blocks.0.1", "new": "transformer_in"}
      assign_to_checkpoint(_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					additional_replacements=[meta_path]  ,					config=_UpperCAmelCase    )

      lowerCamelCase__   	=					unet_state_dict["out.0.weight"]
      lowerCamelCase__   	=					unet_state_dict["out.0.bias"]
      lowerCamelCase__   	=					unet_state_dict["out.2.weight"]
      lowerCamelCase__   	=					unet_state_dict["out.2.bias"]

      # Retrieves the keys for the input blocks only
      lowerCamelCase__   	=					len({'.'.join(layer.split('.'    )[:2]    ) for layer in unet_state_dict if 'input_blocks' in layer}    )
      lowerCamelCase__   	=					{
          layer_id: [key for key in unet_state_dict if f"""input_blocks.{layer_id}""" in key]
          for layer_id in range(_UpperCAmelCase    )
      }

      # Retrieves the keys for the middle blocks only
      lowerCamelCase__   	=					len({'.'.join(layer.split('.'    )[:2]    ) for layer in unet_state_dict if 'middle_block' in layer}    )
      lowerCamelCase__   	=					{
          layer_id: [key for key in unet_state_dict if f"""middle_block.{layer_id}""" in key]
          for layer_id in range(_UpperCAmelCase    )
      }

      # Retrieves the keys for the output blocks only
      lowerCamelCase__   	=					len({'.'.join(layer.split('.'    )[:2]    ) for layer in unet_state_dict if 'output_blocks' in layer}    )
      lowerCamelCase__   	=					{
          layer_id: [key for key in unet_state_dict if f"""output_blocks.{layer_id}""" in key]
          for layer_id in range(_UpperCAmelCase    )
      }

      for i in range(1  ,					_UpperCAmelCase    ):
            lowerCamelCase__   	=					(i - 1) // (config["layers_per_block"] + 1)
            lowerCamelCase__   	=					(i - 1) % (config["layers_per_block"] + 1)

            lowerCamelCase__   	=					[
                key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key and f"""input_blocks.{i}.0.op""" not in key
            ]
            lowerCamelCase__   	=					[key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]
            lowerCamelCase__   	=					[key for key in input_blocks[i] if f"""input_blocks.{i}.2""" in key]

            if f"""input_blocks.{i}.op.weight""" in unet_state_dict:
                  lowerCamelCase__   	=					unet_state_dict.pop(
                      f"""input_blocks.{i}.op.weight"""    )
                  lowerCamelCase__   	=					unet_state_dict.pop(
                      f"""input_blocks.{i}.op.bias"""    )

            lowerCamelCase__   	=					renew_resnet_paths(_UpperCAmelCase    )
            lowerCamelCase__   	=					{"old": f"""input_blocks.{i}.0""", "new": f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
            assign_to_checkpoint(
                _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					additional_replacements=[meta_path]  ,					config=_UpperCAmelCase    )

            lowerCamelCase__   	=					[key for key in resnets if "temopral_conv" in key]
            lowerCamelCase__   	=					renew_temp_conv_paths(_UpperCAmelCase    )
            lowerCamelCase__   	=					{
                "old": f"""input_blocks.{i}.0.temopral_conv""",
                "new": f"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
            }
            assign_to_checkpoint(
                _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					additional_replacements=[meta_path]  ,					config=_UpperCAmelCase    )

            if len(_UpperCAmelCase    ):
                  lowerCamelCase__   	=					renew_attention_paths(_UpperCAmelCase    )
                  lowerCamelCase__   	=					{"old": f"""input_blocks.{i}.1""", "new": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
                  assign_to_checkpoint(
                      _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					additional_replacements=[meta_path]  ,					config=_UpperCAmelCase    )

            if len(_UpperCAmelCase    ):
                  lowerCamelCase__   	=					renew_attention_paths(_UpperCAmelCase    )
                  lowerCamelCase__   	=					{
                      "old": f"""input_blocks.{i}.2""",
                      "new": f"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
                  }
                  assign_to_checkpoint(
                      _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					additional_replacements=[meta_path]  ,					config=_UpperCAmelCase    )

      lowerCamelCase__   	=					middle_blocks[0]
      lowerCamelCase__   	=					[key for key in resnet_a if "temopral_conv" in key]
      lowerCamelCase__   	=					middle_blocks[1]
      lowerCamelCase__   	=					middle_blocks[2]
      lowerCamelCase__   	=					middle_blocks[3]
      lowerCamelCase__   	=					[key for key in resnet_a if "temopral_conv" in key]

      lowerCamelCase__   	=					renew_resnet_paths(_UpperCAmelCase    )
      lowerCamelCase__   	=					{"old": "middle_block.0", "new": "mid_block.resnets.0"}
      assign_to_checkpoint(
          _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					config=_UpperCAmelCase  ,					additional_replacements=[meta_path]    )

      lowerCamelCase__   	=					renew_temp_conv_paths(_UpperCAmelCase    )
      lowerCamelCase__   	=					{"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
      assign_to_checkpoint(
          _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					config=_UpperCAmelCase  ,					additional_replacements=[meta_path]    )

      lowerCamelCase__   	=					renew_resnet_paths(_UpperCAmelCase    )
      lowerCamelCase__   	=					{"old": "middle_block.3", "new": "mid_block.resnets.1"}
      assign_to_checkpoint(
          _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					config=_UpperCAmelCase  ,					additional_replacements=[meta_path]    )

      lowerCamelCase__   	=					renew_temp_conv_paths(_UpperCAmelCase    )
      lowerCamelCase__   	=					{"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
      assign_to_checkpoint(
          _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					config=_UpperCAmelCase  ,					additional_replacements=[meta_path]    )

      lowerCamelCase__   	=					renew_attention_paths(_UpperCAmelCase    )
      lowerCamelCase__   	=					{"old": "middle_block.1", "new": "mid_block.attentions.0"}
      assign_to_checkpoint(
          _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					additional_replacements=[meta_path]  ,					config=_UpperCAmelCase    )

      lowerCamelCase__   	=					renew_attention_paths(_UpperCAmelCase    )
      lowerCamelCase__   	=					{"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
      assign_to_checkpoint(
          _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					additional_replacements=[meta_path]  ,					config=_UpperCAmelCase    )

      for i in range(_UpperCAmelCase    ):
            lowerCamelCase__   	=					i // (config["layers_per_block"] + 1)
            lowerCamelCase__   	=					i % (config["layers_per_block"] + 1)
            lowerCamelCase__   	=					[shave_segments(_UpperCAmelCase  ,					2    ) for name in output_blocks[i]]
            lowerCamelCase__   	=					{}

            for layer in output_block_layers:
                  lowerCamelCase__   	=					layer.split('.'    )[0], shave_segments(_UpperCAmelCase  ,					1    )
                  if layer_id in output_block_list:
                        output_block_list[layer_id].append(_UpperCAmelCase    )
                  else:
                        lowerCamelCase__   	=					[layer_name]

            if len(_UpperCAmelCase    ) > 1:
                  lowerCamelCase__   	=					[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
                  lowerCamelCase__   	=					[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]
                  lowerCamelCase__   	=					[key for key in output_blocks[i] if f"""output_blocks.{i}.2""" in key]

                  lowerCamelCase__   	=					renew_resnet_paths(_UpperCAmelCase    )
                  lowerCamelCase__   	=					renew_resnet_paths(_UpperCAmelCase    )

                  lowerCamelCase__   	=					{"old": f"""output_blocks.{i}.0""", "new": f"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
                  assign_to_checkpoint(
                      _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					additional_replacements=[meta_path]  ,					config=_UpperCAmelCase    )

                  lowerCamelCase__   	=					[key for key in resnets if "temopral_conv" in key]
                  lowerCamelCase__   	=					renew_temp_conv_paths(_UpperCAmelCase    )
                  lowerCamelCase__   	=					{
                      "old": f"""output_blocks.{i}.0.temopral_conv""",
                      "new": f"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
                  }
                  assign_to_checkpoint(
                      _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					additional_replacements=[meta_path]  ,					config=_UpperCAmelCase    )

                  lowerCamelCase__   	=					{k: sorted(_UpperCAmelCase    ) for k, v in output_block_list.items()}
                  if ["conv.bias", "conv.weight"] in output_block_list.values():
                        lowerCamelCase__   	=					list(output_block_list.values()    ).index(['conv.bias', 'conv.weight']    )
                        lowerCamelCase__   	=					unet_state_dict[
                            f"""output_blocks.{i}.{index}.conv.weight"""
                        ]
                        lowerCamelCase__   	=					unet_state_dict[
                            f"""output_blocks.{i}.{index}.conv.bias"""
                        ]

                        # Clear attentions as they have been attributed above.
                        if len(_UpperCAmelCase    ) == 2:
                              lowerCamelCase__   	=					[]

                  if len(_UpperCAmelCase    ):
                        lowerCamelCase__   	=					renew_attention_paths(_UpperCAmelCase    )
                        lowerCamelCase__   	=					{
                            "old": f"""output_blocks.{i}.1""",
                            "new": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
                        }
                        assign_to_checkpoint(
                            _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					additional_replacements=[meta_path]  ,					config=_UpperCAmelCase    )

                  if len(_UpperCAmelCase    ):
                        lowerCamelCase__   	=					renew_attention_paths(_UpperCAmelCase    )
                        lowerCamelCase__   	=					{
                            "old": f"""output_blocks.{i}.2""",
                            "new": f"""up_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
                        }
                        assign_to_checkpoint(
                            _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					additional_replacements=[meta_path]  ,					config=_UpperCAmelCase    )
            else:
                  lowerCamelCase__   	=					renew_resnet_paths(_UpperCAmelCase  ,					n_shave_prefix_segments=1    )
                  for path in resnet_0_paths:
                        lowerCamelCase__   	=					".".join(['output_blocks', str(_UpperCAmelCase    ), path['old']]    )
                        lowerCamelCase__   	=					".".join(['up_blocks', str(_UpperCAmelCase    ), 'resnets', str(_UpperCAmelCase    ), path['new']]    )
                        lowerCamelCase__   	=					unet_state_dict[old_path]

                  lowerCamelCase__   	=					[l for l in output_block_layers if "temopral_conv" in l]
                  for path in temopral_conv_paths:
                        lowerCamelCase__   	=					path.split('temopral_conv.'    )[-1]
                        lowerCamelCase__   	=					".".join(['output_blocks', str(_UpperCAmelCase    ), str(_UpperCAmelCase    ), 'temopral_conv', pruned_path]    )
                        lowerCamelCase__   	=					".".join(['up_blocks', str(_UpperCAmelCase    ), 'temp_convs', str(_UpperCAmelCase    ), pruned_path]    )
                        lowerCamelCase__   	=					unet_state_dict[old_path]

      return new_checkpoint


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE     : List[Any]      =argparse.ArgumentParser()

      parser.add_argument(
          """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
      )
      parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
      __SCREAMING_SNAKE_CASE     : Optional[Any]      =parser.parse_args()

      __SCREAMING_SNAKE_CASE     : Optional[Any]      =torch.load(args.checkpoint_path, map_location="""cpu""")
      __SCREAMING_SNAKE_CASE     : Dict      =UNetaDConditionModel()

      __SCREAMING_SNAKE_CASE     : str      =convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

      __SCREAMING_SNAKE_CASE     : int      =set(unet.state_dict().keys()) - set(converted_ckpt.keys())
      __SCREAMING_SNAKE_CASE     : Any      =set(converted_ckpt.keys()) - set(unet.state_dict().keys())

      assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

      # load state_dict
      unet.load_state_dict(converted_ckpt)

      unet.save_pretrained(args.dump_path)

      # -- finish converting the unet --






'''simple docstring'''

import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
			from comet_ml import OfflineExperiment

if is_tensorboard_available():
			import struct

			import tensorboard.compat.proto.event_pba as event_pba

lowerCAmelCase__      :   Tuple      	=			logging.getLogger(__name__)


@require_tensorboard
class      SCREAMING_SNAKE_CASE__      (   unittest.TestCase		):







					"""simple docstring"""






					def 		lowerCamelCase_   (   self       :      List[str]		):







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







							"""simple docstring"""
							__UpperCAmelCase	:							Optional[int]     =     "test_project_with_log"
							with tempfile.TemporaryDirectory() as dirpath:
									__UpperCAmelCase	:							Any     =     Accelerator(log_with="tensorboard"		,	project_dir=UpperCAmelCase_		)
									accelerator.init_trackers(UpperCAmelCase_		)
									__UpperCAmelCase	:							Optional[Any]     =     {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
									accelerator.log(UpperCAmelCase_		,	step=0		)
									accelerator.end_training()
									# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
									# Names are randomly generated each time
									__UpperCAmelCase	:							Union[str, Any]     =     list(filter(lambda UpperCAmelCase_		: x.is_file()		,	Path(f"{dirpath}/{project_name}"		).iterdir()		)		)[0]
									self.assertNotEqual(str(UpperCAmelCase_		)		,	""		)
					def 		lowerCamelCase_   (   self       :      Tuple		):







							"""simple docstring"""
							__UpperCAmelCase	:							List[str]     =     "test_project_with_log"
							with tempfile.TemporaryDirectory() as dirpath:
									__UpperCAmelCase	:							List[Any]     =     Accelerator(log_with="tensorboard"		,	project_dir=UpperCAmelCase_		)
									accelerator.init_trackers(UpperCAmelCase_		)
									__UpperCAmelCase	:							int     =     {"tensor": torch.tensor(1		)}
									accelerator.log(UpperCAmelCase_		,	step=0		)
									accelerator.end_training()
									# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
									# Names are randomly generated each time
									__UpperCAmelCase	:							Dict     =     list(filter(lambda UpperCAmelCase_		: x.is_file()		,	Path(f"{dirpath}/{project_name}"		).iterdir()		)		)[0]
									# Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
									with open(UpperCAmelCase_		,	"rb"		) as f:
											__UpperCAmelCase	:							Dict     =     f.read()
									__UpperCAmelCase	:							Dict     =     False
									while data:
											__UpperCAmelCase	:							str     =     struct.unpack("Q"		,	data[:8]		)

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

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







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






					def 		lowerCamelCase_   (   self       :      Optional[Any]		):







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






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







					"""simple docstring"""






					def 		lowerCamelCase_   (   self       :      List[str]		):







							"""simple docstring"""
							super().setUp()
							# wandb let's us override where logs are stored to via the WANDB_DIR env var
							self.add_mocks(mock.patch.dict(os.environ		,	{"WANDB_DIR": self.tmpdir}		)		)
					@staticmethod
					def 		lowerCamelCase_   (   UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      bool = True		):







							"""simple docstring"""
							__UpperCAmelCase	:							Dict     =     Rf"{section} ([\S\s]*?)\n\n"
							if record:
									__UpperCAmelCase	:							Optional[Any]     =     Rf"Record: {pattern}"
							__UpperCAmelCase	:							Optional[Any]     =     re.findall(UpperCAmelCase_		,	UpperCAmelCase_		)[0]
							# A config
							if section == "config" or section == "history":
									__UpperCAmelCase	:							Optional[Any]     =     re.findall(R"\"([a-zA-Z0-9_.,]+)"		,	UpperCAmelCase_		)
									return {key: val for key, val in zip(cleaned_record[0::2]		,	cleaned_record[1::2]		)}
							# Everything else
							else:
									return dict(re.findall(R"(\w+): \"([^\s]+)\""		,	UpperCAmelCase_		)		)






					@skip
					def 		lowerCamelCase_   (   self       :      str		):







							"""simple docstring"""
							__UpperCAmelCase	:							Optional[int]     =     "test_project_with_config"
							__UpperCAmelCase	:							List[Any]     =     Accelerator(log_with="wandb"		)
							__UpperCAmelCase	:							Any     =     {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
							__UpperCAmelCase	:							Any     =     {"wandb": {"tags": ["my_tag"]}}
							accelerator.init_trackers(UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		)
							__UpperCAmelCase	:							int     =     {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
							accelerator.log(UpperCAmelCase_		,	step=0		)
							accelerator.end_training()
							# The latest offline log is stored at wandb/latest-run/*.wandb
							for child in Path(f"{self.tmpdir}/wandb/latest-run"		).glob("*"		):
									if child.is_file() and child.suffix == ".wandb":
											__UpperCAmelCase	:							List[str]     =     subprocess.check_output(
											    ["wandb", "sync", "--view", "--verbose", str(UpperCAmelCase_		)]		,	env=os.environ.copy()		).decode("utf8"		,	"ignore"		)
											break

        # Check HPS through careful parsing and cleaning
							__UpperCAmelCase	:							Dict     =     self.parse_log(UpperCAmelCase_		,	"config"		)
							self.assertEqual(logged_items["num_iterations"]		,	"12"		)
							self.assertEqual(logged_items["learning_rate"]		,	"0.01"		)
							self.assertEqual(logged_items["some_boolean"]		,	"false"		)
							self.assertEqual(logged_items["some_string"]		,	"some_value"		)
							self.assertEqual(logged_items["some_string"]		,	"some_value"		)

							# Run tags
							__UpperCAmelCase	:							Union[str, Any]     =     self.parse_log(UpperCAmelCase_		,	"run"		,	UpperCAmelCase_		)
							self.assertEqual(logged_items["tags"]		,	"my_tag"		)

							# Actual logging
							__UpperCAmelCase	:							Optional[Any]     =     self.parse_log(UpperCAmelCase_		,	"history"		)
							self.assertEqual(logged_items["total_loss"]		,	"0.1"		)
							self.assertEqual(logged_items["iteration"]		,	"1"		)
							self.assertEqual(logged_items["my_text"]		,	"some_value"		)
							self.assertEqual(logged_items["_step"]		,	"0"		)






def 						__UpperCamelCase						(			self,       _UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase	:							str     =     run_name
		__UpperCAmelCase	:							Optional[int]     =     OfflineExperiment(project_name=_UpperCAmelCase,       offline_directory=_UpperCAmelCase  )
		logger.info(F"Initialized offline CometML project {self.run_name}"  )
		logger.info("Make sure to log any initial configurations with `self.store_init_configuration` before training!"  )


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







					"""simple docstring"""






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







							"""simple docstring"""
							for log in log_list:
									__UpperCAmelCase	:							List[Any]     =     json.loads(UpperCAmelCase_		)["payload"]
									if is_param and "param" in j.keys():
											if j["param"]["paramName"] == key:
													return j["param"]["paramValue"]
									if "log_other" in j.keys():
											if j["log_other"]["key"] == key:
													return j["log_other"]["val"]
									if "metric" in j.keys():
											if j["metric"]["metricName"] == key:
													return j["metric"]["metricValue"]
					def 		lowerCamelCase_   (   self       :      int		):







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






					def 		lowerCamelCase_   (   self       :      Dict		):







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






class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






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

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







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







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







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







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






					def 		lowerCamelCase_   (   self       :      str		):







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






class      SCREAMING_SNAKE_CASE__      (   unittest.TestCase		):







					"""simple docstring"""






					def 		lowerCamelCase_   (   self       :      Tuple		):







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






					def 		lowerCamelCase_   (   self       :      Dict		):







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




