

'''simple docstring'''







import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase						    =      logging.get_logger(__name__)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Optional[int]:
       A_		   = SwinConfig.from_pretrained(
           """microsoft/swin-tiny-patch4-window7-224""",    out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]			)
       A_		   = MaskFormerConfig(backbone_config=UpperCAmelCase__			)

       A_		   = """huggingface/label-files"""
       if "ade20k-full" in model_name:
              # this should be ok
              A_		   = 8_47
              A_		   = """maskformer-ade20k-full-id2label.json"""
       elif "ade" in model_name:
              # this should be ok
              A_		   = 1_50
              A_		   = """ade20k-id2label.json"""
       elif "coco-stuff" in model_name:
              # this should be ok
              A_		   = 1_71
              A_		   = """maskformer-coco-stuff-id2label.json"""
       elif "coco" in model_name:
              # TODO
              A_		   = 1_33
              A_		   = """coco-panoptic-id2label.json"""
       elif "cityscapes" in model_name:
              # this should be ok
              A_		   = 19
              A_		   = """cityscapes-id2label.json"""
       elif "vistas" in model_name:
              # this should be ok
              A_		   = 65
              A_		   = """mapillary-vistas-id2label.json"""

       A_		   = json.load(open(hf_hub_download(UpperCAmelCase__,    UpperCAmelCase__,    repo_type="""dataset"""			),    """r"""			)			)
       A_		   = {int(UpperCAmelCase__			): v for k, v in idalabel.items()}

       return config
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Optional[int]:
       A_		   = []
       # stem
       # fmt: off
       rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""")			)
       rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""")			)
       rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""")			)
       rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""")			)
       # stages
       for i in range(len(config.backbone_config.depths			)			):
              for j in range(config.backbone_config.depths[i]			):
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')			)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')			)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')			)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')			)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')			)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')			)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')			)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')			)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')			)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')			)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')			)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')			)

              if i < 3:
                     rename_keys.append((F'''backbone.layers.{i}.downsample.reduction.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''')			)
                     rename_keys.append((F'''backbone.layers.{i}.downsample.norm.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''')			)
                     rename_keys.append((F'''backbone.layers.{i}.downsample.norm.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''')			)
              rename_keys.append((F'''backbone.norm{i}.weight''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''')			)
              rename_keys.append((F'''backbone.norm{i}.bias''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''')			)

       # FPN
       rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""")			)
       rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""")			)
       rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""")			)
       for source_index, target_index in zip(range(3,    0,    -1			),    range(0,    3			)			):
              rename_keys.append((F'''sem_seg_head.adapter_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''')			)
              rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''')			)
              rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''')			)
              rename_keys.append((F'''sem_seg_head.layer_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''')			)
              rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''')			)
              rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''')			)
       rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""")			)
       rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""")			)

       # Transformer decoder
       for idx in range(config.decoder_config.decoder_layers			):
              # self-attention out projection
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''')			)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''')			)
              # cross-attention out projection
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''')			)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''')			)
              # MLP 1
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc1.weight''')			)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc1.bias''')			)
              # MLP 2
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc2.weight''')			)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc2.bias''')			)
              # layernorm 1 (self-attention layernorm)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''')			)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''')			)
              # layernorm 2 (cross-attention layernorm)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''')			)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''')			)
              # layernorm 3 (final layernorm)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''')			)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''')			)

       rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""")			)
       rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""")			)

       # heads on top
       rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""")			)

       rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""")			)
       rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""")			)

       rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""")			)
       rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""")			)

       for i in range(3			):
              rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', F'''mask_embedder.{i}.0.weight''')			)
              rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', F'''mask_embedder.{i}.0.bias''')			)
       # fmt: on

       return rename_keys
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     List[str]:
       A_		   = dct.pop(UpperCAmelCase__			)
       A_		   = val
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     Dict:
       A_		   = [int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
       for i in range(len(backbone_config.depths			)			):
              A_		   = num_features[i]
              for j in range(backbone_config.depths[i]			):
                     # fmt: off
                     # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
                     A_		   = state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''			)
                     A_		   = state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias'''			)
                     # next, add query, keys and values (in that order) to the state dict
                     A_		   = in_proj_weight[:dim, :]
                     A_		   = in_proj_bias[: dim]
                     A_		   = in_proj_weight[
                         dim : dim * 2, :
                     ]
                     A_		   = in_proj_bias[
                         dim : dim * 2
                     ]
                     A_		   = in_proj_weight[
                         -dim :, :
                     ]
                     A_		   = in_proj_bias[-dim :]
                     # fmt: on
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     int:
       # fmt: off
       A_		   = config.decoder_config.hidden_size
       for idx in range(config.decoder_config.decoder_layers			):
              # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
              A_		   = state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''			)
              A_		   = state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'''			)
              # next, add query, keys and values (in that order) to the state dict
              A_		   = in_proj_weight[: hidden_size, :]
              A_		   = in_proj_bias[:config.hidden_size]
              A_		   = in_proj_weight[hidden_size : hidden_size * 2, :]
              A_		   = in_proj_bias[hidden_size : hidden_size * 2]
              A_		   = in_proj_weight[-hidden_size :, :]
              A_		   = in_proj_bias[-hidden_size :]
              # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
              A_		   = state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''			)
              A_		   = state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'''			)
              # next, add query, keys and values (in that order) to the state dict
              A_		   = in_proj_weight[: hidden_size, :]
              A_		   = in_proj_bias[:config.hidden_size]
              A_		   = in_proj_weight[hidden_size : hidden_size * 2, :]
              A_		   = in_proj_bias[hidden_size : hidden_size * 2]
              A_		   = in_proj_weight[-hidden_size :, :]
              A_		   = in_proj_bias[-hidden_size :]
       # fmt: on
def  UpperCAmelCase__      ( )     ->     torch.Tensor:
       A_		   = """http://images.cocodataset.org/val2017/000000039769.jpg"""
       A_		   = Image.open(requests.get(UpperCAmelCase__,    stream=UpperCAmelCase__			).raw			)
       return im
@torch.no_grad()
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__ = False			)     ->     Any:
       A_		   = get_maskformer_config(UpperCAmelCase__			)

       # load original state_dict
       with open(UpperCAmelCase__,    """rb"""			) as f:
              A_		   = pickle.load(UpperCAmelCase__			)
       A_		   = data["""model"""]

       # for name, param in state_dict.items():
       #     print(name, param.shape)

       # rename keys
       A_		   = create_rename_keys(UpperCAmelCase__			)
       for src, dest in rename_keys:
              rename_key(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)
       read_in_swin_q_k_v(UpperCAmelCase__,    config.backbone_config			)
       read_in_decoder_q_k_v(UpperCAmelCase__,    UpperCAmelCase__			)

       # update to torch tensors
       for key, value in state_dict.items():
              A_		   = torch.from_numpy(UpperCAmelCase__			)

       # load 🤗 model
       A_		   = MaskFormerForInstanceSegmentation(UpperCAmelCase__			)
       model.eval()

       for name, param in model.named_parameters():
              print(UpperCAmelCase__,    param.shape			)

       A_   ,     A_		   = model.load_state_dict(UpperCAmelCase__,    strict=UpperCAmelCase__			)
       assert missing_keys == [
           "model.pixel_level_module.encoder.model.layernorm.weight",
           "model.pixel_level_module.encoder.model.layernorm.bias",
       ]
       assert len(UpperCAmelCase__			) == 0, F'''Unexpected keys: {unexpected_keys}'''

       # verify results
       A_		   = prepare_img()
       if "vistas" in model_name:
              A_		   = 65
       elif "cityscapes" in model_name:
              A_		   = 6_55_35
       else:
              A_		   = 2_55
       A_		   = True if """ade""" in model_name else False
       A_		   = MaskFormerImageProcessor(ignore_index=UpperCAmelCase__,    reduce_labels=UpperCAmelCase__			)

       A_		   = image_processor(UpperCAmelCase__,    return_tensors="""pt"""			)

       A_		   = model(**UpperCAmelCase__			)

       print("""Logits:""",    outputs.class_queries_logits[0, :3, :3]			)

       if model_name == "maskformer-swin-tiny-ade":
              A_		   = torch.tensor(
                  [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]			)
       assert torch.allclose(outputs.class_queries_logits[0, :3, :3],    UpperCAmelCase__,    atol=1e-4			)
       print("""Looks ok!"""			)

       if pytorch_dump_folder_path is not None:
              print(F'''Saving model and image processor to {pytorch_dump_folder_path}'''			)
              Path(UpperCAmelCase__			).mkdir(exist_ok=UpperCAmelCase__			)
              model.save_pretrained(UpperCAmelCase__			)
              image_processor.save_pretrained(UpperCAmelCase__			)

       if push_to_hub:
              print("""Pushing model and image processor to the hub..."""			)
              model.push_to_hub(F'''nielsr/{model_name}'''			)
              image_processor.push_to_hub(F'''nielsr/{model_name}'''			)


if __name__ == "__main__":
      __lowerCamelCase						    =      argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--model_name''',
          default='''maskformer-swin-tiny-ade''',
          type=str,
          help=('''Name of the MaskFormer model you\'d like to convert''',),
      )
      parser.add_argument(
          '''--checkpoint_path''',
          default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
          type=str,
          help='''Path to the original state dict (.pth file).''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
      )
      parser.add_argument(
          '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
      )

      __lowerCamelCase						    =      parser.parse_args()
      convert_maskformer_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )



'''simple docstring'''







import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
      import sqlitea

      import sqlalchemy


class    A__ ( _snake_case     ):



    def __init__(   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = False						,			**UpperCamelCase__						,			)  -> Optional[Any]:





           '''simple docstring'''
           super().__init__(features=UpperCamelCase__						,			cache_dir=UpperCamelCase__						,			keep_in_memory=UpperCamelCase__						,			**UpperCamelCase__    )
           A_		   = Sql(
               cache_dir=UpperCamelCase__						,			features=UpperCamelCase__						,			sql=UpperCamelCase__						,			con=UpperCamelCase__						,			**UpperCamelCase__						,			)







    def    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           A_		   = None
           A_		   = None
           A_		   = None
           A_		   = None

           self.builder.download_and_prepare(
               download_config=UpperCamelCase__						,			download_mode=UpperCamelCase__						,			verification_mode=UpperCamelCase__						,			base_path=UpperCamelCase__						,			)

           # Build dataset for splits
           A_		   = self.builder.as_dataset(
               split="""train"""						,			verification_mode=UpperCamelCase__						,			in_memory=self.keep_in_memory    )
           return dataset


class    A__ :



    def __init__(   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			**UpperCamelCase__						,			)  -> List[Any]:





           '''simple docstring'''
           if num_proc is not None and num_proc <= 0:
                  raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''    )

           A_		   = dataset
           A_		   = name
           A_		   = con
           A_		   = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           A_		   = num_proc
           A_		   = to_sql_kwargs



    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           A_		   = self.to_sql_kwargs.pop("""sql"""						,			UpperCamelCase__    )
           A_		   = self.to_sql_kwargs.pop("""con"""						,			UpperCamelCase__    )
           A_		   = self.to_sql_kwargs.pop("""index"""						,			UpperCamelCase__    )

           A_		   = self._write(index=UpperCamelCase__						,			**self.to_sql_kwargs    )
           return written



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> Union[str, Any]:





           '''simple docstring'''
           A_   ,     A_   ,     A_		   = args
           A_		   = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
           A_		   = query_table(
               table=self.dataset.data						,			key=slice(UpperCamelCase__						,			offset + self.batch_size    )						,			indices=self.dataset._indices						,			)
           A_		   = batch.to_pandas()
           A_		   = df.to_sql(self.name						,			self.con						,			index=UpperCamelCase__						,			**UpperCamelCase__    )
           return num_rows or len(UpperCamelCase__    )







    def    snake_case_     (   self						,			UpperCamelCase__						,			**UpperCamelCase__    )  -> int:





           '''simple docstring'''
           A_		   = 0

           if self.num_proc is None or self.num_proc == 1:
                  for offset in logging.tqdm(
                      range(0						,			len(self.dataset    )						,			self.batch_size    )						,			unit="""ba"""						,			disable=not logging.is_progress_bar_enabled()						,			desc="""Creating SQL from Arrow format"""						,			):
                         written += self._batch_sql((offset, index, to_sql_kwargs)    )
           else:
                  A_   ,     A_		   = len(self.dataset    ), self.batch_size
                  with multiprocessing.Pool(self.num_proc    ) as pool:
                         for num_rows in logging.tqdm(
                             pool.imap(
                                 self._batch_sql						,			[(offset, index, to_sql_kwargs) for offset in range(0						,			UpperCamelCase__						,			UpperCamelCase__    )]						,			)						,			total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,			unit="""ba"""						,			disable=not logging.is_progress_bar_enabled()						,			desc="""Creating SQL from Arrow format"""						,			):
                                written += num_rows

           return written
