
'''simple docstring'''



import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCamelCase__					:						Optional[int]											=     logging.get_logger(__name__)

UpperCamelCase__					:						str											=     {
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}

class       _a (_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     'oneformer'
       SCREAMING_SNAKE_CASE					=     {'hidden_size': 'hidden_dim'}






       def __init__(							self				,							A__ = None				,							A__ = 2_55				,							A__ = 1_50				,							A__ = 0.1				,							A__ = 2.0				,							A__ = 5.0				,							A__ = 5.0				,							A__ = 0.5				,							A__ = 0.07				,							A__ = 1_25_44				,							A__ = 3.0				,							A__ = 0.75				,							A__ = 0.02				,							A__ = 1.0				,							A__ = 1E-05				,							A__ = False				,							A__ = True				,							A__ = True				,							A__ = [4, 8, 16, 32]				,							A__ = 77				,							A__ = 2_56				,							A__ = 77				,							A__ = 6				,							A__ = 4_94_08				,							A__ = 2				,							A__ = 16				,							A__ = 2_56				,							A__ = 2_56				,							A__ = 2_56				,							A__ = 10_24				,							A__ = "GN"				,							A__ = 6				,							A__ = 10				,							A__ = True				,							A__ = 8				,							A__ = 0.1				,							A__ = 20_48				,							A__ = False				,							A__ = False				,							A__ = 2				,							A__ = 4				,							**A__				,							)			->	List[Any]:
          if backbone_config is None:
             logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""					)
             _SCREAMING_SNAKE_CASE          = CONFIG_MAPPING["""swin"""](
                 image_size=2_24				,							in_channels=3				,							patch_size=4				,							embed_dim=96				,							depths=[2, 2, 6, 2]				,							num_heads=[3, 6, 12, 24]				,							window_size=7				,							drop_path_rate=0.3				,							use_absolute_embeddings=A__				,							out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]				,							)
          elif isinstance(A__				,							A__					):
             _SCREAMING_SNAKE_CASE          = backbone_config.get("""model_type"""					)
             _SCREAMING_SNAKE_CASE          = CONFIG_MAPPING[backbone_model_type]
             _SCREAMING_SNAKE_CASE          = config_class.from_dict(A__					)

          _SCREAMING_SNAKE_CASE          = backbone_config

          _SCREAMING_SNAKE_CASE          = ignore_value
          _SCREAMING_SNAKE_CASE          = num_queries
          _SCREAMING_SNAKE_CASE          = no_object_weight
          _SCREAMING_SNAKE_CASE          = class_weight
          _SCREAMING_SNAKE_CASE          = mask_weight
          _SCREAMING_SNAKE_CASE          = dice_weight
          _SCREAMING_SNAKE_CASE          = contrastive_weight
          _SCREAMING_SNAKE_CASE          = contrastive_temperature
          _SCREAMING_SNAKE_CASE          = train_num_points
          _SCREAMING_SNAKE_CASE          = oversample_ratio
          _SCREAMING_SNAKE_CASE          = importance_sample_ratio
          _SCREAMING_SNAKE_CASE          = init_std
          _SCREAMING_SNAKE_CASE          = init_xavier_std
          _SCREAMING_SNAKE_CASE          = layer_norm_eps
          _SCREAMING_SNAKE_CASE          = is_training
          _SCREAMING_SNAKE_CASE          = use_auxiliary_loss
          _SCREAMING_SNAKE_CASE          = output_auxiliary_logits
          _SCREAMING_SNAKE_CASE          = strides
          _SCREAMING_SNAKE_CASE          = task_seq_len
          _SCREAMING_SNAKE_CASE          = text_encoder_width
          _SCREAMING_SNAKE_CASE          = text_encoder_context_length
          _SCREAMING_SNAKE_CASE          = text_encoder_num_layers
          _SCREAMING_SNAKE_CASE          = text_encoder_vocab_size
          _SCREAMING_SNAKE_CASE          = text_encoder_proj_layers
          _SCREAMING_SNAKE_CASE          = text_encoder_n_ctx
          _SCREAMING_SNAKE_CASE          = conv_dim
          _SCREAMING_SNAKE_CASE          = mask_dim
          _SCREAMING_SNAKE_CASE          = hidden_dim
          _SCREAMING_SNAKE_CASE          = encoder_feedforward_dim
          _SCREAMING_SNAKE_CASE          = norm
          _SCREAMING_SNAKE_CASE          = encoder_layers
          _SCREAMING_SNAKE_CASE          = decoder_layers
          _SCREAMING_SNAKE_CASE          = use_task_norm
          _SCREAMING_SNAKE_CASE          = num_attention_heads
          _SCREAMING_SNAKE_CASE          = dropout
          _SCREAMING_SNAKE_CASE          = dim_feedforward
          _SCREAMING_SNAKE_CASE          = pre_norm
          _SCREAMING_SNAKE_CASE          = enforce_input_proj
          _SCREAMING_SNAKE_CASE          = query_dec_layers
          _SCREAMING_SNAKE_CASE          = common_stride
          _SCREAMING_SNAKE_CASE          = decoder_layers

          super().__init__(**A__					)






       def 				UpperCamelCase (							self					)			->	Dict[str, any]:
          _SCREAMING_SNAKE_CASE          = copy.deepcopy(self.__dict__					)
          _SCREAMING_SNAKE_CASE          = self.backbone_config.to_dict()
          _SCREAMING_SNAKE_CASE          = self.__class__.model_type
          return output








'''simple docstring'''



import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
     import sqlitea

     import sqlalchemy


UpperCamelCase__					:						Tuple											=     datasets.utils.logging.get_logger(__name__)

@dataclass
class       _a (datasets.BuilderConfig):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     None
       SCREAMING_SNAKE_CASE					=     None
       SCREAMING_SNAKE_CASE					=     None
       SCREAMING_SNAKE_CASE					=     True
       SCREAMING_SNAKE_CASE					=     None
       SCREAMING_SNAKE_CASE					=     None
       SCREAMING_SNAKE_CASE					=     None
       SCREAMING_SNAKE_CASE					=     1_00_00
       SCREAMING_SNAKE_CASE					=     None






       def 				UpperCamelCase (							self					)			->	str:
          if self.sql is None:
             raise ValueError("""sql must be specified"""					)
          if self.con is None:
             raise ValueError("""con must be specified"""					)






       def 				UpperCamelCase (							self				,							A__				,							A__ = None				,							)			->	str:
          _SCREAMING_SNAKE_CASE          = config_kwargs.copy()
          # We need to stringify the Selectable object to make its hash deterministic

          # The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
          _SCREAMING_SNAKE_CASE          = config_kwargs["""sql"""]
          if not isinstance(A__				,							A__					):
             if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
                import sqlalchemy

                if isinstance(A__				,							sqlalchemy.sql.Selectable					):
                   _SCREAMING_SNAKE_CASE          = sqlalchemy.create_engine(config_kwargs["""con"""].split("""://"""					)[0] + """://"""					)
                   _SCREAMING_SNAKE_CASE          = str(sql.compile(dialect=engine.dialect					)					)
                   _SCREAMING_SNAKE_CASE          = sql_str
                else:
                   raise TypeError(
                       F"Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(A__					)}: {sql}"					)
             else:
                raise TypeError(
                    F"Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(A__					)}: {sql}"					)
          _SCREAMING_SNAKE_CASE          = config_kwargs["""con"""]
          if not isinstance(A__				,							A__					):
             _SCREAMING_SNAKE_CASE          = id(A__					)
             logger.info(
                 F"SQL connection 'con' of type {type(A__					)} couldn't be hashed properly. To enable hashing, specify 'con' as URI string instead."					)

          return super().create_config_id(A__				,							custom_features=A__					)






       @property
       def 				UpperCamelCase (							self					)			->	Any:
          _SCREAMING_SNAKE_CASE          = {
              """index_col""": self.index_col,
              """columns""": self.columns,
              """params""": self.params,
              """coerce_float""": self.coerce_float,
              """parse_dates""": self.parse_dates,
          }
          return pd_read_sql_kwargs






class       _a (datasets.ArrowBasedBuilder):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     SqlConfig






       def 				UpperCamelCase (							self					)			->	Optional[Any]:
          return datasets.DatasetInfo(features=self.config.features					)






       def 				UpperCamelCase (							self				,							A__					)			->	Dict:
          return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,							gen_kwargs={}					)]






       def 				UpperCamelCase (							self				,							A__					)			->	pa.Table:
          if self.config.features is not None:
             _SCREAMING_SNAKE_CASE          = self.config.features.arrow_schema
             if all(not require_storage_cast(A__					) for feature in self.config.features.values()					):
                # cheaper cast
                _SCREAMING_SNAKE_CASE          = pa.Table.from_arrays([pa_table[field.name] for field in schema]				,							schema=A__					)
             else:
                # more expensive cast; allows str <-> int/float or str to Audio for example
                _SCREAMING_SNAKE_CASE          = table_cast(A__				,							A__					)
          return pa_table






       def 				UpperCamelCase (							self					)			->	Optional[int]:
          _SCREAMING_SNAKE_CASE          = self.config.chunksize
          _SCREAMING_SNAKE_CASE          = pd.read_sql(
              self.config.sql				,							self.config.con				,							chunksize=A__				,							**self.config.pd_read_sql_kwargs					)
          _SCREAMING_SNAKE_CASE          = [sql_reader] if chunksize is None else sql_reader
          for chunk_idx, df in enumerate(A__					):
             _SCREAMING_SNAKE_CASE          = pa.Table.from_pandas(A__					)
             yield chunk_idx, self._cast_table(A__					)






