






from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from PIL import Image

				from ..image_utils import load_image

if is_torch_available():
				from ..models.auto.modeling_auto import (
				    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
				    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
				    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
				    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
				)


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


UpperCamelCase__						:      Union[str, Any]								=					Dict[str, Any]
UpperCamelCase__						:      Dict								=					List[Prediction]







@add_end_docstrings(a_     )
class 				lowerCamelCase_     (	a_     ):
							def __init__(						self		:					Tuple							,*__lowerCamelCase		:					Union[str, Any]							,**__lowerCamelCase		:					str					):





								'''simple docstring'''



								super().__init__(*__lowerCamelCase							,**__lowerCamelCase					)

								if self.framework == "tf":
									raise ValueError(F"""The {self.__class__} is only available in PyTorch."""					)

								requires_backends(self							,'''vision'''					)
								self.check_model_type(
								    dict(
								        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
								        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
								        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
								        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()					)					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple							,**__lowerCamelCase		:					List[Any]					):





								'''simple docstring'''



								a		=			{}
								a		=			{}
								if "subtask" in kwargs:
									a		=			kwargs['''subtask''']
									a		=			kwargs['''subtask''']
								if "threshold" in kwargs:
									a		=			kwargs['''threshold''']
								if "mask_threshold" in kwargs:
									a		=			kwargs['''mask_threshold''']
								if "overlap_mask_area_threshold" in kwargs:
									a		=			kwargs['''overlap_mask_area_threshold''']

								return preprocess_kwargs, {}, postprocess_kwargs
							def __call__(						self		:					Dict							,__lowerCamelCase		:					Any							,**__lowerCamelCase		:					Optional[int]					):





								'''simple docstring'''



								return super().__call__(__lowerCamelCase							,**__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]							,__lowerCamelCase		:					Tuple							,__lowerCamelCase		:					Any=None					):





								'''simple docstring'''



								a		=			load_image(__lowerCamelCase					)
								a		=			[(image.height, image.width)]
								if self.model.config.__class__.__name__ == "OneFormerConfig":
									if subtask is None:
										a		=			{}
									else:
										a		=			{'''task_inputs''': [subtask]}
									a		=			self.image_processor(images=[image]							,return_tensors='''pt'''							,**__lowerCamelCase					)
									a		=			self.tokenizer(
									    inputs['''task_inputs''']							,padding='''max_length'''							,max_length=self.model.config.task_seq_len							,return_tensors=self.framework							,)['''input_ids''']
								else:
									a		=			self.image_processor(images=[image]							,return_tensors='''pt'''					)
								a		=			target_size
								return inputs
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[Any]							,__lowerCamelCase		:					Dict					):





								'''simple docstring'''



								a		=			model_inputs.pop('''target_size'''					)
								a		=			self.model(**__lowerCamelCase					)
								a		=			target_size
								return model_outputs




							def  SCREAMING_SNAKE_CASE_ (						self		:					str							,__lowerCamelCase		:					str							,__lowerCamelCase		:					Optional[Any]=None							,__lowerCamelCase		:					Optional[int]=0.9							,__lowerCamelCase		:					int=0.5							,__lowerCamelCase		:					str=0.5					):





								'''simple docstring'''



								a		=			None
								if subtask in {"panoptic", None} and hasattr(self.image_processor							,'''post_process_panoptic_segmentation'''					):
									a		=			self.image_processor.post_process_panoptic_segmentation
								elif subtask in {"instance", None} and hasattr(self.image_processor							,'''post_process_instance_segmentation'''					):
									a		=			self.image_processor.post_process_instance_segmentation

								if fn is not None:
									a		=			fn(
									    __lowerCamelCase							,threshold=__lowerCamelCase							,mask_threshold=__lowerCamelCase							,overlap_mask_area_threshold=__lowerCamelCase							,target_sizes=model_outputs['''target_size''']							,)[0]

									a		=			[]
									a		=			outputs['''segmentation''']

									for segment in outputs["segments_info"]:
										a		=			(segmentation == segment['''id''']) * 2_55
										a		=			Image.fromarray(mask.numpy().astype(np.uinta					)							,mode='''L'''					)
										a		=			self.model.config.idalabel[segment['''label_id''']]
										a		=			segment['''score''']
										annotation.append({'''score''': score, '''label''': label, '''mask''': mask}					)

								elif subtask in {"semantic", None} and hasattr(self.image_processor							,'''post_process_semantic_segmentation'''					):
									a		=			self.image_processor.post_process_semantic_segmentation(
									    __lowerCamelCase							,target_sizes=model_outputs['''target_size''']					)[0]

									a		=			[]
									a		=			outputs.numpy()
									a		=			np.unique(__lowerCamelCase					)

									for label in labels:
										a		=			(segmentation == label) * 2_55
										a		=			Image.fromarray(mask.astype(np.uinta					)							,mode='''L'''					)
										a		=			self.model.config.idalabel[label]
										annotation.append({'''score''': None, '''label''': label, '''mask''': mask}					)
								else:
									raise ValueError(F"""Subtask {subtask} is not supported for model {type(self.model					)}"""					)
								return annotation













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 				lowerCamelCase_     (	datasets.BuilderConfig     ):
							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  SCREAMING_SNAKE_CASE_ (						self		:					Optional[Any]					):





								'''simple docstring'''



								if self.sql is None:
									raise ValueError('''sql must be specified'''					)
								if self.con is None:
									raise ValueError('''con must be specified'''					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any							,__lowerCamelCase		:					dict							,__lowerCamelCase		:					Optional[datasets.Features] = None							,):





								'''simple docstring'''



								a		=			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
								a		=			config_kwargs['''sql''']
								if not isinstance(__lowerCamelCase							,__lowerCamelCase					):
									if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
										import sqlalchemy

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

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




							@property
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict					):





								'''simple docstring'''



								a		=			{
								    '''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 				lowerCamelCase_     (	datasets.ArrowBasedBuilder     ):
							SCREAMING_SNAKE_CASE_						     =			SqlConfig
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[Any]					):





								'''simple docstring'''



								return datasets.DatasetInfo(features=self.config.features					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]							,__lowerCamelCase		:					int					):





								'''simple docstring'''



								return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,gen_kwargs={}					)]
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple							,__lowerCamelCase		:					pa.Table					):





								'''simple docstring'''



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




							def  SCREAMING_SNAKE_CASE_ (						self		:					str					):





								'''simple docstring'''



								a		=			self.config.chunksize
								a		=			pd.read_sql(
								    self.config.sql							,self.config.con							,chunksize=__lowerCamelCase							,**self.config.pd_read_sql_kwargs					)
								a		=			[sql_reader] if chunksize is None else sql_reader
								for chunk_idx, df in enumerate(__lowerCamelCase					):
									a		=			pa.Table.from_pandas(__lowerCamelCase					)
									yield chunk_idx, self._cast_table(__lowerCamelCase					)





