





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     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   def __init__(    self,		__magic_name__,		__magic_name__,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = False,		**__magic_name__,		) ->      Tuple:






       """simple docstring"""
       super().__init__(features=__magic_name__,		cache_dir=__magic_name__,		keep_in_memory=__magic_name__,		**__magic_name__							)
       UpperCamelCase__							:				Any          =      Sql(
           cache_dir=__magic_name__,		features=__magic_name__,		sql=__magic_name__,		con=__magic_name__,		**__magic_name__,		)




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






       """simple docstring"""
       UpperCamelCase__							:				str          =      None
       UpperCamelCase__							:				List[str]          =      None
       UpperCamelCase__							:				Any          =      None
       UpperCamelCase__							:				Any          =      None

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

       # Build dataset for splits
       UpperCamelCase__							:				List[Any]          =      self.builder.as_dataset(
           split='''train''',		verification_mode=__magic_name__,		in_memory=self.keep_in_memory							)
       return dataset






class     lowercase__       :

   '''simple docstring'''


   def __init__(    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__ = None,		__magic_name__ = None,		**__magic_name__,		) ->      Union[str, 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."							)

       UpperCamelCase__							:				Union[str, Any]          =      dataset
       UpperCamelCase__							:				Dict          =      name
       UpperCamelCase__							:				Union[str, Any]          =      con
       UpperCamelCase__							:				List[str]          =      batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
       UpperCamelCase__							:				Optional[Any]          =      num_proc
       UpperCamelCase__							:				Any          =      to_sql_kwargs




   def       UpperCamelCase__  (    self							) ->      int:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      self.to_sql_kwargs.pop('''sql''',		__magic_name__							)
       UpperCamelCase__							:				Union[str, Any]          =      self.to_sql_kwargs.pop('''con''',		__magic_name__							)
       UpperCamelCase__							:				Union[str, Any]          =      self.to_sql_kwargs.pop('''index''',		__magic_name__							)

       UpperCamelCase__							:				Optional[Any]          =      self._write(index=__magic_name__,		**self.to_sql_kwargs							)
       return written




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






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




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






       """simple docstring"""
       UpperCamelCase__							:				Tuple          =      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:
           UpperCamelCase__   ,UpperCamelCase__							:				str          =      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,		__magic_name__,		__magic_name__							)],		),		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








from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_     							= logging.get_logger(__name__)

UpperCAmelCase_     							= {
    'google/mobilenet_v2_1.4_224': 'https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json',
    'google/mobilenet_v2_1.0_224': 'https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json',
    'google/mobilenet_v2_0.75_160': 'https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json',
    'google/mobilenet_v2_0.35_96': 'https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							List[str]   		=		"mobilenet_v2"




   def __init__(    self,		__magic_name__=3,		__magic_name__=224,		__magic_name__=1.0,		__magic_name__=8,		__magic_name__=8,		__magic_name__=6,		__magic_name__=32,		__magic_name__=True,		__magic_name__=True,		__magic_name__="relu6",		__magic_name__=True,		__magic_name__=0.8,		__magic_name__=0.02,		__magic_name__=0.001,		__magic_name__=255,		**__magic_name__,		) ->      List[Any]:






       """simple docstring"""
       super().__init__(**__magic_name__							)

       if depth_multiplier <= 0:
           raise ValueError('''depth_multiplier must be greater than zero.'''							)

       UpperCamelCase__							:				Union[str, Any]          =      num_channels
       UpperCamelCase__							:				int          =      image_size
       UpperCamelCase__							:				int          =      depth_multiplier
       UpperCamelCase__							:				Tuple          =      depth_divisible_by
       UpperCamelCase__							:				List[str]          =      min_depth
       UpperCamelCase__							:				Optional[int]          =      expand_ratio
       UpperCamelCase__							:				Optional[int]          =      output_stride
       UpperCamelCase__							:				Tuple          =      first_layer_is_expansion
       UpperCamelCase__							:				Union[str, Any]          =      finegrained_output
       UpperCamelCase__							:				str          =      hidden_act
       UpperCamelCase__							:				Optional[Any]          =      tf_padding
       UpperCamelCase__							:				Optional[int]          =      classifier_dropout_prob
       UpperCamelCase__							:				int          =      initializer_range
       UpperCamelCase__							:				Union[str, Any]          =      layer_norm_eps
       UpperCamelCase__							:				Tuple          =      semantic_loss_ignore_index






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							Union[str, Any]   		=		version.parse("1.11"				)




   @property
   def       UpperCamelCase__  (    self							) ->      Mapping[str, Mapping[int, str]]:






       """simple docstring"""
       return OrderedDict([('''pixel_values''', {0: '''batch'''})]							)




   @property
   def       UpperCamelCase__  (    self							) ->      Mapping[str, Mapping[int, str]]:






       """simple docstring"""
       if self.task == "image-classification":
           return OrderedDict([('''logits''', {0: '''batch'''})]							)
       else:
           return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]							)




   @property
   def       UpperCamelCase__  (    self							) ->      float:






       """simple docstring"""
       return 1E-4

