
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 							UpperCAmelCase      (  A_  ):




	'''simple docstring'''





	def __init__(       self					: Tuple							,A					: Union[str, "sqlalchemy.sql.Selectable"]							,A					: Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]							,A					: Optional[Features] = None							,A					: str = None							,A					: bool = False							,**A					: Dict							,):
					super().__init__(features=snake_case__							,cache_dir=snake_case__							,keep_in_memory=snake_case__							,**snake_case__ )
					__A   			= Sql(
					    cache_dir=snake_case__							,features=snake_case__							,sql=snake_case__							,con=snake_case__							,**snake_case__							,)







	def 				UpperCamelCase_  (       self					: Any ):
					__A   			= None
					__A   			= None
					__A   			= None
					__A   			= None

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

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





class 							UpperCAmelCase      :




	'''simple docstring'''





	def __init__(       self					: Union[str, Any]							,A					: Dataset							,A					: str							,A					: Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]							,A					: Optional[int] = None							,A					: Optional[int] = None							,**A					: Dict							,):


					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 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= self.to_sql_kwargs.pop("sql"							,snake_case__ )
					__A   			= self.to_sql_kwargs.pop("con"							,snake_case__ )
					__A   			= self.to_sql_kwargs.pop("index"							,snake_case__ )

					__A   			= self._write(index=snake_case__							,**self.to_sql_kwargs )
					return written






	def 				UpperCamelCase_  (       self					: str							,A					: Tuple ):
					__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(snake_case__							,offset + self.batch_size )							,indices=self.dataset._indices							,)
					__A   			= batch.to_pandas()
					__A   			= df.to_sql(self.name							,self.con							,index=snake_case__							,**snake_case__ )
					return num_rows or len(snake_case__ )







	def 				UpperCamelCase_  (       self					: List[Any]							,A					: str							,**A					: Union[str, Any] ):
					__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   			= 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							,snake_case__							,snake_case__ )]							,)							,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


import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor






@require_tokenizers
@require_vision
class   UpperCAmelCase		(						unittest.TestCase ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	List[str]:


          '''simple docstring'''


          snake_case       :   int							=      tempfile.mkdtemp()

          # fmt: off
          snake_case       :   Optional[int]							=      ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
          # fmt: on
          snake_case       :   List[str]							=      os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["vocab_file"] )
          with open(self.vocab_file    ,						"w"    ,						encoding="utf-8" ) as vocab_writer:
                 vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )

          snake_case       :   int							=      {
              "do_resize": True,
              "size": {"height": 18, "width": 18},
              "do_normalize": True,
              "image_mean": [0.5, 0.5, 0.5],
              "image_std": [0.5, 0.5, 0.5],
          }
          snake_case       :   Optional[Any]							=      os.path.join(self.tmpdirname    ,						snake_case__ )
          with open(self.image_processor_file    ,						"w"    ,						encoding="utf-8" ) as fp:
                 json.dump(snake_case__    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						**snake_case__		:					str )		->	Optional[int]:


          '''simple docstring'''


          return BertTokenizer.from_pretrained(self.tmpdirname    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						**snake_case__		:					List[str] )		->	int:


          '''simple docstring'''


          return ViTImageProcessor.from_pretrained(self.tmpdirname    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Dict:


          '''simple docstring'''


          shutil.rmtree(self.tmpdirname )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	str:


          '''simple docstring'''


          snake_case       :   List[Any]							=      [np.random.randint(2_55    ,						size=(3, 30, 4_00)    ,						dtype=np.uinta )]

          snake_case       :   Optional[int]							=      [Image.fromarray(np.moveaxis(snake_case__    ,						0    ,						-1 ) ) for x in image_inputs]

          return image_inputs



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


          '''simple docstring'''


          snake_case       :   Dict							=      self.get_tokenizer()
          snake_case       :   Optional[Any]							=      self.get_image_processor()

          snake_case       :   Optional[Any]							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          processor.save_pretrained(self.tmpdirname )
          snake_case       :   Any							=      VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )

          self.assertEqual(processor.tokenizer.get_vocab()    ,						tokenizer.get_vocab() )
          self.assertIsInstance(processor.tokenizer    ,						(BertTokenizer, BertTokenizerFast) )

          self.assertEqual(processor.image_processor.to_json_string()    ,						image_processor.to_json_string() )
          self.assertIsInstance(processor.image_processor    ,						snake_case__ )



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


          '''simple docstring'''


          snake_case       :   str							=      VisionTextDualEncoderProcessor(
              tokenizer=self.get_tokenizer()    ,						image_processor=self.get_image_processor() )
          processor.save_pretrained(self.tmpdirname )

          snake_case       :   Optional[int]							=      self.get_tokenizer(bos_token="(BOS)"    ,						eos_token="(EOS)" )
          snake_case       :   Tuple							=      self.get_image_processor(do_normalize=snake_case__    ,						padding_value=1.0 )

          snake_case       :   List[str]							=      VisionTextDualEncoderProcessor.from_pretrained(
              self.tmpdirname    ,						bos_token="(BOS)"    ,						eos_token="(EOS)"    ,						do_normalize=snake_case__    ,						padding_value=1.0 )

          self.assertEqual(processor.tokenizer.get_vocab()    ,						tokenizer_add_kwargs.get_vocab() )
          self.assertIsInstance(processor.tokenizer    ,						(BertTokenizer, BertTokenizerFast) )

          self.assertEqual(processor.image_processor.to_json_string()    ,						image_processor_add_kwargs.to_json_string() )
          self.assertIsInstance(processor.image_processor    ,						snake_case__ )



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


          '''simple docstring'''


          snake_case       :   str							=      self.get_image_processor()
          snake_case       :   Optional[int]							=      self.get_tokenizer()

          snake_case       :   List[Any]							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   Optional[Any]							=      self.prepare_image_inputs()

          snake_case       :   str							=      image_processor(snake_case__    ,						return_tensors="np" )
          snake_case       :   Any							=      processor(images=snake_case__    ,						return_tensors="np" )

          for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()    ,						input_processor[key].sum()    ,						delta=1e-2 )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      self.get_image_processor()
          snake_case       :   int							=      self.get_tokenizer()

          snake_case       :   Any							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   Tuple							=      "lower newer"

          snake_case       :   Tuple							=      processor(text=snake_case__ )

          snake_case       :   Union[str, Any]							=      tokenizer(snake_case__ )

          for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]    ,						encoded_processor[key] )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self.get_image_processor()
          snake_case       :   Dict							=      self.get_tokenizer()

          snake_case       :   Dict							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   int							=      "lower newer"
          snake_case       :   Dict							=      self.prepare_image_inputs()

          snake_case       :   Union[str, Any]							=      processor(text=snake_case__    ,						images=snake_case__ )

          self.assertListEqual(list(inputs.keys() )    ,						["input_ids", "token_type_ids", "attention_mask", "pixel_values"] )

          # test if it raises when no input is passed
          with self.assertRaises(snake_case__ ):
                 processor()



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Tuple:


          '''simple docstring'''


          snake_case       :   Tuple							=      self.get_image_processor()
          snake_case       :   Optional[Any]							=      self.get_tokenizer()

          snake_case       :   Tuple							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   List[str]							=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          snake_case       :   List[Any]							=      processor.batch_decode(snake_case__ )
          snake_case       :   Union[str, Any]							=      tokenizer.batch_decode(snake_case__ )

          self.assertListEqual(snake_case__    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	List[str]:


          '''simple docstring'''


          snake_case       :   str							=      self.get_image_processor()
          snake_case       :   Union[str, Any]							=      self.get_tokenizer()

          snake_case       :   Any							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   Optional[Any]							=      "lower newer"
          snake_case       :   List[Any]							=      self.prepare_image_inputs()

          snake_case       :   Tuple							=      processor(text=snake_case__    ,						images=snake_case__ )

          self.assertListEqual(list(inputs.keys() )    ,						processor.model_input_names )





