import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_vision_available():
	from PIL import Image

	from transformers import FlavaImageProcessor, FlavaProcessor
	from transformers.models.flava.image_processing_flava import (
	    FLAVA_CODEBOOK_MEAN,
	    FLAVA_CODEBOOK_STD,
	    FLAVA_IMAGE_MEAN,
	    FLAVA_IMAGE_STD,
	)




@require_vision
class 					_snake_case						(						unittest.TestCase				):
					def    _lowerCamelCase (  self:				str     )    ->				Dict:
												__UpperCAmelCase						:			Optional[Any]         =   tempfile.mkdtemp()

												# fmt: off
												__UpperCAmelCase						:			Any         =   ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
												# fmt: on
												__UpperCAmelCase						:			int         =   os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["vocab_file"]     )

												with open(self.vocab_file	,  "w"	,  encoding="utf-8"     ) as fp:
																			fp.write("".join([x + "\n" for x in vocab_tokens]     )     )

												__UpperCAmelCase						:			Optional[Any]         =   {
												    "image_mean": FLAVA_IMAGE_MEAN,
												    "image_std": FLAVA_IMAGE_STD,
												    "do_normalize": True,
												    "do_resize": True,
												    "size": 2_24,
												    "do_center_crop": True,
												    "crop_size": 2_24,
												    "input_size_patches": 14,
												    "total_mask_patches": 75,
												    "mask_group_max_patches": None,
												    "mask_group_min_patches": 16,
												    "mask_group_min_aspect_ratio": 0.3,
												    "mask_group_max_aspect_ratio": None,
												    "codebook_do_resize": True,
												    "codebook_size": 1_12,
												    "codebook_do_center_crop": True,
												    "codebook_crop_size": 1_12,
												    "codebook_do_map_pixels": True,
												    "codebook_do_normalize": True,
												    "codebook_image_mean": FLAVA_CODEBOOK_MEAN,
												    "codebook_image_std": FLAVA_CODEBOOK_STD,
												}

												__UpperCAmelCase						:			Dict         =   os.path.join(self.tmpdirname	,  __lowerCamelCase     )
												with open(self.image_processor_file	,  "w"	,  encoding="utf-8"     ) as fp:
																			json.dump(__lowerCamelCase	,  __lowerCamelCase     )
					def    _lowerCamelCase (  self:				Tuple	,  **__lowerCamelCase:				Optional[int]     )    ->				int:
												return BertTokenizer.from_pretrained(self.tmpdirname	,  **__lowerCamelCase     )
					def    _lowerCamelCase (  self:				Union[str, Any]	,  **__lowerCamelCase:				List[Any]     )    ->				int:
												return BertTokenizerFast.from_pretrained(self.tmpdirname	,  **__lowerCamelCase     )
					def    _lowerCamelCase (  self:				List[Any]	,  **__lowerCamelCase:				List[Any]     )    ->				Dict:
												return FlavaImageProcessor.from_pretrained(self.tmpdirname	,  **__lowerCamelCase     )
					def    _lowerCamelCase (  self:				Dict     )    ->				List[str]:
												shutil.rmtree(self.tmpdirname     )
					def    _lowerCamelCase (  self:				List[str]     )    ->				str:
												__UpperCAmelCase						:			Union[str, Any]         =   [np.random.randint(2_55	,  size=(3, 30, 4_00)	,  dtype=np.uinta     )]

												__UpperCAmelCase						:			List[Any]         =   [Image.fromarray(np.moveaxis(__lowerCamelCase	,  0	,  -1     )     ) for x in image_inputs]

												return image_inputs
					def    _lowerCamelCase (  self:				int     )    ->				int:
												__UpperCAmelCase						:			str         =   self.get_tokenizer()
												__UpperCAmelCase						:			List[str]         =   self.get_rust_tokenizer()
												__UpperCAmelCase						:			Any         =   self.get_image_processor()

												__UpperCAmelCase						:			List[str]         =   FlavaProcessor(tokenizer=__lowerCamelCase	,  image_processor=__lowerCamelCase     )
												processor_slow.save_pretrained(self.tmpdirname     )
												__UpperCAmelCase						:			Tuple         =   FlavaProcessor.from_pretrained(self.tmpdirname	,  use_fast=__lowerCamelCase     )

												__UpperCAmelCase						:			Optional[Any]         =   FlavaProcessor(tokenizer=__lowerCamelCase	,  image_processor=__lowerCamelCase     )
												processor_fast.save_pretrained(self.tmpdirname     )
												__UpperCAmelCase						:			Tuple         =   FlavaProcessor.from_pretrained(self.tmpdirname     )

												self.assertEqual(processor_slow.tokenizer.get_vocab()	,  tokenizer_slow.get_vocab()     )
												self.assertEqual(processor_fast.tokenizer.get_vocab()	,  tokenizer_fast.get_vocab()     )
												self.assertEqual(tokenizer_slow.get_vocab()	,  tokenizer_fast.get_vocab()     )
												self.assertIsInstance(processor_slow.tokenizer	,  __lowerCamelCase     )
												self.assertIsInstance(processor_fast.tokenizer	,  __lowerCamelCase     )

												self.assertEqual(processor_slow.image_processor.to_json_string()	,  image_processor.to_json_string()     )
												self.assertEqual(processor_fast.image_processor.to_json_string()	,  image_processor.to_json_string()     )
												self.assertIsInstance(processor_slow.image_processor	,  __lowerCamelCase     )
												self.assertIsInstance(processor_fast.image_processor	,  __lowerCamelCase     )
					def    _lowerCamelCase (  self:				List[str]     )    ->				Any:
												__UpperCAmelCase						:			Dict         =   FlavaProcessor(tokenizer=self.get_tokenizer()	,  image_processor=self.get_image_processor()     )
												processor.save_pretrained(self.tmpdirname     )

												__UpperCAmelCase						:			Union[str, Any]         =   self.get_tokenizer(bos_token="(BOS)"	,  eos_token="(EOS)"     )
												__UpperCAmelCase						:			Union[str, Any]         =   self.get_image_processor(do_normalize=__lowerCamelCase	,  padding_value=1.0     )

												__UpperCAmelCase						:			List[str]         =   FlavaProcessor.from_pretrained(
												    self.tmpdirname	,  bos_token="(BOS)"	,  eos_token="(EOS)"	,  do_normalize=__lowerCamelCase	,  padding_value=1.0     )

												self.assertEqual(processor.tokenizer.get_vocab()	,  tokenizer_add_kwargs.get_vocab()     )
												self.assertIsInstance(processor.tokenizer	,  __lowerCamelCase     )

												self.assertEqual(processor.image_processor.to_json_string()	,  image_processor_add_kwargs.to_json_string()     )
												self.assertIsInstance(processor.image_processor	,  __lowerCamelCase     )
					def    _lowerCamelCase (  self:				int     )    ->				str:
												__UpperCAmelCase						:			Optional[int]         =   self.get_image_processor()
												__UpperCAmelCase						:			List[Any]         =   self.get_tokenizer()

												__UpperCAmelCase						:			List[str]         =   FlavaProcessor(tokenizer=__lowerCamelCase	,  image_processor=__lowerCamelCase     )

												__UpperCAmelCase						:			List[Any]         =   self.prepare_image_inputs()

												__UpperCAmelCase						:			Tuple         =   image_processor(__lowerCamelCase	,  return_tensors="np"     )
												__UpperCAmelCase						:			Union[str, Any]         =   processor(images=__lowerCamelCase	,  return_tensors="np"     )

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

												# With rest of the args
												random.seed(12_34     )
												__UpperCAmelCase						:			List[str]         =   image_processor(
												    __lowerCamelCase	,  return_image_mask=__lowerCamelCase	,  return_codebook_pixels=__lowerCamelCase	,  return_tensors="np"     )
												random.seed(12_34     )
												__UpperCAmelCase						:			List[str]         =   processor(
												    images=__lowerCamelCase	,  return_image_mask=__lowerCamelCase	,  return_codebook_pixels=__lowerCamelCase	,  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    _lowerCamelCase (  self:				Optional[int]     )    ->				str:
												__UpperCAmelCase						:			List[Any]         =   self.get_image_processor()
												__UpperCAmelCase						:			Optional[Any]         =   self.get_tokenizer()

												__UpperCAmelCase						:			Dict         =   FlavaProcessor(tokenizer=__lowerCamelCase	,  image_processor=__lowerCamelCase     )

												__UpperCAmelCase						:			Tuple         =   "lower newer"

												__UpperCAmelCase						:			List[Any]         =   processor(text=__lowerCamelCase     )

												__UpperCAmelCase						:			str         =   tokenizer(__lowerCamelCase     )

												for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key]	,  encoded_processor[key]     )
					def    _lowerCamelCase (  self:				Any     )    ->				List[str]:
												__UpperCAmelCase						:			Dict         =   self.get_image_processor()
												__UpperCAmelCase						:			Any         =   self.get_tokenizer()

												__UpperCAmelCase						:			Tuple         =   FlavaProcessor(tokenizer=__lowerCamelCase	,  image_processor=__lowerCamelCase     )

												__UpperCAmelCase						:			str         =   "lower newer"
												__UpperCAmelCase						:			Tuple         =   self.prepare_image_inputs()

												__UpperCAmelCase						:			int         =   processor(text=__lowerCamelCase	,  images=__lowerCamelCase     )

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

												# add extra args
												__UpperCAmelCase						:			int         =   processor(text=__lowerCamelCase	,  images=__lowerCamelCase	,  return_codebook_pixels=__lowerCamelCase	,  return_image_mask=__lowerCamelCase     )

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

												# test if it raises when no input is passed
												with pytest.raises(__lowerCamelCase     ):
																			processor()
					def    _lowerCamelCase (  self:				List[str]     )    ->				List[str]:
												__UpperCAmelCase						:			Union[str, Any]         =   self.get_image_processor()
												__UpperCAmelCase						:			Optional[Any]         =   self.get_tokenizer()

												__UpperCAmelCase						:			Optional[int]         =   FlavaProcessor(tokenizer=__lowerCamelCase	,  image_processor=__lowerCamelCase     )

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

												__UpperCAmelCase						:			Optional[int]         =   processor.batch_decode(__lowerCamelCase     )
												__UpperCAmelCase						:			List[Any]         =   tokenizer.batch_decode(__lowerCamelCase     )

												self.assertListEqual(__lowerCamelCase	,  __lowerCamelCase     )





					def    _lowerCamelCase (  self:				Union[str, Any]     )    ->				Dict:
												__UpperCAmelCase						:			int         =   self.get_image_processor()
												__UpperCAmelCase						:			Optional[Any]         =   self.get_tokenizer()

												__UpperCAmelCase						:			Tuple         =   FlavaProcessor(tokenizer=__lowerCamelCase	,  image_processor=__lowerCamelCase     )

												__UpperCAmelCase						:			List[str]         =   "lower newer"
												__UpperCAmelCase						:			List[str]         =   self.prepare_image_inputs()

												__UpperCAmelCase						:			Dict         =   processor(text=__lowerCamelCase	,  images=__lowerCamelCase     )

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




import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


_snake_case			=						datasets.utils.logging.get_logger(__name__)




@dataclass
class 					_snake_case						(						datasets.BuilderConfig				):
					lowerCamelCase__:		Optional[datasets.Features]  =     None




class 					_snake_case						(						datasets.ArrowBasedBuilder				):
					lowerCamelCase__:		Union[str, Any]  =     ArrowConfig
					def    _lowerCamelCase (  self:				Optional[int]     )    ->				Optional[int]:
												return datasets.DatasetInfo(features=self.config.features     )
					def    _lowerCamelCase (  self:				List[Any]	,  __lowerCamelCase:				Tuple     )    ->				str:
												if not self.config.data_files:
																			raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''     )
												__UpperCAmelCase						:			Dict         =   dl_manager.download_and_extract(self.config.data_files     )
												if isinstance(__lowerCamelCase	,  (str, list, tuple)     ):
																			__UpperCAmelCase						:			str         =   data_files
																			if isinstance(__lowerCamelCase	,  __lowerCamelCase     ):
																										__UpperCAmelCase						:			List[str]         =   [files]
																			# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																			__UpperCAmelCase						:			List[Any]         =   [dl_manager.iter_files(__lowerCamelCase     ) for file in files]
																			return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,  gen_kwargs={"files": files}     )]
												__UpperCAmelCase						:			Union[str, Any]         =   []
												for split_name, files in data_files.items():
																			if isinstance(__lowerCamelCase	,  __lowerCamelCase     ):
																										__UpperCAmelCase						:			Tuple         =   [files]
																			# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																			__UpperCAmelCase						:			List[Any]         =   [dl_manager.iter_files(__lowerCamelCase     ) for file in files]
																			# Infer features is they are stoed in the arrow schema
																			if self.info.features is None:
																										for file in itertools.chain.from_iterable(__lowerCamelCase     ):
																																	with open(__lowerCamelCase	,  "rb"     ) as f:
																																								__UpperCAmelCase						:			Union[str, Any]         =   datasets.Features.from_arrow_schema(pa.ipc.open_stream(__lowerCamelCase     ).schema     )
																																	break
																			splits.append(datasets.SplitGenerator(name=__lowerCamelCase	,  gen_kwargs={"files": files}     )     )
												return splits
					def    _lowerCamelCase (  self:				str	,  __lowerCamelCase:				pa.Table     )    ->				pa.Table:
												if self.info.features is not None:
																			# more expensive cast to support nested features with keys in a different order
																			# allows str <-> int/float or str to Audio for example
																			__UpperCAmelCase						:			List[str]         =   table_cast(__lowerCamelCase	,  self.info.features.arrow_schema     )
												return pa_table





					def    _lowerCamelCase (  self:				Any	,  __lowerCamelCase:				Any     )    ->				Any:
												for file_idx, file in enumerate(itertools.chain.from_iterable(__lowerCamelCase     )     ):
																			with open(__lowerCamelCase	,  "rb"     ) as f:
																										try:
																																	for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__lowerCamelCase     )     ):
																																								__UpperCAmelCase						:			Optional[int]         =   pa.Table.from_batches([record_batch]     )
																																								# Uncomment for debugging (will print the Arrow table size and elements)
																																								# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																								# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																								yield f'''{file_idx}_{batch_idx}''', self._cast_table(__lowerCamelCase     )
																										except ValueError as e:
																																	logger.error(f'''Failed to read file \'{file}\' with error {type(__lowerCamelCase     )}: {e}'''     )
																																	raise




