
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast




@require_vision
class     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					lowerCamelCase_						=			tempfile.mkdtemp()

					lowerCamelCase_						=			BlipImageProcessor()
					lowerCamelCase_						=			GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model" )

					lowerCamelCase_						=			BlipaProcessor(lowercase						,  lowercase )

					processor.save_pretrained(self.tmpdirname )



	def     SCREAMING_SNAKE_CASE_(    self						,  **lowercase )				->       Union[str, Any]:
					return AutoProcessor.from_pretrained(self.tmpdirname						,  **lowercase ).tokenizer



	def     SCREAMING_SNAKE_CASE_(    self						,  **lowercase )				->       str:
					return AutoProcessor.from_pretrained(self.tmpdirname						,  **lowercase ).image_processor



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					shutil.rmtree(self.tmpdirname )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					lowerCamelCase_						=			[np.random.randint(255						,  size=(3, 30, 400)						,  dtype=np.uinta )]

					lowerCamelCase_						=			[Image.fromarray(np.moveaxis(lowercase						,  0						,  -1 ) ) for x in image_inputs]

					return image_inputs



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			BlipaProcessor(tokenizer=self.get_tokenizer()						,  image_processor=self.get_image_processor() )
					processor.save_pretrained(self.tmpdirname )

					lowerCamelCase_						=			self.get_tokenizer(bos_token="(BOS)"						,  eos_token="(EOS)" )
					lowerCamelCase_						=			self.get_image_processor(do_normalize=lowercase						,  padding_value=1.0 )

					lowerCamelCase_						=			BlipaProcessor.from_pretrained(
					    self.tmpdirname						,  bos_token="(BOS)"						,  eos_token="(EOS)"						,  do_normalize=lowercase						,  padding_value=1.0 )

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

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					lowerCamelCase_						=			self.get_image_processor()
					lowerCamelCase_						=			self.get_tokenizer()

					lowerCamelCase_						=			BlipaProcessor(tokenizer=lowercase						,  image_processor=lowercase )

					lowerCamelCase_						=			self.prepare_image_inputs()

					lowerCamelCase_						=			image_processor(lowercase						,  return_tensors="np" )
					lowerCamelCase_						=			processor(images=lowercase						,  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 )				->       Optional[int]:
					lowerCamelCase_						=			self.get_image_processor()
					lowerCamelCase_						=			self.get_tokenizer()

					lowerCamelCase_						=			BlipaProcessor(tokenizer=lowercase						,  image_processor=lowercase )

					lowerCamelCase_						=			"lower newer"

					lowerCamelCase_						=			processor(text=lowercase )

					lowerCamelCase_						=			tokenizer(lowercase						,  return_token_type_ids=lowercase )

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			self.get_image_processor()
					lowerCamelCase_						=			self.get_tokenizer()

					lowerCamelCase_						=			BlipaProcessor(tokenizer=lowercase						,  image_processor=lowercase )

					lowerCamelCase_						=			"lower newer"
					lowerCamelCase_						=			self.prepare_image_inputs()

					lowerCamelCase_						=			processor(text=lowercase						,  images=lowercase )

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

					# test if it raises when no input is passed
					with pytest.raises(lowercase ):
									processor()



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					lowerCamelCase_						=			self.get_image_processor()
					lowerCamelCase_						=			self.get_tokenizer()

					lowerCamelCase_						=			BlipaProcessor(tokenizer=lowercase						,  image_processor=lowercase )

					lowerCamelCase_						=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					lowerCamelCase_						=			processor.batch_decode(lowercase )
					lowerCamelCase_						=			tokenizer.batch_decode(lowercase )

					self.assertListEqual(lowercase						,  lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					lowerCamelCase_						=			self.get_image_processor()
					lowerCamelCase_						=			self.get_tokenizer()

					lowerCamelCase_						=			BlipaProcessor(tokenizer=lowercase						,  image_processor=lowercase )

					lowerCamelCase_						=			"lower newer"
					lowerCamelCase_						=			self.prepare_image_inputs()

					lowerCamelCase_						=			processor(text=lowercase						,  images=lowercase )

					# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
					self.assertListEqual(list(inputs.keys() )						,  ["pixel_values", "input_ids", "attention_mask"] )




# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

__A				='''2.13.1'''

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('''3.7'''):
			raise ImportWarning(
			    '''To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'''
			)

if version.parse(pyarrow.__version__).major < 8:
			raise ImportWarning(
			    '''To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'''
			    '''If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'''
			)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

__A				=concatenate_datasets
__A				=DownloadConfig
__A				=DownloadManager
__A				=DownloadMode
__A				=DownloadConfig
__A				=DownloadMode
__A				=DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


