

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,
			    BertTokenizerFast,
			    BlipImageProcessor,
			    GPTaTokenizer,
			    InstructBlipProcessor,
			    PreTrainedTokenizerFast,
			)






@require_vision
class      lowerCamelCase_      (	unittest.TestCase	):




	def        lowerCAmelCase_       (     self						:     int							):
		__A :			str		    =			tempfile.mkdtemp()

		__A :			List[str]		    =			BlipImageProcessor()
		__A :			Union[str, Any]		    =			GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""							)
		__A :			str		    =			BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""							)

		__A :			Union[str, Any]		    =			InstructBlipProcessor(__A					,      __A					,      __A							)

		processor.save_pretrained(self.tmpdirname							)




	def        lowerCAmelCase_       (     self						:     Union[str, Any]					,      **__A						:     Any							):
		return AutoProcessor.from_pretrained(self.tmpdirname					,      **__A							).tokenizer




	def        lowerCAmelCase_       (     self						:     Union[str, Any]					,      **__A						:     List[Any]							):
		return AutoProcessor.from_pretrained(self.tmpdirname					,      **__A							).image_processor




	def        lowerCAmelCase_       (     self						:     str					,      **__A						:     Dict							):
		return AutoProcessor.from_pretrained(self.tmpdirname					,      **__A							).qformer_tokenizer




	def        lowerCAmelCase_       (     self						:     Tuple							):
		shutil.rmtree(self.tmpdirname							)




	def        lowerCAmelCase_       (     self						:     str							):
		__A :			List[Any]		    =			[np.random.randint(255					,      size=(3, 30, 400)					,      dtype=np.uinta							)]

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

		return image_inputs




	def        lowerCAmelCase_       (     self						:     str							):
		__A :			Any		    =			InstructBlipProcessor(
		    tokenizer=self.get_tokenizer()					,      image_processor=self.get_image_processor()					,      qformer_tokenizer=self.get_qformer_tokenizer()					,      )
		processor.save_pretrained(self.tmpdirname							)

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

		__A :			Dict		    =			InstructBlipProcessor.from_pretrained(
		    self.tmpdirname					,      bos_token="""(BOS)"""					,      eos_token="""(EOS)"""					,      do_normalize=__A					,      padding_value=1.0							)

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

		self.assertEqual(processor.image_processor.to_json_string()					,      image_processor_add_kwargs.to_json_string()							)
		self.assertIsInstance(processor.image_processor					,      __A							)
		self.assertIsInstance(processor.qformer_tokenizer					,      __A							)




	def        lowerCAmelCase_       (     self						:     Any							):
		__A :			Optional[Any]		    =			self.get_image_processor()
		__A :			List[str]		    =			self.get_tokenizer()
		__A :			int		    =			self.get_qformer_tokenizer()

		__A :			Optional[Any]		    =			InstructBlipProcessor(
		    tokenizer=__A					,      image_processor=__A					,      qformer_tokenizer=__A							)

		__A :			List[str]		    =			self.prepare_image_inputs()

		__A :			Dict		    =			image_processor(__A					,      return_tensors="""np"""							)
		__A :			Dict		    =			processor(images=__A					,      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						:     int							):
		__A :			Optional[Any]		    =			self.get_image_processor()
		__A :			Any		    =			self.get_tokenizer()
		__A :			Any		    =			self.get_qformer_tokenizer()

		__A :			str		    =			InstructBlipProcessor(
		    tokenizer=__A					,      image_processor=__A					,      qformer_tokenizer=__A							)

		__A :			Dict		    =			"""lower newer"""

		__A :			Dict		    =			processor(text=__A							)

		__A :			int		    =			tokenizer(__A					,      return_token_type_ids=__A							)
		__A :			Any		    =			qformer_tokenizer(__A					,      return_token_type_ids=__A							)

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

		for key in encoded_tokens_qformer.keys():
			self.assertListEqual(encoded_tokens_qformer[key]					,      encoded_processor["""qformer_""" + key]							)




	def        lowerCAmelCase_       (     self						:     Any							):
		__A :			Union[str, Any]		    =			self.get_image_processor()
		__A :			Any		    =			self.get_tokenizer()
		__A :			Optional[Any]		    =			self.get_qformer_tokenizer()

		__A :			str		    =			InstructBlipProcessor(
		    tokenizer=__A					,      image_processor=__A					,      qformer_tokenizer=__A							)

		__A :			List[Any]		    =			"""lower newer"""
		__A :			int		    =			self.prepare_image_inputs()

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

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

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




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		__A :			Dict		    =			self.get_image_processor()
		__A :			Optional[Any]		    =			self.get_tokenizer()
		__A :			List[Any]		    =			self.get_qformer_tokenizer()

		__A :			Union[str, Any]		    =			InstructBlipProcessor(
		    tokenizer=__A					,      image_processor=__A					,      qformer_tokenizer=__A							)

		__A :			Optional[int]		    =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

		__A :			Dict		    =			processor.batch_decode(__A							)
		__A :			Union[str, Any]		    =			tokenizer.batch_decode(__A							)

		self.assertListEqual(__A					,      __A							)




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		__A :			Optional[int]		    =			self.get_image_processor()
		__A :			Union[str, Any]		    =			self.get_tokenizer()
		__A :			Tuple		    =			self.get_qformer_tokenizer()

		__A :			Union[str, Any]		    =			InstructBlipProcessor(
		    tokenizer=__A					,      image_processor=__A					,      qformer_tokenizer=__A							)

		__A :			int		    =			"""lower newer"""
		__A :			Optional[int]		    =			self.prepare_image_inputs()

		__A :			List[str]		    =			processor(text=__A					,      images=__A							)

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








import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


UpperCAmelCase_							:					Optional[Any]		 =   [
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.de'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.en'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.fr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.frr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.it'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.simple'''},
    {'''dataset''': '''snli''', '''config_name''': '''plain_text'''},
    {'''dataset''': '''eli5''', '''config_name''': '''LFQA_reddit'''},
    {'''dataset''': '''wiki40b''', '''config_name''': '''en'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.compressed'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.no_index'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.multiset.no_index'''},
    {'''dataset''': '''natural_questions''', '''config_name''': '''default'''},
]
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str=True       )					->				List[Any]:
	if with_config:
		return [
		    {
		        "testcase_name": d["dataset"] + "/" + d["config_name"],
		        "dataset": d["dataset"],
		        "config_name": d["config_name"],
		    }
		    for d in DATASETS_ON_HF_GCP
		]
	else:
		return [
		    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
		]






@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_lowercase	)	)
class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Optional[int]     					=				None
	_lowercase		:   str     					=				None




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     Optional[int]					,      __A						:     Optional[Any]							):
		with TemporaryDirectory() as tmp_dir:
			__A :			List[Any]		    =			dataset_module_factory(__A					,      cache_dir=__A							)

			__A :			Tuple		    =			import_main_class(dataset_module.module_path					,      dataset=__A							)

			__A :			DatasetBuilder		    =			builder_cls(
			    cache_dir=__A					,      config_name=__A					,      hash=dataset_module.hash					,      )

			__A :			List[Any]		    =			"""/""".join(
			    [
			        HF_GCP_BASE_URL,
			        builder_instance._relative_data_dir(with_hash=__A							).replace(os.sep					,      """/"""							),
			        config.DATASET_INFO_FILENAME,
			    ]							)
			__A :			Union[str, Any]		    =			cached_path(__A					,      cache_dir=__A							)
			self.assertTrue(os.path.exists(__A							)							)






@pytest.mark.integration
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Dict       )					->				Optional[Any]:
	__A :			Optional[Any]		    =			tmp_path_factory.mktemp("""test_hf_gcp"""       ) / """test_wikipedia_simple"""
	__A :			Union[str, Any]		    =			dataset_module_factory("""wikipedia"""			,cache_dir=a__       )
	__A :			List[Any]		    =			import_main_class(dataset_module.module_path       )
	__A :			DatasetBuilder		    =			builder_cls(
	    cache_dir=a__			,config_name="""20220301.frr"""			,hash=dataset_module.hash			,)
	# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
	__A :			Any		    =			None
	builder_instance.download_and_prepare()
	__A :			Union[str, Any]		    =			builder_instance.as_dataset()
	assert ds







@pytest.mark.integration
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]       )					->				List[str]:
	__A :			Tuple		    =			dataset_module_factory("""wikipedia"""			,cache_dir=a__       )
	__A :			str		    =			import_main_class(dataset_module.module_path			,dataset=a__       )
	__A :			DatasetBuilder		    =			builder_cls(
	    cache_dir=a__			,config_name="""20220301.frr"""			,hash=dataset_module.hash			,)
	__A :			Optional[int]		    =			builder_instance.as_streaming_dataset()
	assert ds
	assert isinstance(a__			,a__       )
	assert "train" in ds
	assert isinstance(ds["""train"""]			,a__       )
	assert next(iter(ds["""train"""]       )       )





