



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     __a			(  unittest.TestCase					):



	def 		UpperCamelCase				(    self				:     Tuple)->				Optional[int]:
						__lowerCAmelCase						 =tempfile.mkdtemp()

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

						__lowerCAmelCase						 =InstructBlipProcessor(snake_case_					,						snake_case_					,						snake_case_)

						processor.save_pretrained(self.tmpdirname)



	def 		UpperCamelCase				(    self				:     List[Any]					,						**snake_case_				:     Tuple)->				Optional[int]:
						return AutoProcessor.from_pretrained(self.tmpdirname					,						**snake_case_).tokenizer



	def 		UpperCamelCase				(    self				:     Dict					,						**snake_case_				:     str)->				int:
						return AutoProcessor.from_pretrained(self.tmpdirname					,						**snake_case_).image_processor



	def 		UpperCamelCase				(    self				:     Dict					,						**snake_case_				:     Dict)->				Optional[int]:
						return AutoProcessor.from_pretrained(self.tmpdirname					,						**snake_case_).qformer_tokenizer



	def 		UpperCamelCase				(    self				:     Dict)->				Any:
						shutil.rmtree(self.tmpdirname)



	def 		UpperCamelCase				(    self				:     Union[str, Any])->				str:
						__lowerCAmelCase						 =[np.random.randint(2_55					,						size=(3, 30, 4_00)					,						dtype=np.uinta)]

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

						return image_inputs



	def 		UpperCamelCase				(    self				:     Optional[int])->				Dict:
						__lowerCAmelCase						 =InstructBlipProcessor(
						    tokenizer=self.get_tokenizer()					,						image_processor=self.get_image_processor()					,						qformer_tokenizer=self.get_qformer_tokenizer()					,						)
						processor.save_pretrained(self.tmpdirname)

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

						__lowerCAmelCase						 =InstructBlipProcessor.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					,						snake_case_)

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



	def 		UpperCamelCase				(    self				:     Union[str, Any])->				str:
						__lowerCAmelCase						 =self.get_image_processor()
						__lowerCAmelCase						 =self.get_tokenizer()
						__lowerCAmelCase						 =self.get_qformer_tokenizer()

						__lowerCAmelCase						 =InstructBlipProcessor(
						    tokenizer=snake_case_					,						image_processor=snake_case_					,						qformer_tokenizer=snake_case_)

						__lowerCAmelCase						 =self.prepare_image_inputs()

						__lowerCAmelCase						 =image_processor(snake_case_					,						return_tensors="""np""")
						__lowerCAmelCase						 =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 		UpperCamelCase				(    self				:     int)->				Optional[Any]:
						__lowerCAmelCase						 =self.get_image_processor()
						__lowerCAmelCase						 =self.get_tokenizer()
						__lowerCAmelCase						 =self.get_qformer_tokenizer()

						__lowerCAmelCase						 =InstructBlipProcessor(
						    tokenizer=snake_case_					,						image_processor=snake_case_					,						qformer_tokenizer=snake_case_)

						__lowerCAmelCase						 ="""lower newer"""

						__lowerCAmelCase						 =processor(text=snake_case_)

						__lowerCAmelCase						 =tokenizer(snake_case_					,						return_token_type_ids=snake_case_)
						__lowerCAmelCase						 =qformer_tokenizer(snake_case_					,						return_token_type_ids=snake_case_)

						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 		UpperCamelCase				(    self				:     Optional[Any])->				Optional[Any]:
						__lowerCAmelCase						 =self.get_image_processor()
						__lowerCAmelCase						 =self.get_tokenizer()
						__lowerCAmelCase						 =self.get_qformer_tokenizer()

						__lowerCAmelCase						 =InstructBlipProcessor(
						    tokenizer=snake_case_					,						image_processor=snake_case_					,						qformer_tokenizer=snake_case_)

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

						__lowerCAmelCase						 =processor(text=snake_case_					,						images=snake_case_)

						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(snake_case_):
											processor()



	def 		UpperCamelCase				(    self				:     List[Any])->				Dict:
						__lowerCAmelCase						 =self.get_image_processor()
						__lowerCAmelCase						 =self.get_tokenizer()
						__lowerCAmelCase						 =self.get_qformer_tokenizer()

						__lowerCAmelCase						 =InstructBlipProcessor(
						    tokenizer=snake_case_					,						image_processor=snake_case_					,						qformer_tokenizer=snake_case_)

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

						__lowerCAmelCase						 =processor.batch_decode(snake_case_)
						__lowerCAmelCase						 =tokenizer.batch_decode(snake_case_)

						self.assertListEqual(snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     str)->				Optional[int]:
						__lowerCAmelCase						 =self.get_image_processor()
						__lowerCAmelCase						 =self.get_tokenizer()
						__lowerCAmelCase						 =self.get_qformer_tokenizer()

						__lowerCAmelCase						 =InstructBlipProcessor(
						    tokenizer=snake_case_					,						image_processor=snake_case_					,						qformer_tokenizer=snake_case_)

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

						__lowerCAmelCase						 =processor(text=snake_case_					,						images=snake_case_)

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









import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def        __lowerCAmelCase		(	__lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     Any						)	->    Dict:
					assert isinstance(__lowerCamelCase					, __lowerCamelCase						)
					assert dataset.num_rows == 4
					assert dataset.num_columns == 1
					assert dataset.column_names == ["text"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("""keep_in_memory"""					, [False, True]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     str					, __lowerCamelCase						:     Optional[Any]						)	->    List[Any]:
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, cache_dir=__lowerCamelCase					, keep_in_memory=__lowerCamelCase						).read()
					_check_text_dataset(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize(
    """features"""					, [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]					, )
def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     Any					, __lowerCamelCase						:     Tuple						)	->    List[Any]:
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =features.copy() if features else default_expected_features
					__lowerCAmelCase						 =(
					    Features({feature: Value(__lowerCamelCase						) for feature, dtype in features.items()}						) if features is not None else None
					)
					__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, features=__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_dataset(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize("""split"""					, [None, NamedSplit("""train"""						), """train""", """test"""]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     Union[str, Any]					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     Union[str, Any]						)	->    Any:
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, cache_dir=__lowerCamelCase					, split=__lowerCamelCase						).read()
					_check_text_dataset(__lowerCamelCase					, __lowerCamelCase						)
					assert dataset.split == split if split else "train"






@pytest.mark.parametrize("""path_type"""					, [str, list]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     int					, __lowerCamelCase						:     List[str]						)	->    Any:
					if issubclass(__lowerCamelCase					, __lowerCamelCase						):
										__lowerCAmelCase						 =text_path
					elif issubclass(__lowerCamelCase					, __lowerCamelCase						):
										__lowerCAmelCase						 =[text_path]
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_dataset(__lowerCamelCase					, __lowerCamelCase						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     str=("train",)						)	->    Dict:
					assert isinstance(__lowerCamelCase					, __lowerCamelCase						)
					for split in splits:
										__lowerCAmelCase						 =dataset_dict[split]
										assert dataset.num_rows == 4
										assert dataset.num_columns == 1
										assert dataset.column_names == ["text"]
										for feature, expected_dtype in expected_features.items():
															assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("""keep_in_memory"""					, [False, True]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     List[Any]						)	->    Dict:
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__lowerCAmelCase						 =TextDatasetReader({"""train""": text_path}					, cache_dir=__lowerCamelCase					, keep_in_memory=__lowerCamelCase						).read()
					_check_text_datasetdict(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize(
    """features"""					, [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]					, )
def        __lowerCAmelCase		(	__lowerCamelCase						:     Any					, __lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     Union[str, Any]						)	->    Tuple:
					__lowerCAmelCase						 =tmp_path / """cache"""
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =features.copy() if features else default_expected_features
					__lowerCAmelCase						 =(
					    Features({feature: Value(__lowerCamelCase						) for feature, dtype in features.items()}						) if features is not None else None
					)
					__lowerCAmelCase						 =TextDatasetReader({"""train""": text_path}					, features=__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_datasetdict(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize("""split"""					, [None, NamedSplit("""train"""						), """train""", """test"""]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     int					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     Any						)	->    int:
					if split:
										__lowerCAmelCase						 ={split: text_path}
					else:
										__lowerCAmelCase						 ="""train"""
										__lowerCAmelCase						 ={"""train""": text_path, """test""": text_path}
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_datasetdict(__lowerCamelCase					, __lowerCamelCase					, splits=list(path.keys()						)						)
					assert all(dataset[split].split == split for split in path.keys()						)




