






"""simple docstring"""







import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate


@dataclass(frozen=SCREAMING_SNAKE_CASE__							)
class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase    	= field(default="image-classification"  ,  metadata={"include_in_asdict_even_if_is_default": True}							)
						__lowerCamelCase    	= Features({"image": Image()}							)
						__lowerCamelCase    	= Features({"labels": ClassLabel}							)
						__lowerCamelCase    	= "image"
						__lowerCamelCase    	= "labels"




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												if self.label_column not in features:
																		raise ValueError(F'''Column {self.label_column} is not present in features.'''      )
												if not isinstance(features[self.label_column]	,  snake_case__      ):
																		raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''      )
												lowercase__    :				Optional[int]=	copy.deepcopy(self      )
												lowercase__    :				int=	self.label_schema.copy()
												lowercase__    :				List[str]=	features[self.label_column]
												lowercase__    :				str=	label_schema
												return task_template




						@property
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return {
												    self.image_column: "image",
												    self.label_column: "labels",
												}














"""simple docstring"""







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    __UpperCAmelCase(       unittest.TestCase							):




						"""simple docstring"""




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				List[str]=	tempfile.mkdtemp()

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

												lowercase__    :				str=	InstructBlipProcessor(snake_case__	,  snake_case__	,  snake_case__      )

												processor.save_pretrained(self.tmpdirname      )




						def 		UpperCAmelCase_	(       self	,  **snake_case__      ):
												'''simple docstring'''
												return AutoProcessor.from_pretrained(self.tmpdirname	,  **snake_case__      ).tokenizer




						def 		UpperCAmelCase_	(       self	,  **snake_case__      ):
												'''simple docstring'''
												return AutoProcessor.from_pretrained(self.tmpdirname	,  **snake_case__      ).image_processor




						def 		UpperCAmelCase_	(       self	,  **snake_case__      ):
												'''simple docstring'''
												return AutoProcessor.from_pretrained(self.tmpdirname	,  **snake_case__      ).qformer_tokenizer




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												shutil.rmtree(self.tmpdirname      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Union[str, Any]=	[np.random.randint(255	,  size=(3, 30, 400)	,  dtype=np.uinta      )]

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

												return image_inputs




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				str=	InstructBlipProcessor(
												    tokenizer=self.get_tokenizer()	,  image_processor=self.get_image_processor()	,  qformer_tokenizer=self.get_qformer_tokenizer()	,  )
												processor.save_pretrained(self.tmpdirname      )

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

												lowercase__    :				Optional[Any]=	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      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	self.get_image_processor()
												lowercase__    :				Optional[int]=	self.get_tokenizer()
												lowercase__    :				Any=	self.get_qformer_tokenizer()

												lowercase__    :				Any=	InstructBlipProcessor(
												    tokenizer=snake_case__	,  image_processor=snake_case__	,  qformer_tokenizer=snake_case__      )

												lowercase__    :				Tuple=	self.prepare_image_inputs()

												lowercase__    :				int=	image_processor(snake_case__	,  return_tensors="np"      )
												lowercase__    :				List[str]=	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      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	self.get_image_processor()
												lowercase__    :				List[str]=	self.get_tokenizer()
												lowercase__    :				Optional[Any]=	self.get_qformer_tokenizer()

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

												lowercase__    :				Optional[Any]=	"lower newer"

												lowercase__    :				Any=	processor(text=snake_case__      )

												lowercase__    :				Union[str, Any]=	tokenizer(snake_case__	,  return_token_type_ids=snake_case__      )
												lowercase__    :				List[Any]=	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      ):
												'''simple docstring'''
												lowercase__    :				int=	self.get_image_processor()
												lowercase__    :				Optional[int]=	self.get_tokenizer()
												lowercase__    :				Any=	self.get_qformer_tokenizer()

												lowercase__    :				Optional[int]=	InstructBlipProcessor(
												    tokenizer=snake_case__	,  image_processor=snake_case__	,  qformer_tokenizer=snake_case__      )

												lowercase__    :				List[str]=	"lower newer"
												lowercase__    :				Optional[int]=	self.prepare_image_inputs()

												lowercase__    :				Dict=	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      ):
												'''simple docstring'''
												lowercase__    :				Optional[Any]=	self.get_image_processor()
												lowercase__    :				int=	self.get_tokenizer()
												lowercase__    :				Optional[int]=	self.get_qformer_tokenizer()

												lowercase__    :				Any=	InstructBlipProcessor(
												    tokenizer=snake_case__	,  image_processor=snake_case__	,  qformer_tokenizer=snake_case__      )

												lowercase__    :				Dict=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

												lowercase__    :				Optional[Any]=	processor.batch_decode(snake_case__      )
												lowercase__    :				List[Any]=	tokenizer.batch_decode(snake_case__      )

												self.assertListEqual(snake_case__	,  snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				str=	self.get_image_processor()
												lowercase__    :				Optional[int]=	self.get_tokenizer()
												lowercase__    :				Optional[Any]=	self.get_qformer_tokenizer()

												lowercase__    :				int=	InstructBlipProcessor(
												    tokenizer=snake_case__	,  image_processor=snake_case__	,  qformer_tokenizer=snake_case__      )

												lowercase__    :				Union[str, Any]=	"lower newer"
												lowercase__    :				int=	self.prepare_image_inputs()

												lowercase__    :				Dict=	processor(text=snake_case__	,  images=snake_case__      )

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






