



"""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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast


@require_vision
class   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__    :	Union[str, Any]     					=			tempfile.mkdtemp()

             lowercase__    :	Tuple     					=			BlipImageProcessor()
             lowercase__    :	Optional[int]     					=			GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'							)

             lowercase__    :	str     					=			BlipaProcessor(__lowerCAmelCase	,    __lowerCAmelCase							)

             processor.save_pretrained(self.tmpdirname							)


      def 	_UpperCAmelCase (     self	,    **a							) ->      List[Any]:
             return AutoProcessor.from_pretrained(self.tmpdirname	,    **__lowerCAmelCase							).tokenizer


      def 	_UpperCAmelCase (     self	,    **a							) ->      Union[str, Any]:
             return AutoProcessor.from_pretrained(self.tmpdirname	,    **__lowerCAmelCase							).image_processor


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


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Optional[int]     					=			[np.random.randint(2_5_5	,    size=(3, 3_0, 4_0_0)	,    dtype=np.uinta							)]

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

             return image_inputs


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	str     					=			BlipaProcessor(tokenizer=self.get_tokenizer()	,    image_processor=self.get_image_processor()							)
             processor.save_pretrained(self.tmpdirname							)

             lowercase__    :	int     					=			self.get_tokenizer(bos_token='(BOS)'	,    eos_token='(EOS)'							)
             lowercase__    :	Any     					=			self.get_image_processor(do_normalize=__lowerCAmelCase	,    padding_value=1.0							)

             lowercase__    :	Optional[Any]     					=			BlipaProcessor.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 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__    :	Optional[int]     					=			self.get_image_processor()
             lowercase__    :	Any     					=			self.get_tokenizer()

             lowercase__    :	Dict     					=			BlipaProcessor(tokenizer=__lowerCAmelCase	,    image_processor=__lowerCAmelCase							)

             lowercase__    :	Optional[Any]     					=			self.prepare_image_inputs()

             lowercase__    :	Tuple     					=			image_processor(__lowerCAmelCase	,    return_tensors='np'							)
             lowercase__    :	List[str]     					=			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							)


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	Optional[Any]     					=			self.get_image_processor()
             lowercase__    :	Dict     					=			self.get_tokenizer()

             lowercase__    :	Any     					=			BlipaProcessor(tokenizer=__lowerCAmelCase	,    image_processor=__lowerCAmelCase							)

             lowercase__    :	Optional[Any]     					=			'lower newer'

             lowercase__    :	Optional[int]     					=			processor(text=__lowerCAmelCase							)

             lowercase__    :	Union[str, Any]     					=			tokenizer(__lowerCAmelCase	,    return_token_type_ids=__lowerCAmelCase							)

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


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Optional[int]     					=			self.get_image_processor()
             lowercase__    :	Optional[int]     					=			self.get_tokenizer()

             lowercase__    :	List[str]     					=			BlipaProcessor(tokenizer=__lowerCAmelCase	,    image_processor=__lowerCAmelCase							)

             lowercase__    :	Any     					=			'lower newer'
             lowercase__    :	Optional[Any]     					=			self.prepare_image_inputs()

             lowercase__    :	Tuple     					=			processor(text=__lowerCAmelCase	,    images=__lowerCAmelCase							)

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

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


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__    :	int     					=			self.get_image_processor()
             lowercase__    :	Tuple     					=			self.get_tokenizer()

             lowercase__    :	str     					=			BlipaProcessor(tokenizer=__lowerCAmelCase	,    image_processor=__lowerCAmelCase							)

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

             lowercase__    :	int     					=			processor.batch_decode(__lowerCAmelCase							)
             lowercase__    :	Optional[Any]     					=			tokenizer.batch_decode(__lowerCAmelCase							)

             self.assertListEqual(__lowerCAmelCase	,    __lowerCAmelCase							)


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__    :	int     					=			self.get_image_processor()
             lowercase__    :	Union[str, Any]     					=			self.get_tokenizer()

             lowercase__    :	Tuple     					=			BlipaProcessor(tokenizer=__lowerCAmelCase	,    image_processor=__lowerCAmelCase							)

             lowercase__    :	Dict     					=			'lower newer'
             lowercase__    :	Optional[int]     					=			self.prepare_image_inputs()

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

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


"""simple docstring"""





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
       import torch

       from transformers import AutoModelForImageClassification

if is_vision_available():
       from transformers import AutoImageProcessor


@require_torch
@require_vision
class   UpperCAmelCase_			(					unittest.TestCase):


      @slow
      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Optional[Any]     					=			AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'							)
             lowercase__    :	Union[str, Any]     					=			AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'							)
             model.to(a							)

             from datasets import load_dataset

             lowercase__    :	str     					=			load_dataset('nielsr/rvlcdip-demo'							)

             lowercase__    :	Tuple     					=			dataset['train'][0]['image'].convert('RGB'							)

             lowercase__    :	int     					=			image_processor(a	,    return_tensors='pt'							).to(a							)

             # forward pass
             with torch.no_grad():
                    lowercase__    :	List[str]     					=			model(**a							)
                    lowercase__    :	List[Any]     					=			outputs.logits

             lowercase__    :	Union[str, Any]     					=			torch.Size((1, 1_6)							)
             self.assertEqual(logits.shape	,    a							)

             lowercase__    :	Tuple     					=			torch.tensor(
                 [-0.4_158, -0.4_092, -0.4_347]	,    device=a	,    dtype=torch.float	,    )
             self.assertTrue(torch.allclose(logits[0, :3]	,    a	,    atol=1e-4							)							)

