




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor





class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    def __init__(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					int=13       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=3       ,     SCREAMING_SNAKE_CASE__					:					int=224       ,     SCREAMING_SNAKE_CASE__					:					str=30       ,     SCREAMING_SNAKE_CASE__					:					Dict=400       ,     SCREAMING_SNAKE_CASE__					:					Tuple=True       ,     SCREAMING_SNAKE_CASE__					:					str=None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=True       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=[0.5, 0.5, 0.5]       ,     SCREAMING_SNAKE_CASE__					:					str=[0.5, 0.5, 0.5]       ,     )				->	Dict:
         lowerCAmelCase__          =			size if size is not None else {"height": 18, "width": 18}
         lowerCAmelCase__          =			parent
         lowerCAmelCase__          =			batch_size
         lowerCAmelCase__          =			num_channels
         lowerCAmelCase__          =			image_size
         lowerCAmelCase__          =			min_resolution
         lowerCAmelCase__          =			max_resolution
         lowerCAmelCase__          =			do_resize
         lowerCAmelCase__          =			size
         lowerCAmelCase__          =			do_normalize
         lowerCAmelCase__          =			image_mean
         lowerCAmelCase__          =			image_std


    def        a				(	self					:					Dict       )				->	Tuple:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
         }






@require_torch
@require_vision
class 			__lowerCamelCase	(   UpperCamelCase__     ,    unittest.TestCase   ):




    """simple docstring"""

    snake_case__ =				ViTImageProcessor if is_vision_available() else None


    def        a				(	self					:					Tuple       )				->	List[Any]:
         lowerCAmelCase__          =			EfficientFormerImageProcessorTester(self       )


    @property
    def        a				(	self					:					Any       )				->	List[str]:
         return self.image_proc_tester.prepare_image_processor_dict()


    def        a				(	self					:					Tuple       )				->	int:
         lowerCAmelCase__          =			self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "image_mean"       )       )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "image_std"       )       )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "do_normalize"       )       )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "do_resize"       )       )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "size"       )       )


    def        a				(	self					:					Tuple       )				->	List[str]:
         pass


    def        a				(	self					:					Union[str, Any]       )				->	Optional[Any]:
         # Initialize image_processor
         lowerCAmelCase__          =			self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         lowerCAmelCase__          =			prepare_image_inputs(self.image_proc_tester       ,     equal_resolution=SCREAMING_SNAKE_CASE__       )
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,     Image.Image       )

         # Test not batched input
         lowerCAmelCase__          =			image_processor(image_inputs[0]       ,     return_tensors="pt"       ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,     (
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )       ,     )

         # Test batched
         lowerCAmelCase__          =			image_processor(SCREAMING_SNAKE_CASE__       ,     return_tensors="pt"       ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,     (
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )       ,     )


    def        a				(	self					:					List[str]       )				->	str:
         # Initialize image_processor
         lowerCAmelCase__          =			self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         lowerCAmelCase__          =			prepare_image_inputs(self.image_proc_tester       ,     equal_resolution=SCREAMING_SNAKE_CASE__       ,     numpify=SCREAMING_SNAKE_CASE__       )
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,     np.ndarray       )

         # Test not batched input
         lowerCAmelCase__          =			image_processor(image_inputs[0]       ,     return_tensors="pt"       ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,     (
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )       ,     )

         # Test batched
         lowerCAmelCase__          =			image_processor(SCREAMING_SNAKE_CASE__       ,     return_tensors="pt"       ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,     (
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )       ,     )


    def        a				(	self					:					Any       )				->	List[str]:
         # Initialize image_processor
         lowerCAmelCase__          =			self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         lowerCAmelCase__          =			prepare_image_inputs(self.image_proc_tester       ,     equal_resolution=SCREAMING_SNAKE_CASE__       ,     torchify=SCREAMING_SNAKE_CASE__       )
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,     torch.Tensor       )

         # Test not batched input
         lowerCAmelCase__          =			image_processor(image_inputs[0]       ,     return_tensors="pt"       ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,     (
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )       ,     )

         # Test batched
         lowerCAmelCase__          =			image_processor(SCREAMING_SNAKE_CASE__       ,     return_tensors="pt"       ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,     (
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )       ,     )






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 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    def        a				(	self					:					Dict       )				->	Optional[int]:
         lowerCAmelCase__          =			tempfile.mkdtemp()

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

         lowerCAmelCase__          =			BlipaProcessor(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

         processor.save_pretrained(self.tmpdirname       )


    def        a				(	self					:					int       ,     **SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	Optional[Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,     **SCREAMING_SNAKE_CASE__       ).tokenizer


    def        a				(	self					:					Tuple       ,     **SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Optional[Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,     **SCREAMING_SNAKE_CASE__       ).image_processor


    def        a				(	self					:					str       )				->	int:
         shutil.rmtree(self.tmpdirname       )


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

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

         return image_inputs


    def        a				(	self					:					str       )				->	Dict:
         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=SCREAMING_SNAKE_CASE__       ,     padding_value=1.0       )

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

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

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


    def        a				(	self					:					int       )				->	str:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			self.prepare_image_inputs()

         lowerCAmelCase__          =			image_processor(SCREAMING_SNAKE_CASE__       ,     return_tensors="np"       )
         lowerCAmelCase__          =			processor(images=SCREAMING_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        a				(	self					:					Tuple       )				->	int:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			"lower newer"

         lowerCAmelCase__          =			processor(text=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			tokenizer(SCREAMING_SNAKE_CASE__       ,     return_token_type_ids=SCREAMING_SNAKE_CASE__       )

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


    def        a				(	self					:					Dict       )				->	str:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

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

         lowerCAmelCase__          =			processor(text=SCREAMING_SNAKE_CASE__       ,     images=SCREAMING_SNAKE_CASE__       )

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

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


    def        a				(	self					:					str       )				->	List[str]:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

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

         lowerCAmelCase__          =			processor.batch_decode(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			tokenizer.batch_decode(SCREAMING_SNAKE_CASE__       )

         self.assertListEqual(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[str]       )				->	Any:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

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

         lowerCAmelCase__          =			processor(text=SCREAMING_SNAKE_CASE__       ,     images=SCREAMING_SNAKE_CASE__       )

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