





'''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       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def 	UpperCamelCase(					self	):
       _snake_case     		=				tempfile.mkdtemp()

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

       _snake_case     		=				InstructBlipProcessor(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	)

       processor.save_pretrained(self.tmpdirname	)

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       return AutoProcessor.from_pretrained(self.tmpdirname						,   **lowerCamelCase	).tokenizer

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       return AutoProcessor.from_pretrained(self.tmpdirname						,   **lowerCamelCase	).image_processor

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       return AutoProcessor.from_pretrained(self.tmpdirname						,   **lowerCamelCase	).qformer_tokenizer

     def 	UpperCamelCase(					self	):
       shutil.rmtree(self.tmpdirname	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				[np.random.randint(255						,   size=(3, 30, 400)						,   dtype=np.uinta	)]

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

       return image_inputs

     def 	UpperCamelCase(					self	):
       _snake_case     		=				InstructBlipProcessor(
           tokenizer=self.get_tokenizer()						,   image_processor=self.get_image_processor()						,   qformer_tokenizer=self.get_qformer_tokenizer()						,   )
       processor.save_pretrained(self.tmpdirname	)

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

       _snake_case     		=				InstructBlipProcessor.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	)
       self.assertIsInstance(processor.qformer_tokenizer						,   lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_image_processor()
       _snake_case     		=				self.get_tokenizer()
       _snake_case     		=				self.get_qformer_tokenizer()

       _snake_case     		=				InstructBlipProcessor(
           tokenizer=lowerCamelCase						,   image_processor=lowerCamelCase						,   qformer_tokenizer=lowerCamelCase	)

       _snake_case     		=				self.prepare_image_inputs()

       _snake_case     		=				image_processor(lowerCamelCase						,   return_tensors="np"	)
       _snake_case     		=				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	):
       _snake_case     		=				self.get_image_processor()
       _snake_case     		=				self.get_tokenizer()
       _snake_case     		=				self.get_qformer_tokenizer()

       _snake_case     		=				InstructBlipProcessor(
           tokenizer=lowerCamelCase						,   image_processor=lowerCamelCase						,   qformer_tokenizer=lowerCamelCase	)

       _snake_case     		=				"lower newer"

       _snake_case     		=				processor(text=lowerCamelCase	)

       _snake_case     		=				tokenizer(lowerCamelCase						,   return_token_type_ids=lowerCamelCase	)
       _snake_case     		=				qformer_tokenizer(lowerCamelCase						,   return_token_type_ids=lowerCamelCase	)

       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	):
       _snake_case     		=				self.get_image_processor()
       _snake_case     		=				self.get_tokenizer()
       _snake_case     		=				self.get_qformer_tokenizer()

       _snake_case     		=				InstructBlipProcessor(
           tokenizer=lowerCamelCase						,   image_processor=lowerCamelCase						,   qformer_tokenizer=lowerCamelCase	)

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

       _snake_case     		=				processor(text=lowerCamelCase						,   images=lowerCamelCase	)

       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(lowerCamelCase	):
         processor()

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_image_processor()
       _snake_case     		=				self.get_tokenizer()
       _snake_case     		=				self.get_qformer_tokenizer()

       _snake_case     		=				InstructBlipProcessor(
           tokenizer=lowerCamelCase						,   image_processor=lowerCamelCase						,   qformer_tokenizer=lowerCamelCase	)

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

       _snake_case     		=				processor.batch_decode(lowerCamelCase	)
       _snake_case     		=				tokenizer.batch_decode(lowerCamelCase	)

       self.assertListEqual(lowerCamelCase						,   lowerCamelCase	)






     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_image_processor()
       _snake_case     		=				self.get_tokenizer()
       _snake_case     		=				self.get_qformer_tokenizer()

       _snake_case     		=				InstructBlipProcessor(
           tokenizer=lowerCamelCase						,   image_processor=lowerCamelCase						,   qformer_tokenizer=lowerCamelCase	)

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

       _snake_case     		=				processor(text=lowerCamelCase						,   images=lowerCamelCase	)

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









'''simple docstring'''



import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor


class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase=7						,   lowerCamelCase=3						,   lowerCamelCase=30						,   lowerCamelCase=400						,   lowerCamelCase=True						,   lowerCamelCase=None						,   lowerCamelCase=True						,   lowerCamelCase=[0.5, 0.5, 0.5]						,   lowerCamelCase=[0.5, 0.5, 0.5]						,   lowerCamelCase=True						,   lowerCamelCase=1 / 255						,   lowerCamelCase=True						,   ):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       _snake_case     		=				size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
       _snake_case     		=				parent
       _snake_case     		=				batch_size
       _snake_case     		=				num_channels
       _snake_case     		=				min_resolution
       _snake_case     		=				max_resolution
       _snake_case     		=				do_resize
       _snake_case     		=				size
       _snake_case     		=				do_normalize
       _snake_case     		=				image_mean
       _snake_case     		=				image_std
       _snake_case     		=				do_rescale
       _snake_case     		=				rescale_factor
       _snake_case     		=				do_pad

     def 	UpperCamelCase(					self	):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_pad": self.do_pad,
       }






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=False	):
       if not batched:
         _snake_case     		=				image_inputs[0]
         if isinstance(lowerCamelCase						,   Image.Image	):
           _snake_case		,		_snake_case     		=				image.size
         else:
           _snake_case		,		_snake_case     		=				image.shape[1], image.shape[2]
         if w < h:
           _snake_case     		=				int(self.size["shortest_edge"] * h / w	)
           _snake_case     		=				self.size["shortest_edge"]
         elif w > h:
           _snake_case     		=				self.size["shortest_edge"]
           _snake_case     		=				int(self.size["shortest_edge"] * w / h	)
         else:
           _snake_case     		=				self.size["shortest_edge"]
           _snake_case     		=				self.size["shortest_edge"]

       else:
         _snake_case     		=				[]
         for image in image_inputs:
           _snake_case		,		_snake_case     		=				self.get_expected_values([image]	)
           expected_values.append((expected_height, expected_width)	)
         _snake_case     		=				max(lowerCamelCase						,   key=lambda lowerCamelCase	: item[0]	)[0]
         _snake_case     		=				max(lowerCamelCase						,   key=lambda lowerCamelCase	: item[1]	)[1]

       return expected_height, expected_width







@require_torch
@require_vision
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Optional[Any]      =   DetaImageProcessor if is_vision_available() else None

     def 	UpperCamelCase(					self	):
       _snake_case     		=				DetaImageProcessingTester(self	)

     @property
     def 	UpperCamelCase(					self	):
       return self.image_processor_tester.prepare_image_processor_dict()

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       self.assertTrue(hasattr(lowerCamelCase						,   "image_mean"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "image_std"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "do_normalize"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "do_resize"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "do_rescale"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "do_pad"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "size"	)	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.image_processing_class.from_dict(self.image_processor_dict	)
       self.assertEqual(image_processor.size						,   {"shortest_edge": 18, "longest_edge": 1_333}	)
       self.assertEqual(image_processor.do_pad						,   lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       pass

     def 	UpperCamelCase(					self	):
       # Initialize image_processing
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random PIL images
       _snake_case     		=				prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(lowerCamelCase						,   Image.Image	)

       # Test not batched input
       _snake_case     		=				image_processing(image_inputs[0]						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase	)

       self.assertEqual(
           encoded_images.shape						,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,   )

       # Test batched
       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase						,   batched=lowerCamelCase	)

       _snake_case     		=				image_processing(lowerCamelCase						,   return_tensors="pt"	).pixel_values
       self.assertEqual(
           encoded_images.shape						,   (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )						,   )

     def 	UpperCamelCase(					self	):
       # Initialize image_processing
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random numpy tensors
       _snake_case     		=				prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowerCamelCase						,   numpify=lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(lowerCamelCase						,   np.ndarray	)

       # Test not batched input
       _snake_case     		=				image_processing(image_inputs[0]						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase	)

       self.assertEqual(
           encoded_images.shape						,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,   )

       # Test batched
       _snake_case     		=				image_processing(lowerCamelCase						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase						,   batched=lowerCamelCase	)

       self.assertEqual(
           encoded_images.shape						,   (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )						,   )

     def 	UpperCamelCase(					self	):
       # Initialize image_processing
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random PyTorch tensors
       _snake_case     		=				prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowerCamelCase						,   torchify=lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(lowerCamelCase						,   torch.Tensor	)

       # Test not batched input
       _snake_case     		=				image_processing(image_inputs[0]						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase	)

       self.assertEqual(
           encoded_images.shape						,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,   )

       # Test batched
       _snake_case     		=				image_processing(lowerCamelCase						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase						,   batched=lowerCamelCase	)

       self.assertEqual(
           encoded_images.shape						,   (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )						,   )

     @slow
     def 	UpperCamelCase(					self	):
       # prepare image and target
       _snake_case     		=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
       with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"						,   "r"	) as f:
         _snake_case     		=				json.loads(f.read()	)

       _snake_case     		=				{"image_id": 39_769, "annotations": target}

       # encode them
       _snake_case     		=				DetaImageProcessor()
       _snake_case     		=				image_processing(images=lowerCamelCase						,   annotations=lowerCamelCase						,   return_tensors="pt"	)

       # verify pixel values
       _snake_case     		=				torch.Size([1, 3, 800, 1_066]	)
       self.assertEqual(encoding["pixel_values"].shape						,   lowerCamelCase	)

       _snake_case     		=				torch.tensor([0.2796, 0.3138, 0.3481]	)
       self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]						,   lowerCamelCase						,   atol=1e-4	)	)

       # verify area
       _snake_case     		=				torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["area"]						,   lowerCamelCase	)	)
       # verify boxes
       _snake_case     		=				torch.Size([6, 4]	)
       self.assertEqual(encoding["labels"][0]["boxes"].shape						,   lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]						,   lowerCamelCase						,   atol=1e-3	)	)
       # verify image_id
       _snake_case     		=				torch.tensor([39_769]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]						,   lowerCamelCase	)	)
       # verify is_crowd
       _snake_case     		=				torch.tensor([0, 0, 0, 0, 0, 0]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]						,   lowerCamelCase	)	)
       # verify class_labels
       _snake_case     		=				torch.tensor([75, 75, 63, 65, 17, 17]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]						,   lowerCamelCase	)	)
       # verify orig_size
       _snake_case     		=				torch.tensor([480, 640]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]						,   lowerCamelCase	)	)
       # verify size
       _snake_case     		=				torch.tensor([800, 1_066]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"]						,   lowerCamelCase	)	)






     @slow
     def 	UpperCamelCase(					self	):
       # prepare image, target and masks_path
       _snake_case     		=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
       with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"						,   "r"	) as f:
         _snake_case     		=				json.loads(f.read()	)

       _snake_case     		=				{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

       _snake_case     		=				pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"	)

       # encode them
       _snake_case     		=				DetaImageProcessor(format="coco_panoptic"	)
       _snake_case     		=				image_processing(images=lowerCamelCase						,   annotations=lowerCamelCase						,   masks_path=lowerCamelCase						,   return_tensors="pt"	)

       # verify pixel values
       _snake_case     		=				torch.Size([1, 3, 800, 1_066]	)
       self.assertEqual(encoding["pixel_values"].shape						,   lowerCamelCase	)

       _snake_case     		=				torch.tensor([0.2796, 0.3138, 0.3481]	)
       self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]						,   lowerCamelCase						,   atol=1e-4	)	)

       # verify area
       _snake_case     		=				torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["area"]						,   lowerCamelCase	)	)
       # verify boxes
       _snake_case     		=				torch.Size([6, 4]	)
       self.assertEqual(encoding["labels"][0]["boxes"].shape						,   lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]						,   lowerCamelCase						,   atol=1e-3	)	)
       # verify image_id
       _snake_case     		=				torch.tensor([39_769]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]						,   lowerCamelCase	)	)
       # verify is_crowd
       _snake_case     		=				torch.tensor([0, 0, 0, 0, 0, 0]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]						,   lowerCamelCase	)	)
       # verify class_labels
       _snake_case     		=				torch.tensor([17, 17, 63, 75, 75, 93]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]						,   lowerCamelCase	)	)
       # verify masks
       _snake_case     		=				822_873
       self.assertEqual(encoding["labels"][0]["masks"].sum().item()						,   lowerCamelCase	)
       # verify orig_size
       _snake_case     		=				torch.tensor([480, 640]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]						,   lowerCamelCase	)	)
       # verify size
       _snake_case     		=				torch.tensor([800, 1_066]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"]						,   lowerCamelCase	)	)


