


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       a					(       unittest.TestCase      ):

     '''simple docstring'''





     def   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					tempfile.mkdtemp()

         UpperCAmelCase_										=					BlipImageProcessor()
         UpperCAmelCase_										=					GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''    )

         UpperCAmelCase_										=					BlipaProcessor(_A ,   _A    )

         processor.save_pretrained(self.tmpdirname    )
     def   lowerCamelCase_       (     self       :      Tuple ,   **__snake_case       :      str    ):
         return AutoProcessor.from_pretrained(self.tmpdirname ,   **_A    ).tokenizer
     def   lowerCamelCase_       (     self       :      int ,   **__snake_case       :      Optional[int]    ):
         return AutoProcessor.from_pretrained(self.tmpdirname ,   **_A    ).image_processor
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         shutil.rmtree(self.tmpdirname    )
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					[np.random.randint(2_55 ,   size=(3, 30, 4_00) ,   dtype=np.uinta    )]

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

         return image_inputs
     def   lowerCamelCase_       (     self       :      List[str]    ):
         UpperCAmelCase_										=					BlipaProcessor(tokenizer=self.get_tokenizer() ,   image_processor=self.get_image_processor()    )
         processor.save_pretrained(self.tmpdirname    )

         UpperCAmelCase_										=					self.get_tokenizer(bos_token='''(BOS)''' ,   eos_token='''(EOS)'''    )
         UpperCAmelCase_										=					self.get_image_processor(do_normalize=_A ,   padding_value=1.0    )

         UpperCAmelCase_										=					BlipaProcessor.from_pretrained(
             self.tmpdirname ,   bos_token='''(BOS)''' ,   eos_token='''(EOS)''' ,   do_normalize=_A ,   padding_value=1.0    )

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

         self.assertEqual(processor.image_processor.to_json_string() ,   image_processor_add_kwargs.to_json_string()    )
         self.assertIsInstance(processor.image_processor ,   _A    )
     def   lowerCamelCase_       (     self       :      str    ):
         UpperCAmelCase_										=					self.get_image_processor()
         UpperCAmelCase_										=					self.get_tokenizer()

         UpperCAmelCase_										=					BlipaProcessor(tokenizer=_A ,   image_processor=_A    )

         UpperCAmelCase_										=					self.prepare_image_inputs()

         UpperCAmelCase_										=					image_processor(_A ,   return_tensors='''np'''    )
         UpperCAmelCase_										=					processor(images=_A ,   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   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					self.get_image_processor()
         UpperCAmelCase_										=					self.get_tokenizer()

         UpperCAmelCase_										=					BlipaProcessor(tokenizer=_A ,   image_processor=_A    )

         UpperCAmelCase_										=					'''lower newer'''

         UpperCAmelCase_										=					processor(text=_A    )

         UpperCAmelCase_										=					tokenizer(_A ,   return_token_type_ids=_A    )

         for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key] ,   encoded_processor[key]    )
     def   lowerCamelCase_       (     self       :      List[str]    ):
         UpperCAmelCase_										=					self.get_image_processor()
         UpperCAmelCase_										=					self.get_tokenizer()

         UpperCAmelCase_										=					BlipaProcessor(tokenizer=_A ,   image_processor=_A    )

         UpperCAmelCase_										=					'''lower newer'''
         UpperCAmelCase_										=					self.prepare_image_inputs()

         UpperCAmelCase_										=					processor(text=_A ,   images=_A    )

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

         # test if it raises when no input is passed
         with pytest.raises(_A    ):
             processor()
     def   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					self.get_image_processor()
         UpperCAmelCase_										=					self.get_tokenizer()

         UpperCAmelCase_										=					BlipaProcessor(tokenizer=_A ,   image_processor=_A    )

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

         UpperCAmelCase_										=					processor.batch_decode(_A    )
         UpperCAmelCase_										=					tokenizer.batch_decode(_A    )

         self.assertListEqual(_A ,   _A    )




     def   lowerCamelCase_       (     self       :      str    ):
         UpperCAmelCase_										=					self.get_image_processor()
         UpperCAmelCase_										=					self.get_tokenizer()

         UpperCAmelCase_										=					BlipaProcessor(tokenizer=_A ,   image_processor=_A    )

         UpperCAmelCase_										=					'''lower newer'''
         UpperCAmelCase_										=					self.prepare_image_inputs()

         UpperCAmelCase_										=					processor(text=_A ,   images=_A    )

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











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 MobileNetVaImageProcessor



class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     def __init__(     self       :      Tuple ,   __snake_case       :      int ,   __snake_case       :      List[Any]=7 ,   __snake_case       :      Any=3 ,   __snake_case       :      Any=18 ,   __snake_case       :      str=30 ,   __snake_case       :      Any=4_00 ,   __snake_case       :      Optional[int]=True ,   __snake_case       :      str=None ,   __snake_case       :      Any=True ,   __snake_case       :      List[Any]=None ,   ):
         UpperCAmelCase_										=					size if size is not None else {'''shortest_edge''': 20}
         UpperCAmelCase_										=					crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
         UpperCAmelCase_										=					parent
         UpperCAmelCase_										=					batch_size
         UpperCAmelCase_										=					num_channels
         UpperCAmelCase_										=					image_size
         UpperCAmelCase_										=					min_resolution
         UpperCAmelCase_										=					max_resolution
         UpperCAmelCase_										=					do_resize
         UpperCAmelCase_										=					size
         UpperCAmelCase_										=					do_center_crop
         UpperCAmelCase_										=					crop_size




     def   lowerCamelCase_       (     self       :      Any    ):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
         }



@require_torch
@require_vision
class       a					(       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	Union[str, Any]       		=  MobileNetVaImageProcessor if is_vision_available() else None
     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					MobileNetVaImageProcessingTester(self    )
     @property
     def   lowerCamelCase_       (     self       :      List[str]    ):
         return self.image_processor_tester.prepare_image_processor_dict()
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         self.assertTrue(hasattr(__snake_case ,   '''do_resize'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''size'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''do_center_crop'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''crop_size'''    )    )
     def   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					self.image_processing_class.from_dict(self.image_processor_dict    )
         self.assertEqual(image_processor.size ,   {'''shortest_edge''': 20}    )
         self.assertEqual(image_processor.crop_size ,   {'''height''': 18, '''width''': 18}    )

         UpperCAmelCase_										=					self.image_processing_class.from_dict(self.image_processor_dict ,   size=42 ,   crop_size=84    )
         self.assertEqual(image_processor.size ,   {'''shortest_edge''': 42}    )
         self.assertEqual(image_processor.crop_size ,   {'''height''': 84, '''width''': 84}    )
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         pass
     def   lowerCamelCase_       (     self       :      Tuple    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random PIL images
         UpperCAmelCase_										=					prepare_image_inputs(self.image_processor_tester ,   equal_resolution=__snake_case    )
         for image in image_inputs:
             self.assertIsInstance(__snake_case ,   Image.Image    )

         # Test not batched input
         UpperCAmelCase_										=					image_processing(image_inputs[0] ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )

         # Test batched
         UpperCAmelCase_										=					image_processing(__snake_case ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )
     def   lowerCamelCase_       (     self       :      str    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random numpy tensors
         UpperCAmelCase_										=					prepare_image_inputs(self.image_processor_tester ,   equal_resolution=__snake_case ,   numpify=__snake_case    )
         for image in image_inputs:
             self.assertIsInstance(__snake_case ,   np.ndarray    )

         # Test not batched input
         UpperCAmelCase_										=					image_processing(image_inputs[0] ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )

         # Test batched
         UpperCAmelCase_										=					image_processing(__snake_case ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )




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

         # Test not batched input
         UpperCAmelCase_										=					image_processing(image_inputs[0] ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )

         # Test batched
         UpperCAmelCase_										=					image_processing(__snake_case ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )





