
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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast

@require_vision
class __snake_case		(      unittest.TestCase     ):
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      tempfile.mkdtemp()

           lowercase							:     Optional[int]       	=      BlipImageProcessor()
           lowercase							:     List[str]       	=      BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel"""      )

           lowercase							:     Optional[Any]       	=      BlipProcessor(SCREAMING_SNAKE_CASE_		,SCREAMING_SNAKE_CASE_      )

           processor.save_pretrained(self.tmpdirname      )
    def       _SCREAMING_SNAKE_CASE       (  self		,**snake_case      ):



           '''simple docstring'''







           return AutoProcessor.from_pretrained(self.tmpdirname		,**SCREAMING_SNAKE_CASE_      ).tokenizer
    def       _SCREAMING_SNAKE_CASE       (  self		,**snake_case      ):



           '''simple docstring'''







           return AutoProcessor.from_pretrained(self.tmpdirname		,**SCREAMING_SNAKE_CASE_      ).image_processor
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:     List[Any]       	=      [np.random.randint(255		,size=(3, 30, 400)		,dtype=np.uinta      )]

           lowercase							:     Tuple       	=      [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_		,0		,-1      )      ) for x in image_inputs]

           return image_inputs
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      BlipProcessor(tokenizer=self.get_tokenizer()		,image_processor=self.get_image_processor()      )
           processor.save_pretrained(self.tmpdirname      )

           lowercase							:     Union[str, Any]       	=      self.get_tokenizer(bos_token="""(BOS)"""		,eos_token="""(EOS)"""      )
           lowercase							:     Tuple       	=      self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_		,padding_value=1.0      )

           lowercase							:     Optional[int]       	=      BlipProcessor.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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.get_image_processor()
           lowercase							:     Union[str, Any]       	=      self.get_tokenizer()

           lowercase							:     List[Any]       	=      BlipProcessor(tokenizer=SCREAMING_SNAKE_CASE_		,image_processor=SCREAMING_SNAKE_CASE_      )

           lowercase							:     List[str]       	=      self.prepare_image_inputs()

           lowercase							:     List[Any]       	=      image_processor(SCREAMING_SNAKE_CASE_		,return_tensors="""np"""      )
           lowercase							:     List[str]       	=      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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      self.get_image_processor()
           lowercase							:     Tuple       	=      self.get_tokenizer()

           lowercase							:     Optional[int]       	=      BlipProcessor(tokenizer=SCREAMING_SNAKE_CASE_		,image_processor=SCREAMING_SNAKE_CASE_      )

           lowercase							:     List[Any]       	=      'lower newer'

           lowercase							:     str       	=      processor(text=SCREAMING_SNAKE_CASE_      )

           lowercase							:     List[str]       	=      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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.get_image_processor()
           lowercase							:     List[Any]       	=      self.get_tokenizer()

           lowercase							:     Union[str, Any]       	=      BlipProcessor(tokenizer=SCREAMING_SNAKE_CASE_		,image_processor=SCREAMING_SNAKE_CASE_      )

           lowercase							:     Tuple       	=      'lower newer'
           lowercase							:     Tuple       	=      self.prepare_image_inputs()

           lowercase							:     Union[str, Any]       	=      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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      self.get_image_processor()
           lowercase							:     Union[str, Any]       	=      self.get_tokenizer()

           lowercase							:     Any       	=      BlipProcessor(tokenizer=SCREAMING_SNAKE_CASE_		,image_processor=SCREAMING_SNAKE_CASE_      )

           lowercase							:     Union[str, Any]       	=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           lowercase							:     Any       	=      processor.batch_decode(SCREAMING_SNAKE_CASE_      )
           lowercase							:     List[str]       	=      tokenizer.batch_decode(SCREAMING_SNAKE_CASE_      )

           self.assertListEqual(SCREAMING_SNAKE_CASE_		,SCREAMING_SNAKE_CASE_      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      self.get_image_processor()
           lowercase							:     Dict       	=      self.get_tokenizer()

           lowercase							:     Optional[int]       	=      BlipProcessor(tokenizer=SCREAMING_SNAKE_CASE_		,image_processor=SCREAMING_SNAKE_CASE_      )

           lowercase							:     Tuple       	=      'lower newer'
           lowercase							:     str       	=      self.prepare_image_inputs()

           lowercase							:     Union[str, Any]       	=      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"""]      )





import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ImageGPTImageProcessor



class 				lowerCAmelCase_		(  unittest.TestCase							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=7,			SCREAMING_SNAKE_CASE_=3,			SCREAMING_SNAKE_CASE_=18,			SCREAMING_SNAKE_CASE_=30,			SCREAMING_SNAKE_CASE_=400,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=True,			)     ->		Union[str, Any]:
          UpperCamelCase    :  str							=	size if size is not None else {'height': 18, 'width': 18}
          UpperCamelCase    :  int							=	parent
          UpperCamelCase    :  List[Any]							=	batch_size
          UpperCamelCase    :  int							=	num_channels
          UpperCamelCase    :  Any							=	image_size
          UpperCamelCase    :  Optional[int]							=	min_resolution
          UpperCamelCase    :  Optional[Any]							=	max_resolution
          UpperCamelCase    :  Union[str, Any]							=	do_resize
          UpperCamelCase    :  List[Any]							=	size
          UpperCamelCase    :  int							=	do_normalize






       def 					snake_case_       (			self       )     ->		Tuple:
          return {
              # here we create 2 clusters for the sake of simplicity
              "clusters": np.asarray(
                  [
                      [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
                      [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
                  ]       ),
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
          }






@require_torch
@require_vision
class 				lowerCAmelCase_		(  a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					Any        =  ImageGPTImageProcessor if is_vision_available() else None
       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  str							=	ImageGPTImageProcessingTester(self       )
       @property
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          return self.image_processor_tester.prepare_image_processor_dict()
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  int							=	self.image_processing_class(**self.image_processor_dict       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'clusters'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'do_resize'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'size'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'do_normalize'       )       )
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  Tuple							=	self.image_processing_class.from_dict(self.image_processor_dict       )
          self.assertEqual(image_processor.size,			{'height': 18, 'width': 18}       )

          UpperCamelCase    :  Dict							=	self.image_processing_class.from_dict(self.image_processor_dict,			size=42       )
          self.assertEqual(image_processor.size,			{'height': 42, 'width': 42}       )
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  str							=	self.image_processing_class(**self.image_processor_dict       )
          UpperCamelCase    :  int							=	json.loads(image_processor.to_json_string()       )
          for key, value in self.image_processor_dict.items():
             if key == "clusters":
                self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_,			obj[key]       )       )
             else:
                self.assertEqual(obj[key],			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  Tuple							=	self.image_processing_class(**self.image_processor_dict       )

          with tempfile.TemporaryDirectory() as tmpdirname:
             UpperCamelCase    :  List[str]							=	os.path.join(SCREAMING_SNAKE_CASE_,			'image_processor.json'       )
             image_processor_first.to_json_file(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  Tuple							=	self.image_processing_class.from_json_file(SCREAMING_SNAKE_CASE_       ).to_dict()

          UpperCamelCase    :  List[Any]							=	image_processor_first.to_dict()
          for key, value in image_processor_first.items():
             if key == "clusters":
                self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_,			image_processor_second[key]       )       )
             else:
                self.assertEqual(image_processor_first[key],			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  Optional[Any]							=	self.image_processing_class(**self.image_processor_dict       )

          with tempfile.TemporaryDirectory() as tmpdirname:
             image_processor_first.save_pretrained(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  str							=	self.image_processing_class.from_pretrained(SCREAMING_SNAKE_CASE_       ).to_dict()

          UpperCamelCase    :  Union[str, Any]							=	image_processor_first.to_dict()
          for key, value in image_processor_first.items():
             if key == "clusters":
                self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_,			image_processor_second[key]       )       )
             else:
                self.assertEqual(image_processor_first[key],			SCREAMING_SNAKE_CASE_       )






       @unittest.skip('ImageGPT requires clusters at initialization'       )
       def 					snake_case_       (			self       )     ->		str:
          pass






def 		UpperCamelCase	(   )	->     int:
   UpperCamelCase    :  Optional[int]							=	load_dataset('hf-internal-testing/fixtures_image_utils'       ,     split='test'	)

   UpperCamelCase    :  int							=	Image.open(dataset[4]['file']	)
   UpperCamelCase    :  Optional[Any]							=	Image.open(dataset[5]['file']	)

   UpperCamelCase    :  str							=	[imagea, imagea]

   return images



@require_vision
@require_torch
class 				lowerCAmelCase_		(  unittest.TestCase							):
       @slow
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  List[str]							=	ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'       )

          UpperCamelCase    :  List[str]							=	prepare_images()

          # test non-batched
          UpperCamelCase    :  int							=	image_processing(images[0],			return_tensors='pt'       )

          self.assertIsInstance(encoding.input_ids,			torch.LongTensor       )
          self.assertEqual(encoding.input_ids.shape,			(1, 1024)       )

          UpperCamelCase    :  Union[str, Any]							=	[306, 191, 191]
          self.assertEqual(encoding.input_ids[0, :3].tolist(),			SCREAMING_SNAKE_CASE_       )

          # test batched
          UpperCamelCase    :  Tuple							=	image_processing(SCREAMING_SNAKE_CASE_,			return_tensors='pt'       )

          self.assertIsInstance(encoding.input_ids,			torch.LongTensor       )
          self.assertEqual(encoding.input_ids.shape,			(2, 1024)       )

          UpperCamelCase    :  Optional[Any]							=	[303, 13, 13]
          self.assertEqual(encoding.input_ids[1, -3:].tolist(),			SCREAMING_SNAKE_CASE_       )



