





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import CLIPSegProcessor, ViTImageProcessor





@require_vision
class 					A_					(   unittest.TestCase						):







       """simple docstring"""





       def  __UpperCAmelCase			(     self						:						Union[str, Any]	)    ->       str:
         _lowercase						    =			tempfile.mkdtemp()

         # fmt: off
         _lowercase						    =			['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
         # fmt: on
         _lowercase						    =			dict(zip(__A    ,range(len(__A	)	)	)	)
         _lowercase						    =			['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
         _lowercase						    =			{'unk_token': '<unk>'}

         _lowercase						    =			os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['vocab_file']	)
         _lowercase						    =			os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['merges_file']	)
         with open(self.vocab_file    ,'w'    ,encoding='utf-8'	) as fp:
           fp.write(json.dumps(__A	) + '\n'	)
         with open(self.merges_file    ,'w'    ,encoding='utf-8'	) as fp:
           fp.write('\n'.join(__A	)	)

         _lowercase						    =			{
             'do_resize': True,
             'size': 20,
             'do_center_crop': True,
             'crop_size': 18,
             'do_normalize': True,
             'image_mean': [0.48145466, 0.4578275, 0.40821073],
             'image_std': [0.26862954, 0.26130258, 0.27577711],
         }
         _lowercase						    =			os.path.join(self.tmpdirname    ,__A	)
         with open(self.image_processor_file    ,'w'    ,encoding='utf-8'	) as fp:
           json.dump(__A    ,__A	)







       def  __UpperCAmelCase			(     self						:						int    ,**__A						:						Optional[Any]	)    ->       Dict:
         return CLIPTokenizer.from_pretrained(self.tmpdirname    ,**__A	)







       def  __UpperCAmelCase			(     self						:						Optional[int]    ,**__A						:						List[str]	)    ->       Dict:
         return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,**__A	)







       def  __UpperCAmelCase			(     self						:						Dict    ,**__A						:						int	)    ->       List[Any]:
         return ViTImageProcessor.from_pretrained(self.tmpdirname    ,**__A	)







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







       def  __UpperCAmelCase			(     self						:						Optional[int]	)    ->       Optional[Any]:
         _lowercase						    =			[np.random.randint(255    ,size=(3, 30, 400)    ,dtype=np.uinta	)]

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

         return image_inputs







       def  __UpperCAmelCase			(     self						:						Tuple	)    ->       str:
         _lowercase						    =			self.get_tokenizer()
         _lowercase						    =			self.get_rust_tokenizer()
         _lowercase						    =			self.get_image_processor()

         _lowercase						    =			CLIPSegProcessor(tokenizer=__A    ,image_processor=__A	)
         processor_slow.save_pretrained(self.tmpdirname	)
         _lowercase						    =			CLIPSegProcessor.from_pretrained(self.tmpdirname    ,use_fast=__A	)

         _lowercase						    =			CLIPSegProcessor(tokenizer=__A    ,image_processor=__A	)
         processor_fast.save_pretrained(self.tmpdirname	)
         _lowercase						    =			CLIPSegProcessor.from_pretrained(self.tmpdirname	)

         self.assertEqual(processor_slow.tokenizer.get_vocab()    ,tokenizer_slow.get_vocab()	)
         self.assertEqual(processor_fast.tokenizer.get_vocab()    ,tokenizer_fast.get_vocab()	)
         self.assertEqual(tokenizer_slow.get_vocab()    ,tokenizer_fast.get_vocab()	)
         self.assertIsInstance(processor_slow.tokenizer    ,__A	)
         self.assertIsInstance(processor_fast.tokenizer    ,__A	)

         self.assertEqual(processor_slow.image_processor.to_json_string()    ,image_processor.to_json_string()	)
         self.assertEqual(processor_fast.image_processor.to_json_string()    ,image_processor.to_json_string()	)
         self.assertIsInstance(processor_slow.image_processor    ,__A	)
         self.assertIsInstance(processor_fast.image_processor    ,__A	)







       def  __UpperCAmelCase			(     self						:						Any	)    ->       List[Any]:
         _lowercase						    =			CLIPSegProcessor(tokenizer=self.get_tokenizer()    ,image_processor=self.get_image_processor()	)
         processor.save_pretrained(self.tmpdirname	)

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

         _lowercase						    =			CLIPSegProcessor.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  __UpperCAmelCase			(     self						:						Dict	)    ->       Union[str, Any]:
         _lowercase						    =			self.get_image_processor()
         _lowercase						    =			self.get_tokenizer()

         _lowercase						    =			CLIPSegProcessor(tokenizer=__A    ,image_processor=__A	)

         _lowercase						    =			self.prepare_image_inputs()

         _lowercase						    =			image_processor(__A    ,return_tensors='np'	)
         _lowercase						    =			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  __UpperCAmelCase			(     self						:						str	)    ->       Union[str, Any]:
         _lowercase						    =			self.get_image_processor()
         _lowercase						    =			self.get_tokenizer()

         _lowercase						    =			CLIPSegProcessor(tokenizer=__A    ,image_processor=__A	)

         _lowercase						    =			'lower newer'

         _lowercase						    =			processor(text=__A	)

         _lowercase						    =			tokenizer(__A	)

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







       def  __UpperCAmelCase			(     self						:						str	)    ->       Dict:
         _lowercase						    =			self.get_image_processor()
         _lowercase						    =			self.get_tokenizer()

         _lowercase						    =			CLIPSegProcessor(tokenizer=__A    ,image_processor=__A	)

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

         _lowercase						    =			processor(text=__A    ,images=__A	)

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

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







       def  __UpperCAmelCase			(     self						:						Optional[Any]	)    ->       List[Any]:
         _lowercase						    =			self.get_image_processor()
         _lowercase						    =			self.get_tokenizer()

         _lowercase						    =			CLIPSegProcessor(tokenizer=__A    ,image_processor=__A	)

         _lowercase						    =			self.prepare_image_inputs()
         _lowercase						    =			self.prepare_image_inputs()

         _lowercase						    =			processor(images=__A    ,visual_prompt=__A	)

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

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







       def  __UpperCAmelCase			(     self						:						Optional[int]	)    ->       List[Any]:
         _lowercase						    =			self.get_image_processor()
         _lowercase						    =			self.get_tokenizer()

         _lowercase						    =			CLIPSegProcessor(tokenizer=__A    ,image_processor=__A	)

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

         _lowercase						    =			processor.batch_decode(__A	)
         _lowercase						    =			tokenizer.batch_decode(__A	)

         self.assertListEqual(__A    ,__A	)



import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor
class 						lowerCamelCase_							(					unittest.TestCase					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_=7			,							snake_case_=3			,							snake_case_=1_8			,							snake_case_=3_0			,							snake_case_=4_0_0			,							snake_case_=True			,							snake_case_=None			,							snake_case_=True			,							snake_case_=False			,							snake_case_=True			,							snake_case_=True			,							snake_case_=[0.5, 0.5, 0.5]			,							snake_case_=[0.5, 0.5, 0.5]			,							) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = parent
								__lowercase			       = batch_size
								__lowercase			       = num_channels
								__lowercase			       = image_size
								__lowercase			       = min_resolution
								__lowercase			       = max_resolution
								__lowercase			       = do_resize
								__lowercase			       = size if size is not None else {'''height''': 1_8, '''width''': 2_0}
								__lowercase			       = do_thumbnail
								__lowercase			       = do_align_axis
								__lowercase			       = do_pad
								__lowercase			       = do_normalize
								__lowercase			       = image_mean
								__lowercase			       = image_std






					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_thumbnail": self.do_thumbnail,
								    "do_align_long_axis": self.do_align_axis,
								    "do_pad": self.do_pad,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}




@require_torch
@require_vision
class 						lowerCamelCase_							(					lowerCAmelCase__  ,  unittest.TestCase					):



					'''simple docstring'''







					__UpperCAmelCase							  =					DonutImageProcessor if is_vision_available() else None






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = DonutImageProcessingTester(self    )






					@property
					def A   (					self    ) ->      str:







								'''simple docstring'''

								return self.image_processor_tester.prepare_image_processor_dict()






					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

								__lowercase			       = 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_thumbnail'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_align_long_axis'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_pad'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_normalize'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''image_mean'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''image_std'''    )    )






					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class.from_dict(self.image_processor_dict    )
								self.assertEqual(image_processor.size			,							{'''height''': 1_8, '''width''': 2_0}    )

								__lowercase			       = self.image_processing_class.from_dict(self.image_processor_dict			,							size=4_2    )
								self.assertEqual(image_processor.size			,							{'''height''': 4_2, '''width''': 4_2}    )

								# Previous config had dimensions in (width, height) order
								__lowercase			       = self.image_processing_class.from_dict(self.image_processor_dict			,							size=(4_2, 8_4)    )
								self.assertEqual(image_processor.size			,							{'''height''': 8_4, '''width''': 4_2}    )






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								pass






					@is_flaky()
					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								# create random PIL images
								__lowercase			       = 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
								__lowercase			       = 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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)

								# Test batched
								__lowercase			       = 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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)






					@is_flaky()
					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								# create random numpy tensors
								__lowercase			       = 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
								__lowercase			       = 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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)

								# Test batched
								__lowercase			       = 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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)






					@is_flaky()
					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								# create random PyTorch tensors
								__lowercase			       = 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
								__lowercase			       = 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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)

								# Test batched
								__lowercase			       = 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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)


