






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 						UpperCAmelCase__ (							unittest.TestCase							):







		def  A__      (					self		):
						_A	:				Optional[int]   =      tempfile.mkdtemp()

						# fmt: off
						_A	:				Any   =      ['''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
						_A	:				List[Any]   =      dict(zip(A__       ,range(len(A__		)		)		)		)
						_A	:				Dict   =      ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
						_A	:				List[Any]   =      {'''unk_token''': '''<unk>'''}

						_A	:				Dict   =      os.path.join(self.tmpdirname       ,VOCAB_FILES_NAMES['''vocab_file''']		)
						_A	:				Tuple   =      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__		)		)

						_A	:				str   =      {
						    '''do_resize''': True,
						    '''size''': 20,
						    '''do_center_crop''': True,
						    '''crop_size''': 18,
						    '''do_normalize''': True,
						    '''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
						    '''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
						}
						_A	:				Optional[int]   =      os.path.join(self.tmpdirname       ,A__		)
						with open(self.image_processor_file       ,'''w'''       ,encoding='''utf-8'''		) as fp:
										json.dump(A__       ,A__		)







		def  A__      (					self       ,**A__		):
						return CLIPTokenizer.from_pretrained(self.tmpdirname       ,**A__		)







		def  A__      (					self       ,**A__		):
						return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,**A__		)







		def  A__      (					self       ,**A__		):
						return ViTImageProcessor.from_pretrained(self.tmpdirname       ,**A__		)







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







		def  A__      (					self		):
						_A	:				List[str]   =      [np.random.randint(255       ,size=(3, 30, 400)       ,dtype=np.uinta		)]

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

						return image_inputs







		def  A__      (					self		):
						_A	:				Union[str, Any]   =      self.get_tokenizer()
						_A	:				Any   =      self.get_rust_tokenizer()
						_A	:				Dict   =      self.get_image_processor()

						_A	:				Dict   =      CLIPSegProcessor(tokenizer=A__       ,image_processor=A__		)
						processor_slow.save_pretrained(self.tmpdirname		)
						_A	:				Any   =      CLIPSegProcessor.from_pretrained(self.tmpdirname       ,use_fast=A__		)

						_A	:				Union[str, Any]   =      CLIPSegProcessor(tokenizer=A__       ,image_processor=A__		)
						processor_fast.save_pretrained(self.tmpdirname		)
						_A	:				List[Any]   =      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  A__      (					self		):
						_A	:				Union[str, Any]   =      CLIPSegProcessor(tokenizer=self.get_tokenizer()       ,image_processor=self.get_image_processor()		)
						processor.save_pretrained(self.tmpdirname		)

						_A	:				int   =      self.get_tokenizer(bos_token='''(BOS)'''       ,eos_token='''(EOS)'''		)
						_A	:				Tuple   =      self.get_image_processor(do_normalize=A__       ,padding_value=1.0		)

						_A	:				List[str]   =      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  A__      (					self		):
						_A	:				Any   =      self.get_image_processor()
						_A	:				Optional[int]   =      self.get_tokenizer()

						_A	:				Tuple   =      CLIPSegProcessor(tokenizer=A__       ,image_processor=A__		)

						_A	:				Any   =      self.prepare_image_inputs()

						_A	:				Union[str, Any]   =      image_processor(A__       ,return_tensors='''np'''		)
						_A	:				Optional[Any]   =      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  A__      (					self		):
						_A	:				List[Any]   =      self.get_image_processor()
						_A	:				Optional[int]   =      self.get_tokenizer()

						_A	:				Tuple   =      CLIPSegProcessor(tokenizer=A__       ,image_processor=A__		)

						_A	:				Any   =      '''lower newer'''

						_A	:				str   =      processor(text=A__		)

						_A	:				List[str]   =      tokenizer(A__		)

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







		def  A__      (					self		):
						_A	:				List[str]   =      self.get_image_processor()
						_A	:				List[Any]   =      self.get_tokenizer()

						_A	:				Union[str, Any]   =      CLIPSegProcessor(tokenizer=A__       ,image_processor=A__		)

						_A	:				Optional[int]   =      '''lower newer'''
						_A	:				Any   =      self.prepare_image_inputs()

						_A	:				str   =      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  A__      (					self		):
						_A	:				Tuple   =      self.get_image_processor()
						_A	:				Optional[Any]   =      self.get_tokenizer()

						_A	:				str   =      CLIPSegProcessor(tokenizer=A__       ,image_processor=A__		)

						_A	:				Dict   =      self.prepare_image_inputs()
						_A	:				Tuple   =      self.prepare_image_inputs()

						_A	:				List[str]   =      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  A__      (					self		):
						_A	:				Dict   =      self.get_image_processor()
						_A	:				Optional[Any]   =      self.get_tokenizer()

						_A	:				List[str]   =      CLIPSegProcessor(tokenizer=A__       ,image_processor=A__		)

						_A	:				List[str]   =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						_A	:				Any   =      processor.batch_decode(A__		)
						_A	:				str   =      tokenizer.batch_decode(A__		)

						self.assertListEqual(A__       ,A__		)











import warnings


warnings.warn(
    'memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '
    '`from accelerate import find_executable_batch_size` to avoid this warning.',
    FutureWarning,
)



