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    snake_case       (   unittest.TestCase    ):




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		tempfile.mkdtemp()

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

       SCREAMING_SNAKE_CASE__					:					Any      =		os.path.join(self.tmpdirname  ,			VOCAB_FILES_NAMES['vocab_file']					)
       SCREAMING_SNAKE_CASE__					:					str      =		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_					)					)

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		{
           'do_resize': True,
           'size': 20,
           'do_center_crop': True,
           'crop_size': 18,
           'do_normalize': True,
           'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
           'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
       }
       SCREAMING_SNAKE_CASE__					:					Dict      =		os.path.join(self.tmpdirname  ,			a_					)
       with open(self.image_processor_file  ,			'w'  ,			encoding='utf-8'					) as fp:
            json.dump(a_  ,			a_					)




  def 						__lowercase(   self  :						Any  ,			**a_  :						Any					)->							str:




       """simple docstring"""
       return CLIPTokenizer.from_pretrained(self.tmpdirname  ,			**a_					)




  def 						__lowercase(   self  :						Union[str, Any]  ,			**a_  :						Optional[int]					)->							List[str]:




       """simple docstring"""
       return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,			**a_					)




  def 						__lowercase(   self  :						Any  ,			**a_  :						List[str]					)->							int:




       """simple docstring"""
       return ViTImageProcessor.from_pretrained(self.tmpdirname  ,			**a_					)




  def 						__lowercase(   self  :						Tuple					)->							Optional[int]:




       """simple docstring"""
       shutil.rmtree(self.tmpdirname					)




  def 						__lowercase(   self  :						Union[str, Any]					)->							str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		[np.random.randint(255  ,			size=(3, 30, 400)  ,			dtype=np.uinta					)]

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

       return image_inputs




  def 						__lowercase(   self  :						List[Any]					)->							Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.get_tokenizer()
       SCREAMING_SNAKE_CASE__					:					Any      =		self.get_rust_tokenizer()
       SCREAMING_SNAKE_CASE__					:					Dict      =		self.get_image_processor()

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		CLIPSegProcessor(tokenizer=a_  ,			image_processor=a_					)
       processor_slow.save_pretrained(self.tmpdirname					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		CLIPSegProcessor.from_pretrained(self.tmpdirname  ,			use_fast=a_					)

       SCREAMING_SNAKE_CASE__					:					Dict      =		CLIPSegProcessor(tokenizer=a_  ,			image_processor=a_					)
       processor_fast.save_pretrained(self.tmpdirname					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		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 						__lowercase(   self  :						Tuple					)->							List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Dict      =		CLIPSegProcessor(tokenizer=self.get_tokenizer()  ,			image_processor=self.get_image_processor()					)
       processor.save_pretrained(self.tmpdirname					)

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.get_tokenizer(bos_token='(BOS)'  ,			eos_token='(EOS)'					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		self.get_image_processor(do_normalize=a_  ,			padding_value=1.0					)

       SCREAMING_SNAKE_CASE__					:					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 						__lowercase(   self  :						Union[str, Any]					)->							List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.get_image_processor()
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.get_tokenizer()

       SCREAMING_SNAKE_CASE__					:					Tuple      =		CLIPSegProcessor(tokenizer=a_  ,			image_processor=a_					)

       SCREAMING_SNAKE_CASE__					:					str      =		self.prepare_image_inputs()

       SCREAMING_SNAKE_CASE__					:					Dict      =		image_processor(a_  ,			return_tensors='np'					)
       SCREAMING_SNAKE_CASE__					:					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 						__lowercase(   self  :						Tuple					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.get_image_processor()
       SCREAMING_SNAKE_CASE__					:					int      =		self.get_tokenizer()

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		CLIPSegProcessor(tokenizer=a_  ,			image_processor=a_					)

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		'lower newer'

       SCREAMING_SNAKE_CASE__					:					List[str]      =		processor(text=a_					)

       SCREAMING_SNAKE_CASE__					:					str      =		tokenizer(a_					)

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




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.get_image_processor()
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.get_tokenizer()

       SCREAMING_SNAKE_CASE__					:					Tuple      =		CLIPSegProcessor(tokenizer=a_  ,			image_processor=a_					)

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		'lower newer'
       SCREAMING_SNAKE_CASE__					:					str      =		self.prepare_image_inputs()

       SCREAMING_SNAKE_CASE__					:					List[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 						__lowercase(   self  :						Any					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		self.get_image_processor()
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.get_tokenizer()

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		CLIPSegProcessor(tokenizer=a_  ,			image_processor=a_					)

       SCREAMING_SNAKE_CASE__					:					int      =		self.prepare_image_inputs()
       SCREAMING_SNAKE_CASE__					:					str      =		self.prepare_image_inputs()

       SCREAMING_SNAKE_CASE__					:					int      =		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 						__lowercase(   self  :						List[Any]					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.get_image_processor()
       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.get_tokenizer()

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		CLIPSegProcessor(tokenizer=a_  ,			image_processor=a_					)

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       SCREAMING_SNAKE_CASE__					:					Dict      =		processor.batch_decode(a_					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		tokenizer.batch_decode(a_					)

       self.assertListEqual(a_  ,			a_					)










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 CLIPImageProcessor, CLIPProcessor


@require_vision
class     A     (unittest.TestCase       ):
			'''simple docstring'''




			def   a_			(     self  :       Optional[int]  )      ->      Dict:







					"""simple docstring"""






					A__     =  tempfile.mkdtemp()

					# fmt: off
					A__     =  ["""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__     =  dict(zip(__lowerCAmelCase				,      range(len(__lowerCAmelCase  )  )  )  )
					A__     =  ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
					A__     =  {"""unk_token""": """<unk>"""}

					A__     =  os.path.join(self.tmpdirname				,      VOCAB_FILES_NAMES["""vocab_file"""]  )
					A__     =  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(__lowerCAmelCase  ) + """\n"""  )
					with open(self.merges_file				,      """w"""				,      encoding="""utf-8"""  ) as fp:
							fp.write("""\n""".join(__lowerCAmelCase  )  )

					A__     =  {
					    """do_resize""": True,
					    """size""": 20,
					    """do_center_crop""": True,
					    """crop_size""": 18,
					    """do_normalize""": True,
					    """image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
					    """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
					}
					A__     =  os.path.join(self.tmpdirname				,      __lowerCAmelCase  )
					with open(self.image_processor_file				,      """w"""				,      encoding="""utf-8"""  ) as fp:
							json.dump(__lowerCAmelCase				,      __lowerCAmelCase  )






			def   a_			(     self  :       str				,      **__lowerCAmelCase  :       Optional[int]  )      ->      str:







					"""simple docstring"""






					return CLIPTokenizer.from_pretrained(self.tmpdirname				,      **__lowerCAmelCase  )






			def   a_			(     self  :       Dict				,      **__lowerCAmelCase  :       List[Any]  )      ->      Optional[int]:







					"""simple docstring"""






					return CLIPTokenizerFast.from_pretrained(self.tmpdirname				,      **__lowerCAmelCase  )






			def   a_			(     self  :       List[Any]				,      **__lowerCAmelCase  :       List[Any]  )      ->      Tuple:







					"""simple docstring"""






					return CLIPImageProcessor.from_pretrained(self.tmpdirname				,      **__lowerCAmelCase  )






			def   a_			(     self  :       Optional[Any]  )      ->      str:







					"""simple docstring"""






					shutil.rmtree(self.tmpdirname  )






			def   a_			(     self  :       int  )      ->      Tuple:







					"""simple docstring"""






					A__     =  [np.random.randint(2_55				,      size=(3, 30, 4_00)				,      dtype=np.uinta  )]

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

					return image_inputs






			def   a_			(     self  :       List[str]  )      ->      Optional[int]:







					"""simple docstring"""






					A__     =  self.get_tokenizer()
					A__     =  self.get_rust_tokenizer()
					A__     =  self.get_image_processor()

					A__     =  CLIPProcessor(tokenizer=__lowerCAmelCase				,      image_processor=__lowerCAmelCase  )
					processor_slow.save_pretrained(self.tmpdirname  )
					A__     =  CLIPProcessor.from_pretrained(self.tmpdirname				,      use_fast=__lowerCAmelCase  )

					A__     =  CLIPProcessor(tokenizer=__lowerCAmelCase				,      image_processor=__lowerCAmelCase  )
					processor_fast.save_pretrained(self.tmpdirname  )
					A__     =  CLIPProcessor.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				,      __lowerCAmelCase  )
					self.assertIsInstance(processor_fast.tokenizer				,      __lowerCAmelCase  )

					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				,      __lowerCAmelCase  )
					self.assertIsInstance(processor_fast.image_processor				,      __lowerCAmelCase  )






			def   a_			(     self  :       int  )      ->      Any:







					"""simple docstring"""






					A__     =  CLIPProcessor(tokenizer=self.get_tokenizer()				,      image_processor=self.get_image_processor()  )
					processor.save_pretrained(self.tmpdirname  )

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

					A__     =  CLIPProcessor.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  )






			def   a_			(     self  :       List[Any]  )      ->      Union[str, Any]:







					"""simple docstring"""






					A__     =  self.get_image_processor()
					A__     =  self.get_tokenizer()

					A__     =  CLIPProcessor(tokenizer=__lowerCAmelCase				,      image_processor=__lowerCAmelCase  )

					A__     =  self.prepare_image_inputs()

					A__     =  image_processor(__lowerCAmelCase				,      return_tensors="""np"""  )
					A__     =  processor(images=__lowerCAmelCase				,      return_tensors="""np"""  )

					for key in input_image_proc.keys():
							self.assertAlmostEqual(input_image_proc[key].sum()				,      input_processor[key].sum()				,      delta=1e-2  )






			def   a_			(     self  :       Union[str, Any]  )      ->      Tuple:







					"""simple docstring"""






					A__     =  self.get_image_processor()
					A__     =  self.get_tokenizer()

					A__     =  CLIPProcessor(tokenizer=__lowerCAmelCase				,      image_processor=__lowerCAmelCase  )

					A__     =  """lower newer"""

					A__     =  processor(text=__lowerCAmelCase  )

					A__     =  tokenizer(__lowerCAmelCase  )

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






			def   a_			(     self  :       str  )      ->      Optional[Any]:







					"""simple docstring"""






					A__     =  self.get_image_processor()
					A__     =  self.get_tokenizer()

					A__     =  CLIPProcessor(tokenizer=__lowerCAmelCase				,      image_processor=__lowerCAmelCase  )

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

					A__     =  processor(text=__lowerCAmelCase				,      images=__lowerCAmelCase  )

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

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






			def   a_			(     self  :       Optional[int]  )      ->      Tuple:







					"""simple docstring"""






					A__     =  self.get_image_processor()
					A__     =  self.get_tokenizer()

					A__     =  CLIPProcessor(tokenizer=__lowerCAmelCase				,      image_processor=__lowerCAmelCase  )

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

					A__     =  processor.batch_decode(__lowerCAmelCase  )
					A__     =  tokenizer.batch_decode(__lowerCAmelCase  )

					self.assertListEqual(__lowerCAmelCase				,      __lowerCAmelCase  )







			def   a_			(     self  :       Dict  )      ->      str:







					"""simple docstring"""






					A__     =  self.get_image_processor()
					A__     =  self.get_tokenizer()

					A__     =  CLIPProcessor(tokenizer=__lowerCAmelCase				,      image_processor=__lowerCAmelCase  )

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

					A__     =  processor(text=__lowerCAmelCase				,      images=__lowerCAmelCase  )

					self.assertListEqual(list(inputs.keys()  )				,      processor.model_input_names  )



