



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 OwlViTImageProcessor, OwlViTProcessor

@require_vision
class    _A	(     unittest.TestCase   ):



							def 		_lowerCamelCase      (		self							:					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(__SCREAMING_SNAKE_CASE					,		range(len(__SCREAMING_SNAKE_CASE))))
											__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(__SCREAMING_SNAKE_CASE) + '''\n''')
											with open(self.merges_file					,		'''w'''					,		encoding='''utf-8''') as fp:
															fp.write('''\n'''.join(__SCREAMING_SNAKE_CASE))

											__a							=    {
											    '''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							=    os.path.join(self.tmpdirname					,		__SCREAMING_SNAKE_CASE)
											with open(self.image_processor_file					,		'''w'''					,		encoding='''utf-8''') as fp:
															json.dump(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)



							def 		_lowerCamelCase      (		self							:					str					,		**__SCREAMING_SNAKE_CASE							:					Tuple):


											'''simple docstring'''

											return CLIPTokenizer.from_pretrained(self.tmpdirname					,		pad_token='''!'''					,		**__SCREAMING_SNAKE_CASE)



							def 		_lowerCamelCase      (		self							:					Any					,		**__SCREAMING_SNAKE_CASE							:					Any):


											'''simple docstring'''

											return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,		pad_token='''!'''					,		**__SCREAMING_SNAKE_CASE)



							def 		_lowerCamelCase      (		self							:					Any					,		**__SCREAMING_SNAKE_CASE							:					Union[str, Any]):


											'''simple docstring'''

											return OwlViTImageProcessor.from_pretrained(self.tmpdirname					,		**__SCREAMING_SNAKE_CASE)



							def 		_lowerCamelCase      (		self							:					List[str]):


											'''simple docstring'''

											shutil.rmtree(self.tmpdirname)



							def 		_lowerCamelCase      (		self							:					Dict):


											'''simple docstring'''

											__a							=    [np.random.randint(255					,		size=(3, 30, 400)					,		dtype=np.uinta)]

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

											return image_inputs



							def 		_lowerCamelCase      (		self							:					List[str]):


											'''simple docstring'''

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

											__a							=    OwlViTProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,		image_processor=__SCREAMING_SNAKE_CASE)
											processor_slow.save_pretrained(self.tmpdirname)
											__a							=    OwlViTProcessor.from_pretrained(self.tmpdirname					,		use_fast=__SCREAMING_SNAKE_CASE)

											__a							=    OwlViTProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,		image_processor=__SCREAMING_SNAKE_CASE)
											processor_fast.save_pretrained(self.tmpdirname)
											__a							=    OwlViTProcessor.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					,		__SCREAMING_SNAKE_CASE)
											self.assertIsInstance(processor_fast.tokenizer					,		__SCREAMING_SNAKE_CASE)

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



							def 		_lowerCamelCase      (		self							:					Tuple):


											'''simple docstring'''

											__a							=    OwlViTProcessor(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=__SCREAMING_SNAKE_CASE)

											__a							=    OwlViTProcessor.from_pretrained(
											    self.tmpdirname					,		bos_token='''(BOS)'''					,		eos_token='''(EOS)'''					,		do_normalize=__SCREAMING_SNAKE_CASE)

											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 		_lowerCamelCase      (		self							:					Tuple):


											'''simple docstring'''

											__a							=    self.get_image_processor()
											__a							=    self.get_tokenizer()

											__a							=    OwlViTProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,		image_processor=__SCREAMING_SNAKE_CASE)

											__a							=    self.prepare_image_inputs()

											__a							=    image_processor(__SCREAMING_SNAKE_CASE					,		return_tensors='''np''')
											__a							=    processor(images=__SCREAMING_SNAKE_CASE					,		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 		_lowerCamelCase      (		self							:					Optional[int]):


											'''simple docstring'''

											__a							=    self.get_image_processor()
											__a							=    self.get_tokenizer()

											__a							=    OwlViTProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,		image_processor=__SCREAMING_SNAKE_CASE)

											__a							=    '''lower newer'''

											__a							=    processor(text=__SCREAMING_SNAKE_CASE					,		return_tensors='''np''')

											__a							=    tokenizer(__SCREAMING_SNAKE_CASE					,		return_tensors='''np''')

											for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key][0].tolist()					,		encoded_processor[key][0].tolist())



							def 		_lowerCamelCase      (		self							:					Optional[Any]):


											'''simple docstring'''

											__a							=    self.get_image_processor()
											__a							=    self.get_tokenizer()

											__a							=    OwlViTProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,		image_processor=__SCREAMING_SNAKE_CASE)

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

											__a							=    processor(text=__SCREAMING_SNAKE_CASE					,		images=__SCREAMING_SNAKE_CASE)

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

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



							def 		_lowerCamelCase      (		self							:					Any):


											'''simple docstring'''

											__a							=    '''google/owlvit-base-patch32'''
											__a							=    OwlViTProcessor.from_pretrained(__SCREAMING_SNAKE_CASE)

											__a							=    ['''cat''', '''nasa badge''']
											__a							=    processor(text=__SCREAMING_SNAKE_CASE)

											__a							=    16
											self.assertListEqual(list(inputs.keys())					,		['''input_ids''', '''attention_mask'''])
											self.assertEqual(inputs['''input_ids'''].shape					,		(2, seq_length))

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



							def 		_lowerCamelCase      (		self							:					Union[str, Any]):


											'''simple docstring'''

											__a							=    '''google/owlvit-base-patch32'''
											__a							=    OwlViTProcessor.from_pretrained(__SCREAMING_SNAKE_CASE)

											__a							=    [['''cat''', '''nasa badge'''], ['''person''']]
											__a							=    processor(text=__SCREAMING_SNAKE_CASE)

											__a							=    16
											__a							=    len(__SCREAMING_SNAKE_CASE)
											__a							=    max([len(__SCREAMING_SNAKE_CASE) for texts in input_texts])

											self.assertListEqual(list(inputs.keys())					,		['''input_ids''', '''attention_mask'''])
											self.assertEqual(inputs['''input_ids'''].shape					,		(batch_size * num_max_text_queries, seq_length))

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



							def 		_lowerCamelCase      (		self							:					Optional[int]):


											'''simple docstring'''

											__a							=    '''google/owlvit-base-patch32'''
											__a							=    OwlViTProcessor.from_pretrained(__SCREAMING_SNAKE_CASE)

											__a							=    ['''cat''', '''nasa badge''']
											__a							=    processor(text=__SCREAMING_SNAKE_CASE)

											__a							=    16
											__a							=    inputs['''input_ids''']
											__a							=    [
											    [49_406, 2_368, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
											    [49_406, 6_841, 11_301, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
											]

											self.assertListEqual(list(inputs.keys())					,		['''input_ids''', '''attention_mask'''])
											self.assertEqual(inputs['''input_ids'''].shape					,		(2, seq_length))
											self.assertListEqual(list(input_ids[0])					,		predicted_ids[0])
											self.assertListEqual(list(input_ids[1])					,		predicted_ids[1])



							def 		_lowerCamelCase      (		self							:					List[str]):


											'''simple docstring'''

											__a							=    self.get_image_processor()
											__a							=    self.get_tokenizer()

											__a							=    OwlViTProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,		image_processor=__SCREAMING_SNAKE_CASE)

											__a							=    self.prepare_image_inputs()
											__a							=    self.prepare_image_inputs()

											__a							=    processor(images=__SCREAMING_SNAKE_CASE					,		query_images=__SCREAMING_SNAKE_CASE)

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

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





							def 		_lowerCamelCase      (		self							:					List[Any]):


											'''simple docstring'''

											__a							=    self.get_image_processor()
											__a							=    self.get_tokenizer()

											__a							=    OwlViTProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,		image_processor=__SCREAMING_SNAKE_CASE)

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

											__a							=    processor.batch_decode(__SCREAMING_SNAKE_CASE)
											__a							=    tokenizer.batch_decode(__SCREAMING_SNAKE_CASE)

											self.assertListEqual(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)










import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__snake_case						:Optional[Any]						=					logging.get_logger(__name__)


__snake_case						:List[Any]						=					'''▁'''

__snake_case						:List[Any]						=					{'''vocab_file''': '''spiece.model'''}

__snake_case						:Tuple						=					{
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

__snake_case						:List[Any]						=					{
    '''google/reformer-crime-and-punishment''': 52_4288,
}

class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   int						 =						VOCAB_FILES_NAMES
       UpperCamelCase__  :   str						 =						PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase__  :   Dict						 =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase__  :   Optional[int]						 =						['''input_ids''', '''attention_mask''']



       def __init__(		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]="</s>"					,		__SCREAMING_SNAKE_CASE							:					List[Any]="<unk>"					,		__SCREAMING_SNAKE_CASE							:					Any=[]					,		__SCREAMING_SNAKE_CASE							:					Optional[Dict[str, Any]] = None					,		**__SCREAMING_SNAKE_CASE							:					List[Any]					,		):


           '''simple docstring'''

           __a							=    {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=__SCREAMING_SNAKE_CASE					,		unk_token=__SCREAMING_SNAKE_CASE					,		additional_special_tokens=__SCREAMING_SNAKE_CASE					,		sp_model_kwargs=self.sp_model_kwargs					,		**__SCREAMING_SNAKE_CASE					,		)

           __a							=    vocab_file
           __a							=    spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(__SCREAMING_SNAKE_CASE)



       @property
       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           return self.sp_model.get_piece_size()



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE): i for i in range(self.vocab_size)}
           vocab.update(self.added_tokens_encoder)
           return vocab



       def __getstate__(		self							:					Dict):


           '''simple docstring'''

           __a							=    self.__dict__.copy()
           __a							=    None
           return state



       def __setstate__(		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           __a							=    d

           # for backward compatibility
           if not hasattr(self					,		'''sp_model_kwargs'''):
               __a							=    {}

           __a							=    spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(self.vocab_file)



       def 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           return self.sp_model.encode(__SCREAMING_SNAKE_CASE					,		out_type=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           return self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           if index < self.sp_model.get_piece_size():
               __a							=    self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE)
           return token



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           __a							=    []
           __a							=    ''''''
           for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                   out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE) + token
                   __a							=    []
               else:
                   current_sub_tokens.append(__SCREAMING_SNAKE_CASE)
           out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE)
           return out_string.strip()





       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Optional[str] = None):


           '''simple docstring'''

           if not os.path.isdir(__SCREAMING_SNAKE_CASE):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory')
               return
           __a							=    os.path.join(
               __SCREAMING_SNAKE_CASE					,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

           if os.path.abspath(self.vocab_file) != os.path.abspath(__SCREAMING_SNAKE_CASE) and os.path.isfile(self.vocab_file):
               copyfile(self.vocab_file					,		__SCREAMING_SNAKE_CASE)
           elif not os.path.isfile(self.vocab_file):
               with open(__SCREAMING_SNAKE_CASE					,		'''wb''') as fi:
                   __a							=    self.sp_model.serialized_model_proto()
                   fi.write(__SCREAMING_SNAKE_CASE)

           return (out_vocab_file,)





