

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,
					    BertTokenizerFast,
					    BlipImageProcessor,
					    GPTaTokenizer,
					    InstructBlipProcessor,
					    PreTrainedTokenizerFast,
					)

@require_vision
class 					a	(						unittest.TestCase					):


						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										_UpperCAmelCase							:		List[Any]					=     tempfile.mkdtemp()

										_UpperCAmelCase							:		List[str]					=     BlipImageProcessor()
										_UpperCAmelCase							:		Union[str, Any]					=     GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"						)
										_UpperCAmelCase							:		Any					=     BertTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-bert"						)

										_UpperCAmelCase							:		Any					=     InstructBlipProcessor(a__    ,       a__    ,       a__						)

										processor.save_pretrained(self.tmpdirname						)


						def 							_UpperCAmelCase					(							self    ,       **A_						):







										'''simple docstring'''


										return AutoProcessor.from_pretrained(self.tmpdirname    ,       **a__						).tokenizer


						def 							_UpperCAmelCase					(							self    ,       **A_						):







										'''simple docstring'''


										return AutoProcessor.from_pretrained(self.tmpdirname    ,       **a__						).image_processor


						def 							_UpperCAmelCase					(							self    ,       **A_						):







										'''simple docstring'''


										return AutoProcessor.from_pretrained(self.tmpdirname    ,       **a__						).qformer_tokenizer


						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										shutil.rmtree(self.tmpdirname						)


						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


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

										_UpperCAmelCase							:		List[str]					=     [Image.fromarray(np.moveaxis(a__    ,       0    ,       -1						)						) for x in image_inputs]

										return image_inputs


						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										_UpperCAmelCase							:		Optional[Any]					=     InstructBlipProcessor(
										    tokenizer=self.get_tokenizer()    ,       image_processor=self.get_image_processor()    ,       qformer_tokenizer=self.get_qformer_tokenizer()    ,       )
										processor.save_pretrained(self.tmpdirname						)

										_UpperCAmelCase							:		Optional[int]					=     self.get_tokenizer(bos_token="(BOS)"    ,       eos_token="(EOS)"						)
										_UpperCAmelCase							:		str					=     self.get_image_processor(do_normalize=a__    ,       padding_value=1.0						)

										_UpperCAmelCase							:		Tuple					=     InstructBlipProcessor.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__						)
										self.assertIsInstance(processor.qformer_tokenizer    ,       a__						)


						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										_UpperCAmelCase							:		List[str]					=     self.get_image_processor()
										_UpperCAmelCase							:		Optional[int]					=     self.get_tokenizer()
										_UpperCAmelCase							:		Any					=     self.get_qformer_tokenizer()

										_UpperCAmelCase							:		Dict					=     InstructBlipProcessor(
										    tokenizer=a__    ,       image_processor=a__    ,       qformer_tokenizer=a__						)

										_UpperCAmelCase							:		List[Any]					=     self.prepare_image_inputs()

										_UpperCAmelCase							:		List[str]					=     image_processor(a__    ,       return_tensors="np"						)
										_UpperCAmelCase							:		List[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 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										_UpperCAmelCase							:		Any					=     self.get_image_processor()
										_UpperCAmelCase							:		Dict					=     self.get_tokenizer()
										_UpperCAmelCase							:		int					=     self.get_qformer_tokenizer()

										_UpperCAmelCase							:		Tuple					=     InstructBlipProcessor(
										    tokenizer=a__    ,       image_processor=a__    ,       qformer_tokenizer=a__						)

										_UpperCAmelCase							:		List[Any]					=     '''lower newer'''

										_UpperCAmelCase							:		Any					=     processor(text=a__						)

										_UpperCAmelCase							:		Optional[int]					=     tokenizer(a__    ,       return_token_type_ids=a__						)
										_UpperCAmelCase							:		Optional[Any]					=     qformer_tokenizer(a__    ,       return_token_type_ids=a__						)

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

										for key in encoded_tokens_qformer.keys():
														self.assertListEqual(encoded_tokens_qformer[key]    ,       encoded_processor["qformer_" + key]						)


						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										_UpperCAmelCase							:		List[Any]					=     self.get_image_processor()
										_UpperCAmelCase							:		Optional[int]					=     self.get_tokenizer()
										_UpperCAmelCase							:		Optional[Any]					=     self.get_qformer_tokenizer()

										_UpperCAmelCase							:		List[Any]					=     InstructBlipProcessor(
										    tokenizer=a__    ,       image_processor=a__    ,       qformer_tokenizer=a__						)

										_UpperCAmelCase							:		List[Any]					=     '''lower newer'''
										_UpperCAmelCase							:		Tuple					=     self.prepare_image_inputs()

										_UpperCAmelCase							:		Tuple					=     processor(text=a__    ,       images=a__						)

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

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


						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										_UpperCAmelCase							:		int					=     self.get_image_processor()
										_UpperCAmelCase							:		List[Any]					=     self.get_tokenizer()
										_UpperCAmelCase							:		List[Any]					=     self.get_qformer_tokenizer()

										_UpperCAmelCase							:		Optional[Any]					=     InstructBlipProcessor(
										    tokenizer=a__    ,       image_processor=a__    ,       qformer_tokenizer=a__						)

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

										_UpperCAmelCase							:		Tuple					=     processor.batch_decode(a__						)
										_UpperCAmelCase							:		str					=     tokenizer.batch_decode(a__						)

										self.assertListEqual(a__    ,       a__						)





						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


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

										_UpperCAmelCase							:		str					=     InstructBlipProcessor(
										    tokenizer=a__    ,       image_processor=a__    ,       qformer_tokenizer=a__						)

										_UpperCAmelCase							:		str					=     '''lower newer'''
										_UpperCAmelCase							:		str					=     self.prepare_image_inputs()

										_UpperCAmelCase							:		List[Any]					=     processor(text=a__    ,       images=a__						)

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









import copy
import random

from transformers import CLIPTokenizer







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				*a__   ,				**a__):



             """simple docstring"""





             super().__init__(*a__   ,				**a__)
             _lowerCamelCase				:  Optional[Any]						=		{}


      def 					__snake_case      (		self   ,				a__   ,				*a__   ,				**a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		super().add_tokens(a__   ,				*a__   ,				**a__)
             if num_added_tokens == 0:
                    raise ValueError(
                        F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
                        ''' `placeholder_token` that is not already in the tokenizer.''')


      def 					__snake_case      (		self   ,				a__   ,				*a__   ,				a__=1   ,				**a__):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		[]
             if num_vec_per_token == 1:
                    self.try_adding_tokens(a__   ,				*a__   ,				**a__)
                    output.append(a__)
             else:
                    _lowerCamelCase				:  Dict						=		[]
                    for i in range(a__):
                           _lowerCamelCase				:  Optional[Any]						=		placeholder_token + F"""_{i}"""
                           self.try_adding_tokens(a__   ,				*a__   ,				**a__)
                           output.append(a__)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
             for token in self.token_map:
                    if token in placeholder_token:
                           raise ValueError(
                               F"""The tokenizer already has placeholder token {token} that can get confused with"""
                               F""" {placeholder_token}keep placeholder tokens independent""")
             _lowerCamelCase				:  int						=		output


      def 					__snake_case      (		self   ,				a__   ,				a__=False   ,				a__=1.0):



             """simple docstring"""





             if isinstance(a__   ,				a__):
                    _lowerCamelCase				:  Tuple						=		[]
                    for i in range(len(a__)):
                           output.append(self.replace_placeholder_tokens_in_text(text[i]   ,				vector_shuffle=a__))
                    return output
             for placeholder_token in self.token_map:
                    if placeholder_token in text:
                           _lowerCamelCase				:  Union[str, Any]						=		self.token_map[placeholder_token]
                           _lowerCamelCase				:  Optional[Any]						=		tokens[: 1 + int(len(a__) * prop_tokens_to_load)]
                           if vector_shuffle:
                                  _lowerCamelCase				:  int						=		copy.copy(a__)
                                  random.shuffle(a__)
                           _lowerCamelCase				:  List[str]						=		text.replace(a__   ,				''' '''.join(a__))
             return text


      def __call__(		self   ,				a__   ,				*a__   ,				a__=False   ,				a__=1.0   ,				**a__):



             """simple docstring"""





             return super().__call__(
                 self.replace_placeholder_tokens_in_text(
                     a__   ,				vector_shuffle=a__   ,				prop_tokens_to_load=a__)   ,				*a__   ,				**a__   ,				)



      def 					__snake_case      (		self   ,				a__   ,				*a__   ,				a__=False   ,				a__=1.0   ,				**a__):



             """simple docstring"""





             return super().encode(
                 self.replace_placeholder_tokens_in_text(
                     a__   ,				vector_shuffle=a__   ,				prop_tokens_to_load=a__)   ,				*a__   ,				**a__   ,				)

