
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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast





@require_vision
class     lowerCamelCase      (       unittest.TestCase					):







	def  snake_case__     (     self      :Tuple			)		->      Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= tempfile.mkdtemp()

				SCREAMING_SNAKE_CASE     						= BlipImageProcessor()
				SCREAMING_SNAKE_CASE     						= GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''			)

				SCREAMING_SNAKE_CASE     						= BlipaProcessor(lowercase   ,     lowercase			)

				processor.save_pretrained(self.tmpdirname			)







	def  snake_case__     (     self      :Optional[Any]   ,     **lowercase      :Tuple			)		->      Optional[Any]:







				"""simple docstring"""






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







	def  snake_case__     (     self      :List[Any]   ,     **lowercase      :Any			)		->      Union[str, Any]:







				"""simple docstring"""






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







	def  snake_case__     (     self      :Dict			)		->      List[Any]:







				"""simple docstring"""






				shutil.rmtree(self.tmpdirname			)







	def  snake_case__     (     self      :Optional[Any]			)		->      Tuple:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [np.random.randint(2_5_5   ,     size=(3, 3_0, 4_0_0)   ,     dtype=np.uinta			)]

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

				return image_inputs







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= BlipaProcessor(tokenizer=self.get_tokenizer()   ,     image_processor=self.get_image_processor()			)
				processor.save_pretrained(self.tmpdirname			)

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

				SCREAMING_SNAKE_CASE     						= BlipaProcessor.from_pretrained(
				    self.tmpdirname   ,     bos_token='''(BOS)'''   ,     eos_token='''(EOS)'''   ,     do_normalize=lowercase   ,     padding_value=1.0			)

				self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer_add_kwargs.get_vocab()			)
				self.assertIsInstance(processor.tokenizer   ,     lowercase			)

				self.assertEqual(processor.image_processor.to_json_string()   ,     image_processor_add_kwargs.to_json_string()			)
				self.assertIsInstance(processor.image_processor   ,     lowercase			)







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.get_image_processor()
				SCREAMING_SNAKE_CASE     						= self.get_tokenizer()

				SCREAMING_SNAKE_CASE     						= BlipaProcessor(tokenizer=lowercase   ,     image_processor=lowercase			)

				SCREAMING_SNAKE_CASE     						= self.prepare_image_inputs()

				SCREAMING_SNAKE_CASE     						= image_processor(lowercase   ,     return_tensors='''np'''			)
				SCREAMING_SNAKE_CASE     						= processor(images=lowercase   ,     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  snake_case__     (     self      :int			)		->      str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.get_image_processor()
				SCREAMING_SNAKE_CASE     						= self.get_tokenizer()

				SCREAMING_SNAKE_CASE     						= BlipaProcessor(tokenizer=lowercase   ,     image_processor=lowercase			)

				SCREAMING_SNAKE_CASE     						= '''lower newer'''

				SCREAMING_SNAKE_CASE     						= processor(text=lowercase			)

				SCREAMING_SNAKE_CASE     						= tokenizer(lowercase   ,     return_token_type_ids=lowercase			)

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







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.get_image_processor()
				SCREAMING_SNAKE_CASE     						= self.get_tokenizer()

				SCREAMING_SNAKE_CASE     						= BlipaProcessor(tokenizer=lowercase   ,     image_processor=lowercase			)

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

				SCREAMING_SNAKE_CASE     						= processor(text=lowercase   ,     images=lowercase			)

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

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







	def  snake_case__     (     self      :Dict			)		->      Tuple:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.get_image_processor()
				SCREAMING_SNAKE_CASE     						= self.get_tokenizer()

				SCREAMING_SNAKE_CASE     						= BlipaProcessor(tokenizer=lowercase   ,     image_processor=lowercase			)

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

				SCREAMING_SNAKE_CASE     						= processor.batch_decode(lowercase			)
				SCREAMING_SNAKE_CASE     						= tokenizer.batch_decode(lowercase			)

				self.assertListEqual(lowercase   ,     lowercase			)







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.get_image_processor()
				SCREAMING_SNAKE_CASE     						= self.get_tokenizer()

				SCREAMING_SNAKE_CASE     						= BlipaProcessor(tokenizer=lowercase   ,     image_processor=lowercase			)

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

				SCREAMING_SNAKE_CASE     						= processor(text=lowercase   ,     images=lowercase			)

				# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
				self.assertListEqual(list(inputs.keys()			)   ,     ['''pixel_values''', '''input_ids''', '''attention_mask''']			)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase					   =       {
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       ['BridgeTowerImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BridgeTowerForContrastiveLearning',
							    'BridgeTowerForImageAndTextRetrieval',
							    'BridgeTowerForMaskedLM',
							    'BridgeTowerModel',
							    'BridgeTowerPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_bridgetower import (
							    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BridgeTowerConfig,
							    BridgeTowerTextConfig,
							    BridgeTowerVisionConfig,
							)
							from .processing_bridgetower import BridgeTowerProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_bridgetower import BridgeTowerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bridgetower import (
														    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BridgeTowerForContrastiveLearning,
														    BridgeTowerForImageAndTextRetrieval,
														    BridgeTowerForMaskedLM,
														    BridgeTowerModel,
														    BridgeTowerPreTrainedModel,
														)


else:
							import sys

							__lowerCAmelCase					   =       _LazyModule(__name__, globals()['__file__'], _import_structure)