




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 							__UpperCamelCase			( unittest.TestCase  ):





				'''simple docstring'''



				def       __snake_case		( self						):
											lowerCAmelCase   =		tempfile.mkdtemp()

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

											lowerCAmelCase   =		InstructBlipProcessor(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__						)

											processor.save_pretrained(self.tmpdirname						)



				def       __snake_case		( self       , **UpperCAmelCase_						):
											return AutoProcessor.from_pretrained(self.tmpdirname       , **UpperCamelCase__						).tokenizer



				def       __snake_case		( self       , **UpperCAmelCase_						):
											return AutoProcessor.from_pretrained(self.tmpdirname       , **UpperCamelCase__						).image_processor



				def       __snake_case		( self       , **UpperCAmelCase_						):
											return AutoProcessor.from_pretrained(self.tmpdirname       , **UpperCamelCase__						).qformer_tokenizer



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



				def       __snake_case		( self						):
											lowerCAmelCase   =		[np.random.randint(2_55       , size=(3, 30, 4_00)       , dtype=np.uinta						)]

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

											return image_inputs



				def       __snake_case		( self						):
											lowerCAmelCase   =		InstructBlipProcessor(
											    tokenizer=self.get_tokenizer()       , image_processor=self.get_image_processor()       , qformer_tokenizer=self.get_qformer_tokenizer()       , )
											processor.save_pretrained(self.tmpdirname						)

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

											lowerCAmelCase   =		InstructBlipProcessor.from_pretrained(
											    self.tmpdirname       , bos_token='''(BOS)'''       , eos_token='''(EOS)'''       , do_normalize=UpperCamelCase__       , padding_value=1.0						)

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

											self.assertEqual(processor.image_processor.to_json_string()       , image_processor_add_kwargs.to_json_string()						)
											self.assertIsInstance(processor.image_processor       , UpperCamelCase__						)
											self.assertIsInstance(processor.qformer_tokenizer       , UpperCamelCase__						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.get_image_processor()
											lowerCAmelCase   =		self.get_tokenizer()
											lowerCAmelCase   =		self.get_qformer_tokenizer()

											lowerCAmelCase   =		InstructBlipProcessor(
											    tokenizer=UpperCamelCase__       , image_processor=UpperCamelCase__       , qformer_tokenizer=UpperCamelCase__						)

											lowerCAmelCase   =		self.prepare_image_inputs()

											lowerCAmelCase   =		image_processor(UpperCamelCase__       , return_tensors='''np'''						)
											lowerCAmelCase   =		processor(images=UpperCamelCase__       , 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						):
											lowerCAmelCase   =		self.get_image_processor()
											lowerCAmelCase   =		self.get_tokenizer()
											lowerCAmelCase   =		self.get_qformer_tokenizer()

											lowerCAmelCase   =		InstructBlipProcessor(
											    tokenizer=UpperCamelCase__       , image_processor=UpperCamelCase__       , qformer_tokenizer=UpperCamelCase__						)

											lowerCAmelCase   =		'''lower newer'''

											lowerCAmelCase   =		processor(text=UpperCamelCase__						)

											lowerCAmelCase   =		tokenizer(UpperCamelCase__       , return_token_type_ids=UpperCamelCase__						)
											lowerCAmelCase   =		qformer_tokenizer(UpperCamelCase__       , return_token_type_ids=UpperCamelCase__						)

											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       __snake_case		( self						):
											lowerCAmelCase   =		self.get_image_processor()
											lowerCAmelCase   =		self.get_tokenizer()
											lowerCAmelCase   =		self.get_qformer_tokenizer()

											lowerCAmelCase   =		InstructBlipProcessor(
											    tokenizer=UpperCamelCase__       , image_processor=UpperCamelCase__       , qformer_tokenizer=UpperCamelCase__						)

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

											lowerCAmelCase   =		processor(text=UpperCamelCase__       , images=UpperCamelCase__						)

											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(UpperCamelCase__						):
																		processor()



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.get_image_processor()
											lowerCAmelCase   =		self.get_tokenizer()
											lowerCAmelCase   =		self.get_qformer_tokenizer()

											lowerCAmelCase   =		InstructBlipProcessor(
											    tokenizer=UpperCamelCase__       , image_processor=UpperCamelCase__       , qformer_tokenizer=UpperCamelCase__						)

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

											lowerCAmelCase   =		processor.batch_decode(UpperCamelCase__						)
											lowerCAmelCase   =		tokenizer.batch_decode(UpperCamelCase__						)

											self.assertListEqual(UpperCamelCase__       , UpperCamelCase__						)






				def       __snake_case		( self						):
											lowerCAmelCase   =		self.get_image_processor()
											lowerCAmelCase   =		self.get_tokenizer()
											lowerCAmelCase   =		self.get_qformer_tokenizer()

											lowerCAmelCase   =		InstructBlipProcessor(
											    tokenizer=UpperCamelCase__       , image_processor=UpperCamelCase__       , qformer_tokenizer=UpperCamelCase__						)

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

											lowerCAmelCase   =		processor(text=UpperCamelCase__       , images=UpperCamelCase__						)

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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase_						={
    """configuration_efficientformer""": [
        """EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientFormerConfig""",
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=["""EfficientFormerImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=[
		    """EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """EfficientFormerForImageClassification""",
		    """EfficientFormerForImageClassificationWithTeacher""",
		    """EfficientFormerModel""",
		    """EfficientFormerPreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=[
		    """TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFEfficientFormerForImageClassification""",
		    """TFEfficientFormerForImageClassificationWithTeacher""",
		    """TFEfficientFormerModel""",
		    """TFEfficientFormerPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_efficientformer import EfficientFormerImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_efficientformer import (
				    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    EfficientFormerForImageClassification,
				    EfficientFormerForImageClassificationWithTeacher,
				    EfficientFormerModel,
				    EfficientFormerPreTrainedModel,
				)
		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_efficientformer import (
				    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFEfficientFormerForImageClassification,
				    TFEfficientFormerForImageClassificationWithTeacher,
				    TFEfficientFormerModel,
				    TFEfficientFormerPreTrainedModel,
				)

else:
		import sys

		UpperCAmelCase_						=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



