
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        SCREAMING_SNAKE_CASE__    (							unittest.TestCase    ):

							"""simple docstring"""






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





													'''simple docstring'''






													__UpperCamelCase       =			tempfile.mkdtemp()

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

													__UpperCamelCase       =			BlipaProcessor(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)

													processor.save_pretrained(self.tmpdirname			)



							def     A__		(  self     ,	**SCREAMING_SNAKE_CASE_			)->   List[str]:





													'''simple docstring'''






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



							def     A__		(  self     ,	**SCREAMING_SNAKE_CASE_			)->   Dict:





													'''simple docstring'''






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



							def     A__		(  self			)->   int:





													'''simple docstring'''






													shutil.rmtree(self.tmpdirname			)



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





													'''simple docstring'''






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

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

													return image_inputs



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





													'''simple docstring'''






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

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

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

													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     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			self.get_image_processor()
													__UpperCamelCase       =			self.get_tokenizer()

													__UpperCamelCase       =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			self.prepare_image_inputs()

													__UpperCamelCase       =			image_processor(SCREAMING_SNAKE_CASE_     ,	return_tensors='''np'''			)
													__UpperCamelCase       =			processor(images=SCREAMING_SNAKE_CASE_     ,	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     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			self.get_image_processor()
													__UpperCamelCase       =			self.get_tokenizer()

													__UpperCamelCase       =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			'''lower newer'''

													__UpperCamelCase       =			processor(text=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			tokenizer(SCREAMING_SNAKE_CASE_     ,	return_token_type_ids=SCREAMING_SNAKE_CASE_			)

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



							def     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			self.get_image_processor()
													__UpperCamelCase       =			self.get_tokenizer()

													__UpperCamelCase       =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_			)

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

													__UpperCamelCase       =			processor(text=SCREAMING_SNAKE_CASE_     ,	images=SCREAMING_SNAKE_CASE_			)

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

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



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			self.get_image_processor()
													__UpperCamelCase       =			self.get_tokenizer()

													__UpperCamelCase       =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_			)

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

													__UpperCamelCase       =			processor.batch_decode(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			tokenizer.batch_decode(SCREAMING_SNAKE_CASE_			)

													self.assertListEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			self.get_image_processor()
													__UpperCamelCase       =			self.get_tokenizer()

													__UpperCamelCase       =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_			)

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

													__UpperCamelCase       =			processor(text=SCREAMING_SNAKE_CASE_     ,	images=SCREAMING_SNAKE_CASE_			)

													# 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_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase__							:							Dict									=			{"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							Tuple									=			["ViTFeatureExtractor"]
				lowercase__							:							str									=			["ViTImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							List[str]									=			[
				    "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "ViTForImageClassification",
				    "ViTForMaskedImageModeling",
				    "ViTModel",
				    "ViTPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							Tuple									=			[
				    "TFViTForImageClassification",
				    "TFViTModel",
				    "TFViTPreTrainedModel",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							List[Any]									=			[
				    "FlaxViTForImageClassification",
				    "FlaxViTModel",
				    "FlaxViTPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_vit import ViTFeatureExtractor
								from .image_processing_vit import ViTImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_vit import (
								    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ViTForImageClassification,
								    ViTForMaskedImageModeling,
								    ViTModel,
								    ViTPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
				import sys

				lowercase__							:							List[Any]									=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




