






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 							_UpperCamelCase			(       self					)       ->    Dict:
								snake_case_				=							tempfile.mkdtemp()

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

								snake_case_				=							InstructBlipProcessor(a       ,			a       ,			a					)

								processor.save_pretrained(self.tmpdirname					)




	def 							_UpperCamelCase			(       self       ,			**a					)       ->    Optional[Any]:
								return AutoProcessor.from_pretrained(self.tmpdirname       ,			**a					).tokenizer




	def 							_UpperCamelCase			(       self       ,			**a					)       ->    List[str]:
								return AutoProcessor.from_pretrained(self.tmpdirname       ,			**a					).image_processor




	def 							_UpperCamelCase			(       self       ,			**a					)       ->    int:
								return AutoProcessor.from_pretrained(self.tmpdirname       ,			**a					).qformer_tokenizer




	def 							_UpperCamelCase			(       self					)       ->    Union[str, Any]:
								shutil.rmtree(self.tmpdirname					)




	def 							_UpperCamelCase			(       self					)       ->    Union[str, Any]:
								snake_case_				=							[np.random.randint(2_55       ,			size=(3, 30, 4_00)       ,			dtype=np.uinta					)]

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

								return image_inputs




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							InstructBlipProcessor(
								    tokenizer=self.get_tokenizer()       ,			image_processor=self.get_image_processor()       ,			qformer_tokenizer=self.get_qformer_tokenizer()       ,			)
								processor.save_pretrained(self.tmpdirname					)

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

								snake_case_				=							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					)       ->    Optional[int]:
								snake_case_				=							self.get_image_processor()
								snake_case_				=							self.get_tokenizer()
								snake_case_				=							self.get_qformer_tokenizer()

								snake_case_				=							InstructBlipProcessor(
								    tokenizer=a       ,			image_processor=a       ,			qformer_tokenizer=a					)

								snake_case_				=							self.prepare_image_inputs()

								snake_case_				=							image_processor(a       ,			return_tensors='np'					)
								snake_case_				=							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					)       ->    List[str]:
								snake_case_				=							self.get_image_processor()
								snake_case_				=							self.get_tokenizer()
								snake_case_				=							self.get_qformer_tokenizer()

								snake_case_				=							InstructBlipProcessor(
								    tokenizer=a       ,			image_processor=a       ,			qformer_tokenizer=a					)

								snake_case_				=							'lower newer'

								snake_case_				=							processor(text=a					)

								snake_case_				=							tokenizer(a       ,			return_token_type_ids=a					)
								snake_case_				=							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					)       ->    Union[str, Any]:
								snake_case_				=							self.get_image_processor()
								snake_case_				=							self.get_tokenizer()
								snake_case_				=							self.get_qformer_tokenizer()

								snake_case_				=							InstructBlipProcessor(
								    tokenizer=a       ,			image_processor=a       ,			qformer_tokenizer=a					)

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

								snake_case_				=							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					)       ->    Tuple:
								snake_case_				=							self.get_image_processor()
								snake_case_				=							self.get_tokenizer()
								snake_case_				=							self.get_qformer_tokenizer()

								snake_case_				=							InstructBlipProcessor(
								    tokenizer=a       ,			image_processor=a       ,			qformer_tokenizer=a					)

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

								snake_case_				=							processor.batch_decode(a					)
								snake_case_				=							tokenizer.batch_decode(a					)

								self.assertListEqual(a       ,			a					)






	def 							_UpperCamelCase			(       self					)       ->    Any:
								snake_case_				=							self.get_image_processor()
								snake_case_				=							self.get_tokenizer()
								snake_case_				=							self.get_qformer_tokenizer()

								snake_case_				=							InstructBlipProcessor(
								    tokenizer=a       ,			image_processor=a       ,			qformer_tokenizer=a					)

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

								snake_case_				=							processor(text=a       ,			images=a					)

								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


lowercase  							=					{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					[
	    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TapasForMaskedLM",
	    "TapasForQuestionAnswering",
	    "TapasForSequenceClassification",
	    "TapasModel",
	    "TapasPreTrainedModel",
	    "load_tf_weights_in_tapas",
	]
try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					[
	    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFTapasForMaskedLM",
	    "TFTapasForQuestionAnswering",
	    "TFTapasForSequenceClassification",
	    "TFTapasModel",
	    "TFTapasPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
	from .tokenization_tapas import TapasTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tapas import (
		    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TapasForMaskedLM,
		    TapasForQuestionAnswering,
		    TapasForSequenceClassification,
		    TapasModel,
		    TapasPreTrainedModel,
		    load_tf_weights_in_tapas,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_tapas import (
		    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFTapasForMaskedLM,
		    TFTapasForQuestionAnswering,
		    TFTapasForSequenceClassification,
		    TFTapasModel,
		    TFTapasPreTrainedModel,
		)


else:
	import sys

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





