



'''simple docstring'''



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 ):



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__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        UpperCAmelCase_ (    self			,				**UpperCamelCase     )     ->			Any:
						return AutoProcessor.from_pretrained(self.tmpdirname			,				**UpperCamelCase     ).tokenizer



					def        UpperCAmelCase_ (    self			,				**UpperCamelCase     )     ->			Optional[Any]:
						return AutoProcessor.from_pretrained(self.tmpdirname			,				**UpperCamelCase     ).image_processor



					def        UpperCAmelCase_ (    self			,				**UpperCamelCase     )     ->			Dict:
						return AutoProcessor.from_pretrained(self.tmpdirname			,				**UpperCamelCase     ).qformer_tokenizer



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						shutil.rmtree(self.tmpdirname     )



					def        UpperCAmelCase_ (    self     )     ->			int:
						__lowerCAmelCase												=  [np.random.randint(255			,				size=(3, 30, 400)			,				dtype=np.uinta     )]

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

						return image_inputs



					def        UpperCAmelCase_ (    self     )     ->			int:
						__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        UpperCAmelCase_ (    self     )     ->			Dict:
						__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        UpperCAmelCase_ (    self     )     ->			int:
						__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        UpperCAmelCase_ (    self     )     ->			str:
						__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        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
						__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        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__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"]			,				)




'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowerCAmelCase	:		Optional[Any]				=   {
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		List[str]				=   [
	    '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TrOCRForCausalLM''',
	    '''TrOCRPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
	from .processing_trocr import TrOCRProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
	import sys

	lowerCAmelCase	:		Optional[Any]				=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)