
"""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 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				def 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   tempfile.mkdtemp()

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   BlipImageProcessor()
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""    )

											SCREAMING_SNAKE_CASE__						:       str           =   InstructBlipProcessor(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											processor.save_pretrained(self.tmpdirname    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




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



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




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



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											return AutoProcessor.from_pretrained(self.tmpdirname   ,       **SCREAMING_SNAKE_CASE__    ).qformer_tokenizer



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											shutil.rmtree(self.tmpdirname    )



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   [np.random.randint(2_55   ,       size=(3, 30, 4_00)   ,       dtype=np.uinta    )]

											SCREAMING_SNAKE_CASE__						:       int           =   [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__   ,       0   ,       -1    )    ) for x in image_inputs]

											return image_inputs



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   InstructBlipProcessor(
											    tokenizer=self.get_tokenizer()   ,       image_processor=self.get_image_processor()   ,       qformer_tokenizer=self.get_qformer_tokenizer()   ,       )
											processor.save_pretrained(self.tmpdirname    )

											SCREAMING_SNAKE_CASE__						:       str           =   self.get_tokenizer(bos_token="""(BOS)"""   ,       eos_token="""(EOS)"""    )
											SCREAMING_SNAKE_CASE__						:       Any           =   self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__   ,       padding_value=1.0    )

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   InstructBlipProcessor.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__    )
											self.assertIsInstance(processor.qformer_tokenizer   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       Any           =   self.get_tokenizer()
											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.get_qformer_tokenizer()

											SCREAMING_SNAKE_CASE__						:       str           =   InstructBlipProcessor(
											    tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__   ,       qformer_tokenizer=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   image_processor(SCREAMING_SNAKE_CASE__   ,       return_tensors="""np"""    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   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 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       str           =   self.get_tokenizer()
											SCREAMING_SNAKE_CASE__						:       str           =   self.get_qformer_tokenizer()

											SCREAMING_SNAKE_CASE__						:       str           =   InstructBlipProcessor(
											    tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__   ,       qformer_tokenizer=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """lower newer"""

											SCREAMING_SNAKE_CASE__						:       int           =   processor(text=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Any           =   tokenizer(SCREAMING_SNAKE_CASE__   ,       return_token_type_ids=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       int           =   qformer_tokenizer(SCREAMING_SNAKE_CASE__   ,       return_token_type_ids=SCREAMING_SNAKE_CASE__    )

											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 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.get_tokenizer()
											SCREAMING_SNAKE_CASE__						:       int           =   self.get_qformer_tokenizer()

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   InstructBlipProcessor(
											    tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__   ,       qformer_tokenizer=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   """lower newer"""
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE__						:       Any           =   processor(text=SCREAMING_SNAKE_CASE__   ,       images=SCREAMING_SNAKE_CASE__    )

											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(SCREAMING_SNAKE_CASE__    ):
																		processor()



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       int           =   self.get_tokenizer()
											SCREAMING_SNAKE_CASE__						:       Any           =   self.get_qformer_tokenizer()

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   InstructBlipProcessor(
											    tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__   ,       qformer_tokenizer=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   processor.batch_decode(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   tokenizer.batch_decode(SCREAMING_SNAKE_CASE__    )

											self.assertListEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.get_tokenizer()
											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.get_qformer_tokenizer()

											SCREAMING_SNAKE_CASE__						:       List[str]           =   InstructBlipProcessor(
											    tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__   ,       qformer_tokenizer=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   """lower newer"""
											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE__						:       Any           =   processor(text=SCREAMING_SNAKE_CASE__   ,       images=SCREAMING_SNAKE_CASE__    )

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






"""simple docstring"""




from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_torch_available():
					import torch

					from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

UpperCAmelCase__      :    List[str]    							= logging.get_logger(__name__)



@add_end_docstrings(a__  )
class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self   ,       *SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											super().__init__(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )
											requires_backends(self   ,       """vision"""    )
											self.check_model_type(SCREAMING_SNAKE_CASE__    )



				def __call__(self   ,       SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		List[str]:



											"""simple docstring"""




											return super().__call__(SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											return {}, {}, {}



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   load_image(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Any           =   image.size
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.image_processor(images=SCREAMING_SNAKE_CASE__   ,       return_tensors=self.framework    )
											return model_inputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.model(**SCREAMING_SNAKE_CASE__    )
											return model_outputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   model_outputs.predicted_depth
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.nn.functional.interpolate(
											    predicted_depth.unsqueeze(1    )   ,       size=self.image_size[::-1]   ,       mode="""bicubic"""   ,       align_corners=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   prediction.squeeze().cpu().numpy()
											SCREAMING_SNAKE_CASE__						:       Any           =   (output * 2_55 / np.max(SCREAMING_SNAKE_CASE__    )).astype("""uint8"""    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   Image.fromarray(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       str           =   {}
											SCREAMING_SNAKE_CASE__						:       Any           =   predicted_depth
											SCREAMING_SNAKE_CASE__						:       Dict           =   depth
											return output_dict




