




'''simple docstring'''
import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
							from PIL import Image
else:
							class   __UpperCAmelCase				:



												'''simple docstring'''





												@staticmethod
												def     __snake_case						(	*_lowercase     :						Optional[Any]      ,       **_lowercase     :						int)    ->							Optional[Any]:
															pass





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		str:
			A_									= hashlib.mda(image.tobytes()     )
			return m.hexdigest()[:10]





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		Dict:
			A_									= np.array(SCREAMING_SNAKE_CASE_     )
			A_									= npimg.shape
			return {"hash": hashimage(SCREAMING_SNAKE_CASE_     ), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		dict(
					    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if MODEL_FOR_MASK_GENERATION_MAPPING else [])				)
					_UpperCamelCase							=		dict(
					    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])				)



					def     __snake_case						(	self     :						Union[str, Any]      ,       _lowercase     :						int      ,       _lowercase     :						Tuple      ,       _lowercase     :						List[Any])    ->							Optional[int]:
								A_									= MaskGenerationPipeline(model=_lowercase      ,       image_processor=_lowercase)
								return image_segmenter, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]



					def     __snake_case						(	self     :						Any      ,       _lowercase     :						Any      ,       _lowercase     :						Dict)    ->							str:
								pass



					@require_tf
					@unittest.skip('Image segmentation not implemented in TF')
					def     __snake_case						(	self     :						Optional[int])    ->							List[str]:
								pass



					@slow
					@require_torch
					def     __snake_case						(	self     :						int)    ->							str:
								A_									= pipeline('mask-generation'      ,       model='facebook/sam-vit-huge')

								A_									= image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'      ,       points_per_batch=256)

								# Shortening by hashing
								A_									= []
								for i, o in enumerate(outputs['masks']):
											new_outupt += [{"mask": mask_to_test_readable(_lowercase), "scores": outputs["scores"][i]}]

								# fmt: off
								self.assertEqual(
								    nested_simplify(_lowercase      ,       decimals=4)      ,       [
								        {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.04_44},
								        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_21},
								        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.01_67},
								        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.01_32},
								        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.00_53},
								        {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.99_67},
								        {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.9_93},
								        {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.99_09},
								        {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.98_79},
								        {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.98_34},
								        {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.97_16},
								        {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.96_12},
								        {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.95_99},
								        {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.95_52},
								        {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.95_32},
								        {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.95_16},
								        {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.94_99},
								        {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.94_83},
								        {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.94_64},
								        {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.9_43},
								        {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.9_43},
								        {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.94_08},
								        {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.93_35},
								        {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.93_26},
								        {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.92_62},
								        {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.89_99},
								        {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.89_86},
								        {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.89_84},
								        {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.88_73},
								        {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.88_71}
								    ]      ,       )
								# fmt: on



					@require_torch
					@slow
					def     __snake_case						(	self     :						List[str])    ->							List[str]:
								A_									= 'facebook/sam-vit-huge'
								A_									= pipeline('mask-generation'      ,       model=_lowercase)

								A_									= image_segmenter(
								    'http://images.cocodataset.org/val2017/000000039769.jpg'      ,       pred_iou_thresh=1      ,       points_per_batch=256)

								# Shortening by hashing
								A_									= []
								for i, o in enumerate(outputs['masks']):
											new_outupt += [{"mask": mask_to_test_readable(_lowercase), "scores": outputs["scores"][i]}]

								self.assertEqual(
								    nested_simplify(_lowercase      ,       decimals=4)      ,       [
								        {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.04_44},
								        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.02_10},
								        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.01_67},
								        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.01_32},
								        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.00_53},
								    ]      ,       )










'''simple docstring'''
import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		["""image_processor""", """tokenizer"""]
					_UpperCamelCase							=		"""BlipImageProcessor"""
					_UpperCamelCase							=		"""AutoTokenizer"""



					def __init__(	self     :						Union[str, Any]      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						List[Any]      ,       _lowercase     :						List[str])    ->							Union[str, Any]:
								super().__init__(_lowercase      ,       _lowercase)

								# add QFormer tokenizer
								A_									= qformer_tokenizer



					def __call__(	self     :						Optional[int]      ,       _lowercase     :						ImageInput = None      ,       _lowercase     :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,       _lowercase     :						bool = True      ,       _lowercase     :						Union[bool, str, PaddingStrategy] = False      ,       _lowercase     :						Union[bool, str, TruncationStrategy] = None      ,       _lowercase     :						Optional[int] = None      ,       _lowercase     :						int = 0      ,       _lowercase     :						Optional[int] = None      ,       _lowercase     :						Optional[bool] = None      ,       _lowercase     :						bool = False      ,       _lowercase     :						bool = False      ,       _lowercase     :						bool = False      ,       _lowercase     :						bool = False      ,       _lowercase     :						bool = False      ,       _lowercase     :						bool = True      ,       _lowercase     :						Optional[Union[str, TensorType]] = None      ,       **_lowercase     :						List[Any]      ,       )    ->							BatchFeature:
								if images is None and text is None:
											raise ValueError('You have to specify at least images or text.')

								A_									= BatchFeature()

								if text is not None:
											A_									= self.tokenizer(
											    text=_lowercase      ,       add_special_tokens=_lowercase      ,       padding=_lowercase      ,       truncation=_lowercase      ,       max_length=_lowercase      ,       stride=_lowercase      ,       pad_to_multiple_of=_lowercase      ,       return_attention_mask=_lowercase      ,       return_overflowing_tokens=_lowercase      ,       return_special_tokens_mask=_lowercase      ,       return_offsets_mapping=_lowercase      ,       return_token_type_ids=_lowercase      ,       return_length=_lowercase      ,       verbose=_lowercase      ,       return_tensors=_lowercase      ,       **_lowercase      ,       )
											encoding.update(_lowercase)
											A_									= self.qformer_tokenizer(
											    text=_lowercase      ,       add_special_tokens=_lowercase      ,       padding=_lowercase      ,       truncation=_lowercase      ,       max_length=_lowercase      ,       stride=_lowercase      ,       pad_to_multiple_of=_lowercase      ,       return_attention_mask=_lowercase      ,       return_overflowing_tokens=_lowercase      ,       return_special_tokens_mask=_lowercase      ,       return_offsets_mapping=_lowercase      ,       return_token_type_ids=_lowercase      ,       return_length=_lowercase      ,       verbose=_lowercase      ,       return_tensors=_lowercase      ,       **_lowercase      ,       )
											A_									= qformer_text_encoding.pop('input_ids')
											A_									= qformer_text_encoding.pop('attention_mask')

								if images is not None:
											A_									= self.image_processor(_lowercase      ,       return_tensors=_lowercase)
											encoding.update(_lowercase)

								return encoding



					def     __snake_case						(	self     :						List[Any]      ,       *_lowercase     :						Optional[Any]      ,       **_lowercase     :						List[Any])    ->							Union[str, Any]:
								return self.tokenizer.batch_decode(*_lowercase      ,       **_lowercase)



					def     __snake_case						(	self     :						Optional[int]      ,       *_lowercase     :						List[Any]      ,       **_lowercase     :						List[str])    ->							str:
								return self.tokenizer.decode(*_lowercase      ,       **_lowercase)



					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def     __snake_case						(	self     :						Any)    ->							Optional[int]:
								A_									= self.tokenizer.model_input_names
								A_									= self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))



					def     __snake_case						(	self     :						Optional[Any]      ,       _lowercase     :						Optional[int]      ,       **_lowercase     :						int)    ->							Tuple:
								if os.path.isfile(_lowercase):
											raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file')
								os.makedirs(_lowercase      ,       exist_ok=_lowercase)
								A_									= os.path.join(_lowercase      ,       'qformer_tokenizer')
								self.qformer_tokenizer.save_pretrained(_lowercase)
								return super().save_pretrained(_lowercase      ,       **_lowercase)



					@classmethod
					def     __snake_case						(	cls     :						Dict      ,       _lowercase     :						Dict      ,       **_lowercase     :						int)    ->							Any:
								A_									= AutoTokenizer.from_pretrained(_lowercase      ,       subfolder='qformer_tokenizer')
								A_									= cls._get_arguments_from_pretrained(_lowercase      ,       **_lowercase)
								args.append(_lowercase)
								return cls(*_lowercase)




