



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  __lowerCAmelCase     :




			@staticmethod
			def        lowerCamelCase      (						*__UpperCAmelCase	,				**__UpperCAmelCase				):



						'''simple docstring'''
						pass





def 		a__			(		_UpperCamelCase			: Image							):
			__lowerCamelCase													=    hashlib.mda(image.tobytes()							)
			return m.hexdigest()[:10]





def 		a__			(		_UpperCamelCase			: Image							):
			__lowerCamelCase													=    np.array(lowerCAmelCase__							)
			__lowerCamelCase													=    npimg.shape
			return {"hash": hashimage(lowerCAmelCase__							), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class  __lowerCAmelCase     (	unittest.TestCase   ):
	lowerCAmelCase__       					=  dict(
	    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])   )
	lowerCAmelCase__       					=  dict(
	    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])   )




	def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



				'''simple docstring'''
				__lowerCamelCase													=    MaskGenerationPipeline(model=_a	,				image_processor=_a				)
				return image_segmenter, [
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				]




	def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase				):



				'''simple docstring'''
				pass




	@require_tf
	@unittest.skip('''Image segmentation not implemented in TF'''				)
	def        lowerCamelCase      (						self				):



				'''simple docstring'''
				pass




	@slow
	@require_torch
	def        lowerCamelCase      (						self				):



				'''simple docstring'''
				__lowerCamelCase													=    pipeline('''mask-generation'''	,				model='''facebook/sam-vit-huge'''				)

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

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

				# fmt: off
				self.assertEqual(
				    nested_simplify(_a	,				decimals=4				)	,				[
				        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0_444},
				        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.021},
				        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0_167},
				        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0_132},
				        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0_053},
				        {'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (480, 640)}, '''scores''': 0.9_967},
				        {'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (480, 640)}, '''scores''': 0.993},
				        {'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (480, 640)}, '''scores''': 0.9_909},
				        {'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (480, 640)}, '''scores''': 0.9_879},
				        {'''mask''': {'''hash''': '''801064ff79''', '''shape''': (480, 640)}, '''scores''': 0.9_834},
				        {'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (480, 640)}, '''scores''': 0.9_716},
				        {'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (480, 640)}, '''scores''': 0.9_612},
				        {'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (480, 640)}, '''scores''': 0.9_599},
				        {'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (480, 640)}, '''scores''': 0.9_552},
				        {'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (480, 640)}, '''scores''': 0.9_532},
				        {'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (480, 640)}, '''scores''': 0.9_516},
				        {'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (480, 640)}, '''scores''': 0.9_499},
				        {'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (480, 640)}, '''scores''': 0.9_483},
				        {'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (480, 640)}, '''scores''': 0.9_464},
				        {'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (480, 640)}, '''scores''': 0.943},
				        {'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (480, 640)}, '''scores''': 0.943},
				        {'''mask''': {'''hash''': '''c749b25868''', '''shape''': (480, 640)}, '''scores''': 0.9_408},
				        {'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (480, 640)}, '''scores''': 0.9_335},
				        {'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (480, 640)}, '''scores''': 0.9_326},
				        {'''mask''': {'''hash''': '''788b798e24''', '''shape''': (480, 640)}, '''scores''': 0.9_262},
				        {'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (480, 640)}, '''scores''': 0.8_999},
				        {'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (480, 640)}, '''scores''': 0.8_986},
				        {'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (480, 640)}, '''scores''': 0.8_984},
				        {'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (480, 640)}, '''scores''': 0.8_873},
				        {'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (480, 640)}, '''scores''': 0.8_871}
				    ]	,				)
				# fmt: on




	@require_torch
	@slow
	def        lowerCamelCase      (						self				):



				'''simple docstring'''
				__lowerCamelCase													=    '''facebook/sam-vit-huge'''
				__lowerCamelCase													=    pipeline('''mask-generation'''	,				model=_a				)

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

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

				self.assertEqual(
				    nested_simplify(_a	,				decimals=4				)	,				[
				        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0_444},
				        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.0_210},
				        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0_167},
				        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0_132},
				        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0_053},
				    ]	,				)











from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_			=					logging.get_logger(__name__)

a_			=					{
    """uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-pre""": """https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-vcr""": """https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-pre""": """https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-nlvr2""": """https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-pre""": """https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"""
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}



class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  """visual_bert"""




 def __init__(						self	,				__UpperCAmelCase=30522	,				__UpperCAmelCase=768	,				__UpperCAmelCase=512	,				__UpperCAmelCase=12	,				__UpperCAmelCase=12	,				__UpperCAmelCase=3072	,				__UpperCAmelCase="gelu"	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=512	,				__UpperCAmelCase=2	,				__UpperCAmelCase=0.02	,				__UpperCAmelCase=1E-1_2	,				__UpperCAmelCase=False	,				__UpperCAmelCase=True	,				__UpperCAmelCase=1	,				__UpperCAmelCase=0	,				__UpperCAmelCase=2	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    super().__init__(pad_token_id=__UpperCAmelCase	,				bos_token_id=__UpperCAmelCase	,				eos_token_id=__UpperCAmelCase	,				**__UpperCAmelCase				)

    __lowerCamelCase													=    vocab_size
    __lowerCamelCase													=    max_position_embeddings
    __lowerCamelCase													=    hidden_size
    __lowerCamelCase													=    visual_embedding_dim
    __lowerCamelCase													=    num_hidden_layers
    __lowerCamelCase													=    num_attention_heads
    __lowerCamelCase													=    intermediate_size
    __lowerCamelCase													=    hidden_act
    __lowerCamelCase													=    hidden_dropout_prob
    __lowerCamelCase													=    attention_probs_dropout_prob
    __lowerCamelCase													=    initializer_range
    __lowerCamelCase													=    type_vocab_size
    __lowerCamelCase													=    layer_norm_eps
    __lowerCamelCase													=    bypass_transformer
    __lowerCamelCase													=    special_visual_initialize






