'''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       lowercase__		:


											@staticmethod
											def  __A ( *UpperCamelCase__			:       Optional[int]					,	**UpperCamelCase__			:       Dict		):





																	'''simple docstring'''





																	pass
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					str           =							hashlib.mda(image.tobytes()		)
						return m.hexdigest()[:10]
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Tuple           =							np.array(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Any           =							npimg.shape
						return {"hash": hashimage(_lowercase		), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class       lowercase__		(     unittest.TestCase):
						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  __A ( self			:       List[str]					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							MaskGenerationPipeline(model=UpperCamelCase__					,	image_processor=UpperCamelCase__		)
												return image_segmenter, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       Any					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												pass


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





												'''simple docstring'''





												pass


						@slow
						@require_torch
						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							pipeline('''mask-generation'''					,	model='''facebook/sam-vit-huge'''		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg'''					,	points_per_batch=256		)

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

												# fmt: off
												self.assertEqual(
												    nested_simplify(UpperCamelCase__					,	decimals=4		)					,	[
												        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0444},
												        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.021},
												        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0167},
												        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0132},
												        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0053},
												        {'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (480, 640)}, '''scores''': 0.9967},
												        {'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (480, 640)}, '''scores''': 0.993},
												        {'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (480, 640)}, '''scores''': 0.9909},
												        {'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (480, 640)}, '''scores''': 0.9879},
												        {'''mask''': {'''hash''': '''801064ff79''', '''shape''': (480, 640)}, '''scores''': 0.9834},
												        {'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (480, 640)}, '''scores''': 0.9716},
												        {'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (480, 640)}, '''scores''': 0.9612},
												        {'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (480, 640)}, '''scores''': 0.9599},
												        {'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (480, 640)}, '''scores''': 0.9552},
												        {'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (480, 640)}, '''scores''': 0.9532},
												        {'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (480, 640)}, '''scores''': 0.9516},
												        {'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (480, 640)}, '''scores''': 0.9499},
												        {'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (480, 640)}, '''scores''': 0.9483},
												        {'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (480, 640)}, '''scores''': 0.9464},
												        {'''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''': 0.9408},
												        {'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (480, 640)}, '''scores''': 0.9335},
												        {'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (480, 640)}, '''scores''': 0.9326},
												        {'''mask''': {'''hash''': '''788b798e24''', '''shape''': (480, 640)}, '''scores''': 0.9262},
												        {'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (480, 640)}, '''scores''': 0.8999},
												        {'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (480, 640)}, '''scores''': 0.8986},
												        {'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (480, 640)}, '''scores''': 0.8984},
												        {'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (480, 640)}, '''scores''': 0.8873},
												        {'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (480, 640)}, '''scores''': 0.8871}
												    ]					,	)
												# fmt: on



						@require_torch
						@slow
						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							'''facebook/sam-vit-huge'''
												SCREAMING_SNAKE_CASE							:					Tuple           =							pipeline('''mask-generation'''					,	model=UpperCamelCase__		)

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

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

												self.assertEqual(
												    nested_simplify(UpperCamelCase__					,	decimals=4		)					,	[
												        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0444},
												        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.0210},
												        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0167},
												        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0132},
												        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0053},
												    ]					,	)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCamelCase	: Tuple    		=  {
    'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
    'tokenization_ctrl': ['CTRLTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: Dict    		=  [
					    'CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'CTRLForSequenceClassification',
					    'CTRLLMHeadModel',
					    'CTRLModel',
					    'CTRLPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: Any    		=  [
					    'TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFCTRLForSequenceClassification',
					    'TFCTRLLMHeadModel',
					    'TFCTRLModel',
					    'TFCTRLPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
					from .tokenization_ctrl import CTRLTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ctrl import (
										    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CTRLForSequenceClassification,
										    CTRLLMHeadModel,
										    CTRLModel,
										    CTRLPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_ctrl import (
										    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCTRLForSequenceClassification,
										    TFCTRLLMHeadModel,
										    TFCTRLModel,
										    TFCTRLPreTrainedModel,
										)

else:
					import sys

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





