import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor


class   lowercase      (       unittest.TestCase       ):




							def __init__(  self				:   List[Any]		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   str=13		,							_UpperCamelCase				:   List[str]=3		,							_UpperCamelCase				:   Dict=224		,							_UpperCamelCase				:   Union[str, Any]=30		,							_UpperCamelCase				:   Optional[Any]=400		,							_UpperCamelCase				:   int=True		,							_UpperCamelCase				:   Tuple=None		,							_UpperCamelCase				:   List[Any]=True		,							_UpperCamelCase				:   Optional[int]=[0.5, 0.5, 0.5]		,							_UpperCamelCase				:   Union[str, Any]=[0.5, 0.5, 0.5]		,							)   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    size if size is not None else {'''height''': 18, '''width''': 18}
												SCREAMING_SNAKE_CASE           =    parent
												SCREAMING_SNAKE_CASE           =    batch_size
												SCREAMING_SNAKE_CASE           =    num_channels
												SCREAMING_SNAKE_CASE           =    image_size
												SCREAMING_SNAKE_CASE           =    min_resolution
												SCREAMING_SNAKE_CASE           =    max_resolution
												SCREAMING_SNAKE_CASE           =    do_resize
												SCREAMING_SNAKE_CASE           =    size
												SCREAMING_SNAKE_CASE           =    do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean
												SCREAMING_SNAKE_CASE           =    image_std





							def 		__snake_case(  self				:   str )   ->			List[str]:



												'''simple docstring'''

												return {
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_normalize": self.do_normalize,
												    "do_resize": self.do_resize,
												    "size": self.size,
												}



@require_torch
@require_vision
class   lowercase      (       __snake_case			,    unittest.TestCase       ):
							lowercase__  :    Optional[Any]									=							ViTImageProcessor if is_vision_available() else None




							def 		__snake_case(  self				:   Optional[Any] )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    EfficientFormerImageProcessorTester(self )




							@property
							def 		__snake_case(  self				:   Optional[int] )   ->			Optional[Any]:



												'''simple docstring'''

												return self.image_proc_tester.prepare_image_processor_dict()




							def 		__snake_case(  self				:   List[Any] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.image_processing_class(**self.image_processor_dict )
												self.assertTrue(hasattr(a_		,							"image_mean" ) )
												self.assertTrue(hasattr(a_		,							"image_std" ) )
												self.assertTrue(hasattr(a_		,							"do_normalize" ) )
												self.assertTrue(hasattr(a_		,							"do_resize" ) )
												self.assertTrue(hasattr(a_		,							"size" ) )




							def 		__snake_case(  self				:   int )   ->			Any:



												'''simple docstring'''

												pass




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.image_processing_class(**self.image_processor_dict )
												# create random PIL images
												SCREAMING_SNAKE_CASE           =    prepare_image_inputs(self.image_proc_tester		,							equal_resolution=a_ )
												for image in image_inputs:
																	self.assertIsInstance(a_		,							Image.Image )

												# Test not batched input
												SCREAMING_SNAKE_CASE           =    image_processor(image_inputs[0]		,							return_tensors="pt" ).pixel_values
												self.assertEqual(
												    encoded_images.shape		,							(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size["height"],
												        self.image_proc_tester.size["width"],
												    )		,							)

												# Test batched
												SCREAMING_SNAKE_CASE           =    image_processor(a_		,							return_tensors="pt" ).pixel_values
												self.assertEqual(
												    encoded_images.shape		,							(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size["height"],
												        self.image_proc_tester.size["width"],
												    )		,							)




							def 		__snake_case(  self				:   Tuple )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.image_processing_class(**self.image_processor_dict )
												# create random numpy tensors
												SCREAMING_SNAKE_CASE           =    prepare_image_inputs(self.image_proc_tester		,							equal_resolution=a_		,							numpify=a_ )
												for image in image_inputs:
																	self.assertIsInstance(a_		,							np.ndarray )

												# Test not batched input
												SCREAMING_SNAKE_CASE           =    image_processor(image_inputs[0]		,							return_tensors="pt" ).pixel_values
												self.assertEqual(
												    encoded_images.shape		,							(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size["height"],
												        self.image_proc_tester.size["width"],
												    )		,							)

												# Test batched
												SCREAMING_SNAKE_CASE           =    image_processor(a_		,							return_tensors="pt" ).pixel_values
												self.assertEqual(
												    encoded_images.shape		,							(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size["height"],
												        self.image_proc_tester.size["width"],
												    )		,							)





							def 		__snake_case(  self				:   Any )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.image_processing_class(**self.image_processor_dict )
												# create random PyTorch tensors
												SCREAMING_SNAKE_CASE           =    prepare_image_inputs(self.image_proc_tester		,							equal_resolution=a_		,							torchify=a_ )
												for image in image_inputs:
																	self.assertIsInstance(a_		,							torch.Tensor )

												# Test not batched input
												SCREAMING_SNAKE_CASE           =    image_processor(image_inputs[0]		,							return_tensors="pt" ).pixel_values
												self.assertEqual(
												    encoded_images.shape		,							(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size["height"],
												        self.image_proc_tester.size["width"],
												    )		,							)

												# Test batched
												SCREAMING_SNAKE_CASE           =    image_processor(a_		,							return_tensors="pt" ).pixel_values
												self.assertEqual(
												    encoded_images.shape		,							(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size["height"],
												        self.image_proc_tester.size["width"],
												    )		,							)







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
							from PIL import Image


class   lowercase      (       a       ):
							lowercase__  :    Optional[Any]									=							(
							    """This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."""
							    """It takes two arguments named `image` which should be the original image, and `label` which should be a text """
							    """describing the elements what should be identified in the segmentation mask. The tool returns the mask."""
							)
							lowercase__  :    Optional[int]									=							"""CIDAS/clipseg-rd64-refined"""
							lowercase__  :    Tuple									=							"""image_segmenter"""
							lowercase__  :    Optional[Any]									=							CLIPSegForImageSegmentation

							lowercase__  :    int									=							["""image""", """text"""]
							lowercase__  :    List[str]									=							["""image"""]




							def __init__(  self				:   str		,							*_UpperCamelCase				:   str		,							**_UpperCamelCase				:   Union[str, Any] )   ->			Optional[int]:



												'''simple docstring'''

												requires_backends(self		,							["vision"] )
												super().__init__(*_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   "Image"		,							_UpperCamelCase				:   str )   ->			Optional[int]:



												'''simple docstring'''

												return self.pre_processor(text=[label]		,							images=[image]		,							padding=_UpperCamelCase		,							return_tensors="pt" )




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   str )   ->			Union[str, Any]:



												'''simple docstring'''

												with torch.no_grad():
																	SCREAMING_SNAKE_CASE           =    self.model(**_UpperCamelCase ).logits
												return logits





							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Optional[Any] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    outputs.cpu().detach().numpy()
												SCREAMING_SNAKE_CASE           =    0
												SCREAMING_SNAKE_CASE           =    1
												return Image.fromarray((array * 255).astype(np.uinta ) )




