import unittest

import numpy as np
from datasets import load_dataset

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 SegformerImageProcessor



class     snake_case_			(						unittest.TestCase			):






   def __init__( self							:	int      ,		lowercase_							:	Any      ,		lowercase_							:	Any=7      ,		lowercase_							:	Optional[Any]=3      ,		lowercase_							:	Optional[Any]=30      ,		lowercase_							:	List[Any]=4_00      ,		lowercase_							:	Tuple=True      ,		lowercase_							:	List[str]=None      ,		lowercase_							:	str=True      ,		lowercase_							:	Tuple=[0.5, 0.5, 0.5]      ,		lowercase_							:	int=[0.5, 0.5, 0.5]      ,		lowercase_							:	str=False      ,		)   ->		Tuple:
       lowercase__					:  Tuple          =							size if size is not None else {'''height''': 30, '''width''': 30}
       lowercase__					:  List[Any]          =							parent
       lowercase__					:  int          =							batch_size
       lowercase__					:  int          =							num_channels
       lowercase__					:  Union[str, Any]          =							min_resolution
       lowercase__					:  Tuple          =							max_resolution
       lowercase__					:  Tuple          =							do_resize
       lowercase__					:  List[str]          =							size
       lowercase__					:  int          =							do_normalize
       lowercase__					:  int          =							image_mean
       lowercase__					:  str          =							image_std
       lowercase__					:  List[Any]          =							do_reduce_labels







   def  __UpperCamelCase	( self							:	Optional[Any]    )   ->		List[str]:
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_reduce_labels": self.do_reduce_labels,
       }



def      lowercase_							(     ):
    lowercase__					:  Optional[int]          =							load_dataset("hf-internal-testing/fixtures_ade20k"						,      split="test")

    lowercase__					:  Optional[Any]          =							Image.open(dataset[0]["file"])
    lowercase__					:  Tuple          =							Image.open(dataset[1]["file"])

    return image, map

def      lowercase_							(     ):
    lowercase__					:  Tuple          =							load_dataset("hf-internal-testing/fixtures_ade20k"						,      split="test")

    lowercase__					:  Tuple          =							Image.open(dataset[0]["file"])
    lowercase__					:  Tuple          =							Image.open(dataset[1]["file"])
    lowercase__					:  Optional[int]          =							Image.open(dataset[2]["file"])
    lowercase__					:  Union[str, Any]          =							Image.open(dataset[3]["file"])

    return [imagea, imagea], [mapa, mapa]



@require_torch
@require_vision
class     snake_case_			(						lowerCAmelCase_    ,unittest.TestCase			):
   __A      :						List[Any]   =							SegformerImageProcessor if is_vision_available() else None






   def  __UpperCamelCase	( self							:	Optional[int]    )   ->		str:
       lowercase__					:  List[str]          =							SegformerImageProcessingTester(self    )






   @property
   def  __UpperCamelCase	( self							:	str    )   ->		List[str]:
       return self.image_processor_tester.prepare_image_processor_dict()






   def  __UpperCamelCase	( self							:	Tuple    )   ->		List[str]:
       lowercase__					:  Optional[Any]          =							self.image_processing_class(**self.image_processor_dict    )
       self.assertTrue(hasattr(UpperCamelCase_      ,		"do_resize"    )    )
       self.assertTrue(hasattr(UpperCamelCase_      ,		"size"    )    )
       self.assertTrue(hasattr(UpperCamelCase_      ,		"do_normalize"    )    )
       self.assertTrue(hasattr(UpperCamelCase_      ,		"image_mean"    )    )
       self.assertTrue(hasattr(UpperCamelCase_      ,		"image_std"    )    )
       self.assertTrue(hasattr(UpperCamelCase_      ,		"do_reduce_labels"    )    )






   def  __UpperCamelCase	( self							:	Optional[Any]    )   ->		Optional[Any]:
       lowercase__					:  str          =							self.image_processing_class.from_dict(self.image_processor_dict    )
       self.assertEqual(image_processor.size      ,		{"height": 30, "width": 30}    )
       self.assertEqual(image_processor.do_reduce_labels      ,		UpperCamelCase_    )

       lowercase__					:  Tuple          =							self.image_processing_class.from_dict(self.image_processor_dict      ,		size=42      ,		reduce_labels=UpperCamelCase_    )
       self.assertEqual(image_processor.size      ,		{"height": 42, "width": 42}    )
       self.assertEqual(image_processor.do_reduce_labels      ,		UpperCamelCase_    )






   def  __UpperCamelCase	( self							:	Dict    )   ->		List[Any]:
       pass






   def  __UpperCamelCase	( self							:	Any    )   ->		List[Any]:
       # Initialize image_processing
       lowercase__					:  List[Any]          =							self.image_processing_class(**self.image_processor_dict    )
       # create random PIL images
       lowercase__					:  Dict          =							prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=UpperCamelCase_    )
       for image in image_inputs:
           self.assertIsInstance(UpperCamelCase_      ,		Image.Image    )

       # Test not batched input
       lowercase__					:  int          =							image_processing(image_inputs[0]      ,		return_tensors="pt"    ).pixel_values
       self.assertEqual(
           encoded_images.shape      ,		(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )      ,		)

       # Test batched
       lowercase__					:  Optional[Any]          =							image_processing(UpperCamelCase_      ,		return_tensors="pt"    ).pixel_values
       self.assertEqual(
           encoded_images.shape      ,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )      ,		)






   def  __UpperCamelCase	( self							:	str    )   ->		str:
       # Initialize image_processing
       lowercase__					:  Union[str, Any]          =							self.image_processing_class(**self.image_processor_dict    )
       # create random numpy tensors
       lowercase__					:  List[str]          =							prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=UpperCamelCase_      ,		numpify=UpperCamelCase_    )
       for image in image_inputs:
           self.assertIsInstance(UpperCamelCase_      ,		np.ndarray    )

       # Test not batched input
       lowercase__					:  Any          =							image_processing(image_inputs[0]      ,		return_tensors="pt"    ).pixel_values
       self.assertEqual(
           encoded_images.shape      ,		(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )      ,		)

       # Test batched
       lowercase__					:  Optional[Any]          =							image_processing(UpperCamelCase_      ,		return_tensors="pt"    ).pixel_values
       self.assertEqual(
           encoded_images.shape      ,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )      ,		)






   def  __UpperCamelCase	( self							:	List[str]    )   ->		Union[str, Any]:
       # Initialize image_processing
       lowercase__					:  List[Any]          =							self.image_processing_class(**self.image_processor_dict    )
       # create random PyTorch tensors
       lowercase__					:  str          =							prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=UpperCamelCase_      ,		torchify=UpperCamelCase_    )
       for image in image_inputs:
           self.assertIsInstance(UpperCamelCase_      ,		torch.Tensor    )

       # Test not batched input
       lowercase__					:  Optional[int]          =							image_processing(image_inputs[0]      ,		return_tensors="pt"    ).pixel_values
       self.assertEqual(
           encoded_images.shape      ,		(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )      ,		)

       # Test batched
       lowercase__					:  str          =							image_processing(UpperCamelCase_      ,		return_tensors="pt"    ).pixel_values
       self.assertEqual(
           encoded_images.shape      ,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )      ,		)






   def  __UpperCamelCase	( self							:	str    )   ->		Optional[int]:
       # Initialize image_processing
       lowercase__					:  Any          =							self.image_processing_class(**self.image_processor_dict    )
       # create random PyTorch tensors
       lowercase__					:  Optional[Any]          =							prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=UpperCamelCase_      ,		torchify=UpperCamelCase_    )
       lowercase__					:  Tuple          =							[]
       for image in image_inputs:
           self.assertIsInstance(UpperCamelCase_      ,		torch.Tensor    )
           maps.append(torch.zeros(image.shape[-2:]    ).long()    )

       # Test not batched input
       lowercase__					:  Optional[Any]          =							image_processing(image_inputs[0]      ,		maps[0]      ,		return_tensors="pt"    )
       self.assertEqual(
           encoding["pixel_values"].shape      ,		(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )      ,		)
       self.assertEqual(
           encoding["labels"].shape      ,		(
               1,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )      ,		)
       self.assertEqual(encoding["labels"].dtype      ,		torch.long    )
       self.assertTrue(encoding["labels"].min().item() >= 0    )
       self.assertTrue(encoding["labels"].max().item() <= 2_55    )

       # Test batched
       lowercase__					:  Union[str, Any]          =							image_processing(UpperCamelCase_      ,		UpperCamelCase_      ,		return_tensors="pt"    )
       self.assertEqual(
           encoding["pixel_values"].shape      ,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )      ,		)
       self.assertEqual(
           encoding["labels"].shape      ,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )      ,		)
       self.assertEqual(encoding["labels"].dtype      ,		torch.long    )
       self.assertTrue(encoding["labels"].min().item() >= 0    )
       self.assertTrue(encoding["labels"].max().item() <= 2_55    )

       # Test not batched input (PIL images)
       lowercase__					:  int          =							prepare_semantic_single_inputs()

       lowercase__					:  Union[str, Any]          =							image_processing(UpperCamelCase_      ,		UpperCamelCase_      ,		return_tensors="pt"    )
       self.assertEqual(
           encoding["pixel_values"].shape      ,		(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )      ,		)
       self.assertEqual(
           encoding["labels"].shape      ,		(
               1,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )      ,		)
       self.assertEqual(encoding["labels"].dtype      ,		torch.long    )
       self.assertTrue(encoding["labels"].min().item() >= 0    )
       self.assertTrue(encoding["labels"].max().item() <= 2_55    )

       # Test batched input (PIL images)
       lowercase__					:  Any          =							prepare_semantic_batch_inputs()

       lowercase__					:  Union[str, Any]          =							image_processing(UpperCamelCase_      ,		UpperCamelCase_      ,		return_tensors="pt"    )
       self.assertEqual(
           encoding["pixel_values"].shape      ,		(
               2,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )      ,		)
       self.assertEqual(
           encoding["labels"].shape      ,		(
               2,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )      ,		)
       self.assertEqual(encoding["labels"].dtype      ,		torch.long    )
       self.assertTrue(encoding["labels"].min().item() >= 0    )
       self.assertTrue(encoding["labels"].max().item() <= 2_55    )







   def  __UpperCamelCase	( self							:	List[str]    )   ->		Optional[int]:
       # Initialize image_processing
       lowercase__					:  Any          =							self.image_processing_class(**self.image_processor_dict    )

       # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
       lowercase__					:  int          =							prepare_semantic_single_inputs()
       lowercase__					:  Dict          =							image_processing(UpperCamelCase_      ,		UpperCamelCase_      ,		return_tensors="pt"    )
       self.assertTrue(encoding["labels"].min().item() >= 0    )
       self.assertTrue(encoding["labels"].max().item() <= 1_50    )

       lowercase__					:  Optional[Any]          =							True
       lowercase__					:  Optional[Any]          =							image_processing(UpperCamelCase_      ,		UpperCamelCase_      ,		return_tensors="pt"    )
       self.assertTrue(encoding["labels"].min().item() >= 0    )
       self.assertTrue(encoding["labels"].max().item() <= 2_55    )





'''simple docstring'''






from typing import TYPE_CHECKING

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


__lowercase	=    {'''configuration_detr''': ['''DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetrConfig''', '''DetrOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    ['''DetrFeatureExtractor''']
							__lowercase	=    ['''DetrImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''DetrForObjectDetection''',
							    '''DetrForSegmentation''',
							    '''DetrModel''',
							    '''DetrPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_detr import DetrFeatureExtractor
														from .image_processing_detr import DetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_detr import (
														    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DetrForObjectDetection,
														    DetrForSegmentation,
														    DetrModel,
														    DetrPreTrainedModel,
														)

else:
							import sys

							__lowercase	=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






