



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    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







				def __init__(			self  ,			snake_case__  ,			snake_case__=7  ,			snake_case__=3  ,			snake_case__=30  ,			snake_case__=400  ,			snake_case__=True  ,			snake_case__=None  ,			snake_case__=True  ,			snake_case__=[0.5, 0.5, 0.5]  ,			snake_case__=[0.5, 0.5, 0.5]  ,			snake_case__=False  ,			):

									'''simple docstring'''


									UpperCamelCase_			= size if size is not None else {"height": 30, "width": 30}
									UpperCamelCase_			= parent
									UpperCamelCase_			= batch_size
									UpperCamelCase_			= num_channels
									UpperCamelCase_			= min_resolution
									UpperCamelCase_			= max_resolution
									UpperCamelCase_			= do_resize
									UpperCamelCase_			= size
									UpperCamelCase_			= do_normalize
									UpperCamelCase_			= image_mean
									UpperCamelCase_			= image_std
									UpperCamelCase_			= do_reduce_labels

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									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  _lowerCAmelCase     ():
					UpperCamelCase_			= load_dataset("hf-internal-testing/fixtures_ade20k" ,						split="test")

					UpperCamelCase_			= Image.open(dataset[0]["file"])
					UpperCamelCase_			= Image.open(dataset[1]["file"])

					return image, map



def  _lowerCAmelCase     ():
					UpperCamelCase_			= load_dataset("hf-internal-testing/fixtures_ade20k" ,						split="test")

					UpperCamelCase_			= Image.open(dataset[0]["file"])
					UpperCamelCase_			= Image.open(dataset[1]["file"])
					UpperCamelCase_			= Image.open(dataset[2]["file"])
					UpperCamelCase_			= Image.open(dataset[3]["file"])

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






@require_torch
@require_vision
class    _lowercase (a_   , unittest.TestCase   ):
				'''simple docstring'''







				lowercase__     =    SegformerImageProcessor if is_vision_available() else None

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= SegformerImageProcessingTester(self     )

				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return self.image_processor_tester.prepare_image_processor_dict()

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.image_processing_class(**self.image_processor_dict     )
									self.assertTrue(hasattr(snake_case__  ,			"do_resize"     )     )
									self.assertTrue(hasattr(snake_case__  ,			"size"     )     )
									self.assertTrue(hasattr(snake_case__  ,			"do_normalize"     )     )
									self.assertTrue(hasattr(snake_case__  ,			"image_mean"     )     )
									self.assertTrue(hasattr(snake_case__  ,			"image_std"     )     )
									self.assertTrue(hasattr(snake_case__  ,			"do_reduce_labels"     )     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= 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  ,			snake_case__     )

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

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									pass

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.image_processing_class(**self.image_processor_dict     )
									# create random PIL images
									UpperCamelCase_			= prepare_image_inputs(self.image_processor_tester  ,			equal_resolution=snake_case__     )
									for image in image_inputs:
														self.assertIsInstance(snake_case__  ,			Image.Image     )

									# Test not batched input
									UpperCamelCase_			= 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
									UpperCamelCase_			= image_processing(snake_case__  ,			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    _lowerCamelCase       (			self     ):

									'''simple docstring'''


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

									# Test not batched input
									UpperCamelCase_			= 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
									UpperCamelCase_			= image_processing(snake_case__  ,			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    _lowerCamelCase       (			self     ):

									'''simple docstring'''


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

									# Test not batched input
									UpperCamelCase_			= 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
									UpperCamelCase_			= image_processing(snake_case__  ,			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    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.image_processing_class(**self.image_processor_dict     )
									# create random PyTorch tensors
									UpperCamelCase_			= prepare_image_inputs(self.image_processor_tester  ,			equal_resolution=snake_case__  ,			torchify=snake_case__     )
									UpperCamelCase_			= []
									for image in image_inputs:
														self.assertIsInstance(snake_case__  ,			torch.Tensor     )
														maps.append(torch.zeros(image.shape[-2:]     ).long()     )

									# Test not batched input
									UpperCamelCase_			= 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() <= 255     )

									# Test batched
									UpperCamelCase_			= image_processing(snake_case__  ,			snake_case__  ,			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() <= 255     )

									# Test not batched input (PIL images)
									UpperCamelCase_	,     UpperCamelCase_			= prepare_semantic_single_inputs()

									UpperCamelCase_			= image_processing(snake_case__  ,			snake_case__  ,			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() <= 255     )

									# Test batched input (PIL images)
									UpperCamelCase_	,     UpperCamelCase_			= prepare_semantic_batch_inputs()

									UpperCamelCase_			= image_processing(snake_case__  ,			snake_case__  ,			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() <= 255     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= 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
									UpperCamelCase_	,     UpperCamelCase_			= prepare_semantic_single_inputs()
									UpperCamelCase_			= image_processing(snake_case__  ,			snake_case__  ,			return_tensors="pt"     )
									self.assertTrue(encoding["labels"].min().item() >= 0     )
									self.assertTrue(encoding["labels"].max().item() <= 150     )

									UpperCamelCase_			= True
									UpperCamelCase_			= image_processing(snake_case__  ,			snake_case__  ,			return_tensors="pt"     )
									self.assertTrue(encoding["labels"].min().item() >= 0     )
									self.assertTrue(encoding["labels"].max().item() <= 255     )





from typing import TYPE_CHECKING

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


UpperCAmelCase :				List[Any]							={
    """configuration_retribert""": ["""RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RetriBertConfig"""],
    """tokenization_retribert""": ["""RetriBertTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				int							=["""RetriBertTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				str							=[
	    """RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RetriBertModel""",
	    """RetriBertPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
	from .tokenization_retribert import RetriBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_retribert_fast import RetriBertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_retribert import (
		    RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RetriBertModel,
		    RetriBertPreTrainedModel,
		)

else:
	import sys

	UpperCAmelCase :				List[Any]							=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
