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     ,   UpperCAmelCase     ,   UpperCAmelCase=7     ,   UpperCAmelCase=3     ,   UpperCAmelCase=30     ,   UpperCAmelCase=400     ,   UpperCAmelCase=True     ,   UpperCAmelCase=None     ,   UpperCAmelCase=True     ,   UpperCAmelCase=[0.5, 0.5, 0.5]     ,   UpperCAmelCase=[0.5, 0.5, 0.5]     ,   UpperCAmelCase=False     ,   ):







														'''simple docstring'''






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



							def     _UpperCAmelCase       (    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			(  ):



							"""simple docstring"""


							_lowercase				=	load_dataset("""hf-internal-testing/fixtures_ade20k""" ,split="""test"""					)

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

							return image, map


def 					__lowerCAmelCase			(  ):



							"""simple docstring"""


							_lowercase				=	load_dataset("""hf-internal-testing/fixtures_ade20k""" ,split="""test"""					)

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

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







@require_torch
@require_vision
class _lowercase					(       _UpperCAmelCase   ,				unittest.TestCase						):





							"""simple docstring"""




							lowerCAmelCase__       =					SegformerImageProcessor if is_vision_available() else None



							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






														_lowercase				=	SegformerImageProcessingTester(self  )



							@property
							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






														return self.image_processor_tester.prepare_image_processor_dict()



							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






														_lowercase				=	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  ):







														'''simple docstring'''






														_lowercase				=	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				=	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  ):







														'''simple docstring'''






														pass



							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






														_lowercase				=	self.image_processing_class(**self.image_processor_dict  )
														# create random PIL images
														_lowercase				=	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				=	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				=	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  ):







														'''simple docstring'''






														_lowercase				=	self.image_processing_class(**self.image_processor_dict  )
														# create random numpy tensors
														_lowercase				=	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				=	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				=	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  ):







														'''simple docstring'''






														_lowercase				=	self.image_processing_class(**self.image_processor_dict  )
														# create random PyTorch tensors
														_lowercase				=	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				=	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				=	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  ):







														'''simple docstring'''






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

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

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

														_lowercase				=	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() <= 255  )

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

														_lowercase				=	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() <= 255  )



							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






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

														_lowercase				=	True
														_lowercase				=	image_processing(UpperCAmelCase     ,   UpperCAmelCase     ,   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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A_:      int        =			{
    'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
    'tokenization_bart': ['BartTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_:      List[str]        =			['BartTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_:      int        =			[
				    'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BartForCausalLM',
				    'BartForConditionalGeneration',
				    'BartForQuestionAnswering',
				    'BartForSequenceClassification',
				    'BartModel',
				    'BartPretrainedModel',
				    'PretrainedBartModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_:      Any        =			[
				    'TFBartForConditionalGeneration',
				    'TFBartForSequenceClassification',
				    'TFBartModel',
				    'TFBartPretrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_:      str        =			[
				    'FlaxBartDecoderPreTrainedModel',
				    'FlaxBartForCausalLM',
				    'FlaxBartForConditionalGeneration',
				    'FlaxBartForQuestionAnswering',
				    'FlaxBartForSequenceClassification',
				    'FlaxBartModel',
				    'FlaxBartPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
				from .tokenization_bart import BartTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bart_fast import BartTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bart import (
								    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BartForCausalLM,
								    BartForConditionalGeneration,
								    BartForQuestionAnswering,
								    BartForSequenceClassification,
								    BartModel,
								    BartPretrainedModel,
								    PretrainedBartModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_bart import (
								    TFBartForConditionalGeneration,
								    TFBartForSequenceClassification,
								    TFBartModel,
								    TFBartPretrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_bart import (
								    FlaxBartDecoderPreTrainedModel,
								    FlaxBartForCausalLM,
								    FlaxBartForConditionalGeneration,
								    FlaxBartForQuestionAnswering,
								    FlaxBartForSequenceClassification,
								    FlaxBartModel,
								    FlaxBartPreTrainedModel,
								)

else:
				import sys

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


