





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 	UpperCamelCase_     (     unittest.TestCase       ):



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





					def      __a    (		self    :    Dict	):
							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 		_SCREAMING_SNAKE_CASE    (   ):
		lowerCamelCase_  :    str   	=			load_dataset('hf-internal-testing/fixtures_ade20k'       ,split='test'	)

		lowerCamelCase_  :    List[str]   	=			Image.open(dataset[0]['file']	)
		lowerCamelCase_  :    List[Any]   	=			Image.open(dataset[1]['file']	)

		return image, map


def 		_SCREAMING_SNAKE_CASE    (   ):
		lowerCamelCase_  :    str   	=			load_dataset('hf-internal-testing/fixtures_ade20k'       ,split='test'	)

		lowerCamelCase_  :    Any   	=			Image.open(dataset[0]['file']	)
		lowerCamelCase_  :    str   	=			Image.open(dataset[1]['file']	)
		lowerCamelCase_  :    Optional[int]   	=			Image.open(dataset[2]['file']	)
		lowerCamelCase_  :    Dict   	=			Image.open(dataset[3]['file']	)

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






@require_torch
@require_vision
class 	UpperCamelCase_     (     _SCREAMING_SNAKE_CASE     ,    unittest.TestCase       ):
					_a			:	Optional[int]							=				SegformerImageProcessor if is_vision_available() else None



					def      __a    (		self    :    Optional[Any]	):
							lowerCamelCase_  :    List[Any]   	=			SegformerImageProcessingTester(self	)



					@property
					def      __a    (		self    :    Any	):
							return self.image_processor_tester.prepare_image_processor_dict()



					def      __a    (		self    :    Union[str, Any]	):
							lowerCamelCase_  :    Dict   	=			self.image_processing_class(**self.image_processor_dict	)
							self.assertTrue(hasattr(__A					,			'do_resize'	)	)
							self.assertTrue(hasattr(__A					,			'size'	)	)
							self.assertTrue(hasattr(__A					,			'do_normalize'	)	)
							self.assertTrue(hasattr(__A					,			'image_mean'	)	)
							self.assertTrue(hasattr(__A					,			'image_std'	)	)
							self.assertTrue(hasattr(__A					,			'do_reduce_labels'	)	)



					def      __a    (		self    :    Dict	):
							lowerCamelCase_  :    List[Any]   	=			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					,			__A	)

							lowerCamelCase_  :    Any   	=			self.image_processing_class.from_dict(self.image_processor_dict					,			size=42					,			reduce_labels=__A	)
							self.assertEqual(image_processor.size					,			{'height': 42, 'width': 42}	)
							self.assertEqual(image_processor.do_reduce_labels					,			__A	)



					def      __a    (		self    :    List[Any]	):
							pass



					def      __a    (		self    :    Union[str, Any]	):
							# Initialize image_processing
							lowerCamelCase_  :    Union[str, Any]   	=			self.image_processing_class(**self.image_processor_dict	)
							# create random PIL images
							lowerCamelCase_  :    List[Any]   	=			prepare_image_inputs(self.image_processor_tester					,			equal_resolution=__A	)
							for image in image_inputs:
									self.assertIsInstance(__A					,			Image.Image	)

							# Test not batched input
							lowerCamelCase_  :    List[str]   	=			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
							lowerCamelCase_  :    Tuple   	=			image_processing(__A					,			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      __a    (		self    :    Optional[int]	):
							# Initialize image_processing
							lowerCamelCase_  :    List[str]   	=			self.image_processing_class(**self.image_processor_dict	)
							# create random numpy tensors
							lowerCamelCase_  :    Optional[Any]   	=			prepare_image_inputs(self.image_processor_tester					,			equal_resolution=__A					,			numpify=__A	)
							for image in image_inputs:
									self.assertIsInstance(__A					,			np.ndarray	)

							# Test not batched input
							lowerCamelCase_  :    Tuple   	=			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
							lowerCamelCase_  :    Dict   	=			image_processing(__A					,			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      __a    (		self    :    Dict	):
							# Initialize image_processing
							lowerCamelCase_  :    str   	=			self.image_processing_class(**self.image_processor_dict	)
							# create random PyTorch tensors
							lowerCamelCase_  :    Union[str, Any]   	=			prepare_image_inputs(self.image_processor_tester					,			equal_resolution=__A					,			torchify=__A	)
							for image in image_inputs:
									self.assertIsInstance(__A					,			torch.Tensor	)

							# Test not batched input
							lowerCamelCase_  :    List[str]   	=			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
							lowerCamelCase_  :    List[str]   	=			image_processing(__A					,			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      __a    (		self    :    int	):
							# Initialize image_processing
							lowerCamelCase_  :    Optional[int]   	=			self.image_processing_class(**self.image_processor_dict	)
							# create random PyTorch tensors
							lowerCamelCase_  :    int   	=			prepare_image_inputs(self.image_processor_tester					,			equal_resolution=__A					,			torchify=__A	)
							lowerCamelCase_  :    Any   	=			[]
							for image in image_inputs:
									self.assertIsInstance(__A					,			torch.Tensor	)
									maps.append(torch.zeros(image.shape[-2:]	).long()	)

							# Test not batched input
							lowerCamelCase_  :    Union[str, 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
							lowerCamelCase_  :    Tuple   	=			image_processing(__A					,			__A					,			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)
							lowerCamelCase_	,			lowerCamelCase_  :    Optional[Any]   	=			prepare_semantic_single_inputs()

							lowerCamelCase_  :    Any   	=			image_processing(__A					,			__A					,			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)
							lowerCamelCase_	,			lowerCamelCase_  :    Dict   	=			prepare_semantic_batch_inputs()

							lowerCamelCase_  :    Tuple   	=			image_processing(__A					,			__A					,			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      __a    (		self    :    int	):
							# Initialize image_processing
							lowerCamelCase_  :    List[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
							lowerCamelCase_	,			lowerCamelCase_  :    Optional[Any]   	=			prepare_semantic_single_inputs()
							lowerCamelCase_  :    Optional[Any]   	=			image_processing(__A					,			__A					,			return_tensors='pt'	)
							self.assertTrue(encoding['labels'].min().item() >= 0	)
							self.assertTrue(encoding['labels'].max().item() <= 1_50	)

							lowerCamelCase_  :    Union[str, Any]   	=			True
							lowerCamelCase_  :    int   	=			image_processing(__A					,			__A					,			return_tensors='pt'	)
							self.assertTrue(encoding['labels'].min().item() >= 0	)
							self.assertTrue(encoding['labels'].max().item() <= 2_55	)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowercase		:     int 					={
    """configuration_marian""": ["""MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarianConfig""", """MarianOnnxConfig"""],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:     Any 					=["""MarianTokenizer"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:     Any 					=[
		    """MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """MarianForCausalLM""",
		    """MarianModel""",
		    """MarianMTModel""",
		    """MarianPreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:     Dict 					=["""TFMarianModel""", """TFMarianMTModel""", """TFMarianPreTrainedModel"""]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:     Optional[Any] 					=["""FlaxMarianModel""", """FlaxMarianMTModel""", """FlaxMarianPreTrainedModel"""]

if TYPE_CHECKING:
		from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_marian import MarianTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_marian import (
				    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MarianForCausalLM,
				    MarianModel,
				    MarianMTModel,
				    MarianPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
		import sys

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