from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowercase_  							=			logging.get_logger(__name__)

lowercase_  							=			{
    "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}







class      SCREAMING_SNAKE_CASE__					(  lowerCAmelCase_    ):
       A							:	Tuple          =	'''mbart'''
       A							:	List[Any]          =	['''past_key_values''']
       A							:	Tuple          =	{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

       def __init__(							self					:     int			,			_lowerCAmelCase					:     Optional[int]=5_02_65			,			_lowerCAmelCase					:     str=10_24			,			_lowerCAmelCase					:     Optional[Any]=12			,			_lowerCAmelCase					:     Optional[int]=40_96			,			_lowerCAmelCase					:     int=16			,			_lowerCAmelCase					:     Dict=12			,			_lowerCAmelCase					:     Optional[int]=40_96			,			_lowerCAmelCase					:     Dict=16			,			_lowerCAmelCase					:     List[Any]=0.0			,			_lowerCAmelCase					:     Optional[int]=0.0			,			_lowerCAmelCase					:     Union[str, Any]=True			,			_lowerCAmelCase					:     Any=True			,			_lowerCAmelCase					:     Union[str, Any]="gelu"			,			_lowerCAmelCase					:     str=10_24			,			_lowerCAmelCase					:     Dict=0.1			,			_lowerCAmelCase					:     str=0.0			,			_lowerCAmelCase					:     Optional[Any]=0.0			,			_lowerCAmelCase					:     List[Any]=0.02			,			_lowerCAmelCase					:     Optional[int]=0.0			,			_lowerCAmelCase					:     Tuple=False			,			_lowerCAmelCase					:     str=1			,			_lowerCAmelCase					:     List[Any]=0			,			_lowerCAmelCase					:     Dict=2			,			_lowerCAmelCase					:     int=2			,			**_lowerCAmelCase					:     Tuple			,			):
           __snake_case       :				List[str] 			=    vocab_size
           __snake_case       :				str 			=    max_position_embeddings
           __snake_case       :				List[Any] 			=    d_model
           __snake_case       :				Union[str, Any] 			=    encoder_ffn_dim
           __snake_case       :				Optional[Any] 			=    encoder_layers
           __snake_case       :				Union[str, Any] 			=    encoder_attention_heads
           __snake_case       :				Tuple 			=    decoder_ffn_dim
           __snake_case       :				Tuple 			=    decoder_layers
           __snake_case       :				Tuple 			=    decoder_attention_heads
           __snake_case       :				List[str] 			=    dropout
           __snake_case       :				Union[str, Any] 			=    attention_dropout
           __snake_case       :				List[Any] 			=    activation_dropout
           __snake_case       :				Optional[Any] 			=    activation_function
           __snake_case       :				int 			=    init_std
           __snake_case       :				Dict 			=    encoder_layerdrop
           __snake_case       :				str 			=    decoder_layerdrop
           __snake_case       :				str 			=    classifier_dropout
           __snake_case       :				str 			=    use_cache
           __snake_case       :				List[str] 			=    encoder_layers
           __snake_case       :				str 			=    scale_embedding  # scale factor will be sqrt(d_model) if True
           super().__init__(
               pad_token_id=UpperCamelCase_			,			bos_token_id=UpperCamelCase_			,			eos_token_id=UpperCamelCase_			,			is_encoder_decoder=UpperCamelCase_			,			forced_eos_token_id=UpperCamelCase_			,			**UpperCamelCase_			,			)







class      SCREAMING_SNAKE_CASE__					(  lowerCAmelCase_    ):

       @property
       def 			snake_case__    (							self					:     List[Any]			):
           if self.task in ["default", "seq2seq-lm"]:
               __snake_case       :				Union[str, Any] 			=    OrderedDict(
                   [
                       ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                       ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                   ]			)

               if self.use_past:
                   __snake_case       :				List[Any] 			=    {0: '''batch'''}
                   __snake_case       :				List[str] 			=    {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
               else:
                   __snake_case       :				Tuple 			=    {0: '''batch''', 1: '''decoder_sequence'''}
                   __snake_case       :				Optional[int] 			=    {0: '''batch''', 1: '''decoder_sequence'''}

               if self.use_past:
                   self.fill_with_past_key_values_(UpperCamelCase_			,			direction="""inputs"""			)
           elif self.task == "causal-lm":
               # TODO: figure this case out.
               __snake_case       :				List[str] 			=    OrderedDict(
                   [
                       ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                       ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                   ]			)
               if self.use_past:
                   __snake_case       :				Any 			=    self.num_layers
                   for i in range(UpperCamelCase_			):
                       __snake_case       :				Tuple 			=    {0: '''batch''', 2: '''past_sequence + sequence'''}
                       __snake_case       :				Union[str, Any] 			=    {0: '''batch''', 2: '''past_sequence + sequence'''}
           else:
               __snake_case       :				Optional[int] 			=    OrderedDict(
                   [
                       ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                       ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                       ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                       ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
                   ]			)

           return common_inputs

       @property
       def 			snake_case__    (							self					:     Any			):
           if self.task in ["default", "seq2seq-lm"]:
               __snake_case       :				int 			=    super().outputs
           else:
               __snake_case       :				Optional[int] 			=    super(UpperCamelCase_			,			self			).outputs
               if self.use_past:
                   __snake_case       :				Optional[int] 			=    self.num_layers
                   for i in range(UpperCamelCase_			):
                       __snake_case       :				int 			=    {0: '''batch''', 2: '''past_sequence + sequence'''}
                       __snake_case       :				str 			=    {0: '''batch''', 2: '''past_sequence + sequence'''}
           return common_outputs

       def 			snake_case__    (							self					:     str			,			_lowerCAmelCase					:     PreTrainedTokenizer			,			_lowerCAmelCase					:     int = -1			,			_lowerCAmelCase					:     int = -1			,			_lowerCAmelCase					:     bool = False			,			_lowerCAmelCase					:     Optional[TensorType] = None			,			):
           __snake_case       :				int 			=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               UpperCamelCase_			,			UpperCamelCase_			,			UpperCamelCase_			,			UpperCamelCase_			,			UpperCamelCase_			)

           # Generate decoder inputs
           __snake_case       :				Union[str, Any] 			=    seq_length if not self.use_past else 1
           __snake_case       :				int 			=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               UpperCamelCase_			,			UpperCamelCase_			,			UpperCamelCase_			,			UpperCamelCase_			,			UpperCamelCase_			)
           __snake_case       :				List[Any] 			=    {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
           __snake_case       :				Any 			=    dict(**UpperCamelCase_			,			**UpperCamelCase_			)

           if self.use_past:
               if not is_torch_available():
                   raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""			)
               else:
                   import torch
               __snake_case       :				Tuple 			=    common_inputs['''input_ids'''].shape
               __snake_case       :				Any 			=    common_inputs['''decoder_input_ids'''].shape[1]
               __snake_case       :				Union[str, Any] 			=    self.num_attention_heads
               __snake_case       :				Tuple 			=    (
                   batch,
                   num_encoder_attention_heads,
                   encoder_seq_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               __snake_case       :				List[Any] 			=    decoder_seq_length + 3
               __snake_case       :				Optional[Any] 			=    (
                   batch,
                   num_decoder_attention_heads,
                   decoder_past_length,
                   self._config.hidden_size // num_decoder_attention_heads,
               )

               __snake_case       :				Dict 			=    torch.cat(
                   [common_inputs["""decoder_attention_mask"""], torch.ones(UpperCamelCase_			,			UpperCamelCase_			)]			,			dim=1			)

               __snake_case       :				List[Any] 			=    []
               # If the number of encoder and decoder layers are present in the model configuration, both are considered
               __snake_case       :				List[str] 			=    self.num_layers
               __snake_case       :				Optional[int] 			=    min(UpperCamelCase_			,			UpperCamelCase_			)
               __snake_case       :				str 			=    max(UpperCamelCase_			,			UpperCamelCase_			) - min_num_layers
               __snake_case       :				Union[str, Any] 			=    '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

               for _ in range(UpperCamelCase_			):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(UpperCamelCase_			),
                           torch.zeros(UpperCamelCase_			),
                           torch.zeros(UpperCamelCase_			),
                           torch.zeros(UpperCamelCase_			),
                       )			)
               # TODO: test this.
               __snake_case       :				Tuple 			=    encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
               for _ in range(UpperCamelCase_			,			UpperCamelCase_			):
                   common_inputs["past_key_values"].append((torch.zeros(UpperCamelCase_			), torch.zeros(UpperCamelCase_			))			)
           return common_inputs

       def 			snake_case__    (							self					:     int			,			_lowerCAmelCase					:     PreTrainedTokenizer			,			_lowerCAmelCase					:     int = -1			,			_lowerCAmelCase					:     int = -1			,			_lowerCAmelCase					:     bool = False			,			_lowerCAmelCase					:     Optional[TensorType] = None			,			):
           __snake_case       :				Tuple 			=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               UpperCamelCase_			,			UpperCamelCase_			,			UpperCamelCase_			,			UpperCamelCase_			,			UpperCamelCase_			)

           if self.use_past:
               if not is_torch_available():
                   raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""			)
               else:
                   import torch
               __snake_case       :				str 			=    common_inputs['''input_ids'''].shape
               # Not using the same length for past_key_values
               __snake_case       :				str 			=    seqlen + 2
               __snake_case       :				Tuple 			=    self.num_layers
               __snake_case       :				List[str] 			=    self.num_attention_heads
               __snake_case       :				List[Any] 			=    (
                   batch,
                   num_encoder_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )

               __snake_case       :				Union[str, Any] 			=    common_inputs['''attention_mask'''].dtype
               __snake_case       :				Any 			=    torch.cat(
                   [common_inputs["""attention_mask"""], torch.ones(UpperCamelCase_			,			UpperCamelCase_			,			dtype=UpperCamelCase_			)]			,			dim=1			)
               __snake_case       :				int 			=    [
                   (torch.zeros(UpperCamelCase_			), torch.zeros(UpperCamelCase_			)) for _ in range(UpperCamelCase_			)
               ]
           return common_inputs

       def 			snake_case__    (							self					:     int			,			_lowerCAmelCase					:     PreTrainedTokenizer			,			_lowerCAmelCase					:     int = -1			,			_lowerCAmelCase					:     int = -1			,			_lowerCAmelCase					:     bool = False			,			_lowerCAmelCase					:     Optional[TensorType] = None			,			):
           # Copied from OnnxConfig.generate_dummy_inputs
           # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           __snake_case       :				Dict 			=    compute_effective_axis_dimension(
               UpperCamelCase_			,			fixed_dimension=OnnxConfig.default_fixed_batch			,			num_token_to_add=0			)

           # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
           __snake_case       :				List[str] 			=    tokenizer.num_special_tokens_to_add(UpperCamelCase_			)
           __snake_case       :				List[Any] 			=    compute_effective_axis_dimension(
               UpperCamelCase_			,			fixed_dimension=OnnxConfig.default_fixed_sequence			,			num_token_to_add=UpperCamelCase_			)

           # Generate dummy inputs according to compute batch and sequence
           __snake_case       :				Tuple 			=    [''' '''.join([tokenizer.unk_token]			) * seq_length] * batch_size
           __snake_case       :				str 			=    dict(tokenizer(UpperCamelCase_			,			return_tensors=UpperCamelCase_			)			)
           return common_inputs

       def 			snake_case__    (							self					:     Union[str, Any]			,			_lowerCAmelCase					:     PreTrainedTokenizer			,			_lowerCAmelCase					:     int = -1			,			_lowerCAmelCase					:     int = -1			,			_lowerCAmelCase					:     bool = False			,			_lowerCAmelCase					:     Optional[TensorType] = None			,			):
           if self.task in ["default", "seq2seq-lm"]:
               __snake_case       :				List[Any] 			=    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                   UpperCamelCase_			,			batch_size=UpperCamelCase_			,			seq_length=UpperCamelCase_			,			is_pair=UpperCamelCase_			,			framework=UpperCamelCase_			)

           elif self.task == "causal-lm":
               __snake_case       :				Any 			=    self._generate_dummy_inputs_for_causal_lm(
                   UpperCamelCase_			,			batch_size=UpperCamelCase_			,			seq_length=UpperCamelCase_			,			is_pair=UpperCamelCase_			,			framework=UpperCamelCase_			)
           else:
               __snake_case       :				Dict 			=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                   UpperCamelCase_			,			batch_size=UpperCamelCase_			,			seq_length=UpperCamelCase_			,			is_pair=UpperCamelCase_			,			framework=UpperCamelCase_			)

           return common_inputs





       def 			snake_case__    (							self					:     Any			,			_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     str			,			_lowerCAmelCase					:     Tuple			,			_lowerCAmelCase					:     List[str]			):
           if self.task in ["default", "seq2seq-lm"]:
               __snake_case       :				List[Any] 			=    super()._flatten_past_key_values_(UpperCamelCase_			,			UpperCamelCase_			,			UpperCamelCase_			,			UpperCamelCase_			)
           else:
               __snake_case       :				Dict 			=    super(UpperCamelCase_			,			self			)._flatten_past_key_values_(
                   UpperCamelCase_			,			UpperCamelCase_			,			UpperCamelCase_			,			UpperCamelCase_			)





'''simple docstring'''






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 ):
	"""simple docstring"""





	def __init__(						self				: int					,						UpperCamelCase_				: Any					,						UpperCamelCase_				: Any=7					,						UpperCamelCase_				: Optional[Any]=3					,						UpperCamelCase_				: Optional[Any]=30					,						UpperCamelCase_				: List[Any]=400					,						UpperCamelCase_				: Tuple=True					,						UpperCamelCase_				: List[str]=None					,						UpperCamelCase_				: str=True					,						UpperCamelCase_				: Tuple=[0.5, 0.5, 0.5]					,						UpperCamelCase_				: int=[0.5, 0.5, 0.5]					,						UpperCamelCase_				: str=False					,						):
			lowerCAmelCase_   :       Tuple    							=size if size is not None else {'''height''': 30, '''width''': 30}
			lowerCAmelCase_   :       List[Any]    							=parent
			lowerCAmelCase_   :       int    							=batch_size
			lowerCAmelCase_   :       int    							=num_channels
			lowerCAmelCase_   :       Union[str, Any]    							=min_resolution
			lowerCAmelCase_   :       Tuple    							=max_resolution
			lowerCAmelCase_   :       Tuple    							=do_resize
			lowerCAmelCase_   :       List[str]    							=size
			lowerCAmelCase_   :       int    							=do_normalize
			lowerCAmelCase_   :       int    							=image_mean
			lowerCAmelCase_   :       str    							=image_std
			lowerCAmelCase_   :       List[Any]    							=do_reduce_labels





	def 	__A      (						self				: Optional[Any]      ):
			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_   :       Optional[int]    							=load_dataset('''hf-internal-testing/fixtures_ade20k'''  ,					split='''test'''  )

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

		return image, map







def 		SCREAMING_SNAKE_CASE__					(							):
		lowerCAmelCase_   :       Tuple    							=load_dataset('''hf-internal-testing/fixtures_ade20k'''  ,					split='''test'''  )

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

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






@require_torch
@require_vision
class 							_snake_case     (  lowerCAmelCase_						,    unittest.TestCase ):
	"""simple docstring"""





	_UpperCamelCase							:  List[Any]								=		SegformerImageProcessor if is_vision_available() else None




	def 	__A      (						self				: Optional[int]      ):
			lowerCAmelCase_   :       List[str]    							=SegformerImageProcessingTester(self      )




	@property
	def 	__A      (						self				: str      ):
			return self.image_processor_tester.prepare_image_processor_dict()




	def 	__A      (						self				: Tuple      ):
			lowerCAmelCase_   :       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 	__A      (						self				: Optional[Any]      ):
			lowerCAmelCase_   :       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_      )

			lowerCAmelCase_   :       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 	__A      (						self				: Dict      ):
			pass




	def 	__A      (						self				: Any      ):
			# Initialize image_processing
			lowerCAmelCase_   :       List[Any]    							=self.image_processing_class(**self.image_processor_dict      )
			# create random PIL images
			lowerCAmelCase_   :       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
			lowerCAmelCase_   :       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
			lowerCAmelCase_   :       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 	__A      (						self				: str      ):
			# Initialize image_processing
			lowerCAmelCase_   :       Union[str, Any]    							=self.image_processing_class(**self.image_processor_dict      )
			# create random numpy tensors
			lowerCAmelCase_   :       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
			lowerCAmelCase_   :       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
			lowerCAmelCase_   :       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 	__A      (						self				: List[str]      ):
			# Initialize image_processing
			lowerCAmelCase_   :       List[Any]    							=self.image_processing_class(**self.image_processor_dict      )
			# create random PyTorch tensors
			lowerCAmelCase_   :       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
			lowerCAmelCase_   :       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
			lowerCAmelCase_   :       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 	__A      (						self				: str      ):
			# Initialize image_processing
			lowerCAmelCase_   :       Any    							=self.image_processing_class(**self.image_processor_dict      )
			# create random PyTorch tensors
			lowerCAmelCase_   :       Optional[Any]    							=prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase_					,						torchify=UpperCamelCase_      )
			lowerCAmelCase_   :       Tuple    							=[]
			for image in image_inputs:
					self.assertIsInstance(UpperCamelCase_					,						torch.Tensor      )
					maps.append(torch.zeros(image.shape[-2:]      ).long()      )

			# Test not batched input
			lowerCAmelCase_   :       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() <= 255      )

			# Test batched
			lowerCAmelCase_   :       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() <= 255      )

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

			lowerCAmelCase_   :       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() <= 255      )

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

			lowerCAmelCase_   :       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() <= 255      )





	def 	__A      (						self				: List[str]      ):
			# Initialize image_processing
			lowerCAmelCase_   :       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_   :       int    							=prepare_semantic_single_inputs()
			lowerCAmelCase_   :       Dict    							=image_processing(UpperCamelCase_					,						UpperCamelCase_					,						return_tensors='''pt'''      )
			self.assertTrue(encoding['''labels'''].min().item() >= 0      )
			self.assertTrue(encoding['''labels'''].max().item() <= 150      )

			lowerCAmelCase_   :       Optional[Any]    							=True
			lowerCAmelCase_   :       Optional[Any]    							=image_processing(UpperCamelCase_					,						UpperCamelCase_					,						return_tensors='''pt'''      )
			self.assertTrue(encoding['''labels'''].min().item() >= 0      )
			self.assertTrue(encoding['''labels'''].max().item() <= 255      )






