



"""simple docstring"""





from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class       lowercase__      (   _lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase     ):
							'''simple docstring'''







							_UpperCAmelCase   					=       [r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']






							@register_to_config
							def __init__(	self						,							snake_case						,							snake_case						,							snake_case = None						,							snake_case = 50257						,							snake_case = 1024						,							snake_case = 768						,							snake_case = 12						,							snake_case = 12						,							snake_case = None						,							snake_case = "gelu_new"						,							snake_case = 0.1						,							snake_case = 0.1						,							snake_case = 0.1						,							snake_case = 1E-5						,							snake_case = 0.02						,							snake_case = True						,							snake_case = True						,							snake_case = False						,							snake_case = False						,							)      ->				Tuple:
									super().__init__()

									_UpperCAmelCase							=		prefix_length

									if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
											raise ValueError(
											    f'`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'
											    f' `n_embd`: {n_embd} are not equal.'    )

									_UpperCAmelCase							=		prefix_inner_dim
									_UpperCAmelCase							=		prefix_hidden_dim

									_UpperCAmelCase							=		(
									    nn.Linear(self.prefix_inner_dim						,							self.prefix_hidden_dim    )
									    if self.prefix_hidden_dim is not None
									    else nn.Identity()
									)
									_UpperCAmelCase							=		(
									    nn.Linear(self.prefix_hidden_dim						,							_SCREAMING_SNAKE_CASE    ) if self.prefix_hidden_dim is not None else nn.Identity()
									)

									_UpperCAmelCase							=		GPTaConfig(
									    vocab_size=_SCREAMING_SNAKE_CASE						,							n_positions=_SCREAMING_SNAKE_CASE						,							n_embd=_SCREAMING_SNAKE_CASE						,							n_layer=_SCREAMING_SNAKE_CASE						,							n_head=_SCREAMING_SNAKE_CASE						,							n_inner=_SCREAMING_SNAKE_CASE						,							activation_function=_SCREAMING_SNAKE_CASE						,							resid_pdrop=_SCREAMING_SNAKE_CASE						,							embd_pdrop=_SCREAMING_SNAKE_CASE						,							attn_pdrop=_SCREAMING_SNAKE_CASE						,							layer_norm_epsilon=_SCREAMING_SNAKE_CASE						,							initializer_range=_SCREAMING_SNAKE_CASE						,							scale_attn_weights=_SCREAMING_SNAKE_CASE						,							use_cache=_SCREAMING_SNAKE_CASE						,							scale_attn_by_inverse_layer_idx=_SCREAMING_SNAKE_CASE						,							reorder_and_upcast_attn=_SCREAMING_SNAKE_CASE						,							)
									_UpperCAmelCase							=		GPTaLMHeadModel(_SCREAMING_SNAKE_CASE    )






							def 							lowerCamelCase_ (	self						,							snake_case						,							snake_case						,							snake_case = None						,							snake_case = None						,							)      ->				Union[str, Any]:
									_UpperCAmelCase							=		self.transformer.transformer.wte(_SCREAMING_SNAKE_CASE    )
									_UpperCAmelCase							=		self.encode_prefix(_SCREAMING_SNAKE_CASE    )
									_UpperCAmelCase							=		self.decode_prefix(_SCREAMING_SNAKE_CASE    )
									_UpperCAmelCase							=		torch.cat((prefix_embeds, embedding_text)						,							dim=1    )

									if labels is not None:
											_UpperCAmelCase							=		self.get_dummy_token(input_ids.shape[0]						,							input_ids.device    )
											_UpperCAmelCase							=		torch.cat((dummy_token, input_ids)						,							dim=1    )
									_UpperCAmelCase							=		self.transformer(inputs_embeds=_SCREAMING_SNAKE_CASE						,							labels=_SCREAMING_SNAKE_CASE						,							attention_mask=_SCREAMING_SNAKE_CASE    )
									if self.prefix_hidden_dim is not None:
											return out, hidden
									else:
											return out






							def 							lowerCamelCase_ (	self						,							snake_case						,							snake_case    )      ->				torch.Tensor:
									return torch.zeros(_SCREAMING_SNAKE_CASE						,							self.prefix_length						,							dtype=torch.intaa						,							device=_SCREAMING_SNAKE_CASE    )






							def 							lowerCamelCase_ (	self						,							snake_case    )      ->				str:
									return self.encode_prefix(_SCREAMING_SNAKE_CASE    )






							@torch.no_grad()
							def 							lowerCamelCase_ (	self						,							snake_case						,							snake_case						,							snake_case    )      ->				Dict:
									_UpperCAmelCase							=		torch.split(_SCREAMING_SNAKE_CASE						,							1						,							dim=0    )
									_UpperCAmelCase							=		[]
									_UpperCAmelCase							=		[]
									for feature in features:
											_UpperCAmelCase							=		self.decode_prefix(feature.to(_SCREAMING_SNAKE_CASE    )    )  # back to the clip feature
											# Only support beam search for now
											_UpperCAmelCase							=		self.generate_beam(
											    input_embeds=_SCREAMING_SNAKE_CASE						,							device=_SCREAMING_SNAKE_CASE						,							eos_token_id=_SCREAMING_SNAKE_CASE    )
											generated_tokens.append(output_tokens[0]    )
											generated_seq_lengths.append(seq_lengths[0]    )
									_UpperCAmelCase							=		torch.stack(_SCREAMING_SNAKE_CASE    )
									_UpperCAmelCase							=		torch.stack(_SCREAMING_SNAKE_CASE    )
									return generated_tokens, generated_seq_lengths






							@torch.no_grad()
							def 							lowerCamelCase_ (	self						,							snake_case=None						,							snake_case=None						,							snake_case=None						,							snake_case = 5						,							snake_case = 67						,							snake_case = 1.0						,							snake_case = None						,							)      ->				Optional[int]:
									_UpperCAmelCase							=		eos_token_id
									_UpperCAmelCase							=		None
									_UpperCAmelCase							=		None
									_UpperCAmelCase							=		torch.ones(_SCREAMING_SNAKE_CASE						,							device=_SCREAMING_SNAKE_CASE						,							dtype=torch.int    )
									_UpperCAmelCase							=		torch.zeros(_SCREAMING_SNAKE_CASE						,							device=_SCREAMING_SNAKE_CASE						,							dtype=torch.bool    )

									if input_embeds is not None:
											_UpperCAmelCase							=		input_embeds
									else:
											_UpperCAmelCase							=		self.transformer.transformer.wte(_SCREAMING_SNAKE_CASE    )

									for i in range(_SCREAMING_SNAKE_CASE    ):
											_UpperCAmelCase							=		self.transformer(inputs_embeds=_SCREAMING_SNAKE_CASE    )
											_UpperCAmelCase							=		outputs.logits
											_UpperCAmelCase							=		logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
											_UpperCAmelCase							=		logits.softmax(-1    ).log()

											if scores is None:
													_UpperCAmelCase							=		logits.topk(_SCREAMING_SNAKE_CASE						,							-1    )
													_UpperCAmelCase							=		generated.expand(_SCREAMING_SNAKE_CASE						,							*generated.shape[1:]    )
													_UpperCAmelCase							=		next_tokens.permute(1						,							0    ), scores.squeeze(0    )
													if tokens is None:
															_UpperCAmelCase							=		next_tokens
													else:
															_UpperCAmelCase							=		tokens.expand(_SCREAMING_SNAKE_CASE						,							*tokens.shape[1:]    )
															_UpperCAmelCase							=		torch.cat((tokens, next_tokens)						,							dim=1    )
											else:
													_UpperCAmelCase							=		-float(np.inf    )
													_UpperCAmelCase							=		0
													_UpperCAmelCase							=		scores[:, None] + logits
													seq_lengths[~is_stopped] += 1
													_UpperCAmelCase							=		scores_sum / seq_lengths[:, None]
													_UpperCAmelCase							=		scores_sum_average.view(-1    ).topk(_SCREAMING_SNAKE_CASE						,							-1    )
													_UpperCAmelCase							=		next_tokens // scores_sum.shape[1]
													_UpperCAmelCase							=		seq_lengths[next_tokens_source]
													_UpperCAmelCase							=		next_tokens % scores_sum.shape[1]
													_UpperCAmelCase							=		next_tokens.unsqueeze(1    )
													_UpperCAmelCase							=		tokens[next_tokens_source]
													_UpperCAmelCase							=		torch.cat((tokens, next_tokens)						,							dim=1    )
													_UpperCAmelCase							=		generated[next_tokens_source]
													_UpperCAmelCase							=		scores_sum_average * seq_lengths
													_UpperCAmelCase							=		is_stopped[next_tokens_source]

											_UpperCAmelCase							=		self.transformer.transformer.wte(next_tokens.squeeze()    ).view(generated.shape[0]						,							1						,							-1    )
											_UpperCAmelCase							=		torch.cat((generated, next_token_embed)						,							dim=1    )
											_UpperCAmelCase							=		is_stopped + next_tokens.eq(_SCREAMING_SNAKE_CASE    ).squeeze()
											if is_stopped.all():
													break

									_UpperCAmelCase							=		scores / seq_lengths
									_UpperCAmelCase							=		scores.argsort(descending=_SCREAMING_SNAKE_CASE    )
									# tokens tensors are already padded to max_seq_length
									_UpperCAmelCase							=		[tokens[i] for i in order]
									_UpperCAmelCase							=		torch.stack(_SCREAMING_SNAKE_CASE						,							dim=0    )
									_UpperCAmelCase							=		torch.tensor([seq_lengths[i] for i in order]						,							dtype=seq_lengths.dtype    )
									return output_texts, seq_lengths







"""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 BeitImageProcessor
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	def __init__(      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Any     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=7     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=3     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=18     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=30     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=400     ,       _SCREAMING_SNAKE_CASE				:       Dict=True     ,       _SCREAMING_SNAKE_CASE				:       str=None     ,       _SCREAMING_SNAKE_CASE				:       Any=True     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=None     ,       _SCREAMING_SNAKE_CASE				:       Dict=True     ,       _SCREAMING_SNAKE_CASE				:       List[str]=[0.5, 0.5, 0.5]     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=[0.5, 0.5, 0.5]     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=False     ,       )						->	int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     size if size is not None else {'height': 20, 'width': 20}
					SCREAMING_SNAKE_CASE						:							int							=     crop_size if crop_size is not None else {'height': 18, 'width': 18}
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     parent
					SCREAMING_SNAKE_CASE						:							Dict							=     batch_size
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     num_channels
					SCREAMING_SNAKE_CASE						:							Tuple							=     image_size
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     min_resolution
					SCREAMING_SNAKE_CASE						:							List[str]							=     max_resolution
					SCREAMING_SNAKE_CASE						:							Tuple							=     do_resize
					SCREAMING_SNAKE_CASE						:							str							=     size
					SCREAMING_SNAKE_CASE						:							Dict							=     do_center_crop
					SCREAMING_SNAKE_CASE						:							List[Any]							=     crop_size
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     do_normalize
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     image_mean
					SCREAMING_SNAKE_CASE						:							Dict							=     image_std
					SCREAMING_SNAKE_CASE						:							Any							=     do_reduce_labels







	def 			_lowerCAmelCase     (      self				:       Dict    )						->	str:





					"""simple docstring"""


					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_reduce_labels": self.do_reduce_labels,
					}




def  __snake_case							(	)     ->       List[str]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Dict							=     load_dataset('hf-internal-testing/fixtures_ade20k'							,		split='test'     )

				SCREAMING_SNAKE_CASE						:							Any							=     Image.open(dataset[0]['file']     )
				SCREAMING_SNAKE_CASE						:							int							=     Image.open(dataset[1]['file']     )

				return image, map
def  __snake_case							(	)     ->       Optional[Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							int							=     load_dataset('hf-internal-testing/fixtures_ade20k'							,		split='test'     )

				SCREAMING_SNAKE_CASE						:							Optional[int]							=     Image.open(ds[0]['file']     )
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     Image.open(ds[1]['file']     )
				SCREAMING_SNAKE_CASE						:							Tuple							=     Image.open(ds[2]['file']     )
				SCREAMING_SNAKE_CASE						:							Tuple							=     Image.open(ds[3]['file']     )

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







@require_torch
@require_vision
class     lowerCAmelCase__						(	_lowerCamelCase							,							unittest.TestCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Tuple  						= BeitImageProcessor if is_vision_available() else None





	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     BeitImageProcessingTester(self    )





	@property
	def 			_lowerCAmelCase     (      self				:       Optional[int]    )						->	int:





					"""simple docstring"""


					return self.image_processor_tester.prepare_image_processor_dict()





	def 			_lowerCAmelCase     (      self				:       int    )						->	Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     self.image_processing_class(**self.image_processor_dict    )
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'do_resize'    )    )
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'size'    )    )
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'do_center_crop'    )    )
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'center_crop'    )    )
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'do_normalize'    )    )
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'image_mean'    )    )
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'image_std'    )    )





	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     self.image_processing_class.from_dict(self.image_processor_dict    )
					self.assertEqual(image_processor.size     ,       {'height': 20, 'width': 20}    )
					self.assertEqual(image_processor.crop_size     ,       {'height': 18, 'width': 18}    )
					self.assertEqual(image_processor.do_reduce_labels     ,       _SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.image_processing_class.from_dict(
					    self.image_processor_dict     ,       size=42     ,       crop_size=84     ,       reduce_labels=_SCREAMING_SNAKE_CASE    )
					self.assertEqual(image_processor.size     ,       {'height': 42, 'width': 42}    )
					self.assertEqual(image_processor.crop_size     ,       {'height': 84, 'width': 84}    )
					self.assertEqual(image_processor.do_reduce_labels     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       str    )						->	Dict:





					"""simple docstring"""


					pass





	def 			_lowerCAmelCase     (      self				:       Any    )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.image_processing_class(**self.image_processor_dict    )
					# create random PIL images
					SCREAMING_SNAKE_CASE						:							List[str]							=     prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=_SCREAMING_SNAKE_CASE    )
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       Image.Image    )

					# Test not batched input
					SCREAMING_SNAKE_CASE						:							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.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )

					# Test batched
					SCREAMING_SNAKE_CASE						:							str							=     image_processing(_SCREAMING_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.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )





	def 			_lowerCAmelCase     (      self				:       Union[str, Any]    )						->	Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     self.image_processing_class(**self.image_processor_dict    )
					# create random numpy tensors
					SCREAMING_SNAKE_CASE						:							Dict							=     prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=_SCREAMING_SNAKE_CASE     ,       numpify=_SCREAMING_SNAKE_CASE    )
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       np.ndarray    )

					# Test not batched input
					SCREAMING_SNAKE_CASE						:							Dict							=     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.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )

					# Test batched
					SCREAMING_SNAKE_CASE						:							str							=     image_processing(_SCREAMING_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.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )





	def 			_lowerCAmelCase     (      self				:       str    )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     self.image_processing_class(**self.image_processor_dict    )
					# create random PyTorch tensors
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=_SCREAMING_SNAKE_CASE     ,       torchify=_SCREAMING_SNAKE_CASE    )
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       torch.Tensor    )

					# Test not batched input
					SCREAMING_SNAKE_CASE						:							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.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )

					# Test batched
					SCREAMING_SNAKE_CASE						:							str							=     image_processing(_SCREAMING_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.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )





	def 			_lowerCAmelCase     (      self				:       int    )						->	Optional[Any]:





					"""simple docstring"""


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

					# Test not batched input
					SCREAMING_SNAKE_CASE						:							int							=     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.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )
					self.assertEqual(
					    encoding['labels'].shape     ,       (
					        1,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_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
					SCREAMING_SNAKE_CASE						:							List[str]							=     image_processing(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_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.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )
					self.assertEqual(
					    encoding['labels'].shape     ,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_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)
					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							str							=     prepare_semantic_single_inputs()

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     image_processing(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       return_tensors='pt'    )
					self.assertEqual(
					    encoding['pixel_values'].shape     ,       (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )
					self.assertEqual(
					    encoding['labels'].shape     ,       (
					        1,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_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)
					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							int							=     prepare_semantic_batch_inputs()

					SCREAMING_SNAKE_CASE						:							Any							=     image_processing(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       return_tensors='pt'    )
					self.assertEqual(
					    encoding['pixel_values'].shape     ,       (
					        2,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )
					self.assertEqual(
					    encoding['labels'].shape     ,       (
					        2,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_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				:       Optional[int]    )						->	Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[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
					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Any							=     prepare_semantic_single_inputs()
					SCREAMING_SNAKE_CASE						:							int							=     image_processing(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       return_tensors='pt'    )
					self.assertTrue(encoding['labels'].min().item() >= 0    )
					self.assertTrue(encoding['labels'].max().item() <= 150    )

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





