



"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class     lowerCAmelCase__						:


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Any  						= PegasusConfig
	_SCREAMING_SNAKE_CASE	:		int  						= {}
	_SCREAMING_SNAKE_CASE	:		Optional[Any]  						= '''gelu'''





	def __init__(      self				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=13     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=7     ,       _SCREAMING_SNAKE_CASE				:       str=True     ,       _SCREAMING_SNAKE_CASE				:       str=False     ,       _SCREAMING_SNAKE_CASE				:       Tuple=99     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=32     ,       _SCREAMING_SNAKE_CASE				:       str=2     ,       _SCREAMING_SNAKE_CASE				:       Tuple=4     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=37     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=0.1     ,       _SCREAMING_SNAKE_CASE				:       Tuple=0.1     ,       _SCREAMING_SNAKE_CASE				:       List[str]=40     ,       _SCREAMING_SNAKE_CASE				:       Tuple=2     ,       _SCREAMING_SNAKE_CASE				:       Tuple=1     ,       _SCREAMING_SNAKE_CASE				:       int=0     ,       )						->	Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     parent
					SCREAMING_SNAKE_CASE						:							List[Any]							=     batch_size
					SCREAMING_SNAKE_CASE						:							List[str]							=     seq_length
					SCREAMING_SNAKE_CASE						:							str							=     is_training
					SCREAMING_SNAKE_CASE						:							List[str]							=     use_labels
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     vocab_size
					SCREAMING_SNAKE_CASE						:							List[Any]							=     hidden_size
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     num_hidden_layers
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     num_attention_heads
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     intermediate_size

					SCREAMING_SNAKE_CASE						:							List[str]							=     hidden_dropout_prob
					SCREAMING_SNAKE_CASE						:							Any							=     attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     max_position_embeddings
					SCREAMING_SNAKE_CASE						:							List[Any]							=     eos_token_id
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     pad_token_id
					SCREAMING_SNAKE_CASE						:							str							=     bos_token_id





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     ids_tensor([self.batch_size, self.seq_length - 1]     ,       self.vocab_size    )
					SCREAMING_SNAKE_CASE						:							str							=     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )     ,       1    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     tf.concat([input_ids, eos_tensor]     ,       axis=1    )

					SCREAMING_SNAKE_CASE						:							int							=     ids_tensor([self.batch_size, self.seq_length]     ,       self.vocab_size    )

					SCREAMING_SNAKE_CASE						:							int							=     self.config_cls(
					    vocab_size=self.vocab_size     ,       d_model=self.hidden_size     ,       encoder_layers=self.num_hidden_layers     ,       decoder_layers=self.num_hidden_layers     ,       encoder_attention_heads=self.num_attention_heads     ,       decoder_attention_heads=self.num_attention_heads     ,       encoder_ffn_dim=self.intermediate_size     ,       decoder_ffn_dim=self.intermediate_size     ,       dropout=self.hidden_dropout_prob     ,       attention_dropout=self.attention_probs_dropout_prob     ,       max_position_embeddings=self.max_position_embeddings     ,       eos_token_ids=[2]     ,       bos_token_id=self.bos_token_id     ,       pad_token_id=self.pad_token_id     ,       decoder_start_token_id=self.pad_token_id     ,       **self.config_updates     ,       )
					SCREAMING_SNAKE_CASE						:							Any							=     prepare_pegasus_inputs_dict(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
					return config, inputs_dict







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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     TFPegasusModel(config=_SCREAMING_SNAKE_CASE    ).get_decoder()
					SCREAMING_SNAKE_CASE						:							Tuple							=     inputs_dict['input_ids']

					SCREAMING_SNAKE_CASE						:							Any							=     input_ids[:1, :]
					SCREAMING_SNAKE_CASE						:							List[Any]							=     inputs_dict['attention_mask'][:1, :]
					SCREAMING_SNAKE_CASE						:							Tuple							=     inputs_dict['head_mask']
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     1

					# first forward pass
					SCREAMING_SNAKE_CASE						:							List[str]							=     model(_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE     ,       head_mask=_SCREAMING_SNAKE_CASE     ,       use_cache=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Any							=     outputs.to_tuple()

					# create hypothetical next token and extent to next_input_ids
					SCREAMING_SNAKE_CASE						:							str							=     ids_tensor((self.batch_size, 3)     ,       config.vocab_size    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     tf.cast(ids_tensor((self.batch_size, 3)     ,       2    )     ,       tf.inta    )

					# append to next input_ids and
					SCREAMING_SNAKE_CASE						:							str							=     tf.concat([input_ids, next_tokens]     ,       axis=-1    )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     tf.concat([attention_mask, next_attn_mask]     ,       axis=-1    )

					SCREAMING_SNAKE_CASE						:							List[Any]							=     model(_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE    )[0]
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     model(_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE     ,       past_key_values=_SCREAMING_SNAKE_CASE    )[0]

					self.parent.assertEqual(next_tokens.shape[1]     ,       output_from_past.shape[1]    )

					# select random slice
					SCREAMING_SNAKE_CASE						:							Dict							=     int(ids_tensor((1,)     ,       output_from_past.shape[-1]    )    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     output_from_no_past[:, -3:, random_slice_idx]
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     output_from_past[:, :, random_slice_idx]

					# test that outputs are equal for slice
					tf.debugging.assert_near(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       rtol=1E-3    )




def  __snake_case							(	__A      :						Dict							,		__A      :						int							,		__A      :						List[Any]							,		__A      :						Optional[Any]=None							,		__A      :						Optional[Any]=None							,		__A      :						str=None							,		__A      :						Dict=None							,		__A      :						Optional[int]=None							,		)     ->       List[Any]:






				'''simple docstring'''
				if attention_mask is None:
								SCREAMING_SNAKE_CASE						:							str							=     tf.cast(tf.math.not_equal(__A							,		config.pad_token_id     )							,		tf.inta     )
				if decoder_attention_mask is None:
								SCREAMING_SNAKE_CASE						:							Optional[int]							=     tf.concat(
								    [
								        tf.ones(decoder_input_ids[:, :1].shape							,		dtype=tf.inta     ),
								        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]							,		config.pad_token_id     )							,		tf.inta     ),
								    ]							,		axis=-1							,		)
				if head_mask is None:
								SCREAMING_SNAKE_CASE						:							int							=     tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
				if decoder_head_mask is None:
								SCREAMING_SNAKE_CASE						:							str							=     tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
				if cross_attn_head_mask is None:
								SCREAMING_SNAKE_CASE						:							Tuple							=     tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": decoder_attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				    "cross_attn_head_mask": cross_attn_head_mask,
				}
@require_tf
class     lowerCAmelCase__						(	_lowerCamelCase							,							_lowerCamelCase							,							unittest.TestCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Tuple  						= (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
	_SCREAMING_SNAKE_CASE	:		Tuple  						= (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
	_SCREAMING_SNAKE_CASE	:		Optional[Any]  						= (
	    {
	        '''conversational''': TFPegasusForConditionalGeneration,
	        '''feature-extraction''': TFPegasusModel,
	        '''summarization''': TFPegasusForConditionalGeneration,
	        '''text2text-generation''': TFPegasusForConditionalGeneration,
	        '''translation''': TFPegasusForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	_SCREAMING_SNAKE_CASE	:		List[str]  						= True
	_SCREAMING_SNAKE_CASE	:		Dict  						= False
	_SCREAMING_SNAKE_CASE	:		List[str]  						= False





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     TFPegasusModelTester(self    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     ConfigTester(self     ,       config_class=_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Dict    )						->	Tuple:





					"""simple docstring"""


					self.config_tester.run_common_tests()







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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_decoder_model_past_large_inputs(*_SCREAMING_SNAKE_CASE    )




@require_sentencepiece
@require_tokenizers
@require_tf
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		int  						= [
	    ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
	    ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
	]
	_SCREAMING_SNAKE_CASE	:		List[Any]  						= [
	    '''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
	    ''' reduce the risk of wildfires.''',
	    '''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
	]  # differs slightly from pytorch, likely due to numerical differences in linear layers
	_SCREAMING_SNAKE_CASE	:		List[Any]  						= '''google/pegasus-xsum'''





	@cached_property
	def 			_lowerCAmelCase     (      self				:       Dict    )						->	List[str]:





					"""simple docstring"""


					return AutoTokenizer.from_pretrained(self.model_name    )





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
					return model





	def 			_lowerCAmelCase     (      self				:       Dict     ,       **_SCREAMING_SNAKE_CASE				:       Optional[int]    )						->	Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     self.translate_src_text(**_SCREAMING_SNAKE_CASE    )
					assert self.expected_text == generated_words





	def 			_lowerCAmelCase     (      self				:       List[str]     ,       **_SCREAMING_SNAKE_CASE				:       Dict    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     self.tokenizer(self.src_text     ,       **_SCREAMING_SNAKE_CASE     ,       padding=_SCREAMING_SNAKE_CASE     ,       return_tensors='tf'    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.model.generate(
					    model_inputs.input_ids     ,       attention_mask=model_inputs.attention_mask     ,       num_beams=2     ,       use_cache=_SCREAMING_SNAKE_CASE     ,       )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.tokenizer.batch_decode(generated_ids.numpy()     ,       skip_special_tokens=_SCREAMING_SNAKE_CASE    )
					return generated_words







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





					"""simple docstring"""


					self._assert_generated_batch_equal_expected()










"""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    )





