




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.modeling_flax_pytorch_utils import (
        convert_pytorch_state_dict_to_flax,
        load_flax_weights_in_pytorch_model,
    )
    from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
    import torch







class __snake_case     :







   def __init__( self						:		str   ,		_snake_case						:		Tuple   ,		_snake_case						:		str=14   ,		_snake_case						:		Optional[int]=7   ,		_snake_case						:		Any=True   ,		_snake_case						:		str=True   ,		_snake_case						:		Optional[Any]=False   ,		_snake_case						:		int=True   ,		_snake_case						:		List[Any]=99   ,		_snake_case						:		List[Any]=32   ,		_snake_case						:		int=4   ,		_snake_case						:		str=4   ,		_snake_case						:		str=[[["global", "local"], 2]]   ,		_snake_case						:		Optional[Any]=37   ,		_snake_case						:		int="gelu"   ,		_snake_case						:		Dict=0.1   ,		_snake_case						:		Optional[Any]=0.1   ,		_snake_case						:		Optional[Any]=512   ,		_snake_case						:		str=7   ,		_snake_case						:		str=0.0_2   ,		):

        """simple docstring"""


        UpperCAmelCase_		=				parent
        UpperCAmelCase_		=				batch_size
        UpperCAmelCase_		=				seq_length
        UpperCAmelCase_		=				is_training
        UpperCAmelCase_		=				use_input_mask
        UpperCAmelCase_		=				use_token_type_ids
        UpperCAmelCase_		=				use_labels
        UpperCAmelCase_		=				vocab_size
        UpperCAmelCase_		=				hidden_size
        UpperCAmelCase_		=				num_hidden_layers
        UpperCAmelCase_		=				num_attention_heads
        UpperCAmelCase_		=				attention_types
        UpperCAmelCase_		=				intermediate_size
        UpperCAmelCase_		=				hidden_act
        UpperCAmelCase_		=				hidden_dropout_prob
        UpperCAmelCase_		=				attention_probs_dropout_prob
        UpperCAmelCase_		=				max_position_embeddings
        UpperCAmelCase_		=				window_size
        UpperCAmelCase_		=				initializer_range
        UpperCAmelCase_		=				None
        UpperCAmelCase_		=				vocab_size - 1
        UpperCAmelCase_		=				vocab_size - 1
        UpperCAmelCase_		=				vocab_size - 1







   def lowerCamelCase		( self						:		Optional[int]):

        """simple docstring"""


        UpperCAmelCase_		=				ids_tensor([self.batch_size, self.seq_length]   ,		self.vocab_size)

        UpperCAmelCase_		=				None
        if self.use_input_mask:
             UpperCAmelCase_		=				random_attention_mask([self.batch_size, self.seq_length])

        UpperCAmelCase_		=				GPTNeoConfig(
            vocab_size=self.vocab_size   ,		hidden_size=self.hidden_size   ,		num_layers=self.num_hidden_layers   ,		num_heads=self.num_attention_heads   ,		max_position_embeddings=self.max_position_embeddings   ,		use_cache=_snake_case   ,		bos_token_id=self.bos_token_id   ,		eos_token_id=self.eos_token_id   ,		pad_token_id=self.pad_token_id   ,		window_size=self.window_size   ,		attention_types=self.attention_types   ,		)

        return (config, input_ids, input_mask)







   def lowerCamelCase		( self						:		Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_		=				self.prepare_config_and_inputs()
        UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_		=				config_and_inputs
        UpperCAmelCase_		=				{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
        return config, inputs_dict







   def lowerCamelCase		( self						:		Optional[Any]   ,		_snake_case						:		Any   ,		_snake_case						:		Optional[Any]   ,		_snake_case						:		Optional[Any]   ,		_snake_case						:		Dict):

        """simple docstring"""


        UpperCAmelCase_		=				20
        UpperCAmelCase_		=				model_class_name(_snake_case)

        UpperCAmelCase_		=				model.init_cache(input_ids.shape[0]   ,		_snake_case)
        UpperCAmelCase_		=				jnp.ones((input_ids.shape[0], max_decoder_length)   ,		dtype='''i4''')

        UpperCAmelCase_		=				jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1)[None, :]   ,		(input_ids.shape[0], input_ids.shape[-1] - 1))
        UpperCAmelCase_		=				model(
            input_ids[:, :-1]   ,		attention_mask=_snake_case   ,		past_key_values=_snake_case   ,		position_ids=_snake_case   ,		)

        UpperCAmelCase_		=				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,		dtype='''i4''')
        UpperCAmelCase_		=				model(
            input_ids[:, -1:]   ,		attention_mask=_snake_case   ,		past_key_values=outputs_cache.past_key_values   ,		position_ids=_snake_case   ,		)

        UpperCAmelCase_		=				model(_snake_case)

        UpperCAmelCase_		=				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
        self.parent.assertTrue(diff < 1e-3   ,		msg=F"""Max diff is {diff}""")







   def lowerCamelCase		( self						:		Tuple   ,		_snake_case						:		List[str]   ,		_snake_case						:		str   ,		_snake_case						:		Optional[int]   ,		_snake_case						:		Dict):

        """simple docstring"""


        UpperCAmelCase_		=				20
        UpperCAmelCase_		=				model_class_name(_snake_case)

        UpperCAmelCase_		=				jnp.concatenate(
            [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))]   ,		axis=-1   ,		)

        UpperCAmelCase_		=				model.init_cache(input_ids.shape[0]   ,		_snake_case)
        UpperCAmelCase_		=				jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1)[None, :]   ,		(input_ids.shape[0], input_ids.shape[-1] - 1))

        UpperCAmelCase_		=				model(
            input_ids[:, :-1]   ,		attention_mask=_snake_case   ,		past_key_values=_snake_case   ,		position_ids=_snake_case   ,		)
        UpperCAmelCase_		=				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,		dtype='''i4''')
        UpperCAmelCase_		=				model(
            input_ids[:, -1:]   ,		past_key_values=outputs_cache.past_key_values   ,		attention_mask=_snake_case   ,		position_ids=_snake_case   ,		)

        UpperCAmelCase_		=				model(_snake_case   ,		attention_mask=_snake_case)

        UpperCAmelCase_		=				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
        self.parent.assertTrue(diff < 1e-3   ,		msg=F"""Max diff is {diff}""")







@require_flax
class __snake_case     (     a					,    a					,    unittest.TestCase						):
   UpperCAmelCase__    :       Tuple		=    (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
   UpperCAmelCase__    :       Dict		=    (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()







   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


        UpperCAmelCase_		=				FlaxGPTNeoModelTester(self)







   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        for model_class_name in self.all_model_classes:
             UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_		=				self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward(_snake_case   ,		_snake_case   ,		_snake_case   ,		_snake_case)







   def lowerCamelCase		( self						:		str):

        """simple docstring"""


        for model_class_name in self.all_model_classes:
             UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_		=				self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward_with_attn_mask(
                 _snake_case   ,		_snake_case   ,		_snake_case   ,		_snake_case)







   @slow
   def lowerCamelCase		( self						:		Dict):

        """simple docstring"""


        UpperCAmelCase_		=				GPTaTokenizer.from_pretrained('''gpt2'''   ,		pad_token='''<|endoftext|>'''   ,		padding_side='''left''')
        UpperCAmelCase_		=				tokenizer(['''Hello this is a long string''', '''Hey''']   ,		return_tensors='''np'''   ,		padding=_snake_case   ,		truncation=_snake_case)

        UpperCAmelCase_		=				FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M''')
        UpperCAmelCase_		=				False
        UpperCAmelCase_		=				model.config.eos_token_id

        UpperCAmelCase_		=				jax.jit(model.generate)

        UpperCAmelCase_		=				jit_generate(
            inputs['''input_ids''']   ,		attention_mask=inputs['''attention_mask''']   ,		pad_token_id=tokenizer.pad_token_id).sequences

        UpperCAmelCase_		=				tokenizer.batch_decode(_snake_case   ,		skip_special_tokens=_snake_case)

        UpperCAmelCase_		=				[
            '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
            '''Hey, I\'m a little late to the party. I\'m going to''',
        ]

        self.assertListEqual(_snake_case   ,		_snake_case)







   @is_pt_flax_cross_test
   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_	,			UpperCAmelCase_		=				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__):
                  # prepare inputs
                  UpperCAmelCase_		=				self._prepare_for_class(_snake_case   ,		_snake_case)
                  UpperCAmelCase_		=				{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

                  # load corresponding PyTorch class
                  UpperCAmelCase_		=				model_class.__name__[4:]  # Skip the "Flax" at the beginning
                  UpperCAmelCase_		=				getattr(_snake_case   ,		_snake_case)

                  UpperCAmelCase_	,			UpperCAmelCase_		=				pt_inputs['''input_ids'''].shape
                  UpperCAmelCase_		=				np.random.randint(0   ,		seq_length - 1   ,		size=(batch_size,))
                  for batch_idx, start_index in enumerate(_snake_case):
                       UpperCAmelCase_		=				0
                       UpperCAmelCase_		=				1
                       UpperCAmelCase_		=				0
                       UpperCAmelCase_		=				1
                  UpperCAmelCase_		=				pt_model_class(_snake_case).eval()
                  UpperCAmelCase_		=				model_class(_snake_case   ,		dtype=jnp.floataa)

                  UpperCAmelCase_		=				convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,		_snake_case)
                  UpperCAmelCase_		=				fx_state

                  with torch.no_grad():
                       UpperCAmelCase_		=				pt_model(**_snake_case).to_tuple()

                  UpperCAmelCase_		=				fx_model(**_snake_case).to_tuple()
                  self.assertEqual(len(_snake_case)   ,		len(_snake_case)   ,		'''Output lengths differ between Flax and PyTorch''')
                  for fx_output, pt_output in zip(_snake_case   ,		_snake_case):
                       self.assert_almost_equals(fx_output[:, -1]   ,		pt_output[:, -1].numpy()   ,		4e-2)

                  with tempfile.TemporaryDirectory() as tmpdirname:
                       pt_model.save_pretrained(_snake_case)
                       UpperCAmelCase_		=				model_class.from_pretrained(_snake_case   ,		from_pt=_snake_case)

                  UpperCAmelCase_		=				fx_model_loaded(**_snake_case).to_tuple()
                  self.assertEqual(
                      len(_snake_case)   ,		len(_snake_case)   ,		'''Output lengths differ between Flax and PyTorch''')
                  for fx_output_loaded, pt_output in zip(_snake_case   ,		_snake_case):
                       self.assert_almost_equals(fx_output_loaded[:, -1]   ,		pt_output[:, -1].numpy()   ,		4e-2)







   @is_pt_flax_cross_test
   def lowerCamelCase		( self						:		List[str]):

        """simple docstring"""


        UpperCAmelCase_	,			UpperCAmelCase_		=				self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__):
                  # prepare inputs
                  UpperCAmelCase_		=				self._prepare_for_class(_snake_case   ,		_snake_case)
                  UpperCAmelCase_		=				{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

                  # load corresponding PyTorch class
                  UpperCAmelCase_		=				model_class.__name__[4:]  # Skip the "Flax" at the beginning
                  UpperCAmelCase_		=				getattr(_snake_case   ,		_snake_case)

                  UpperCAmelCase_		=				pt_model_class(_snake_case).eval()
                  UpperCAmelCase_		=				model_class(_snake_case   ,		dtype=jnp.floataa)

                  UpperCAmelCase_		=				load_flax_weights_in_pytorch_model(_snake_case   ,		fx_model.params)
                  UpperCAmelCase_	,			UpperCAmelCase_		=				pt_inputs['''input_ids'''].shape
                  UpperCAmelCase_		=				np.random.randint(0   ,		seq_length - 1   ,		size=(batch_size,))
                  for batch_idx, start_index in enumerate(_snake_case):
                       UpperCAmelCase_		=				0
                       UpperCAmelCase_		=				1
                       UpperCAmelCase_		=				0
                       UpperCAmelCase_		=				1

                  # make sure weights are tied in PyTorch
                  pt_model.tie_weights()

                  with torch.no_grad():
                       UpperCAmelCase_		=				pt_model(**_snake_case).to_tuple()

                  UpperCAmelCase_		=				fx_model(**_snake_case).to_tuple()
                  self.assertEqual(len(_snake_case)   ,		len(_snake_case)   ,		'''Output lengths differ between Flax and PyTorch''')
                  for fx_output, pt_output in zip(_snake_case   ,		_snake_case):
                       self.assert_almost_equals(fx_output[:, -1]   ,		pt_output[:, -1].numpy()   ,		4e-2)

                  with tempfile.TemporaryDirectory() as tmpdirname:
                       fx_model.save_pretrained(_snake_case)
                       UpperCAmelCase_		=				pt_model_class.from_pretrained(_snake_case   ,		from_flax=_snake_case)

                  with torch.no_grad():
                       UpperCAmelCase_		=				pt_model_loaded(**_snake_case).to_tuple()

                  self.assertEqual(
                      len(_snake_case)   ,		len(_snake_case)   ,		'''Output lengths differ between Flax and PyTorch''')
                  for fx_output, pt_output in zip(_snake_case   ,		_snake_case):
                       self.assert_almost_equals(fx_output[:, -1]   ,		pt_output[:, -1].numpy()   ,		4e-2)







   @slow
   def lowerCamelCase		( self						:		str):

        """simple docstring"""


        for model_class_name in self.all_model_classes:
             UpperCAmelCase_		=				model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M''')
             UpperCAmelCase_		=				model(np.ones((1, 1)))
             self.assertIsNotNone(_snake_case)












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						):







   def __init__( self						:		List[Any]   ,		_snake_case						:		List[str]   ,		_snake_case						:		Any=7   ,		_snake_case						:		Any=3   ,		_snake_case						:		str=30   ,		_snake_case						:		str=400   ,		_snake_case						:		Any=True   ,		_snake_case						:		Dict=None   ,		_snake_case						:		List[str]=True   ,		_snake_case						:		Union[str, Any]=[0.5, 0.5, 0.5]   ,		_snake_case						:		Tuple=[0.5, 0.5, 0.5]   ,		_snake_case						:		str=False   ,		):

        """simple docstring"""


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







   def lowerCamelCase		( self						:		Tuple):

        """simple docstring"""


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







def   A		()  -> List[str]:







     """simple docstring"""


     UpperCAmelCase_		=				load_dataset('''hf-internal-testing/fixtures_ade20k'''  ,							split='''test'''			)

     UpperCAmelCase_		=				Image.open(dataset[0]['''file''']			)
     UpperCAmelCase_		=				Image.open(dataset[1]['''file''']			)

     return image, map


def   A		()  -> str:







     """simple docstring"""


     UpperCAmelCase_		=				load_dataset('''hf-internal-testing/fixtures_ade20k'''  ,							split='''test'''			)

     UpperCAmelCase_		=				Image.open(dataset[0]['''file''']			)
     UpperCAmelCase_		=				Image.open(dataset[1]['''file''']			)
     UpperCAmelCase_		=				Image.open(dataset[2]['''file''']			)
     UpperCAmelCase_		=				Image.open(dataset[3]['''file''']			)

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







@require_torch
@require_vision
class __snake_case     (     a					,    unittest.TestCase						):
   UpperCAmelCase__    :       int		=    SegformerImageProcessor if is_vision_available() else None







   def lowerCamelCase		( self						:		Tuple):

        """simple docstring"""


        UpperCAmelCase_		=				SegformerImageProcessingTester(self)







   @property
   def lowerCamelCase		( self						:		int):

        """simple docstring"""


        return self.image_processor_tester.prepare_image_processor_dict()







   def lowerCamelCase		( self						:		List[Any]):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(_snake_case   ,		'''do_resize'''))
        self.assertTrue(hasattr(_snake_case   ,		'''size'''))
        self.assertTrue(hasattr(_snake_case   ,		'''do_normalize'''))
        self.assertTrue(hasattr(_snake_case   ,		'''image_mean'''))
        self.assertTrue(hasattr(_snake_case   ,		'''image_std'''))
        self.assertTrue(hasattr(_snake_case   ,		'''do_reduce_labels'''))







   def lowerCamelCase		( self						:		Optional[int]):

        """simple docstring"""


        UpperCAmelCase_		=				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   ,		_snake_case)

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







   def lowerCamelCase		( self						:		Dict):

        """simple docstring"""


        pass







   def lowerCamelCase		( self						:		Dict):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        UpperCAmelCase_		=				prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=_snake_case)
        for image in image_inputs:
             self.assertIsInstance(_snake_case   ,		Image.Image)

        # Test not batched input
        UpperCAmelCase_		=				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
        UpperCAmelCase_		=				image_processing(_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.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)







   def lowerCamelCase		( self						:		Optional[int]):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        UpperCAmelCase_		=				prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=_snake_case   ,		numpify=_snake_case)
        for image in image_inputs:
             self.assertIsInstance(_snake_case   ,		np.ndarray)

        # Test not batched input
        UpperCAmelCase_		=				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
        UpperCAmelCase_		=				image_processing(_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.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)







   def lowerCamelCase		( self						:		Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        UpperCAmelCase_		=				prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=_snake_case   ,		torchify=_snake_case)
        for image in image_inputs:
             self.assertIsInstance(_snake_case   ,		torch.Tensor)

        # Test not batched input
        UpperCAmelCase_		=				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
        UpperCAmelCase_		=				image_processing(_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.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)







   def lowerCamelCase		( self						:		str):

        """simple docstring"""


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

        # Test not batched input
        UpperCAmelCase_		=				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
        UpperCAmelCase_		=				image_processing(_snake_case   ,		_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.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)
        UpperCAmelCase_	,			UpperCAmelCase_		=				prepare_semantic_single_inputs()

        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		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)
        UpperCAmelCase_	,			UpperCAmelCase_		=				prepare_semantic_batch_inputs()

        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		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 lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_		=				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
        UpperCAmelCase_	,			UpperCAmelCase_		=				prepare_semantic_single_inputs()
        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 150)

        UpperCAmelCase_		=				True
        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 255)






