



from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


a_      :      Dict    =     logging.get_logger(__name__)


a_      :      Dict    =     OrderedDict(
    [
        # Base model mapping
        ('albert', 'FlaxAlbertModel'),
        ('bart', 'FlaxBartModel'),
        ('beit', 'FlaxBeitModel'),
        ('bert', 'FlaxBertModel'),
        ('big_bird', 'FlaxBigBirdModel'),
        ('blenderbot', 'FlaxBlenderbotModel'),
        ('blenderbot-small', 'FlaxBlenderbotSmallModel'),
        ('clip', 'FlaxCLIPModel'),
        ('distilbert', 'FlaxDistilBertModel'),
        ('electra', 'FlaxElectraModel'),
        ('gpt-sw3', 'FlaxGPT2Model'),
        ('gpt2', 'FlaxGPT2Model'),
        ('gpt_neo', 'FlaxGPTNeoModel'),
        ('gptj', 'FlaxGPTJModel'),
        ('longt5', 'FlaxLongT5Model'),
        ('marian', 'FlaxMarianModel'),
        ('mbart', 'FlaxMBartModel'),
        ('mt5', 'FlaxMT5Model'),
        ('opt', 'FlaxOPTModel'),
        ('pegasus', 'FlaxPegasusModel'),
        ('regnet', 'FlaxRegNetModel'),
        ('resnet', 'FlaxResNetModel'),
        ('roberta', 'FlaxRobertaModel'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormModel'),
        ('roformer', 'FlaxRoFormerModel'),
        ('t5', 'FlaxT5Model'),
        ('vision-text-dual-encoder', 'FlaxVisionTextDualEncoderModel'),
        ('vit', 'FlaxViTModel'),
        ('wav2vec2', 'FlaxWav2Vec2Model'),
        ('whisper', 'FlaxWhisperModel'),
        ('xglm', 'FlaxXGLMModel'),
        ('xlm-roberta', 'FlaxXLMRobertaModel'),
    ]
)

a_      :      int    =     OrderedDict(
    [
        # Model for pre-training mapping
        ('albert', 'FlaxAlbertForPreTraining'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForPreTraining'),
        ('big_bird', 'FlaxBigBirdForPreTraining'),
        ('electra', 'FlaxElectraForPreTraining'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
        ('wav2vec2', 'FlaxWav2Vec2ForPreTraining'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

a_      :      List[Any]    =     OrderedDict(
    [
        # Model for Masked LM mapping
        ('albert', 'FlaxAlbertForMaskedLM'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForMaskedLM'),
        ('big_bird', 'FlaxBigBirdForMaskedLM'),
        ('distilbert', 'FlaxDistilBertForMaskedLM'),
        ('electra', 'FlaxElectraForMaskedLM'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

a_      :      Tuple    =     OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('blenderbot', 'FlaxBlenderbotForConditionalGeneration'),
        ('blenderbot-small', 'FlaxBlenderbotSmallForConditionalGeneration'),
        ('encoder-decoder', 'FlaxEncoderDecoderModel'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('marian', 'FlaxMarianMTModel'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('pegasus', 'FlaxPegasusForConditionalGeneration'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
    ]
)

a_      :      Union[str, Any]    =     OrderedDict(
    [
        # Model for Image-classsification
        ('beit', 'FlaxBeitForImageClassification'),
        ('regnet', 'FlaxRegNetForImageClassification'),
        ('resnet', 'FlaxResNetForImageClassification'),
        ('vit', 'FlaxViTForImageClassification'),
    ]
)

a_      :      Dict    =     OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

a_      :      Union[str, Any]    =     OrderedDict(
    [
        # Model for Causal LM mapping
        ('bart', 'FlaxBartForCausalLM'),
        ('bert', 'FlaxBertForCausalLM'),
        ('big_bird', 'FlaxBigBirdForCausalLM'),
        ('electra', 'FlaxElectraForCausalLM'),
        ('gpt-sw3', 'FlaxGPT2LMHeadModel'),
        ('gpt2', 'FlaxGPT2LMHeadModel'),
        ('gpt_neo', 'FlaxGPTNeoForCausalLM'),
        ('gptj', 'FlaxGPTJForCausalLM'),
        ('opt', 'FlaxOPTForCausalLM'),
        ('roberta', 'FlaxRobertaForCausalLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForCausalLM'),
        ('xglm', 'FlaxXGLMForCausalLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForCausalLM'),
    ]
)

a_      :      Dict    =     OrderedDict(
    [
        # Model for Sequence Classification mapping
        ('albert', 'FlaxAlbertForSequenceClassification'),
        ('bart', 'FlaxBartForSequenceClassification'),
        ('bert', 'FlaxBertForSequenceClassification'),
        ('big_bird', 'FlaxBigBirdForSequenceClassification'),
        ('distilbert', 'FlaxDistilBertForSequenceClassification'),
        ('electra', 'FlaxElectraForSequenceClassification'),
        ('mbart', 'FlaxMBartForSequenceClassification'),
        ('roberta', 'FlaxRobertaForSequenceClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForSequenceClassification'),
        ('roformer', 'FlaxRoFormerForSequenceClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForSequenceClassification'),
    ]
)

a_      :      List[str]    =     OrderedDict(
    [
        # Model for Question Answering mapping
        ('albert', 'FlaxAlbertForQuestionAnswering'),
        ('bart', 'FlaxBartForQuestionAnswering'),
        ('bert', 'FlaxBertForQuestionAnswering'),
        ('big_bird', 'FlaxBigBirdForQuestionAnswering'),
        ('distilbert', 'FlaxDistilBertForQuestionAnswering'),
        ('electra', 'FlaxElectraForQuestionAnswering'),
        ('mbart', 'FlaxMBartForQuestionAnswering'),
        ('roberta', 'FlaxRobertaForQuestionAnswering'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForQuestionAnswering'),
        ('roformer', 'FlaxRoFormerForQuestionAnswering'),
        ('xlm-roberta', 'FlaxXLMRobertaForQuestionAnswering'),
    ]
)

a_      :      Optional[int]    =     OrderedDict(
    [
        # Model for Token Classification mapping
        ('albert', 'FlaxAlbertForTokenClassification'),
        ('bert', 'FlaxBertForTokenClassification'),
        ('big_bird', 'FlaxBigBirdForTokenClassification'),
        ('distilbert', 'FlaxDistilBertForTokenClassification'),
        ('electra', 'FlaxElectraForTokenClassification'),
        ('roberta', 'FlaxRobertaForTokenClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForTokenClassification'),
        ('roformer', 'FlaxRoFormerForTokenClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForTokenClassification'),
    ]
)

a_      :      List[str]    =     OrderedDict(
    [
        # Model for Multiple Choice mapping
        ('albert', 'FlaxAlbertForMultipleChoice'),
        ('bert', 'FlaxBertForMultipleChoice'),
        ('big_bird', 'FlaxBigBirdForMultipleChoice'),
        ('distilbert', 'FlaxDistilBertForMultipleChoice'),
        ('electra', 'FlaxElectraForMultipleChoice'),
        ('roberta', 'FlaxRobertaForMultipleChoice'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMultipleChoice'),
        ('roformer', 'FlaxRoFormerForMultipleChoice'),
        ('xlm-roberta', 'FlaxXLMRobertaForMultipleChoice'),
    ]
)

a_      :      Optional[Any]    =     OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

a_      :      Tuple    =     OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

a_      :      Dict    =     OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

a_      :      int    =     _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
a_      :      Optional[Any]    =     _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
a_      :      Union[str, Any]    =     _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
a_      :      Dict    =     _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
a_      :      Dict    =     _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
a_      :      Optional[int]    =     _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
a_      :      int    =     _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
a_      :      Optional[Any]    =     _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
a_      :      str    =     _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
a_      :      Union[str, Any]    =     _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
a_      :      Dict    =     _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
a_      :      Optional[Any]    =     _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
a_      :      List[str]    =     _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
a_      :      Optional[Any]    =     _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class    __UpperCamelCase			(       _BaseAutoModelClass				):

 """simple docstring"""



 _lowercase   :    str      						=					FLAX_MODEL_MAPPING





a_      :      Any    =     auto_class_update(FlaxAutoModel)
class    __UpperCamelCase			(       _BaseAutoModelClass				):

 """simple docstring"""



 _lowercase   :    Tuple      						=					FLAX_MODEL_FOR_PRETRAINING_MAPPING





a_      :      List[str]    =     auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')
class    __UpperCamelCase			(       _BaseAutoModelClass				):

 """simple docstring"""



 _lowercase   :    List[str]      						=					FLAX_MODEL_FOR_CAUSAL_LM_MAPPING





a_      :      Tuple    =     auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')
class    __UpperCamelCase			(       _BaseAutoModelClass				):

 """simple docstring"""



 _lowercase   :    List[Any]      						=					FLAX_MODEL_FOR_MASKED_LM_MAPPING





a_      :      Optional[Any]    =     auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')
class    __UpperCamelCase			(       _BaseAutoModelClass				):

 """simple docstring"""



 _lowercase   :    Optional[Any]      						=					FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





a_      :      Dict    =     auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)
class    __UpperCamelCase			(       _BaseAutoModelClass				):

 """simple docstring"""



 _lowercase   :    Any      						=					FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING





a_      :      str    =     auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)
class    __UpperCamelCase			(       _BaseAutoModelClass				):

 """simple docstring"""



 _lowercase   :    List[str]      						=					FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING





a_      :      int    =     auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')
class    __UpperCamelCase			(       _BaseAutoModelClass				):

 """simple docstring"""



 _lowercase   :    Optional[Any]      						=					FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING





a_      :      int    =     auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)
class    __UpperCamelCase			(       _BaseAutoModelClass				):

 """simple docstring"""



 _lowercase   :    Any      						=					FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING





a_      :      List[Any]    =     auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')
class    __UpperCamelCase			(       _BaseAutoModelClass				):

 """simple docstring"""



 _lowercase   :    str      						=					FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING





a_      :      Dict    =     auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)
class    __UpperCamelCase			(       _BaseAutoModelClass				):

 """simple docstring"""



 _lowercase   :    Union[str, Any]      						=					FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING





a_      :      str    =     auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)
class    __UpperCamelCase			(       _BaseAutoModelClass				):

 """simple docstring"""



 _lowercase   :    List[str]      						=					FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING





a_      :      Optional[int]    =     auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')
class    __UpperCamelCase			(       _BaseAutoModelClass				):

 """simple docstring"""



 _lowercase   :    int      						=					FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING





a_      :      Optional[int]    =     auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)







import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import UperNetForSemanticSegmentation
		from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor



class    a_ :






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase=13				,			__UpperCamelCase=32				,			__UpperCamelCase=3				,			__UpperCamelCase=4				,			__UpperCamelCase=[10, 20, 30, 40]				,			__UpperCamelCase=[2, 2, 3, 2]				,			__UpperCamelCase=True				,			__UpperCamelCase=True				,			__UpperCamelCase=37				,			__UpperCamelCase="gelu"				,			__UpperCamelCase=10				,			__UpperCamelCase=0.02				,			__UpperCamelCase=["stage2", "stage3", "stage4"]				,			__UpperCamelCase=3				,			__UpperCamelCase=None				,			):
									_lowercase					       =					parent
									_lowercase					       =					batch_size
									_lowercase					       =					image_size
									_lowercase					       =					num_channels
									_lowercase					       =					num_stages
									_lowercase					       =					hidden_sizes
									_lowercase					       =					depths
									_lowercase					       =					is_training
									_lowercase					       =					use_labels
									_lowercase					       =					intermediate_size
									_lowercase					       =					hidden_act
									_lowercase					       =					type_sequence_label_size
									_lowercase					       =					initializer_range
									_lowercase					       =					out_features
									_lowercase					       =					num_labels
									_lowercase					       =					scope
									_lowercase					       =					num_stages






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

									_lowercase					       =					None
									if self.use_labels:
															_lowercase					       =					ids_tensor([self.batch_size]				,			self.type_sequence_label_size  )

									_lowercase					       =					self.get_config()

									return config, pixel_values, labels






			def    UpperCamelCase_				(      self  ):
									return ConvNextConfig(
									    num_channels=self.num_channels				,			num_stages=self.num_stages				,			hidden_sizes=self.hidden_sizes				,			depths=self.depths				,			is_training=self.is_training				,			intermediate_size=self.intermediate_size				,			hidden_act=self.hidden_act				,			out_features=self.out_features				,			)






			def    UpperCamelCase_				(      self  ):
									return UperNetConfig(
									    backbone_config=self.get_backbone_config()				,			hidden_size=512				,			pool_scales=[1, 2, 3, 6]				,			use_auxiliary_head=__UpperCamelCase				,			auxiliary_loss_weight=0.4				,			auxiliary_in_channels=40				,			auxiliary_channels=256				,			auxiliary_num_convs=1				,			auxiliary_concat_input=__UpperCamelCase				,			loss_ignore_index=255				,			num_labels=self.num_labels				,			)






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					UperNetForSemanticSegmentation(config=__UpperCamelCase  )
									model.to(__UpperCamelCase  )
									model.eval()
									_lowercase					       =					model(__UpperCamelCase  )
									self.parent.assertEqual(
									    result.logits.shape				,			(self.batch_size, self.num_labels, self.image_size, self.image_size)  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.prepare_config_and_inputs()
									(
									    (
									    _lowercase
									)       ,						(
									    _lowercase
									)       ,						(
									    _lowercase
									)       ,						
									)					       =					config_and_inputs
									_lowercase					       =					{"""pixel_values""": pixel_values}
									return config, inputs_dict







@require_torch
class    a_ (			_a					,			_a					,			unittest.TestCase    ):
			a	:			Optional[int]					   =							(UperNetForSemanticSegmentation,) if is_torch_available() else ()
			a	:			Optional[Any]					   =							{'''image-segmentation''': UperNetForSemanticSegmentation} if is_torch_available() else {}
			a	:			Any					   =							False
			a	:			Union[str, Any]					   =							False
			a	:			Optional[Any]					   =							False
			a	:			int					   =							False
			a	:			Optional[int]					   =							False
			a	:			List[str]					   =							False






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					UperNetModelTester(self  )
									_lowercase					       =					ConfigTester(self				,			config_class=__UpperCamelCase				,			has_text_modality=__UpperCamelCase				,			hidden_size=37  )






			def    UpperCamelCase_				(      self  ):
									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()






			def    UpperCamelCase_				(      self  ):
									return






			def    UpperCamelCase_				(      self  ):
									_lowercase       ,						_lowercase					       =					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															_lowercase					       =					model_class(__UpperCamelCase  )
															_lowercase					       =					inspect.signature(model.forward  )
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															_lowercase					       =					[*signature.parameters.keys()]

															_lowercase					       =					["""pixel_values"""]
															self.assertListEqual(arg_names[:1]				,			__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase  )






			@unittest.skip(reason="""UperNet does not use inputs_embeds"""  )
			def    UpperCamelCase_				(      self  ):
									pass






			@unittest.skip(reason="""UperNet does not support input and output embeddings"""  )
			def    UpperCamelCase_				(      self  ):
									pass






			@unittest.skip(reason="""UperNet does not have a base model"""  )
			def    UpperCamelCase_				(      self  ):
									pass






			@unittest.skip(reason="""UperNet does not have a base model"""  )
			def    UpperCamelCase_				(      self  ):
									pass






			@require_torch_multi_gpu
			@unittest.skip(reason="""UperNet has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""  )
			def    UpperCamelCase_				(      self  ):
									pass






			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
			def    UpperCamelCase_				(      self  ):
									pass






			def    UpperCamelCase_				(      self  ):
									def check_hidden_states_output(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
															_lowercase					       =					model_class(__UpperCamelCase  )
															model.to(__UpperCamelCase  )
															model.eval()

															with torch.no_grad():
																					_lowercase					       =					model(**self._prepare_for_class(__UpperCamelCase				,			__UpperCamelCase  )  )

															_lowercase					       =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															_lowercase					       =					self.model_tester.num_stages
															self.assertEqual(len(__UpperCamelCase  )				,			expected_num_stages + 1  )

															# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]  )				,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4]				,			)

									_lowercase       ,						_lowercase					       =					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															_lowercase					       =					True
															check_hidden_states_output(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  )

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															_lowercase					       =					True

															check_hidden_states_output(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									_lowercase       ,						_lowercase					       =					self.model_tester.prepare_config_and_inputs_for_common()

									_lowercase					       =					_config_zero_init(__UpperCamelCase  )
									_lowercase					       =					_config_zero_init(configs_no_init.backbone_config  )
									for model_class in self.all_model_classes:
															_lowercase					       =					model_class(config=__UpperCamelCase  )
															for name, param in model.named_parameters():
																					if param.requires_grad:
																											self.assertIn(
																											    ((param.data.mean() * 1E9).round() / 1E9).item()				,			[0.0, 1.0]				,			msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""				,			)






			@unittest.skip(reason="""UperNet does not have tied weights"""  )
			def    UpperCamelCase_				(      self  ):
									pass






			@slow
			def    UpperCamelCase_				(      self  ):
									for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_lowercase					       =					UperNetForSemanticSegmentation.from_pretrained(__UpperCamelCase  )
															self.assertIsNotNone(__UpperCamelCase  )







def 	UpperCamelCase__  (     )  ->     str:
						_lowercase					       =					hf_hub_download(
						    repo_id="""hf-internal-testing/fixtures_ade20k"""		,   repo_type="""dataset"""		,   filename="""ADE_val_00000001.jpg"""       )
						_lowercase					       =					Image.open(SCREAMING_SNAKE_CASE_       ).convert("""RGB"""       )
						return image



@require_torch
@require_vision
@slow
class    a_ (			unittest.TestCase    ):






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					AutoImageProcessor.from_pretrained("""openmmlab/upernet-swin-tiny"""  )
									_lowercase					       =					UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-swin-tiny"""  ).to(__UpperCamelCase  )

									_lowercase					       =					prepare_img()
									_lowercase					       =					processor(images=__UpperCamelCase				,			return_tensors="""pt"""  ).to(__UpperCamelCase  )

									with torch.no_grad():
															_lowercase					       =					model(**__UpperCamelCase  )

									_lowercase					       =					torch.Size((1, model.config.num_labels, 512, 512)  )
									self.assertEqual(outputs.logits.shape				,			__UpperCamelCase  )

									_lowercase					       =					torch.tensor(
									    [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]  ).to(__UpperCamelCase  )
									self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]				,			__UpperCamelCase				,			atol=1E-4  )  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					AutoImageProcessor.from_pretrained("""openmmlab/upernet-convnext-tiny"""  )
									_lowercase					       =					UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-convnext-tiny"""  ).to(__UpperCamelCase  )

									_lowercase					       =					prepare_img()
									_lowercase					       =					processor(images=__UpperCamelCase				,			return_tensors="""pt"""  ).to(__UpperCamelCase  )

									with torch.no_grad():
															_lowercase					       =					model(**__UpperCamelCase  )

									_lowercase					       =					torch.Size((1, model.config.num_labels, 512, 512)  )
									self.assertEqual(outputs.logits.shape				,			__UpperCamelCase  )

									_lowercase					       =					torch.tensor(
									    [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]  ).to(__UpperCamelCase  )
									self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]				,			__UpperCamelCase				,			atol=1E-4  )  )