






'''simple docstring'''




import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
		import torch

		from transformers import (
		    MODEL_MAPPING,
		    SegformerForImageClassification,
		    SegformerForSemanticSegmentation,
		    SegformerModel,
		)
		from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import SegformerImageProcessor







class    A_	(UpperCAmelCase_							):
							"""simple docstring"""




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


										'''simple docstring'''




										snake_case_				:					List[str]   =       self.config_class(**self.inputs_dict				)
										self.parent.assertTrue(hasattr(_snake_case       ,							"hidden_sizes"				)				)
										self.parent.assertTrue(hasattr(_snake_case       ,							"num_attention_heads"				)				)
										self.parent.assertTrue(hasattr(_snake_case       ,							"num_encoder_blocks"				)				)







class    A_	:
							"""simple docstring"""




							def __init__(		self			:List[Any]       ,							lowerCAmelCase__			:Union[str, Any]       ,							lowerCAmelCase__			:Union[str, Any]=13       ,							lowerCAmelCase__			:Union[str, Any]=64       ,							lowerCAmelCase__			:int=3       ,							lowerCAmelCase__			:Dict=4       ,							lowerCAmelCase__			:Dict=[2, 2, 2, 2]       ,							lowerCAmelCase__			:Dict=[8, 4, 2, 1]       ,							lowerCAmelCase__			:Any=[16, 32, 64, 128]       ,							lowerCAmelCase__			:int=[1, 4, 8, 16]       ,							lowerCAmelCase__			:Any=[1, 2, 4, 8]       ,							lowerCAmelCase__			:Union[str, Any]=True       ,							lowerCAmelCase__			:List[Any]=True       ,							lowerCAmelCase__			:Any="gelu"       ,							lowerCAmelCase__			:str=0.1       ,							lowerCAmelCase__			:Any=0.1       ,							lowerCAmelCase__			:Optional[int]=0.0_2       ,							lowerCAmelCase__			:str=3       ,							lowerCAmelCase__			:str=None       ,							)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					int   =       parent
										snake_case_				:					Union[str, Any]   =       batch_size
										snake_case_				:					List[Any]   =       image_size
										snake_case_				:					Dict   =       num_channels
										snake_case_				:					Optional[Any]   =       num_encoder_blocks
										snake_case_				:					List[Any]   =       sr_ratios
										snake_case_				:					Union[str, Any]   =       depths
										snake_case_				:					int   =       hidden_sizes
										snake_case_				:					Optional[Any]   =       downsampling_rates
										snake_case_				:					List[str]   =       num_attention_heads
										snake_case_				:					Any   =       is_training
										snake_case_				:					Dict   =       use_labels
										snake_case_				:					List[Any]   =       hidden_act
										snake_case_				:					Dict   =       hidden_dropout_prob
										snake_case_				:					int   =       attention_probs_dropout_prob
										snake_case_				:					Optional[Any]   =       initializer_range
										snake_case_				:					int   =       num_labels
										snake_case_				:					Dict   =       scope

							def 							_A	(		self			:Optional[Any]				)				->  str:


										'''simple docstring'''




										snake_case_				:					List[str]   =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

										snake_case_				:					str   =       None
										if self.use_labels:
													snake_case_				:					str   =       ids_tensor([self.batch_size, self.image_size, self.image_size]       ,							self.num_labels				)

										snake_case_				:					List[Any]   =       self.get_config()
										return config, pixel_values, labels

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


										'''simple docstring'''




										return SegformerConfig(
										    image_size=self.image_size       ,							num_channels=self.num_channels       ,							num_encoder_blocks=self.num_encoder_blocks       ,							depths=self.depths       ,							hidden_sizes=self.hidden_sizes       ,							num_attention_heads=self.num_attention_heads       ,							hidden_act=self.hidden_act       ,							hidden_dropout_prob=self.hidden_dropout_prob       ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,							initializer_range=self.initializer_range       ,							)

							def 							_A	(		self			:Union[str, Any]       ,							lowerCAmelCase__			:List[Any]       ,							lowerCAmelCase__			:Any       ,							lowerCAmelCase__			:Union[str, Any]				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					str   =       SegformerModel(config=_snake_case				)
										model.to(_snake_case				)
										model.eval()
										snake_case_				:					Optional[Any]   =       model(_snake_case				)
										snake_case_				:					List[Any]   =       self.image_size // (self.downsampling_rates[-1] * 2)
										self.parent.assertEqual(
										    result.last_hidden_state.shape       ,							(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)				)

							def 							_A	(		self			:int       ,							lowerCAmelCase__			:Optional[int]       ,							lowerCAmelCase__			:Optional[Any]       ,							lowerCAmelCase__			:Optional[int]				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       self.num_labels
										snake_case_				:					Any   =       SegformerForSemanticSegmentation(_snake_case				)
										model.to(_snake_case				)
										model.eval()
										snake_case_				:					int   =       model(_snake_case				)
										self.parent.assertEqual(
										    result.logits.shape       ,							(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)				)
										snake_case_				:					Union[str, Any]   =       model(_snake_case       ,							labels=_snake_case				)
										self.parent.assertEqual(
										    result.logits.shape       ,							(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)				)
										self.parent.assertGreater(result.loss       ,							0.0				)

							def 							_A	(		self			:int       ,							lowerCAmelCase__			:Union[str, Any]       ,							lowerCAmelCase__			:Tuple       ,							lowerCAmelCase__			:Any				)				->  str:


										'''simple docstring'''




										snake_case_				:					List[str]   =       1
										snake_case_				:					Optional[Any]   =       SegformerForSemanticSegmentation(config=_snake_case				)
										model.to(_snake_case				)
										model.eval()
										snake_case_				:					List[str]   =       torch.randint(0       ,							1       ,							(self.batch_size, self.image_size, self.image_size)				).to(_snake_case				)
										snake_case_				:					Optional[int]   =       model(_snake_case       ,							labels=_snake_case				)
										self.parent.assertGreater(result.loss       ,							0.0				)






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


										'''simple docstring'''




										snake_case_				:					Dict   =       self.prepare_config_and_inputs()
										snake_case_,	snake_case_,	snake_case_				:					Tuple   =       config_and_inputs
										snake_case_				:					List[Any]   =       {"pixel_values": pixel_values}
										return config, inputs_dict







@require_torch
class    A_	(UpperCAmelCase_						,					UpperCAmelCase_						,					unittest.TestCase							):
							"""simple docstring"""




							a__   		=	(
							    (
							        SegformerModel,
							        SegformerForSemanticSegmentation,
							        SegformerForImageClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							a__   		=	(
							    {
							        "feature-extraction": SegformerModel,
							        "image-classification": SegformerForImageClassification,
							        "image-segmentation": SegformerForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							a__   		=	True
							a__   		=	False
							a__   		=	False
							a__   		=	False

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


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       SegformerModelTester(self				)
										snake_case_				:					str   =       SegformerConfigTester(self       ,							config_class=_snake_case				)

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


										'''simple docstring'''




										self.config_tester.run_common_tests()

							def 							_A	(		self			:Optional[Any]				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_snake_case				)

							def 							_A	(		self			:Dict				)				->  str:


										'''simple docstring'''




										snake_case_				:					int   =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_binary_image_segmentation(*_snake_case				)

							def 							_A	(		self			:int				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Any   =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_segmentation(*_snake_case				)

							@unittest.skip("SegFormer does not use inputs_embeds"				)
							def 							_A	(		self			:Dict				)				->  Tuple:


										'''simple docstring'''




										pass

							@unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods"				)
							def 							_A	(		self			:Dict				)				->  List[str]:


										'''simple docstring'''




										pass

							def 							_A	(		self			:str				)				->  Optional[int]:


										'''simple docstring'''




										snake_case_,	snake_case_				:					List[str]   =       self.model_tester.prepare_config_and_inputs_for_common()

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

													snake_case_				:					Optional[Any]   =       ["pixel_values"]
													self.assertListEqual(arg_names[:1]       ,							_snake_case				)

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


										'''simple docstring'''




										snake_case_,	snake_case_				:					Dict   =       self.model_tester.prepare_config_and_inputs_for_common()
										snake_case_				:					Optional[Any]   =       True

										for model_class in self.all_model_classes:
													snake_case_				:					Tuple   =       True
													snake_case_				:					int   =       False
													snake_case_				:					List[Any]   =       True
													snake_case_				:					Optional[int]   =       model_class(_snake_case				)
													model.to(_snake_case				)
													model.eval()
													with torch.no_grad():
																snake_case_				:					int   =       model(**self._prepare_for_class(_snake_case       ,							_snake_case				)				)
													snake_case_				:					List[Any]   =       outputs.attentions

													snake_case_				:					List[str]   =       sum(self.model_tester.depths				)
													self.assertEqual(len(_snake_case				)       ,							_snake_case				)

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													snake_case_				:					Any   =       True
													snake_case_				:					Dict   =       model_class(_snake_case				)
													model.to(_snake_case				)
													model.eval()
													with torch.no_grad():
																snake_case_				:					List[Any]   =       model(**self._prepare_for_class(_snake_case       ,							_snake_case				)				)
													snake_case_				:					List[Any]   =       outputs.attentions

													self.assertEqual(len(_snake_case				)       ,							_snake_case				)

													# verify the first attentions (first block, first layer)
													snake_case_				:					Dict   =       (self.model_tester.image_size // 4) ** 2
													snake_case_				:					List[str]   =       (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(attentions[0].shape[-3:]				)       ,							[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]       ,							)

													# verify the last attentions (last block, last layer)
													snake_case_				:					Optional[int]   =       (self.model_tester.image_size // 32) ** 2
													snake_case_				:					Dict   =       (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
													self.assertListEqual(
													    list(attentions[-1].shape[-3:]				)       ,							[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]       ,							)
													snake_case_				:					int   =       len(_snake_case				)

													# Check attention is always last and order is fine
													snake_case_				:					List[Any]   =       True
													snake_case_				:					Any   =       True
													snake_case_				:					Dict   =       model_class(_snake_case				)
													model.to(_snake_case				)
													model.eval()
													with torch.no_grad():
																snake_case_				:					Optional[int]   =       model(**self._prepare_for_class(_snake_case       ,							_snake_case				)				)

													self.assertEqual(out_len + 1       ,							len(_snake_case				)				)

													snake_case_				:					Any   =       outputs.attentions

													self.assertEqual(len(_snake_case				)       ,							_snake_case				)
													# verify the first attentions (first block, first layer)
													snake_case_				:					int   =       (self.model_tester.image_size // 4) ** 2
													snake_case_				:					Optional[int]   =       (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]				)       ,							[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]       ,							)

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


										'''simple docstring'''




										def check_hidden_states_output(lowerCAmelCase__			:Union[str, Any]       ,							lowerCAmelCase__			:Union[str, Any]       ,							lowerCAmelCase__			:str				):
													snake_case_				:					Tuple   =       model_class(_snake_case				)
													model.to(_snake_case				)
													model.eval()

													with torch.no_grad():
																snake_case_				:					Optional[Any]   =       model(**self._prepare_for_class(_snake_case       ,							_snake_case				)				)

													snake_case_				:					int   =       outputs.hidden_states

													snake_case_				:					Union[str, Any]   =       self.model_tester.num_encoder_blocks
													self.assertEqual(len(_snake_case				)       ,							_snake_case				)

													# verify the first hidden states (first block)
													self.assertListEqual(
													    list(hidden_states[0].shape[-3:]				)       ,							[
													        self.model_tester.hidden_sizes[0],
													        self.model_tester.image_size // 4,
													        self.model_tester.image_size // 4,
													    ]       ,							)

										snake_case_,	snake_case_				:					int   =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													snake_case_				:					Tuple   =       True
													check_hidden_states_output(_snake_case       ,							_snake_case       ,							_snake_case				)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													snake_case_				:					List[Any]   =       True

													check_hidden_states_output(_snake_case       ,							_snake_case       ,							_snake_case				)

							def 							_A	(		self			:Tuple				)				->  Optional[Any]:


										'''simple docstring'''




										if not self.model_tester.is_training:
													return

										snake_case_,	snake_case_				:					str   =       self.model_tester.prepare_config_and_inputs_for_common()
										snake_case_				:					List[str]   =       True

										for model_class in self.all_model_classes:
													if model_class in get_values(_snake_case				):
																continue

													snake_case_				:					Optional[int]   =       model_class(_snake_case				)
													model.to(_snake_case				)
													model.train()
													snake_case_				:					Union[str, Any]   =       self._prepare_for_class(_snake_case       ,							_snake_case       ,							return_labels=_snake_case				)
													snake_case_				:					Optional[Any]   =       model(**_snake_case				).loss
													loss.backward()

							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."				)
							def 							_A	(		self			:Optional[Any]				)				->  Union[str, Any]:


										'''simple docstring'''




										pass






							@slow
							def 							_A	(		self			:Dict				)				->  Tuple:


										'''simple docstring'''




										for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													snake_case_				:					int   =       SegformerModel.from_pretrained(_snake_case				)
													self.assertIsNotNone(_snake_case				)

def 	__UpperCAmelCase       ( )->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					List[Any]   =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
			return image







@require_torch
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							@slow
							def 							_A	(		self			:Dict				)				->  Optional[int]:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       SegformerImageProcessor(
										    image_scale=(512, 512)       ,							keep_ratio=_snake_case       ,							align=_snake_case       ,							do_random_crop=_snake_case				)
										snake_case_				:					Tuple   =       SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"				).to(
										    _snake_case				)

										snake_case_				:					str   =       prepare_img()
										snake_case_				:					List[Any]   =       image_processor(images=_snake_case       ,							return_tensors="pt"				)
										snake_case_				:					str   =       encoded_inputs.pixel_values.to(_snake_case				)

										with torch.no_grad():
													snake_case_				:					List[Any]   =       model(_snake_case				)

										snake_case_				:					Optional[int]   =       torch.Size((1, model.config.num_labels, 128, 128)				)
										self.assertEqual(outputs.logits.shape       ,							_snake_case				)

										snake_case_				:					Dict   =       torch.tensor(
										    [
										        [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
										        [[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
										        [[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
										    ]				).to(_snake_case				)
										self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]       ,							_snake_case       ,							atol=1E-4				)				)

							@slow
							def 							_A	(		self			:List[Any]				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       SegformerImageProcessor(
										    image_scale=(512, 512)       ,							keep_ratio=_snake_case       ,							align=_snake_case       ,							do_random_crop=_snake_case				)
										snake_case_				:					Optional[Any]   =       SegformerForSemanticSegmentation.from_pretrained(
										    "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"				).to(_snake_case				)

										snake_case_				:					Union[str, Any]   =       prepare_img()
										snake_case_				:					List[str]   =       image_processor(images=_snake_case       ,							return_tensors="pt"				)
										snake_case_				:					Any   =       encoded_inputs.pixel_values.to(_snake_case				)

										with torch.no_grad():
													snake_case_				:					List[Any]   =       model(_snake_case				)

										snake_case_				:					List[str]   =       torch.Size((1, model.config.num_labels, 128, 128)				)
										self.assertEqual(outputs.logits.shape       ,							_snake_case				)

										snake_case_				:					str   =       torch.tensor(
										    [
										        [[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
										        [[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
										        [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
										    ]				).to(_snake_case				)
										self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]       ,							_snake_case       ,							atol=1E-1				)				)






							@slow
							def 							_A	(		self			:List[Any]				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       SegformerImageProcessor(
										    image_scale=(512, 512)       ,							keep_ratio=_snake_case       ,							align=_snake_case       ,							do_random_crop=_snake_case				)
										snake_case_				:					int   =       SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"				).to(
										    _snake_case				)

										snake_case_				:					Optional[int]   =       prepare_img()
										snake_case_				:					Optional[Any]   =       image_processor(images=_snake_case       ,							return_tensors="pt"				)
										snake_case_				:					Tuple   =       encoded_inputs.pixel_values.to(_snake_case				)

										with torch.no_grad():
													snake_case_				:					List[Any]   =       model(_snake_case				)

										snake_case_				:					Any   =       outputs.logits.detach().cpu()

										snake_case_				:					Union[str, Any]   =       image_processor.post_process_semantic_segmentation(outputs=_snake_case       ,							target_sizes=[(500, 300)]				)
										snake_case_				:					int   =       torch.Size((500, 300)				)
										self.assertEqual(segmentation[0].shape       ,							_snake_case				)

										snake_case_				:					Dict   =       image_processor.post_process_semantic_segmentation(outputs=_snake_case				)
										snake_case_				:					Optional[Any]   =       torch.Size((128, 128)				)
										self.assertEqual(segmentation[0].shape       ,							_snake_case				)












'''simple docstring'''




from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__lowerCamelCase   :					Dict   							=					{
    '''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
    '''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase   :					int   							=					[
		    '''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''GPTNeoXJapaneseForCausalLM''',
		    '''GPTNeoXJapaneseLayer''',
		    '''GPTNeoXJapaneseModel''',
		    '''GPTNeoXJapanesePreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
		from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gpt_neox_japanese import (
				    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTNeoXJapaneseForCausalLM,
				    GPTNeoXJapaneseLayer,
				    GPTNeoXJapaneseModel,
				    GPTNeoXJapanesePreTrainedModel,
				)


else:
		import sys

		__lowerCamelCase   :					Optional[int]   							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




