
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        UpperCAmelCase_  (       lowerCamelCase__	):

						'''simple docstring'''




						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.config_class(**self.inputs_dict		)
													self.parent.assertTrue(hasattr(__A				,							'hidden_sizes'		)		)
													self.parent.assertTrue(hasattr(__A				,							'num_attention_heads'		)		)
													self.parent.assertTrue(hasattr(__A				,							'num_encoder_blocks'		)		)







class        UpperCAmelCase_  :

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A=13				,							_A=64				,							_A=3				,							_A=4				,							_A=[2, 2, 2, 2]				,							_A=[8, 4, 2, 1]				,							_A=[16, 32, 64, 128]				,							_A=[1, 4, 8, 16]				,							_A=[1, 2, 4, 8]				,							_A=True				,							_A=True				,							_A="gelu"				,							_A=0.1				,							_A=0.1				,							_A=0.0_2				,							_A=3				,							_A=None				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= parent
													__SCREAMING_SNAKE_CASE									= batch_size
													__SCREAMING_SNAKE_CASE									= image_size
													__SCREAMING_SNAKE_CASE									= num_channels
													__SCREAMING_SNAKE_CASE									= num_encoder_blocks
													__SCREAMING_SNAKE_CASE									= sr_ratios
													__SCREAMING_SNAKE_CASE									= depths
													__SCREAMING_SNAKE_CASE									= hidden_sizes
													__SCREAMING_SNAKE_CASE									= downsampling_rates
													__SCREAMING_SNAKE_CASE									= num_attention_heads
													__SCREAMING_SNAKE_CASE									= is_training
													__SCREAMING_SNAKE_CASE									= use_labels
													__SCREAMING_SNAKE_CASE									= hidden_act
													__SCREAMING_SNAKE_CASE									= hidden_dropout_prob
													__SCREAMING_SNAKE_CASE									= attention_probs_dropout_prob
													__SCREAMING_SNAKE_CASE									= initializer_range
													__SCREAMING_SNAKE_CASE									= num_labels
													__SCREAMING_SNAKE_CASE									= scope






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

													__SCREAMING_SNAKE_CASE									= self.get_config()
													return config, pixel_values, labels






						def 	_A     (   self		):







													'''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				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= SegformerModel(config=__A		)
													model.to(__A		)
													model.eval()
													__SCREAMING_SNAKE_CASE									= model(__A		)
													__SCREAMING_SNAKE_CASE									= 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				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.num_labels
													__SCREAMING_SNAKE_CASE									= SegformerForSemanticSegmentation(__A		)
													model.to(__A		)
													model.eval()
													__SCREAMING_SNAKE_CASE									= model(__A		)
													self.parent.assertEqual(
													    result.logits.shape				,							(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)		)
													__SCREAMING_SNAKE_CASE									= model(__A				,							labels=__A		)
													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				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 1
													__SCREAMING_SNAKE_CASE									= SegformerForSemanticSegmentation(config=__A		)
													model.to(__A		)
													model.eval()
													__SCREAMING_SNAKE_CASE									= torch.randint(0				,							1				,							(self.batch_size, self.image_size, self.image_size)		).to(__A		)
													__SCREAMING_SNAKE_CASE									= model(__A				,							labels=__A		)
													self.parent.assertGreater(result.loss				,							0.0		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.prepare_config_and_inputs()
													__SCREAMING_SNAKE_CASE									= config_and_inputs
													__SCREAMING_SNAKE_CASE									= {'''pixel_values''': pixel_values}
													return config, inputs_dict







@require_torch
class        UpperCAmelCase_  (       lowerCamelCase__   ,   lowerCamelCase__   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			List[str]      =  (
						    (
						        SegformerModel,
						        SegformerForSemanticSegmentation,
						        SegformerForImageClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCamelCase__      :			str      =  (
						    {
						        '''feature-extraction''': SegformerModel,
						        '''image-classification''': SegformerForImageClassification,
						        '''image-segmentation''': SegformerForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						UpperCamelCase__      :			Union[str, Any]      =  True
						UpperCamelCase__      :			List[str]      =  False
						UpperCamelCase__      :			List[str]      =  False
						UpperCamelCase__      :			Any      =  False






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= SegformerModelTester(self		)
													__SCREAMING_SNAKE_CASE									= SegformerConfigTester(self				,							config_class=__A		)






						def 	_A     (   self		):







													'''simple docstring'''

													self.config_tester.run_common_tests()






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__A		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_binary_image_segmentation(*__A		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_segmentation(*__A		)






						@unittest.skip('SegFormer does not use inputs_embeds'		)
						def 	_A     (   self		):







													'''simple docstring'''

													pass






						@unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods'		)
						def 	_A     (   self		):







													'''simple docstring'''

													pass






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs_for_common()

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

																				__SCREAMING_SNAKE_CASE									= ['''pixel_values''']
																				self.assertListEqual(arg_names[:1]				,							__A		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs_for_common()
													__SCREAMING_SNAKE_CASE									= True

													for model_class in self.all_model_classes:
																				__SCREAMING_SNAKE_CASE									= True
																				__SCREAMING_SNAKE_CASE									= False
																				__SCREAMING_SNAKE_CASE									= True
																				__SCREAMING_SNAKE_CASE									= model_class(__A		)
																				model.to(__A		)
																				model.eval()
																				with torch.no_grad():
																											__SCREAMING_SNAKE_CASE									= model(**self._prepare_for_class(__A				,							__A		)		)
																				__SCREAMING_SNAKE_CASE									= outputs.attentions

																				__SCREAMING_SNAKE_CASE									= sum(self.model_tester.depths		)
																				self.assertEqual(len(__A		)				,							__A		)

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

																				self.assertEqual(len(__A		)				,							__A		)

																				# verify the first attentions (first block, first layer)
																				__SCREAMING_SNAKE_CASE									= (self.model_tester.image_size // 4) ** 2
																				__SCREAMING_SNAKE_CASE									= (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)
																				__SCREAMING_SNAKE_CASE									= (self.model_tester.image_size // 32) ** 2
																				__SCREAMING_SNAKE_CASE									= (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]				,							)
																				__SCREAMING_SNAKE_CASE									= len(__A		)

																				# Check attention is always last and order is fine
																				__SCREAMING_SNAKE_CASE									= True
																				__SCREAMING_SNAKE_CASE									= True
																				__SCREAMING_SNAKE_CASE									= model_class(__A		)
																				model.to(__A		)
																				model.eval()
																				with torch.no_grad():
																											__SCREAMING_SNAKE_CASE									= model(**self._prepare_for_class(__A				,							__A		)		)

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

																				__SCREAMING_SNAKE_CASE									= outputs.attentions

																				self.assertEqual(len(__A		)				,							__A		)
																				# verify the first attentions (first block, first layer)
																				__SCREAMING_SNAKE_CASE									= (self.model_tester.image_size // 4) ** 2
																				__SCREAMING_SNAKE_CASE									= (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		):







													'''simple docstring'''

													def check_hidden_states_output(_A				,							_A				,							_A		):
																				__SCREAMING_SNAKE_CASE									= model_class(__A		)
																				model.to(__A		)
																				model.eval()

																				with torch.no_grad():
																											__SCREAMING_SNAKE_CASE									= model(**self._prepare_for_class(__A				,							__A		)		)

																				__SCREAMING_SNAKE_CASE									= outputs.hidden_states

																				__SCREAMING_SNAKE_CASE									= self.model_tester.num_encoder_blocks
																				self.assertEqual(len(__A		)				,							__A		)

																				# 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,
																				    ]				,							)

													__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				__SCREAMING_SNAKE_CASE									= True
																				check_hidden_states_output(__A				,							__A				,							__A		)

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

																				check_hidden_states_output(__A				,							__A				,							__A		)






						def 	_A     (   self		):







													'''simple docstring'''

													if not self.model_tester.is_training:
																				return

													__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs_for_common()
													__SCREAMING_SNAKE_CASE									= True

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

																				__SCREAMING_SNAKE_CASE									= model_class(__A		)
																				model.to(__A		)
																				model.train()
																				__SCREAMING_SNAKE_CASE									= self._prepare_for_class(__A				,							__A				,							return_labels=__A		)
																				__SCREAMING_SNAKE_CASE									= model(**__A		).loss
																				loss.backward()






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







													'''simple docstring'''

													pass






						@slow
						def 	_A     (   self		):







													'''simple docstring'''

													for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__SCREAMING_SNAKE_CASE									= SegformerModel.from_pretrained(__A		)
																				self.assertIsNotNone(__A		)







def      __lowercase       (			)    ->			List[Any]:
							__SCREAMING_SNAKE_CASE									= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
							return image

@require_torch
class        UpperCAmelCase_  (       unittest.TestCase	):

						'''simple docstring'''




						@slow
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= SegformerImageProcessor(
													    image_scale=(512, 512)				,							keep_ratio=__A				,							align=__A				,							do_random_crop=__A		)
													__SCREAMING_SNAKE_CASE									= SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'		).to(
													    __A		)

													__SCREAMING_SNAKE_CASE									= prepare_img()
													__SCREAMING_SNAKE_CASE									= image_processor(images=__A				,							return_tensors='pt'		)
													__SCREAMING_SNAKE_CASE									= encoded_inputs.pixel_values.to(__A		)

													with torch.no_grad():
																				__SCREAMING_SNAKE_CASE									= model(__A		)

													__SCREAMING_SNAKE_CASE									= torch.Size((1, model.config.num_labels, 128, 128)		)
													self.assertEqual(outputs.logits.shape				,							__A		)

													__SCREAMING_SNAKE_CASE									= 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]],
													        [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
													        [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
													    ]		).to(__A		)
													self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]				,							__A				,							atol=1e-4		)		)






						@slow
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= SegformerImageProcessor(
													    image_scale=(512, 512)				,							keep_ratio=__A				,							align=__A				,							do_random_crop=__A		)
													__SCREAMING_SNAKE_CASE									= SegformerForSemanticSegmentation.from_pretrained(
													    'nvidia/segformer-b1-finetuned-cityscapes-1024-1024'		).to(__A		)

													__SCREAMING_SNAKE_CASE									= prepare_img()
													__SCREAMING_SNAKE_CASE									= image_processor(images=__A				,							return_tensors='pt'		)
													__SCREAMING_SNAKE_CASE									= encoded_inputs.pixel_values.to(__A		)

													with torch.no_grad():
																				__SCREAMING_SNAKE_CASE									= model(__A		)

													__SCREAMING_SNAKE_CASE									= torch.Size((1, model.config.num_labels, 128, 128)		)
													self.assertEqual(outputs.logits.shape				,							__A		)

													__SCREAMING_SNAKE_CASE									= torch.tensor(
													    [
													        [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
													        [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
													        [[-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(__A		)
													self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]				,							__A				,							atol=1e-1		)		)






						@slow
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= SegformerImageProcessor(
													    image_scale=(512, 512)				,							keep_ratio=__A				,							align=__A				,							do_random_crop=__A		)
													__SCREAMING_SNAKE_CASE									= SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'		).to(
													    __A		)

													__SCREAMING_SNAKE_CASE									= prepare_img()
													__SCREAMING_SNAKE_CASE									= image_processor(images=__A				,							return_tensors='pt'		)
													__SCREAMING_SNAKE_CASE									= encoded_inputs.pixel_values.to(__A		)

													with torch.no_grad():
																				__SCREAMING_SNAKE_CASE									= model(__A		)

													__SCREAMING_SNAKE_CASE									= outputs.logits.detach().cpu()

													__SCREAMING_SNAKE_CASE									= image_processor.post_process_semantic_segmentation(outputs=__A				,							target_sizes=[(500, 300)]		)
													__SCREAMING_SNAKE_CASE									= torch.Size((500, 300)		)
													self.assertEqual(segmentation[0].shape				,							__A		)

													__SCREAMING_SNAKE_CASE									= image_processor.post_process_semantic_segmentation(outputs=__A		)
													__SCREAMING_SNAKE_CASE									= torch.Size((128, 128)		)
													self.assertEqual(segmentation[0].shape				,							__A		)











from __future__ import annotations

from collections.abc import Generator






def      __lowercase       (			)    ->			Generator[int, None, None]:
							__SCREAMING_SNAKE_CASE									= {}
							__SCREAMING_SNAKE_CASE									= 2
							while True:
														__SCREAMING_SNAKE_CASE									= factor_map.pop(a__							,       a__       )
														if factor:
																					__SCREAMING_SNAKE_CASE									= factor + prime
																					while x in factor_map:
																												x += factor
																					__SCREAMING_SNAKE_CASE									= factor
														else:
																					__SCREAMING_SNAKE_CASE									= prime
																					yield prime
														prime += 1






def      __lowercase       (			a__ = 1E10       )    ->			int:
							__SCREAMING_SNAKE_CASE									= sieve()

							__SCREAMING_SNAKE_CASE									= 1
							while True:
														__SCREAMING_SNAKE_CASE									= next(a__       )
														if (2 * prime * n) > limit:
																					return n
														# Ignore the next prime as the reminder will be 2.
														next(a__       )
														n += 2


if __name__ == "__main__":
			print(solution())






