


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 		_snake_case  (				_snake_case							):




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:int										=	self.config_class(**self.inputs_dict   )
       self.parent.assertTrue(hasattr(_lowerCamelCase ,  '''hidden_sizes'''   )   )
       self.parent.assertTrue(hasattr(_lowerCamelCase ,  '''num_attention_heads'''   )   )
       self.parent.assertTrue(hasattr(_lowerCamelCase ,  '''num_encoder_blocks'''   )   )







class 		_snake_case  :




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase=13 ,  _lowerCamelCase=64 ,  _lowerCamelCase=3 ,  _lowerCamelCase=4 ,  _lowerCamelCase=[2, 2, 2, 2] ,  _lowerCamelCase=[8, 4, 2, 1] ,  _lowerCamelCase=[16, 32, 64, 128] ,  _lowerCamelCase=[1, 4, 8, 16] ,  _lowerCamelCase=[1, 2, 4, 8] ,  _lowerCamelCase=True ,  _lowerCamelCase=True ,  _lowerCamelCase="gelu" ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=0.02 ,  _lowerCamelCase=3 ,  _lowerCamelCase=None ,  ):
       a						:List[str]										=	parent
       a						:Dict										=	batch_size
       a						:int										=	image_size
       a						:List[str]										=	num_channels
       a						:int										=	num_encoder_blocks
       a						:Union[str, Any]										=	sr_ratios
       a						:Any										=	depths
       a						:str										=	hidden_sizes
       a						:Any										=	downsampling_rates
       a						:List[str]										=	num_attention_heads
       a						:Dict										=	is_training
       a						:Optional[int]										=	use_labels
       a						:Any										=	hidden_act
       a						:Tuple										=	hidden_dropout_prob
       a						:Any										=	attention_probs_dropout_prob
       a						:Any										=	initializer_range
       a						:List[str]										=	num_labels
       a						:str										=	scope




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[Any]										=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

       a						:Optional[int]										=	None
       if self.use_labels:
             a						:Tuple										=	ids_tensor([self.batch_size, self.image_size, self.image_size] ,  self.num_labels   )

       a						:Any										=	self.get_config()
       return config, pixel_values, labels




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       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 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
       a						:List[Any]										=	SegformerModel(config=_lowerCamelCase   )
       model.to(_lowerCamelCase   )
       model.eval()
       a						:Optional[Any]										=	model(_lowerCamelCase   )
       a						:Tuple										=	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 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
       a						:Optional[Any]										=	self.num_labels
       a						:Any										=	SegformerForSemanticSegmentation(_lowerCamelCase   )
       model.to(_lowerCamelCase   )
       model.eval()
       a						:Optional[int]										=	model(_lowerCamelCase   )
       self.parent.assertEqual(
           result.logits.shape ,  (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)   )
       a						:Tuple										=	model(_lowerCamelCase ,  labels=_lowerCamelCase   )
       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 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
       a						:Any										=	1
       a						:List[str]										=	SegformerForSemanticSegmentation(config=_lowerCamelCase   )
       model.to(_lowerCamelCase   )
       model.eval()
       a						:Optional[int]										=	torch.randint(0 ,  1 ,  (self.batch_size, self.image_size, self.image_size)   ).to(_lowerCamelCase   )
       a						:str										=	model(_lowerCamelCase ,  labels=_lowerCamelCase   )
       self.parent.assertGreater(result.loss ,  0.0   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Any										=	self.prepare_config_and_inputs()
       a						:Optional[Any]										=	config_and_inputs
       a						:Dict										=	{'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_torch
class 		_snake_case  (				_snake_case						,   _snake_case						,   unittest.TestCase							):
 SCREAMING_SNAKE_CASE__				=	(
     (
         SegformerModel,
         SegformerForSemanticSegmentation,
         SegformerForImageClassification,
     )
     if is_torch_available()
     else ()
 )
 SCREAMING_SNAKE_CASE__				=	(
     {
         'feature-extraction': SegformerModel,
         'image-classification': SegformerForImageClassification,
         'image-segmentation': SegformerForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 SCREAMING_SNAKE_CASE__				=	True
 SCREAMING_SNAKE_CASE__				=	False
 SCREAMING_SNAKE_CASE__				=	False
 SCREAMING_SNAKE_CASE__				=	False




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:int										=	SegformerModelTester(self   )
       a						:Union[str, Any]										=	SegformerConfigTester(self ,  config_class=_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       self.config_tester.run_common_tests()




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[Any]										=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[Any]										=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_binary_image_segmentation(*_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[str]										=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_segmentation(*_lowerCamelCase   )




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




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




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Dict										=	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             a						:List[str]										=	model_class(_lowerCamelCase   )
             a						:Tuple										=	inspect.signature(model.forward   )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             a						:Dict										=	[*signature.parameters.keys()]

             a						:int										=	['''pixel_values''']
             self.assertListEqual(arg_names[:1] ,  _lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Tuple										=	self.model_tester.prepare_config_and_inputs_for_common()
       a						:Dict										=	True

       for model_class in self.all_model_classes:
             a						:List[str]										=	True
             a						:List[str]										=	False
             a						:Tuple										=	True
             a						:Dict										=	model_class(_lowerCamelCase   )
             model.to(_lowerCamelCase   )
             model.eval()
             with torch.no_grad():
                   a						:Tuple										=	model(**self._prepare_for_class(_lowerCamelCase ,  _lowerCamelCase   )   )
             a						:List[str]										=	outputs.attentions

             a						:Any										=	sum(self.model_tester.depths   )
             self.assertEqual(len(_lowerCamelCase   ) ,  _lowerCamelCase   )

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

             self.assertEqual(len(_lowerCamelCase   ) ,  _lowerCamelCase   )

             # verify the first attentions (first block, first layer)
             a						:Any										=	(self.model_tester.image_size // 4) ** 2
             a						:List[Any]										=	(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)
             a						:str										=	(self.model_tester.image_size // 32) ** 2
             a						:Optional[Any]										=	(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] ,  )
             a						:Tuple										=	len(_lowerCamelCase   )

             # Check attention is always last and order is fine
             a						:str										=	True
             a						:List[str]										=	True
             a						:Optional[Any]										=	model_class(_lowerCamelCase   )
             model.to(_lowerCamelCase   )
             model.eval()
             with torch.no_grad():
                   a						:Union[str, Any]										=	model(**self._prepare_for_class(_lowerCamelCase ,  _lowerCamelCase   )   )

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

             a						:Any										=	outputs.attentions

             self.assertEqual(len(_lowerCamelCase   ) ,  _lowerCamelCase   )
             # verify the first attentions (first block, first layer)
             a						:Union[str, Any]										=	(self.model_tester.image_size // 4) ** 2
             a						:List[Any]										=	(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 	SCREAMING_SNAKE_CASE__    (		self   ):
       def check_hidden_states_output(_lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
             a						:Optional[int]										=	model_class(_lowerCamelCase   )
             model.to(_lowerCamelCase   )
             model.eval()

             with torch.no_grad():
                   a						:Any										=	model(**self._prepare_for_class(_lowerCamelCase ,  _lowerCamelCase   )   )

             a						:List[Any]										=	outputs.hidden_states

             a						:Optional[int]										=	self.model_tester.num_encoder_blocks
             self.assertEqual(len(_lowerCamelCase   ) ,  _lowerCamelCase   )

             # 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,
                 ] ,  )

       a						:Any										=	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             a						:List[str]										=	True
             check_hidden_states_output(_lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             a						:Tuple										=	True

             check_hidden_states_output(_lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       if not self.model_tester.is_training:
             return

       a						:Dict										=	self.model_tester.prepare_config_and_inputs_for_common()
       a						:List[str]										=	True

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

             a						:str										=	model_class(_lowerCamelCase   )
             model.to(_lowerCamelCase   )
             model.train()
             a						:Tuple										=	self._prepare_for_class(_lowerCamelCase ,  _lowerCamelCase ,  return_labels=_lowerCamelCase   )
             a						:int										=	model(**_lowerCamelCase   ).loss
             loss.backward()




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




 @slow
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             a						:Optional[int]										=	SegformerModel.from_pretrained(_lowerCamelCase   )
             self.assertIsNotNone(_lowerCamelCase   )







def     __lowerCamelCase						(			):




      """simple docstring"""





      a						:Tuple										=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
      return image







@require_torch
class 		_snake_case  (				unittest.TestCase							):




 @slow
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       # only resize + normalize
       a						:Optional[int]										=	SegformerImageProcessor(
           image_scale=(512, 512) ,  keep_ratio=_lowerCamelCase ,  align=_lowerCamelCase ,  do_random_crop=_lowerCamelCase   )
       a						:Tuple										=	SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512'''   ).to(
           _lowerCamelCase   )

       a						:int										=	prepare_img()
       a						:Dict										=	image_processor(images=_lowerCamelCase ,  return_tensors='''pt'''   )
       a						:Any										=	encoded_inputs.pixel_values.to(_lowerCamelCase   )

       with torch.no_grad():
             a						:List[Any]										=	model(_lowerCamelCase   )

       a						:Dict										=	torch.Size((1, model.config.num_labels, 128, 128)   )
       self.assertEqual(outputs.logits.shape ,  _lowerCamelCase   )

       a						:Union[str, Any]										=	torch.tensor(
           [
               [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
               [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
               [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
           ]   ).to(_lowerCamelCase   )
       self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] ,  _lowerCamelCase ,  atol=1e-4   )   )




 @slow
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       # only resize + normalize
       a						:int										=	SegformerImageProcessor(
           image_scale=(512, 512) ,  keep_ratio=_lowerCamelCase ,  align=_lowerCamelCase ,  do_random_crop=_lowerCamelCase   )
       a						:Tuple										=	SegformerForSemanticSegmentation.from_pretrained(
           '''nvidia/segformer-b1-finetuned-cityscapes-1024-1024'''   ).to(_lowerCamelCase   )

       a						:Union[str, Any]										=	prepare_img()
       a						:Any										=	image_processor(images=_lowerCamelCase ,  return_tensors='''pt'''   )
       a						:int										=	encoded_inputs.pixel_values.to(_lowerCamelCase   )

       with torch.no_grad():
             a						:Optional[int]										=	model(_lowerCamelCase   )

       a						:Union[str, Any]										=	torch.Size((1, model.config.num_labels, 128, 128)   )
       self.assertEqual(outputs.logits.shape ,  _lowerCamelCase   )

       a						:int										=	torch.tensor(
           [
               [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
               [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
               [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
           ]   ).to(_lowerCamelCase   )
       self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] ,  _lowerCamelCase ,  atol=1e-1   )   )




 @slow
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       # only resize + normalize
       a						:int										=	SegformerImageProcessor(
           image_scale=(512, 512) ,  keep_ratio=_lowerCamelCase ,  align=_lowerCamelCase ,  do_random_crop=_lowerCamelCase   )
       a						:Optional[Any]										=	SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512'''   ).to(
           _lowerCamelCase   )

       a						:str										=	prepare_img()
       a						:List[str]										=	image_processor(images=_lowerCamelCase ,  return_tensors='''pt'''   )
       a						:Optional[Any]										=	encoded_inputs.pixel_values.to(_lowerCamelCase   )

       with torch.no_grad():
             a						:Any										=	model(_lowerCamelCase   )

       a						:Union[str, Any]										=	outputs.logits.detach().cpu()

       a						:Dict										=	image_processor.post_process_semantic_segmentation(outputs=_lowerCamelCase ,  target_sizes=[(500, 300)]   )
       a						:Optional[Any]										=	torch.Size((500, 300)   )
       self.assertEqual(segmentation[0].shape ,  _lowerCamelCase   )

       a						:int										=	image_processor.post_process_semantic_segmentation(outputs=_lowerCamelCase   )
       a						:Union[str, Any]										=	torch.Size((128, 128)   )
       self.assertEqual(segmentation[0].shape ,  _lowerCamelCase   )









def     __lowerCamelCase						(			UpperCAmelCase_						:		str						):




      """simple docstring"""





      if n_term == "":
            return []
      a						:list										=	[]
      for temp in range(int(UpperCAmelCase_						)						):
            series.append(F'''1/{temp + 1}''' if series else '''1'''						)
      return series


if __name__ == "__main__":
     snake_case    :   Tuple	    =					input('''Enter the last number (nth term) of the Harmonic Series''')
     print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
     print(harmonic_series(nth_term))





