


import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase      =   logging.get_logger(__name__)






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Union[str, Any]:




						"""simple docstring"""

						snake_case_	=				original_name.split('''.''' )[0]
						snake_case_	=				key.split('''.''' )
						snake_case_	=				int(key_list[key_list.index(SCREAMING_SNAKE_CASE ) - 2] )
						snake_case_	=				int(key_list[key_list.index(SCREAMING_SNAKE_CASE ) - 1] )
						snake_case_	=				orig_block_num - offset

						snake_case_	=				key.replace(f'''{orig_block_num}.{layer_num}.{original_name}''' , f'''block.{new_block_num}.{layer_num}.{new_name}''' )
						return key






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Optional[int]:




						"""simple docstring"""

						snake_case_	=				OrderedDict()
						snake_case_	,   snake_case_	=				0, 0
						for key, value in state_dict.items():
												if key.startswith('''network''' ):
																		snake_case_	=				key.replace('''network''' , '''poolformer.encoder''' )
												if "proj" in key:
																		# Works for the first embedding as well as the internal embedding layers
																		if key.endswith('''bias''' ) and "patch_embed" not in key:
																								patch_emb_offset += 1
																		snake_case_	=				key[: key.find('''proj''' )]
																		snake_case_	=				key.replace(SCREAMING_SNAKE_CASE , f'''patch_embeddings.{total_embed_found}.''' )
																		snake_case_	=				key.replace('''proj''' , '''projection''' )
																		if key.endswith('''bias''' ):
																								total_embed_found += 1
												if "patch_embeddings" in key:
																		snake_case_	=				'''poolformer.encoder.''' + key
												if "mlp.fc1" in key:
																		snake_case_	=				replace_key_with_offset(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''mlp.fc1''' , '''output.conv1''' )
												if "mlp.fc2" in key:
																		snake_case_	=				replace_key_with_offset(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''mlp.fc2''' , '''output.conv2''' )
												if "norm1" in key:
																		snake_case_	=				replace_key_with_offset(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''norm1''' , '''before_norm''' )
												if "norm2" in key:
																		snake_case_	=				replace_key_with_offset(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''norm2''' , '''after_norm''' )
												if "layer_scale_1" in key:
																		snake_case_	=				replace_key_with_offset(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''layer_scale_1''' , '''layer_scale_1''' )
												if "layer_scale_2" in key:
																		snake_case_	=				replace_key_with_offset(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''layer_scale_2''' , '''layer_scale_2''' )
												if "head" in key:
																		snake_case_	=				key.replace('''head''' , '''classifier''' )
												snake_case_	=				value
						return new_state_dict






def 	__lowerCAmelCase   ()->  str:




						"""simple docstring"""

						snake_case_	=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						snake_case_	=				Image.open(requests.get(SCREAMING_SNAKE_CASE , stream=SCREAMING_SNAKE_CASE ).raw )

						return image






@torch.no_grad()
def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Any:




						"""simple docstring"""

						snake_case_	=				PoolFormerConfig()

						# set attributes based on model_name
						snake_case_	=				'''huggingface/label-files'''
						snake_case_	=				model_name[-3:]
						snake_case_	=				1000
						snake_case_	=				'''imagenet-1k-id2label.json'''
						snake_case_	=				(1, 1000)

						# set config attributes
						snake_case_	=				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , repo_type='''dataset''' ) , '''r''' ) )
						snake_case_	=				{int(SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
						snake_case_	=				idalabel
						snake_case_	=				{v: k for k, v in idalabel.items()}
						if size == "s12":
												snake_case_	=				[2, 2, 6, 2]
												snake_case_	=				[64, 128, 320, 512]
												snake_case_	=				4.0
												snake_case_	=				0.9
						elif size == "s24":
												snake_case_	=				[4, 4, 12, 4]
												snake_case_	=				[64, 128, 320, 512]
												snake_case_	=				4.0
												snake_case_	=				0.9
						elif size == "s36":
												snake_case_	=				[6, 6, 18, 6]
												snake_case_	=				[64, 128, 320, 512]
												snake_case_	=				4.0
												snake_case_	=				1E-6
												snake_case_	=				0.9
						elif size == "m36":
												snake_case_	=				[6, 6, 18, 6]
												snake_case_	=				[96, 192, 384, 768]
												snake_case_	=				4.0
												snake_case_	=				1E-6
												snake_case_	=				0.9_5
						elif size == "m48":
												snake_case_	=				[8, 8, 24, 8]
												snake_case_	=				[96, 192, 384, 768]
												snake_case_	=				4.0
												snake_case_	=				1E-6
												snake_case_	=				0.9_5
						else:
												raise ValueError(f'''Size {size} not supported''' )

						# load image processor
						snake_case_	=				PoolFormerImageProcessor(crop_pct=SCREAMING_SNAKE_CASE )

						# Prepare image
						snake_case_	=				prepare_img()
						snake_case_	=				image_processor(images=SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).pixel_values

						logger.info(f'''Converting model {model_name}...''' )

						# load original state dict
						snake_case_	=				torch.load(SCREAMING_SNAKE_CASE , map_location=torch.device('''cpu''' ) )

						# rename keys
						snake_case_	=				rename_keys(SCREAMING_SNAKE_CASE )

						# create HuggingFace model and load state dict
						snake_case_	=				PoolFormerForImageClassification(SCREAMING_SNAKE_CASE )
						model.load_state_dict(SCREAMING_SNAKE_CASE )
						model.eval()

						# Define image processor
						snake_case_	=				PoolFormerImageProcessor(crop_pct=SCREAMING_SNAKE_CASE )
						snake_case_	=				image_processor(images=prepare_img() , return_tensors='''pt''' ).pixel_values

						# forward pass
						snake_case_	=				model(SCREAMING_SNAKE_CASE )
						snake_case_	=				outputs.logits

						# define expected logit slices for different models
						if size == "s12":
												snake_case_	=				torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9] )
						elif size == "s24":
												snake_case_	=				torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5] )
						elif size == "s36":
												snake_case_	=				torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8] )
						elif size == "m36":
												snake_case_	=				torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8] )
						elif size == "m48":
												snake_case_	=				torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3] )
						else:
												raise ValueError(f'''Size {size} not supported''' )

						# verify logits
						assert logits.shape == expected_shape
						assert torch.allclose(logits[0, :3] , SCREAMING_SNAKE_CASE , atol=1E-2 )

						# finally, save model and image processor
						logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
						Path(SCREAMING_SNAKE_CASE ).mkdir(exist_ok=SCREAMING_SNAKE_CASE )
						model.save_pretrained(SCREAMING_SNAKE_CASE )
						print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
						image_processor.save_pretrained(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
			UpperCAmelCase      =   argparse.ArgumentParser()

			parser.add_argument(
			    """--model_name""",
			    default="""poolformer_s12""",
			    type=str,
			    help="""Name of the model you'd like to convert.""",
			)
			parser.add_argument(
			    """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
			)
			UpperCAmelCase      =   parser.parse_args()
			convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



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 lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.config_class(**self.inputs_dict )
       self.parent.assertTrue(hasattr(_UpperCAmelCase		,				'''hidden_sizes''' ) )
       self.parent.assertTrue(hasattr(_UpperCAmelCase		,				'''num_attention_heads''' ) )
       self.parent.assertTrue(hasattr(_UpperCAmelCase		,				'''num_encoder_blocks''' ) )







class lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=13		,				_UpperCAmelCase=64		,				_UpperCAmelCase=3		,				_UpperCAmelCase=4		,				_UpperCAmelCase=[2, 2, 2, 2]		,				_UpperCAmelCase=[8, 4, 2, 1]		,				_UpperCAmelCase=[16, 32, 64, 1_28]		,				_UpperCAmelCase=[1, 4, 8, 16]		,				_UpperCAmelCase=[1, 2, 4, 8]		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=3		,				_UpperCAmelCase=None		,				):
       snake_case_	=				parent
       snake_case_	=				batch_size
       snake_case_	=				image_size
       snake_case_	=				num_channels
       snake_case_	=				num_encoder_blocks
       snake_case_	=				sr_ratios
       snake_case_	=				depths
       snake_case_	=				hidden_sizes
       snake_case_	=				downsampling_rates
       snake_case_	=				num_attention_heads
       snake_case_	=				is_training
       snake_case_	=				use_labels
       snake_case_	=				hidden_act
       snake_case_	=				hidden_dropout_prob
       snake_case_	=				attention_probs_dropout_prob
       snake_case_	=				initializer_range
       snake_case_	=				num_labels
       snake_case_	=				scope



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

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



 def 						UpperCamelCase__					(     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 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				SegformerModel(config=_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       snake_case_	=				model(_UpperCAmelCase )
       snake_case_	=				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 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				self.num_labels
       snake_case_	=				SegformerForSemanticSegmentation(_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       snake_case_	=				model(_UpperCAmelCase )
       self.parent.assertEqual(
           result.logits.shape		,				(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
       snake_case_	=				model(_UpperCAmelCase		,				labels=_UpperCAmelCase )
       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 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				1
       snake_case_	=				SegformerForSemanticSegmentation(config=_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       snake_case_	=				torch.randint(0		,				1		,				(self.batch_size, self.image_size, self.image_size) ).to(_UpperCAmelCase )
       snake_case_	=				model(_UpperCAmelCase		,				labels=_UpperCAmelCase )
       self.parent.assertGreater(result.loss		,				0.0 )





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







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

 '''simple docstring'''

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

 __snake_case												=  True
 __snake_case												=  False
 __snake_case												=  False
 __snake_case												=  False



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				SegformerModelTester(self )
       snake_case_	=				SegformerConfigTester(self		,				config_class=_UpperCAmelCase )



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



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_binary_image_segmentation(*_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_segmentation(*_UpperCAmelCase )



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



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



 def 						UpperCamelCase__					(     self ):
       snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()

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

             snake_case_	=				['''pixel_values''']
             self.assertListEqual(arg_names[:1]		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()
       snake_case_	=				True

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

             snake_case_	=				sum(self.model_tester.depths )
             self.assertEqual(len(_UpperCAmelCase )		,				_UpperCAmelCase )

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

             self.assertEqual(len(_UpperCAmelCase )		,				_UpperCAmelCase )

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

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

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

             snake_case_	=				outputs.attentions

             self.assertEqual(len(_UpperCAmelCase )		,				_UpperCAmelCase )
             # verify the first attentions (first block, first layer)
             snake_case_	=				(self.model_tester.image_size // 4) ** 2
             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 						UpperCamelCase__					(     self ):
       def check_hidden_states_output(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
             snake_case_	=				model_class(_UpperCAmelCase )
             model.to(_UpperCAmelCase )
             model.eval()

             with torch.no_grad():
                   snake_case_	=				model(**self._prepare_for_class(_UpperCAmelCase		,				_UpperCAmelCase ) )

             snake_case_	=				outputs.hidden_states

             snake_case_	=				self.model_tester.num_encoder_blocks
             self.assertEqual(len(_UpperCAmelCase )		,				_UpperCAmelCase )

             # 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_	=				self.model_tester.prepare_config_and_inputs_for_common()

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

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

             check_hidden_states_output(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )



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

       snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()
       snake_case_	=				True

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

             snake_case_	=				model_class(_UpperCAmelCase )
             model.to(_UpperCAmelCase )
             model.train()
             snake_case_	=				self._prepare_for_class(_UpperCAmelCase		,				_UpperCAmelCase		,				return_labels=_UpperCAmelCase )
             snake_case_	=				model(**_UpperCAmelCase ).loss
             loss.backward()



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





 @slow
 def 						UpperCamelCase__					(     self ):
       for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             snake_case_	=				SegformerModel.from_pretrained(_UpperCAmelCase )
             self.assertIsNotNone(_UpperCAmelCase )






def 	__lowerCAmelCase   ()->  List[str]:




      """simple docstring"""

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







@require_torch
class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 @slow
 def 						UpperCamelCase__					(     self ):
       # only resize + normalize
       snake_case_	=				SegformerImageProcessor(
           image_scale=(5_12, 5_12)		,				keep_ratio=_UpperCAmelCase		,				align=_UpperCAmelCase		,				do_random_crop=_UpperCAmelCase )
       snake_case_	=				SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512''' ).to(
           _UpperCAmelCase )

       snake_case_	=				prepare_img()
       snake_case_	=				image_processor(images=_UpperCAmelCase		,				return_tensors='''pt''' )
       snake_case_	=				encoded_inputs.pixel_values.to(_UpperCAmelCase )

       with torch.no_grad():
             snake_case_	=				model(_UpperCAmelCase )

       snake_case_	=				torch.Size((1, model.config.num_labels, 1_28, 1_28) )
       self.assertEqual(outputs.logits.shape		,				_UpperCAmelCase )

       snake_case_	=				torch.tensor(
           [
               [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
               [[-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(_UpperCAmelCase )
       self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]		,				_UpperCAmelCase		,				atol=1E-4 ) )



 @slow
 def 						UpperCamelCase__					(     self ):
       # only resize + normalize
       snake_case_	=				SegformerImageProcessor(
           image_scale=(5_12, 5_12)		,				keep_ratio=_UpperCAmelCase		,				align=_UpperCAmelCase		,				do_random_crop=_UpperCAmelCase )
       snake_case_	=				SegformerForSemanticSegmentation.from_pretrained(
           '''nvidia/segformer-b1-finetuned-cityscapes-1024-1024''' ).to(_UpperCAmelCase )

       snake_case_	=				prepare_img()
       snake_case_	=				image_processor(images=_UpperCAmelCase		,				return_tensors='''pt''' )
       snake_case_	=				encoded_inputs.pixel_values.to(_UpperCAmelCase )

       with torch.no_grad():
             snake_case_	=				model(_UpperCAmelCase )

       snake_case_	=				torch.Size((1, model.config.num_labels, 1_28, 1_28) )
       self.assertEqual(outputs.logits.shape		,				_UpperCAmelCase )

       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_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
           ] ).to(_UpperCAmelCase )
       self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]		,				_UpperCAmelCase		,				atol=1E-1 ) )





 @slow
 def 						UpperCamelCase__					(     self ):
       # only resize + normalize
       snake_case_	=				SegformerImageProcessor(
           image_scale=(5_12, 5_12)		,				keep_ratio=_UpperCAmelCase		,				align=_UpperCAmelCase		,				do_random_crop=_UpperCAmelCase )
       snake_case_	=				SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512''' ).to(
           _UpperCAmelCase )

       snake_case_	=				prepare_img()
       snake_case_	=				image_processor(images=_UpperCAmelCase		,				return_tensors='''pt''' )
       snake_case_	=				encoded_inputs.pixel_values.to(_UpperCAmelCase )

       with torch.no_grad():
             snake_case_	=				model(_UpperCAmelCase )

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

       snake_case_	=				image_processor.post_process_semantic_segmentation(outputs=_UpperCAmelCase		,				target_sizes=[(5_00, 3_00)] )
       snake_case_	=				torch.Size((5_00, 3_00) )
       self.assertEqual(segmentation[0].shape		,				_UpperCAmelCase )

       snake_case_	=				image_processor.post_process_semantic_segmentation(outputs=_UpperCAmelCase )
       snake_case_	=				torch.Size((1_28, 1_28) )
       self.assertEqual(segmentation[0].shape		,				_UpperCAmelCase )