
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_=1						)						->						List[str]:
					'''simple docstring'''


					if n_shave_prefix_segments >= 0:
										return ".".join(path.split('.'						)[n_shave_prefix_segments:]						)
					else:
										return ".".join(path.split('.'						)[:n_shave_prefix_segments]						)






def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_=0						)						->						Any:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					[]
					for old_item in old_list:
										SCREAMING_SNAKE_CASE__						=					old_item.replace('in_layers.0'      ,      'norm1'						)
										SCREAMING_SNAKE_CASE__						=					new_item.replace('in_layers.2'      ,      'conv1'						)

										SCREAMING_SNAKE_CASE__						=					new_item.replace('out_layers.0'      ,      'norm2'						)
										SCREAMING_SNAKE_CASE__						=					new_item.replace('out_layers.3'      ,      'conv2'						)

										SCREAMING_SNAKE_CASE__						=					new_item.replace('emb_layers.1'      ,      'time_emb_proj'						)
										SCREAMING_SNAKE_CASE__						=					new_item.replace('skip_connection'      ,      'conv_shortcut'						)

										SCREAMING_SNAKE_CASE__						=					shave_segments(_UpperCamelCase      ,      n_shave_prefix_segments=_UpperCamelCase						)

										mapping.append({'old': old_item, 'new': new_item}						)

					return mapping






def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_=0						)						->						str:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					[]
					for old_item in old_list:
										SCREAMING_SNAKE_CASE__						=					old_item

										SCREAMING_SNAKE_CASE__						=					new_item.replace('norm.weight'      ,      'group_norm.weight'						)
										SCREAMING_SNAKE_CASE__						=					new_item.replace('norm.bias'      ,      'group_norm.bias'						)

										SCREAMING_SNAKE_CASE__						=					new_item.replace('proj_out.weight'      ,      'proj_attn.weight'						)
										SCREAMING_SNAKE_CASE__						=					new_item.replace('proj_out.bias'      ,      'proj_attn.bias'						)

										SCREAMING_SNAKE_CASE__						=					shave_segments(_UpperCamelCase      ,      n_shave_prefix_segments=_UpperCamelCase						)

										mapping.append({'old': old_item, 'new': new_item}						)

					return mapping






def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_      ,      UpperCamelCase_      ,      UpperCamelCase_=None      ,      UpperCamelCase_=None      ,      UpperCamelCase_=None						)						->						Dict:
					'''simple docstring'''


					assert isinstance(_UpperCamelCase      ,      _UpperCamelCase						), "Paths should be a list of dicts containing 'old' and 'new' keys."

					# Splits the attention layers into three variables.
					if attention_paths_to_split is not None:
										for path, path_map in attention_paths_to_split.items():
															SCREAMING_SNAKE_CASE__						=					old_checkpoint[path]
															SCREAMING_SNAKE_CASE__						=					old_tensor.shape[0] // 3

															SCREAMING_SNAKE_CASE__						=					(-1, channels) if len(old_tensor.shape						) == 3 else (-1)

															SCREAMING_SNAKE_CASE__						=					old_tensor.shape[0] // config['num_head_channels'] // 3

															SCREAMING_SNAKE_CASE__						=					old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]						)
															SCREAMING_SNAKE_CASE__		,					SCREAMING_SNAKE_CASE__		,					SCREAMING_SNAKE_CASE__						=					old_tensor.split(channels // num_heads      ,      dim=1						)

															SCREAMING_SNAKE_CASE__						=					query.reshape(_UpperCamelCase						)
															SCREAMING_SNAKE_CASE__						=					key.reshape(_UpperCamelCase						)
															SCREAMING_SNAKE_CASE__						=					value.reshape(_UpperCamelCase						)

					for path in paths:
										SCREAMING_SNAKE_CASE__						=					path['new']

										# These have already been assigned
										if attention_paths_to_split is not None and new_path in attention_paths_to_split:
															continue

										# Global renaming happens here
										SCREAMING_SNAKE_CASE__						=					new_path.replace('middle_block.0'      ,      'mid_block.resnets.0'						)
										SCREAMING_SNAKE_CASE__						=					new_path.replace('middle_block.1'      ,      'mid_block.attentions.0'						)
										SCREAMING_SNAKE_CASE__						=					new_path.replace('middle_block.2'      ,      'mid_block.resnets.1'						)

										if additional_replacements is not None:
															for replacement in additional_replacements:
																				SCREAMING_SNAKE_CASE__						=					new_path.replace(replacement['old']      ,      replacement['new']						)

        # proj_attn.weight has to be converted from conv 1D to linear
										if "proj_attn.weight" in new_path:
															SCREAMING_SNAKE_CASE__						=					old_checkpoint[path['old']][:, :, 0]
										else:
															SCREAMING_SNAKE_CASE__						=					old_checkpoint[path['old']]






def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_						)						->						Tuple:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					{}

					SCREAMING_SNAKE_CASE__						=					checkpoint['time_embed.0.weight']
					SCREAMING_SNAKE_CASE__						=					checkpoint['time_embed.0.bias']
					SCREAMING_SNAKE_CASE__						=					checkpoint['time_embed.2.weight']
					SCREAMING_SNAKE_CASE__						=					checkpoint['time_embed.2.bias']

					SCREAMING_SNAKE_CASE__						=					checkpoint['input_blocks.0.0.weight']
					SCREAMING_SNAKE_CASE__						=					checkpoint['input_blocks.0.0.bias']

					SCREAMING_SNAKE_CASE__						=					checkpoint['out.0.weight']
					SCREAMING_SNAKE_CASE__						=					checkpoint['out.0.bias']
					SCREAMING_SNAKE_CASE__						=					checkpoint['out.2.weight']
					SCREAMING_SNAKE_CASE__						=					checkpoint['out.2.bias']

					# Retrieves the keys for the input blocks only
					SCREAMING_SNAKE_CASE__						=					len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'input_blocks' in layer}						)
					SCREAMING_SNAKE_CASE__						=					{
					    layer_id: [key for key in checkpoint if F'input_blocks.{layer_id}' in key]
					    for layer_id in range(_UpperCamelCase						)
					}

					# Retrieves the keys for the middle blocks only
					SCREAMING_SNAKE_CASE__						=					len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'middle_block' in layer}						)
					SCREAMING_SNAKE_CASE__						=					{
					    layer_id: [key for key in checkpoint if F'middle_block.{layer_id}' in key]
					    for layer_id in range(_UpperCamelCase						)
					}

					# Retrieves the keys for the output blocks only
					SCREAMING_SNAKE_CASE__						=					len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'output_blocks' in layer}						)
					SCREAMING_SNAKE_CASE__						=					{
					    layer_id: [key for key in checkpoint if F'output_blocks.{layer_id}' in key]
					    for layer_id in range(_UpperCamelCase						)
					}

					for i in range(1      ,      _UpperCamelCase						):
										SCREAMING_SNAKE_CASE__						=					(i - 1) // (config['num_res_blocks'] + 1)
										SCREAMING_SNAKE_CASE__						=					(i - 1) % (config['num_res_blocks'] + 1)

										SCREAMING_SNAKE_CASE__						=					[key for key in input_blocks[i] if F'input_blocks.{i}.0' in key]
										SCREAMING_SNAKE_CASE__						=					[key for key in input_blocks[i] if F'input_blocks.{i}.1' in key]

										if F'input_blocks.{i}.0.op.weight' in checkpoint:
															SCREAMING_SNAKE_CASE__						=					checkpoint[
															    F'input_blocks.{i}.0.op.weight'
															]
															SCREAMING_SNAKE_CASE__						=					checkpoint[
															    F'input_blocks.{i}.0.op.bias'
															]
															continue

										SCREAMING_SNAKE_CASE__						=					renew_resnet_paths(_UpperCamelCase						)
										SCREAMING_SNAKE_CASE__						=					{'old': F'input_blocks.{i}.0', 'new': F'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
										SCREAMING_SNAKE_CASE__						=					{'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
										assign_to_checkpoint(
										    _UpperCamelCase      ,      _UpperCamelCase      ,      _UpperCamelCase      ,      additional_replacements=[meta_path, resnet_op]      ,      config=_UpperCamelCase						)

										if len(_UpperCamelCase						):
															SCREAMING_SNAKE_CASE__						=					renew_attention_paths(_UpperCamelCase						)
															SCREAMING_SNAKE_CASE__						=					{
															    'old': F'input_blocks.{i}.1',
															    'new': F'down_blocks.{block_id}.attentions.{layer_in_block_id}',
															}
															SCREAMING_SNAKE_CASE__						=					{
															    F'input_blocks.{i}.1.qkv.bias': {
															        'key': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
															        'query': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
															        'value': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
															    },
															    F'input_blocks.{i}.1.qkv.weight': {
															        'key': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
															        'query': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
															        'value': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
															    },
															}
															assign_to_checkpoint(
															    _UpperCamelCase      ,      _UpperCamelCase      ,      _UpperCamelCase      ,      additional_replacements=[meta_path]      ,      attention_paths_to_split=_UpperCamelCase      ,      config=_UpperCamelCase      ,      )

					SCREAMING_SNAKE_CASE__						=					middle_blocks[0]
					SCREAMING_SNAKE_CASE__						=					middle_blocks[1]
					SCREAMING_SNAKE_CASE__						=					middle_blocks[2]

					SCREAMING_SNAKE_CASE__						=					renew_resnet_paths(_UpperCamelCase						)
					assign_to_checkpoint(_UpperCamelCase      ,      _UpperCamelCase      ,      _UpperCamelCase      ,      config=_UpperCamelCase						)

					SCREAMING_SNAKE_CASE__						=					renew_resnet_paths(_UpperCamelCase						)
					assign_to_checkpoint(_UpperCamelCase      ,      _UpperCamelCase      ,      _UpperCamelCase      ,      config=_UpperCamelCase						)

					SCREAMING_SNAKE_CASE__						=					renew_attention_paths(_UpperCamelCase						)
					SCREAMING_SNAKE_CASE__						=					{
					    'middle_block.1.qkv.bias': {
					        'key': 'mid_block.attentions.0.key.bias',
					        'query': 'mid_block.attentions.0.query.bias',
					        'value': 'mid_block.attentions.0.value.bias',
					    },
					    'middle_block.1.qkv.weight': {
					        'key': 'mid_block.attentions.0.key.weight',
					        'query': 'mid_block.attentions.0.query.weight',
					        'value': 'mid_block.attentions.0.value.weight',
					    },
					}
					assign_to_checkpoint(
					    _UpperCamelCase      ,      _UpperCamelCase      ,      _UpperCamelCase      ,      attention_paths_to_split=_UpperCamelCase      ,      config=_UpperCamelCase						)

					for i in range(_UpperCamelCase						):
										SCREAMING_SNAKE_CASE__						=					i // (config['num_res_blocks'] + 1)
										SCREAMING_SNAKE_CASE__						=					i % (config['num_res_blocks'] + 1)
										SCREAMING_SNAKE_CASE__						=					[shave_segments(_UpperCamelCase      ,      2						) for name in output_blocks[i]]
										SCREAMING_SNAKE_CASE__						=					{}

										for layer in output_block_layers:
															SCREAMING_SNAKE_CASE__		,					SCREAMING_SNAKE_CASE__						=					layer.split('.'						)[0], shave_segments(_UpperCamelCase      ,      1						)
															if layer_id in output_block_list:
																				output_block_list[layer_id].append(_UpperCamelCase						)
															else:
																				SCREAMING_SNAKE_CASE__						=					[layer_name]

										if len(_UpperCamelCase						) > 1:
															SCREAMING_SNAKE_CASE__						=					[key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
															SCREAMING_SNAKE_CASE__						=					[key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]

															SCREAMING_SNAKE_CASE__						=					renew_resnet_paths(_UpperCamelCase						)
															SCREAMING_SNAKE_CASE__						=					renew_resnet_paths(_UpperCamelCase						)

															SCREAMING_SNAKE_CASE__						=					{'old': F'output_blocks.{i}.0', 'new': F'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
															assign_to_checkpoint(_UpperCamelCase      ,      _UpperCamelCase      ,      _UpperCamelCase      ,      additional_replacements=[meta_path]      ,      config=_UpperCamelCase						)

															if ["conv.weight", "conv.bias"] in output_block_list.values():
																				SCREAMING_SNAKE_CASE__						=					list(output_block_list.values()						).index(['conv.weight', 'conv.bias']						)
																				SCREAMING_SNAKE_CASE__						=					checkpoint[
																				    F'output_blocks.{i}.{index}.conv.weight'
																				]
																				SCREAMING_SNAKE_CASE__						=					checkpoint[
																				    F'output_blocks.{i}.{index}.conv.bias'
																				]

																				# Clear attentions as they have been attributed above.
																				if len(_UpperCamelCase						) == 2:
																									SCREAMING_SNAKE_CASE__						=					[]

															if len(_UpperCamelCase						):
																				SCREAMING_SNAKE_CASE__						=					renew_attention_paths(_UpperCamelCase						)
																				SCREAMING_SNAKE_CASE__						=					{
																				    'old': F'output_blocks.{i}.1',
																				    'new': F'up_blocks.{block_id}.attentions.{layer_in_block_id}',
																				}
																				SCREAMING_SNAKE_CASE__						=					{
																				    F'output_blocks.{i}.1.qkv.bias': {
																				        'key': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
																				        'query': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
																				        'value': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
																				    },
																				    F'output_blocks.{i}.1.qkv.weight': {
																				        'key': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
																				        'query': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
																				        'value': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
																				    },
																				}
																				assign_to_checkpoint(
																				    _UpperCamelCase      ,      _UpperCamelCase      ,      _UpperCamelCase      ,      additional_replacements=[meta_path]      ,      attention_paths_to_split=to_split if any('qkv' in key for key in attentions						) else None      ,      config=_UpperCamelCase      ,      )
										else:
															SCREAMING_SNAKE_CASE__						=					renew_resnet_paths(_UpperCamelCase      ,      n_shave_prefix_segments=1						)
															for path in resnet_0_paths:
																				SCREAMING_SNAKE_CASE__						=					'.'.join(['output_blocks', str(_UpperCamelCase						), path['old']]						)
																				SCREAMING_SNAKE_CASE__						=					'.'.join(['up_blocks', str(_UpperCamelCase						), 'resnets', str(_UpperCamelCase						), path['new']]						)

																				SCREAMING_SNAKE_CASE__						=					checkpoint[old_path]

					return new_checkpoint


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

				parser.add_argument(
				    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
				)

				parser.add_argument(
				    """--config_file""",
				    default=None,
				    type=str,
				    required=True,
				    help="""The config json file corresponding to the architecture.""",
				)

				parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

				__snake_case				      =    parser.parse_args()

				__snake_case				      =    torch.load(args.checkpoint_path)

				with open(args.config_file) as f:
								__snake_case				      =    json.loads(f.read())

				__snake_case				      =    convert_ldm_checkpoint(checkpoint, config)

				if "ldm" in config:
								del config["ldm"]

				__snake_case				      =    UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				try:
								__snake_case				      =    DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
								__snake_case				      =    VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

								__snake_case				      =    LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
								pipe.save_pretrained(args.dump_path)
				except:  # noqa: E722
								model.save_pretrained(args.dump_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 A   (					self    ) ->      List[str]:







								'''simple docstring'''

								__lowercase			       = 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 						lowerCamelCase_							:



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_=1_3			,							snake_case_=6_4			,							snake_case_=3			,							snake_case_=4			,							snake_case_=[2, 2, 2, 2]			,							snake_case_=[8, 4, 2, 1]			,							snake_case_=[1_6, 3_2, 6_4, 1_2_8]			,							snake_case_=[1, 4, 8, 1_6]			,							snake_case_=[1, 2, 4, 8]			,							snake_case_=True			,							snake_case_=True			,							snake_case_="gelu"			,							snake_case_=0.1			,							snake_case_=0.1			,							snake_case_=0.0_2			,							snake_case_=3			,							snake_case_=None			,							) ->      int:







								'''simple docstring'''

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






					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

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

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

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






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







								'''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			,							snake_case_			,							snake_case_			,							snake_case_    ) ->      Tuple:







								'''simple docstring'''

								__lowercase			       = SegformerModel(config=snake_case_    )
								model.to(snake_case_    )
								model.eval()
								__lowercase			       = model(snake_case_    )
								__lowercase			       = __lowercase			       = 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			,							snake_case_			,							snake_case_			,							snake_case_    ) ->      str:







								'''simple docstring'''

								__lowercase			       = self.num_labels
								__lowercase			       = SegformerForSemanticSegmentation(snake_case_    )
								model.to(snake_case_    )
								model.eval()
								__lowercase			       = model(snake_case_    )
								self.parent.assertEqual(
								    result.logits.shape			,							(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)    )
								__lowercase			       = 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			,							snake_case_			,							snake_case_			,							snake_case_    ) ->      Optional[int]:







								'''simple docstring'''

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






					def A   (					self    ) ->      List[Any]:







								'''simple docstring'''

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




@require_torch
class 						lowerCamelCase_							(					lowerCAmelCase__  ,  lowerCAmelCase__  ,  unittest.TestCase					):



					'''simple docstring'''







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

					__UpperCAmelCase							  =					True
					__UpperCAmelCase							  =					False
					__UpperCAmelCase							  =					False
					__UpperCAmelCase							  =					False






					def A   (					self    ) ->      Any:







								'''simple docstring'''

								__lowercase			       = SegformerModelTester(self    )
								__lowercase			       = SegformerConfigTester(self			,							config_class=snake_case_    )






					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								self.config_tester.run_common_tests()






					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case_    )






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_binary_image_segmentation(*snake_case_    )






					def A   (					self    ) ->      int:







								'''simple docstring'''

								__lowercase			       = 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    ) ->      List[Any]:







								'''simple docstring'''

								pass






					@unittest.skip('''SegFormer does not have get_input_embeddings method and get_output_embeddings methods'''    )
					def A   (					self    ) ->      str:







								'''simple docstring'''

								pass






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







								'''simple docstring'''

								__lowercase       ,			__lowercase			       = self.model_tester.prepare_config_and_inputs_for_common()

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

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






					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

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

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

											__lowercase			       = 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"]
											__lowercase			       = True
											__lowercase			       = model_class(snake_case_    )
											model.to(snake_case_    )
											model.eval()
											with torch.no_grad():
														__lowercase			       = model(**self._prepare_for_class(snake_case_			,							snake_case_    )    )
											__lowercase			       = outputs.attentions

											self.assertEqual(len(snake_case_    )			,							snake_case_    )

											# verify the first attentions (first block, first layer)
											__lowercase			       = (self.model_tester.image_size // 4) ** 2
											__lowercase			       = (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)
											__lowercase			       = (self.model_tester.image_size // 3_2) ** 2
											__lowercase			       = (self.model_tester.image_size // (3_2 * 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]			,							)
											__lowercase			       = len(snake_case_    )

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

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

											__lowercase			       = outputs.attentions

											self.assertEqual(len(snake_case_    )			,							snake_case_    )
											# verify the first attentions (first block, first layer)
											__lowercase			       = (self.model_tester.image_size // 4) ** 2
											__lowercase			       = (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    ) ->      Dict:







								'''simple docstring'''

								def check_hidden_states_output(snake_case_			,							snake_case_			,							snake_case_    ):
											__lowercase			       = model_class(snake_case_    )
											model.to(snake_case_    )
											model.eval()

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

											__lowercase			       = outputs.hidden_states

											__lowercase			       = 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,
											    ]			,							)

								__lowercase       ,			__lowercase			       = self.model_tester.prepare_config_and_inputs_for_common()

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

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

											check_hidden_states_output(snake_case_			,							snake_case_			,							snake_case_    )






					def A   (					self    ) ->      str:







								'''simple docstring'''

								if not self.model_tester.is_training:
											return

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

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

											__lowercase			       = model_class(snake_case_    )
											model.to(snake_case_    )
											model.train()
											__lowercase			       = self._prepare_for_class(snake_case_			,							snake_case_			,							return_labels=snake_case_    )
											__lowercase			       = 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    ) ->      List[Any]:







								'''simple docstring'''

								pass






					@slow
					def A   (					self    ) ->      str:







								'''simple docstring'''

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




def 		lowercase_	(  ):


			'''simple docstring'''







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





@require_torch
class 						lowerCamelCase_							(					unittest.TestCase					):



					'''simple docstring'''







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







								'''simple docstring'''

								__lowercase			       = SegformerImageProcessor(
								    image_scale=(5_1_2, 5_1_2)			,							keep_ratio=snake_case_			,							align=snake_case_			,							do_random_crop=snake_case_    )
								__lowercase			       = SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512'''    ).to(
								    snake_case_    )

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

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

								__lowercase			       = torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8)    )
								self.assertEqual(outputs.logits.shape			,							snake_case_    )

								__lowercase			       = 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    ) ->      int:







								'''simple docstring'''

								__lowercase			       = SegformerImageProcessor(
								    image_scale=(5_1_2, 5_1_2)			,							keep_ratio=snake_case_			,							align=snake_case_			,							do_random_crop=snake_case_    )
								__lowercase			       = SegformerForSemanticSegmentation.from_pretrained(
								    '''nvidia/segformer-b1-finetuned-cityscapes-1024-1024'''    ).to(snake_case_    )

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

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

								__lowercase			       = torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8)    )
								self.assertEqual(outputs.logits.shape			,							snake_case_    )

								__lowercase			       = 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    ) ->      int:







								'''simple docstring'''

								__lowercase			       = SegformerImageProcessor(
								    image_scale=(5_1_2, 5_1_2)			,							keep_ratio=snake_case_			,							align=snake_case_			,							do_random_crop=snake_case_    )
								__lowercase			       = SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512'''    ).to(
								    snake_case_    )

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

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

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

								__lowercase			       = image_processor.post_process_semantic_segmentation(outputs=snake_case_			,							target_sizes=[(5_0_0, 3_0_0)]    )
								__lowercase			       = torch.Size((5_0_0, 3_0_0)    )
								self.assertEqual(segmentation[0].shape			,							snake_case_    )

								__lowercase			       = image_processor.post_process_semantic_segmentation(outputs=snake_case_    )
								__lowercase			       = torch.Size((1_2_8, 1_2_8)    )
								self.assertEqual(segmentation[0].shape			,							snake_case_    )


