


"""simple docstring"""


import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 torch import nn

	from transformers import (
	    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
	    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
	    MODEL_MAPPING,
	    DeiTForImageClassification,
	    DeiTForImageClassificationWithTeacher,
	    DeiTForMaskedImageModeling,
	    DeiTModel,
	)
	from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import DeiTImageProcessor
class 						_UpperCamelCase						:
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a=13					,     __a=30					,     __a=2					,     __a=3					,     __a=True					,     __a=True					,     __a=32					,     __a=5					,     __a=4					,     __a=37					,     __a="gelu"					,     __a=0.1					,     __a=0.1					,     __a=10					,     __a=0.0_2					,     __a=3					,     __a=None					,     __a=2					,     ):
										__lowerCAmelCase       					=    parent
										__lowerCAmelCase       					=    batch_size
										__lowerCAmelCase       					=    image_size
										__lowerCAmelCase       					=    patch_size
										__lowerCAmelCase       					=    num_channels
										__lowerCAmelCase       					=    is_training
										__lowerCAmelCase       					=    use_labels
										__lowerCAmelCase       					=    hidden_size
										__lowerCAmelCase       					=    num_hidden_layers
										__lowerCAmelCase       					=    num_attention_heads
										__lowerCAmelCase       					=    intermediate_size
										__lowerCAmelCase       					=    hidden_act
										__lowerCAmelCase       					=    hidden_dropout_prob
										__lowerCAmelCase       					=    attention_probs_dropout_prob
										__lowerCAmelCase       					=    type_sequence_label_size
										__lowerCAmelCase       					=    initializer_range
										__lowerCAmelCase       					=    scope
										__lowerCAmelCase       					=    encoder_stride

										# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
										__lowerCAmelCase       					=    (image_size // patch_size) ** 2
										__lowerCAmelCase       					=    num_patches + 2

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

										__lowerCAmelCase       					=    None
										if self.use_labels:
													__lowerCAmelCase       					=    ids_tensor([self.batch_size]					,     self.type_sequence_label_size			)

										__lowerCAmelCase       					=    self.get_config()

										return config, pixel_values, labels

							def     snake_case       (  self			):
										return DeiTConfig(
										    image_size=self.image_size					,     patch_size=self.patch_size					,     num_channels=self.num_channels					,     hidden_size=self.hidden_size					,     num_hidden_layers=self.num_hidden_layers					,     num_attention_heads=self.num_attention_heads					,     intermediate_size=self.intermediate_size					,     hidden_act=self.hidden_act					,     hidden_dropout_prob=self.hidden_dropout_prob					,     attention_probs_dropout_prob=self.attention_probs_dropout_prob					,     is_decoder=__a					,     initializer_range=self.initializer_range					,     encoder_stride=self.encoder_stride					,     )

							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    DeiTModel(config=__a			)
										model.to(__a			)
										model.eval()
										__lowerCAmelCase       					=    model(__a			)
										self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, self.seq_length, self.hidden_size)			)

							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    DeiTForMaskedImageModeling(config=__a			)
										model.to(__a			)
										model.eval()
										__lowerCAmelCase       					=    model(__a			)
										self.parent.assertEqual(
										    result.reconstruction.shape					,     (self.batch_size, self.num_channels, self.image_size, self.image_size)			)

										# test greyscale images
										__lowerCAmelCase       					=    1
										__lowerCAmelCase       					=    DeiTForMaskedImageModeling(__a			)
										model.to(__a			)
										model.eval()

										__lowerCAmelCase       					=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
										__lowerCAmelCase       					=    model(__a			)
										self.parent.assertEqual(result.reconstruction.shape					,     (self.batch_size, 1, self.image_size, self.image_size)			)

							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    self.type_sequence_label_size
										__lowerCAmelCase       					=    DeiTForImageClassification(__a			)
										model.to(__a			)
										model.eval()
										__lowerCAmelCase       					=    model(__a					,     labels=__a			)
										self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.type_sequence_label_size)			)

										# test greyscale images
										__lowerCAmelCase       					=    1
										__lowerCAmelCase       					=    DeiTForImageClassification(__a			)
										model.to(__a			)
										model.eval()

										__lowerCAmelCase       					=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
										__lowerCAmelCase       					=    model(__a					,     labels=__a			)
										self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.type_sequence_label_size)			)



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.prepare_config_and_inputs()
										(
										    (
										    __lowerCAmelCase			
										)					,			(
										    __lowerCAmelCase			
										)					,			(
										    __lowerCAmelCase			
										)					,						
										)       					=    config_and_inputs
										__lowerCAmelCase       					=    {"pixel_values": pixel_values}
										return config, inputs_dict
@require_torch
class 						_UpperCamelCase						(     lowerCAmelCase__ ,lowerCAmelCase__ ,unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					Dict          =(
							    (
							        DeiTModel,
							        DeiTForImageClassification,
							        DeiTForImageClassificationWithTeacher,
							        DeiTForMaskedImageModeling,
							    )
							    if is_torch_available()
							    else ()
							)
							__UpperCAmelCase		:					str          =(
							    {
							        """feature-extraction""": DeiTModel,
							        """image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
							    }
							    if is_torch_available()
							    else {}
							)

							__UpperCAmelCase		:					List[Any]          =False
							__UpperCAmelCase		:					Optional[int]          =False
							__UpperCAmelCase		:					int          =False

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    DeiTModelTester(self			)
										__lowerCAmelCase       					=    ConfigTester(self					,     config_class=__a					,     has_text_modality=__a					,     hidden_size=37			)

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

							@unittest.skip(reason="DeiT does not use inputs_embeds"			)
							def     snake_case       (  self			):
										pass

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													__lowerCAmelCase       					=    model_class(__a			)
													self.assertIsInstance(model.get_input_embeddings()					,     (nn.Module)			)
													__lowerCAmelCase       					=    model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(__a					,     nn.Linear			)			)

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()

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

													__lowerCAmelCase       					=    ["pixel_values"]
													self.assertListEqual(arg_names[:1]					,     __a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_image_modeling(*__a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__a			)

							def     snake_case       (  self					,     __a					,     __a					,     __a=False			):
										__lowerCAmelCase       					=    super()._prepare_for_class(__a					,     __a					,     return_labels=__a			)

										if return_labels:
													if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
																del inputs_dict["labels"]

										return inputs_dict

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

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

										for model_class in self.all_model_classes:
													# DeiTForImageClassificationWithTeacher supports inference-only
													if (
													    model_class in get_values(__a			)
													    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
													):
																continue
													__lowerCAmelCase       					=    model_class(__a			)
													model.to(__a			)
													model.train()
													__lowerCAmelCase       					=    self._prepare_for_class(__a					,     __a					,     return_labels=__a			)
													__lowerCAmelCase       					=    model(**__a			).loss
													loss.backward()

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()
										if not self.model_tester.is_training:
													return

										__lowerCAmelCase       					=    False
										__lowerCAmelCase       					=    True

										for model_class in self.all_model_classes:
													if model_class in get_values(__a			) or not model_class.supports_gradient_checkpointing:
																continue
													# DeiTForImageClassificationWithTeacher supports inference-only
													if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
																continue
													__lowerCAmelCase       					=    model_class(__a			)
													model.gradient_checkpointing_enable()
													model.to(__a			)
													model.train()
													__lowerCAmelCase       					=    self._prepare_for_class(__a					,     __a					,     return_labels=__a			)
													__lowerCAmelCase       					=    model(**__a			).loss
													loss.backward()

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()

										__lowerCAmelCase       					=    [
										    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
										    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
										    {"title": "regression", "num_labels": 1, "dtype": torch.float},
										]

										for model_class in self.all_model_classes:
													if (
													    model_class
													    not in [
													        *get_values(__a			),
													        *get_values(__a			),
													    ]
													    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
													):
																continue

													for problem_type in problem_types:
																with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"			):
																			__lowerCAmelCase       					=    problem_type["title"]
																			__lowerCAmelCase       					=    problem_type["num_labels"]

																			__lowerCAmelCase       					=    model_class(__a			)
																			model.to(__a			)
																			model.train()

																			__lowerCAmelCase       					=    self._prepare_for_class(__a					,     __a					,     return_labels=__a			)

																			if problem_type["num_labels"] > 1:
																						__lowerCAmelCase       					=    inputs["labels"].unsqueeze(1			).repeat(1					,     problem_type["num_labels"]			)

																			__lowerCAmelCase       					=    inputs["labels"].to(problem_type["dtype"]			)

																			# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																			# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																			# they have the same size." which is a symptom something in wrong for the regression problem.
																			# See https://github.com/huggingface/transformers/issues/11780
																			with warnings.catch_warnings(record=__a			) as warning_list:
																						__lowerCAmelCase       					=    model(**__a			).loss
																			for w in warning_list:
																						if "Using a target size that is different to the input size" in str(w.message			):
																									raise ValueError(
																									    f"Something is going wrong in the regression problem: intercepted {w.message}"			)

																			loss.backward()



							@slow
							def     snake_case       (  self			):
										for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__lowerCAmelCase       					=    DeiTModel.from_pretrained(__a			)
													self.assertIsNotNone(__a			)


def _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
			return image


@require_torch
@require_vision
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							@cached_property
							def     snake_case       (  self			):
										return (
										    DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"			)
										    if is_vision_available()
										    else None
										)

							@slow
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"			).to(
										    __a			)

										__lowerCAmelCase       					=    self.default_image_processor
										__lowerCAmelCase       					=    prepare_img()
										__lowerCAmelCase       					=    image_processor(images=__a					,     return_tensors="pt"			).to(__a			)

										# forward pass
										with torch.no_grad():
													__lowerCAmelCase       					=    model(**__a			)

										# verify the logits
										__lowerCAmelCase       					=    torch.Size((1, 10_00)			)
										self.assertEqual(outputs.logits.shape					,     __a			)

										__lowerCAmelCase       					=    torch.tensor([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]			).to(__a			)

										self.assertTrue(torch.allclose(outputs.logits[0, :3]					,     __a					,     atol=1e-4			)			)



							@slow
							@require_accelerate
							@require_torch_gpu
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    DeiTModel.from_pretrained(
										    "facebook/deit-base-distilled-patch16-224"					,     torch_dtype=torch.floataa					,     device_map="auto"			)
										__lowerCAmelCase       					=    self.default_image_processor

										__lowerCAmelCase       					=    prepare_img()
										__lowerCAmelCase       					=    image_processor(images=__a					,     return_tensors="pt"			)
										__lowerCAmelCase       					=    inputs.pixel_values.to(__a			)

										# forward pass to make sure inference works in fp16
										with torch.no_grad():
													__lowerCAmelCase       					=    model(__a			)








"""simple docstring"""


import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    384
			__lowerCAmelCase       					=    7
			if "tiny" in model_name:
						__lowerCAmelCase       					=    96
						__lowerCAmelCase       					=    (2, 2, 6, 2)
						__lowerCAmelCase       					=    (3, 6, 12, 24)
			elif "small" in model_name:
						__lowerCAmelCase       					=    96
						__lowerCAmelCase       					=    (2, 2, 18, 2)
						__lowerCAmelCase       					=    (3, 6, 12, 24)
			elif "base" in model_name:
						__lowerCAmelCase       					=    128
						__lowerCAmelCase       					=    (2, 2, 18, 2)
						__lowerCAmelCase       					=    (4, 8, 16, 32)
						__lowerCAmelCase       					=    12
						__lowerCAmelCase       					=    512
			elif "large" in model_name:
						__lowerCAmelCase       					=    192
						__lowerCAmelCase       					=    (2, 2, 18, 2)
						__lowerCAmelCase       					=    (6, 12, 24, 48)
						__lowerCAmelCase       					=    12
						__lowerCAmelCase       					=    768

			# set label information
			__lowerCAmelCase       					=    150
			__lowerCAmelCase       					=    "huggingface/label-files"
			__lowerCAmelCase       					=    "ade20k-id2label.json"
			__lowerCAmelCase       					=    json.load(open(hf_hub_download(_UpperCamelCase					,				_UpperCamelCase					,				repo_type="dataset"   )					,				"r"   )   )
			__lowerCAmelCase       					=    {int(_UpperCamelCase   ): v for k, v in idalabel.items()}
			__lowerCAmelCase       					=    {v: k for k, v in idalabel.items()}

			__lowerCAmelCase       					=    SwinConfig(
			    embed_dim=_UpperCamelCase					,				depths=_UpperCamelCase					,				num_heads=_UpperCamelCase					,				window_size=_UpperCamelCase					,				out_features=["stage1", "stage2", "stage3", "stage4"]					,				)
			__lowerCAmelCase       					=    UperNetConfig(
			    backbone_config=_UpperCamelCase					,				auxiliary_in_channels=_UpperCamelCase					,				num_labels=_UpperCamelCase					,				idalabel=_UpperCamelCase					,				labelaid=_UpperCamelCase					,				)

			return config


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    []

			# fmt: off
			# stem
			rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight")   )
			rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias")   )
			rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight")   )
			rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.embeddings.norm.bias")   )
			# stages
			for i in range(len(config.backbone_config.depths   )   ):
						for j in range(config.backbone_config.depths[i]   ):
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias")   )

						if i < 3:
									rename_keys.append((f"backbone.stages.{i}.downsample.reduction.weight", f"backbone.encoder.layers.{i}.downsample.reduction.weight")   )
									rename_keys.append((f"backbone.stages.{i}.downsample.norm.weight", f"backbone.encoder.layers.{i}.downsample.norm.weight")   )
									rename_keys.append((f"backbone.stages.{i}.downsample.norm.bias", f"backbone.encoder.layers.{i}.downsample.norm.bias")   )
						rename_keys.append((f"backbone.norm{i}.weight", f"backbone.hidden_states_norms.stage{i+1}.weight")   )
						rename_keys.append((f"backbone.norm{i}.bias", f"backbone.hidden_states_norms.stage{i+1}.bias")   )

			# decode head
			rename_keys.extend(
			    [
			        ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
			        ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
			        ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
			        ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
			    ]   )
			# fmt: on

			return rename_keys


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    dct.pop(_UpperCamelCase   )
			__lowerCAmelCase       					=    val


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    [int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
			for i in range(len(backbone_config.depths   )   ):
						__lowerCAmelCase       					=    num_features[i]
						for j in range(backbone_config.depths[i]   ):
									# fmt: off
									# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
									__lowerCAmelCase       					=    state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"   )
									__lowerCAmelCase       					=    state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"   )
									# next, add query, keys and values (in that order) to the state dict
									__lowerCAmelCase       					=    in_proj_weight[:dim, :]
									__lowerCAmelCase       					=    in_proj_bias[: dim]
									__lowerCAmelCase       					=    in_proj_weight[
									    dim : dim * 2, :
									]
									__lowerCAmelCase       					=    in_proj_bias[
									    dim : dim * 2
									]
									__lowerCAmelCase       					=    in_proj_weight[
									    -dim :, :
									]
									__lowerCAmelCase       					=    in_proj_bias[-dim :]
									# fmt: on


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase					,			__lowerCAmelCase       					=    x.shape
			__lowerCAmelCase       					=    x.reshape(_UpperCamelCase					,				4					,				in_channel // 4   )
			__lowerCAmelCase       					=    x[:, [0, 2, 1, 3], :].transpose(1					,				2   ).reshape(_UpperCamelCase					,				_UpperCamelCase   )
			return x


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase					,			__lowerCAmelCase       					=    x.shape
			__lowerCAmelCase       					=    x.reshape(_UpperCamelCase					,				in_channel // 4					,				4   )
			__lowerCAmelCase       					=    x[:, :, [0, 2, 1, 3]].transpose(1					,				2   ).reshape(_UpperCamelCase					,				_UpperCamelCase   )

			return x


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    x.shape[0]
			__lowerCAmelCase       					=    x.reshape(4					,				in_channel // 4   )
			__lowerCAmelCase       					=    x[[0, 2, 1, 3], :].transpose(0					,				1   ).reshape(_UpperCamelCase   )
			return x


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    x.shape[0]
			__lowerCAmelCase       					=    x.reshape(in_channel // 4					,				4   )
			__lowerCAmelCase       					=    x[:, [0, 2, 1, 3]].transpose(0					,				1   ).reshape(_UpperCamelCase   )
			return x


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    {
			    "upernet-swin-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth",
			    "upernet-swin-small": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth",
			    "upernet-swin-base": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth",
			    "upernet-swin-large": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth",
			}
			__lowerCAmelCase       					=    model_name_to_url[model_name]
			__lowerCAmelCase       					=    torch.hub.load_state_dict_from_url(_UpperCamelCase					,				map_location="cpu"					,				file_name=_UpperCamelCase   )[
			    "state_dict"
			]

			for name, param in state_dict.items():
						print(_UpperCamelCase					,				param.shape   )

			__lowerCAmelCase       					=    get_upernet_config(_UpperCamelCase   )
			__lowerCAmelCase       					=    UperNetForSemanticSegmentation(_UpperCamelCase   )
			model.eval()

			# replace "bn" => "batch_norm"
			for key in state_dict.copy().keys():
						__lowerCAmelCase       					=    state_dict.pop(_UpperCamelCase   )
						if "bn" in key:
									__lowerCAmelCase       					=    key.replace("bn"					,				"batch_norm"   )
						__lowerCAmelCase       					=    val

			# rename keys
			__lowerCAmelCase       					=    create_rename_keys(_UpperCamelCase   )
			for src, dest in rename_keys:
						rename_key(_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   )
			read_in_q_k_v(_UpperCamelCase					,				config.backbone_config   )

			# fix downsample parameters
			for key, value in state_dict.items():
						if "downsample" in key:
									if "reduction" in key:
												__lowerCAmelCase       					=    reverse_correct_unfold_reduction_order(_UpperCamelCase   )
									if "norm" in key:
												__lowerCAmelCase       					=    reverse_correct_unfold_norm_order(_UpperCamelCase   )

			model.load_state_dict(_UpperCamelCase   )

			# verify on image
			__lowerCAmelCase       					=    "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
			__lowerCAmelCase       					=    Image.open(requests.get(_UpperCamelCase					,				stream=_UpperCamelCase   ).raw   ).convert("RGB"   )

			__lowerCAmelCase       					=    SegformerImageProcessor()
			__lowerCAmelCase       					=    processor(_UpperCamelCase					,				return_tensors="pt"   ).pixel_values

			with torch.no_grad():
						__lowerCAmelCase       					=    model(_UpperCamelCase   )
						__lowerCAmelCase       					=    outputs.logits

			print(logits.shape   )
			print("First values of logits:"					,				logits[0, 0, :3, :3]   )
			# assert values
			if model_name == "upernet-swin-tiny":
						__lowerCAmelCase       					=    torch.tensor(
						    [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]   )
			elif model_name == "upernet-swin-small":
						__lowerCAmelCase       					=    torch.tensor(
						    [[-7.19_21, -7.19_21, -6.95_32], [-7.19_21, -7.19_21, -6.95_32], [-7.09_08, -7.09_08, -6.85_34]]   )
			elif model_name == "upernet-swin-base":
						__lowerCAmelCase       					=    torch.tensor(
						    [[-6.58_51, -6.58_51, -6.43_30], [-6.58_51, -6.58_51, -6.43_30], [-6.47_63, -6.47_63, -6.32_54]]   )
			elif model_name == "upernet-swin-large":
						__lowerCAmelCase       					=    torch.tensor(
						    [[-7.52_97, -7.52_97, -7.38_02], [-7.52_97, -7.52_97, -7.38_02], [-7.40_44, -7.40_44, -7.25_86]]   )
			print("Logits:"					,				outputs.logits[0, 0, :3, :3]   )
			assert torch.allclose(outputs.logits[0, 0, :3, :3]					,				_UpperCamelCase					,				atol=1e-4   )
			print("Looks ok!"   )

			if pytorch_dump_folder_path is not None:
						print(f"Saving model {model_name} to {pytorch_dump_folder_path}"   )
						model.save_pretrained(_UpperCamelCase   )
						print(f"Saving processor to {pytorch_dump_folder_path}"   )
						processor.save_pretrained(_UpperCamelCase   )

			if push_to_hub:
						print(f"Pushing model and processor for {model_name} to hub"   )
						model.push_to_hub(f"openmmlab/{model_name}"   )
						processor.push_to_hub(f"openmmlab/{model_name}"   )


if __name__ == "__main__":
	A  :     int    					=    argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--model_name",
	    default="upernet-swin-tiny",
	    type=str,
	    choices=[f'''upernet-swin-{size}''' for size in ["tiny", "small", "base", "large"]],
	    help="Name of the Swin + UperNet model you'd like to convert.",
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
	)
	parser.add_argument(
	    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
	)

	A  :     List[Any]    					=    parser.parse_args()
	convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




