
'''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       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 3_84
				SCREAMING_SNAKE_CASE  :						List[Any]      					= 7
				if "tiny" in model_name:
								SCREAMING_SNAKE_CASE  :						str      					= 96
								SCREAMING_SNAKE_CASE  :						List[Any]      					= (2, 2, 6, 2)
								SCREAMING_SNAKE_CASE  :						Dict      					= (3, 6, 12, 24)
				elif "small" in model_name:
								SCREAMING_SNAKE_CASE  :						Tuple      					= 96
								SCREAMING_SNAKE_CASE  :						str      					= (2, 2, 18, 2)
								SCREAMING_SNAKE_CASE  :						Any      					= (3, 6, 12, 24)
				elif "base" in model_name:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= 1_28
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= (2, 2, 18, 2)
								SCREAMING_SNAKE_CASE  :						int      					= (4, 8, 16, 32)
								SCREAMING_SNAKE_CASE  :						List[Any]      					= 12
								SCREAMING_SNAKE_CASE  :						int      					= 5_12
				elif "large" in model_name:
								SCREAMING_SNAKE_CASE  :						Any      					= 1_92
								SCREAMING_SNAKE_CASE  :						Any      					= (2, 2, 18, 2)
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= (6, 12, 24, 48)
								SCREAMING_SNAKE_CASE  :						Any      					= 12
								SCREAMING_SNAKE_CASE  :						Tuple      					= 7_68

				# set label information
				SCREAMING_SNAKE_CASE  :						Any      					= 1_50
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= """huggingface/label-files"""
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= """ade20k-id2label.json"""
				SCREAMING_SNAKE_CASE  :						Dict      					= json.load(open(hf_hub_download(snake_case_    ,	snake_case_    ,	repo_type='''dataset'''       )    ,	'''r'''       )       )
				SCREAMING_SNAKE_CASE  :						str      					= {int(snake_case_       ): v for k, v in idalabel.items()}
				SCREAMING_SNAKE_CASE  :						Tuple      					= {v: k for k, v in idalabel.items()}

				SCREAMING_SNAKE_CASE  :						Any      					= SwinConfig(
				    embed_dim=snake_case_    ,	depths=snake_case_    ,	num_heads=snake_case_    ,	window_size=snake_case_    ,	out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']    ,	)
				SCREAMING_SNAKE_CASE  :						Any      					= UperNetConfig(
				    backbone_config=snake_case_    ,	auxiliary_in_channels=snake_case_    ,	num_labels=snake_case_    ,	idalabel=snake_case_    ,	labelaid=snake_case_    ,	)

				return config






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]       ):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= []

				# 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       __lowercase  (_SCREAMING_SNAKE_CASE      :Tuple    ,	_SCREAMING_SNAKE_CASE      :Dict    ,	_SCREAMING_SNAKE_CASE      :Dict       ):
				SCREAMING_SNAKE_CASE  :						str      					= dct.pop(snake_case_       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= val






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Tuple    ,	_SCREAMING_SNAKE_CASE      :Any       ):
				SCREAMING_SNAKE_CASE  :						Dict      					= [int(backbone_config.embed_dim * 2**i       ) for i in range(len(backbone_config.depths       )       )]
				for i in range(len(backbone_config.depths       )       ):
								SCREAMING_SNAKE_CASE  :						str      					= 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)
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= state_dict.pop(F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'''       )
												SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 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
												SCREAMING_SNAKE_CASE  :						Optional[int]      					= in_proj_weight[:dim, :]
												SCREAMING_SNAKE_CASE  :						Tuple      					= in_proj_bias[: dim]
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= in_proj_weight[
												    dim : dim * 2, :
												]
												SCREAMING_SNAKE_CASE  :						Tuple      					= in_proj_bias[
												    dim : dim * 2
												]
												SCREAMING_SNAKE_CASE  :						int      					= in_proj_weight[
												    -dim :, :
												]
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= in_proj_bias[-dim :]
												# fmt: on






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Dict       ):
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= x.shape
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= x.reshape(snake_case_    ,	4    ,	in_channel // 4       )
				SCREAMING_SNAKE_CASE  :						Tuple      					= x[:, [0, 2, 1, 3], :].transpose(1    ,	2       ).reshape(snake_case_    ,	snake_case_       )
				return x






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Any       ):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= x.shape
				SCREAMING_SNAKE_CASE  :						str      					= x.reshape(snake_case_    ,	in_channel // 4    ,	4       )
				SCREAMING_SNAKE_CASE  :						Any      					= x[:, :, [0, 2, 1, 3]].transpose(1    ,	2       ).reshape(snake_case_    ,	snake_case_       )

				return x






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Dict       ):
				SCREAMING_SNAKE_CASE  :						Any      					= x.shape[0]
				SCREAMING_SNAKE_CASE  :						List[Any]      					= x.reshape(4    ,	in_channel // 4       )
				SCREAMING_SNAKE_CASE  :						Dict      					= x[[0, 2, 1, 3], :].transpose(0    ,	1       ).reshape(snake_case_       )
				return x






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Tuple       ):
				SCREAMING_SNAKE_CASE  :						List[str]      					= x.shape[0]
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= x.reshape(in_channel // 4    ,	4       )
				SCREAMING_SNAKE_CASE  :						str      					= x[:, [0, 2, 1, 3]].transpose(0    ,	1       ).reshape(snake_case_       )
				return x






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :Any       ):
				SCREAMING_SNAKE_CASE  :						Dict      					= {
				    """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""",
				}
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= model_name_to_url[model_name]
				SCREAMING_SNAKE_CASE  :						int      					= torch.hub.load_state_dict_from_url(snake_case_    ,	map_location='''cpu'''    ,	file_name=snake_case_       )[
				    """state_dict"""
				]

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

				SCREAMING_SNAKE_CASE  :						Tuple      					= get_upernet_config(snake_case_       )
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= UperNetForSemanticSegmentation(snake_case_       )
				model.eval()

				# replace "bn" => "batch_norm"
				for key in state_dict.copy().keys():
								SCREAMING_SNAKE_CASE  :						Any      					= state_dict.pop(snake_case_       )
								if "bn" in key:
												SCREAMING_SNAKE_CASE  :						List[Any]      					= key.replace('''bn'''    ,	'''batch_norm'''       )
								SCREAMING_SNAKE_CASE  :						List[Any]      					= val

				# rename keys
				SCREAMING_SNAKE_CASE  :						Tuple      					= create_rename_keys(snake_case_       )
				for src, dest in rename_keys:
								rename_key(snake_case_    ,	snake_case_    ,	snake_case_       )
				read_in_q_k_v(snake_case_    ,	config.backbone_config       )

				# fix downsample parameters
				for key, value in state_dict.items():
								if "downsample" in key:
												if "reduction" in key:
																SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= reverse_correct_unfold_reduction_order(snake_case_       )
												if "norm" in key:
																SCREAMING_SNAKE_CASE  :						str      					= reverse_correct_unfold_norm_order(snake_case_       )

				model.load_state_dict(snake_case_       )

				# verify on image
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
				SCREAMING_SNAKE_CASE  :						int      					= Image.open(requests.get(snake_case_    ,	stream=snake_case_       ).raw       ).convert('''RGB'''       )

				SCREAMING_SNAKE_CASE  :						int      					= SegformerImageProcessor()
				SCREAMING_SNAKE_CASE  :						List[Any]      					= processor(snake_case_    ,	return_tensors='''pt'''       ).pixel_values

				with torch.no_grad():
								SCREAMING_SNAKE_CASE  :						Any      					= model(snake_case_       )
								SCREAMING_SNAKE_CASE  :						str      					= outputs.logits

				print(logits.shape       )
				print('''First values of logits:'''    ,	logits[0, 0, :3, :3]       )
				# assert values
				if model_name == "upernet-swin-tiny":
								SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.tensor(
								    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]       )
				elif model_name == "upernet-swin-small":
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torch.tensor(
								    [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]       )
				elif model_name == "upernet-swin-base":
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= torch.tensor(
								    [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]       )
				elif model_name == "upernet-swin-large":
								SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.tensor(
								    [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]       )
				print('''Logits:'''    ,	outputs.logits[0, 0, :3, :3]       )
				assert torch.allclose(outputs.logits[0, 0, :3, :3]    ,	snake_case_    ,	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(snake_case_       )
								print(F'''Saving processor to {pytorch_dump_folder_path}'''       )
								processor.save_pretrained(snake_case_       )

				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__":
	snake_case_			=     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."""
	)

	snake_case_			=     parser.parse_args()
	convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''

import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]       ):
				return x + 2




class 	a__     (				unittest.TestCase	):


	def 				lowercase__  (self : List[str]      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''x = 3'''
					SCREAMING_SNAKE_CASE  :						Dict      					= {}
					SCREAMING_SNAKE_CASE  :						Any      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					assert result == 3
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3}      )

					SCREAMING_SNAKE_CASE  :						str      					= '''x = y'''
					SCREAMING_SNAKE_CASE  :						int      					= {'''y''': 5}
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 5, '''y''': 5}      )


	def 				lowercase__  (self : Optional[int]      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= '''y = add_two(x)'''
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						str      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 5}      )

					# Won't work without the tool
					with CaptureStdout() as out:
									SCREAMING_SNAKE_CASE  :						Tuple      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					assert result is None
					assert "tried to execute add_two" in out.out


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''x = 3'''
					SCREAMING_SNAKE_CASE  :						Any      					= {}
					SCREAMING_SNAKE_CASE  :						str      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					assert result == 3
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3}      )


	def 				lowercase__  (self : Optional[int]      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''test_dict = {\'x\': x, \'y\': add_two(x)}'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						List[Any]      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 5}      )
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}      )


	def 				lowercase__  (self : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''x = 3\ny = 5'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= {}
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 5}      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''text = f\'This is x: {x}.\''''
					SCREAMING_SNAKE_CASE  :						List[str]      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						Dict      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == "This is x: 3."
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''text''': '''This is x: 3.'''}      )


	def 				lowercase__  (self : int      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''if x <= 3:\n    y = 2\nelse:\n    y = 5'''
					SCREAMING_SNAKE_CASE  :						int      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						List[str]      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == 2
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 2}      )

					SCREAMING_SNAKE_CASE  :						Any      					= {'''x''': 8}
					SCREAMING_SNAKE_CASE  :						int      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 8, '''y''': 5}      )


	def 				lowercase__  (self : Any      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''test_list = [x, add_two(x)]'''
					SCREAMING_SNAKE_CASE  :						List[str]      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						Tuple      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					self.assertListEqual(__UpperCAmelCase,					[3, 5]      )
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''test_list''': [3, 5]}      )


	def 				lowercase__  (self : int      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= '''y = x'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						str      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					assert result == 3
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 3}      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= '''test_list = [x, add_two(x)]\ntest_list[1]'''
					SCREAMING_SNAKE_CASE  :						int      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''test_list''': [3, 5]}      )

					SCREAMING_SNAKE_CASE  :						Dict      					= '''test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}      )






	def 				lowercase__  (self : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''x = 0\nfor i in range(3):\n    x = i'''
					SCREAMING_SNAKE_CASE  :						List[Any]      					= {}
					SCREAMING_SNAKE_CASE  :						List[str]      					= evaluate(__UpperCAmelCase,					{'''range''': range},					state=__UpperCAmelCase      )
					assert result == 2
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 2, '''i''': 2}      )




