



'''simple docstring'''

import argparse
import json
from pathlib import Path

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

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				# initialize config
				if "resnet-50" in model_name:
								SCREAMING_SNAKE_CASE  :						str      					= ResNetConfig.from_pretrained('''microsoft/resnet-50'''       )
				elif "resnet-101" in model_name:
								SCREAMING_SNAKE_CASE  :						str      					= ResNetConfig.from_pretrained('''microsoft/resnet-101'''       )
				else:
								raise ValueError('''Model name should include either resnet50 or resnet101'''       )

				SCREAMING_SNAKE_CASE  :						Any      					= DetrConfig(use_timm_backbone=_SCREAMING_SNAKE_CASE    ,	backbone_config=_SCREAMING_SNAKE_CASE       )

				# set label attributes
				SCREAMING_SNAKE_CASE  :						List[Any]      					= '''panoptic''' in model_name
				if is_panoptic:
								SCREAMING_SNAKE_CASE  :						Any      					= 2_50
				else:
								SCREAMING_SNAKE_CASE  :						str      					= 91
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''huggingface/label-files'''
								SCREAMING_SNAKE_CASE  :						List[str]      					= '''coco-detection-id2label.json'''
								SCREAMING_SNAKE_CASE  :						List[str]      					= json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	repo_type='''dataset'''       )    ,	'''r'''       )       )
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= {int(_SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
								SCREAMING_SNAKE_CASE  :						List[str]      					= idalabel
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= {v: k for k, v in idalabel.items()}

				return config, is_panoptic






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]       ):
				# here we list all keys to be renamed (original name on the left, our name on the right)
				SCREAMING_SNAKE_CASE  :						int      					= []

				# stem
				# fmt: off
				rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight''')       )
				rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight''')       )
				rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias''')       )
				rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean''')       )
				rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_var''')       )
				# stages
				for stage_idx in range(len(config.backbone_config.depths       )       ):
								for layer_idx in range(config.backbone_config.depths[stage_idx]       ):
												# shortcut
												if layer_idx == 0:
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
																    )       )
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
																    )       )
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
																    )       )
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
																    )       )
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
																    )       )
												# 3 convs
												for i in range(3       ):
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
																    )       )
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
																    )       )
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
																    )       )
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
																    )       )
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
																    )       )
    # fmt: on

				for i in range(config.encoder_layers       ):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append(
								    (
								        F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
								        F'''encoder.layers.{i}.self_attn.out_proj.weight''',
								    )       )
								rename_keys.append(
								    (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')       )
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight''')       )
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias''')       )
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight''')       )
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias''')       )
								rename_keys.append(
								    (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')       )
								rename_keys.append(
								    (F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias''')       )
								rename_keys.append(
								    (F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight''')       )
								rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias''')       )
								# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
								rename_keys.append(
								    (
								        F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
								        F'''decoder.layers.{i}.self_attn.out_proj.weight''',
								    )       )
								rename_keys.append(
								    (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')       )
								rename_keys.append(
								    (
								        F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
								        F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
								    )       )
								rename_keys.append(
								    (
								        F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
								        F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
								    )       )
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight''')       )
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias''')       )
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight''')       )
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias''')       )
								rename_keys.append(
								    (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')       )
								rename_keys.append(
								    (F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias''')       )
								rename_keys.append(
								    (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')       )
								rename_keys.append(
								    (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')       )
								rename_keys.append(
								    (F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight''')       )
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias''')       )

				# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
				rename_keys.extend(
				    [
				        ('''input_proj.weight''', '''input_projection.weight'''),
				        ('''input_proj.bias''', '''input_projection.bias'''),
				        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
				        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
				        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
				        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
				        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
				        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
				        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
				        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
				        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
				        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
				        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
				    ]       )

				return rename_keys






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Union[str, Any]    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :Any       ):
				SCREAMING_SNAKE_CASE  :						Tuple      					= state_dict.pop(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Any      					= val






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]=False       ):
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= ''''''
				if is_panoptic:
								SCREAMING_SNAKE_CASE  :						List[Any]      					= '''detr.'''

				# first: transformer encoder
				for i in range(6       ):
								# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
								SCREAMING_SNAKE_CASE  :						Tuple      					= state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''       )
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''       )
								# next, add query, keys and values (in that order) to the state dict
								SCREAMING_SNAKE_CASE  :						str      					= in_proj_weight[:2_56, :]
								SCREAMING_SNAKE_CASE  :						str      					= in_proj_bias[:2_56]
								SCREAMING_SNAKE_CASE  :						int      					= in_proj_weight[2_56:5_12, :]
								SCREAMING_SNAKE_CASE  :						List[str]      					= in_proj_bias[2_56:5_12]
								SCREAMING_SNAKE_CASE  :						Any      					= in_proj_weight[-2_56:, :]
								SCREAMING_SNAKE_CASE  :						Tuple      					= in_proj_bias[-2_56:]
				# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
				for i in range(6       ):
								# read in weights + bias of input projection layer of self-attention
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''       )
								SCREAMING_SNAKE_CASE  :						Dict      					= state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''       )
								# next, add query, keys and values (in that order) to the state dict
								SCREAMING_SNAKE_CASE  :						str      					= in_proj_weight[:2_56, :]
								SCREAMING_SNAKE_CASE  :						Dict      					= in_proj_bias[:2_56]
								SCREAMING_SNAKE_CASE  :						List[str]      					= in_proj_weight[2_56:5_12, :]
								SCREAMING_SNAKE_CASE  :						int      					= in_proj_bias[2_56:5_12]
								SCREAMING_SNAKE_CASE  :						Tuple      					= in_proj_weight[-2_56:, :]
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= in_proj_bias[-2_56:]
								# read in weights + bias of input projection layer of cross-attention
								SCREAMING_SNAKE_CASE  :						List[str]      					= state_dict.pop(
								    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''       )
								SCREAMING_SNAKE_CASE  :						Tuple      					= state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''       )
								# next, add query, keys and values (in that order) of cross-attention to the state dict
								SCREAMING_SNAKE_CASE  :						Any      					= in_proj_weight_cross_attn[:2_56, :]
								SCREAMING_SNAKE_CASE  :						Dict      					= in_proj_bias_cross_attn[:2_56]
								SCREAMING_SNAKE_CASE  :						List[Any]      					= in_proj_weight_cross_attn[2_56:5_12, :]
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= in_proj_bias_cross_attn[2_56:5_12]
								SCREAMING_SNAKE_CASE  :						List[Any]      					= in_proj_weight_cross_attn[-2_56:, :]
								SCREAMING_SNAKE_CASE  :						Tuple      					= in_proj_bias_cross_attn[-2_56:]






def       __lowercase  ():
				SCREAMING_SNAKE_CASE  :						List[str]      					= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				SCREAMING_SNAKE_CASE  :						Dict      					= Image.open(requests.get(_SCREAMING_SNAKE_CASE    ,	stream=_SCREAMING_SNAKE_CASE       ).raw       )

				return im






@torch.no_grad()
def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[int]    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]=None    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]=False       ):
				SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Any      					= get_detr_config(_SCREAMING_SNAKE_CASE       )

				# load original model from torch hub
				SCREAMING_SNAKE_CASE  :						str      					= {
				    '''detr-resnet-50''': '''detr_resnet50''',
				    '''detr-resnet-101''': '''detr_resnet101''',
				}
				logger.info(F'''Converting model {model_name}...'''       )
				SCREAMING_SNAKE_CASE  :						Tuple      					= torch.hub.load('''facebookresearch/detr'''    ,	model_name_to_original_name[model_name]    ,	pretrained=_SCREAMING_SNAKE_CASE       ).eval()
				SCREAMING_SNAKE_CASE  :						List[str]      					= detr.state_dict()
				# rename keys
				for src, dest in create_rename_keys(_SCREAMING_SNAKE_CASE       ):
								if is_panoptic:
												SCREAMING_SNAKE_CASE  :						Optional[Any]      					= '''detr.''' + src
								rename_key(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
				# query, key and value matrices need special treatment
				read_in_q_k_v(_SCREAMING_SNAKE_CASE    ,	is_panoptic=_SCREAMING_SNAKE_CASE       )
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				SCREAMING_SNAKE_CASE  :						Tuple      					= '''detr.model.''' if is_panoptic else '''model.'''
				for key in state_dict.copy().keys():
								if is_panoptic:
												if (
												    key.startswith('''detr'''       )
												    and not key.startswith('''class_labels_classifier'''       )
												    and not key.startswith('''bbox_predictor'''       )
												):
																SCREAMING_SNAKE_CASE  :						int      					= state_dict.pop(_SCREAMING_SNAKE_CASE       )
																SCREAMING_SNAKE_CASE  :						int      					= val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																SCREAMING_SNAKE_CASE  :						Optional[int]      					= state_dict.pop(_SCREAMING_SNAKE_CASE       )
																SCREAMING_SNAKE_CASE  :						Tuple      					= val
												elif key.startswith('''bbox_attention'''       ) or key.startswith('''mask_head'''       ):
																continue
												else:
																SCREAMING_SNAKE_CASE  :						Tuple      					= state_dict.pop(_SCREAMING_SNAKE_CASE       )
																SCREAMING_SNAKE_CASE  :						Any      					= val
								else:
												if not key.startswith('''class_labels_classifier'''       ) and not key.startswith('''bbox_predictor'''       ):
																SCREAMING_SNAKE_CASE  :						str      					= state_dict.pop(_SCREAMING_SNAKE_CASE       )
																SCREAMING_SNAKE_CASE  :						Optional[int]      					= val

    # finally, create HuggingFace model and load state dict
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= DetrForSegmentation(_SCREAMING_SNAKE_CASE       ) if is_panoptic else DetrForObjectDetection(_SCREAMING_SNAKE_CASE       )
				model.load_state_dict(_SCREAMING_SNAKE_CASE       )
				model.eval()

				# verify our conversion on an image
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''coco_panoptic''' if is_panoptic else '''coco_detection'''
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= DetrImageProcessor(format=_SCREAMING_SNAKE_CASE       )

				SCREAMING_SNAKE_CASE  :						str      					= processor(images=prepare_img()    ,	return_tensors='''pt'''       )
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= encoding['''pixel_values''']

				SCREAMING_SNAKE_CASE  :						Dict      					= detr(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= model(_SCREAMING_SNAKE_CASE       )

				assert torch.allclose(outputs.logits    ,	original_outputs['''pred_logits''']    ,	atol=1E-3       )
				assert torch.allclose(outputs.pred_boxes    ,	original_outputs['''pred_boxes''']    ,	atol=1E-3       )
				if is_panoptic:
								assert torch.allclose(outputs.pred_masks    ,	original_outputs['''pred_masks''']    ,	atol=1E-4       )
				print('''Looks ok!'''       )

				if pytorch_dump_folder_path is not None:
								# Save model and image processor
								logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''       )
								Path(_SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE       )
								model.save_pretrained(_SCREAMING_SNAKE_CASE       )
								processor.save_pretrained(_SCREAMING_SNAKE_CASE       )

				if push_to_hub:
								# Upload model and image processor to the hub
								logger.info('''Uploading PyTorch model and image processor to the hub...'''       )
								model.push_to_hub(F'''nielsr/{model_name}'''       )
								processor.push_to_hub(F'''nielsr/{model_name}'''       )


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

	parser.add_argument(
	    """--model_name""",
	    default="""detr-resnet-50""",
	    type=str,
	    choices=["""detr-resnet-50""", """detr-resnet-101"""],
	    help="""Name of the DETR model you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
	)
	parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the model to the hub or not.""")
	snake_case_			=     parser.parse_args()
	convert_detr_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}      )




