
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_  :	List[Any] 				=					logging.get_logger(__name__)







def     A__   (  lowerCamelCase  )					->		List[str]:
				UpperCamelCase_:							Optional[Any]		=  SwinConfig(
				    embed_dim=1_92       ,      depths=(2, 2, 18, 2)       ,      num_heads=(6, 12, 24, 48)       ,      window_size=12       ,      out_features=["""stage2""", """stage3""", """stage4"""]       ,      )

				UpperCamelCase_:							List[Any]		=  DetaConfig(
				    backbone_config=lowerCamelCase       ,      num_queries=9_00       ,      encoder_ffn_dim=20_48       ,      decoder_ffn_dim=20_48       ,      num_feature_levels=5       ,      assign_first_stage=lowerCamelCase       ,      with_box_refine=lowerCamelCase       ,      two_stage=lowerCamelCase       ,      )

				# set labels
				UpperCamelCase_:							Any		=  """huggingface/label-files"""
				if "o365" in model_name:
								UpperCamelCase_:							List[Any]		=  3_66
								UpperCamelCase_:							int		=  """object365-id2label.json"""
				else:
								UpperCamelCase_:							int		=  91
								UpperCamelCase_:							Union[str, Any]		=  """coco-detection-id2label.json"""

				UpperCamelCase_:							int		=  num_labels
				UpperCamelCase_:							List[str]		=  json.load(open(cached_download(hf_hub_url(lowerCamelCase       ,      lowerCamelCase       ,      repo_type="""dataset"""  )  )       ,      """r"""  )  )
				UpperCamelCase_:							Optional[int]		=  {int(lowerCamelCase  ): v for k, v in idalabel.items()}
				UpperCamelCase_:							List[Any]		=  idalabel
				UpperCamelCase_:							str		=  {v: k for k, v in idalabel.items()}

				return config







def     A__   (  lowerCamelCase  )					->		Any:
				UpperCamelCase_:							Optional[Any]		=  []

				# stem
				# fmt: off
				rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight""")  )
				rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias""")  )
				rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight""")  )
				rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.model.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.0.body.layers.{i}.blocks.{j}.norm1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')  )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')  )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')  )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')  )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')  )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')  )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')  )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')  )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')  )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')  )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')  )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')  )

								if i < 3:
												rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.reduction.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.reduction.weight''')  )
												rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.weight''')  )
												rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.bias''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.bias''')  )

				rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight""")  )
				rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias""")  )
				rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight""")  )
				rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias""")  )
				rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight""")  )
				rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias""")  )

				# transformer encoder
				for i in range(config.encoder_layers  ):
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')  )
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')  )
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', F'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')  )
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', F'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')  )
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', F'''model.encoder.layers.{i}.self_attn.value_proj.weight''')  )
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', F'''model.encoder.layers.{i}.self_attn.value_proj.bias''')  )
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', F'''model.encoder.layers.{i}.self_attn.output_proj.weight''')  )
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', F'''model.encoder.layers.{i}.self_attn.output_proj.bias''')  )
								rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.weight''', F'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')  )
								rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')  )
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''model.encoder.layers.{i}.fc1.weight''')  )
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''model.encoder.layers.{i}.fc1.bias''')  )
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''model.encoder.layers.{i}.fc2.weight''')  )
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''model.encoder.layers.{i}.fc2.bias''')  )
								rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''model.encoder.layers.{i}.final_layer_norm.weight''')  )
								rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''model.encoder.layers.{i}.final_layer_norm.bias''')  )

				# transformer decoder
				for i in range(config.decoder_layers  ):
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')  )
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')  )
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')  )
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')  )
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')  )
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')  )
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')  )
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')  )
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.weight''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')  )
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')  )
								rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''model.decoder.layers.{i}.self_attn.out_proj.weight''')  )
								rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''model.decoder.layers.{i}.self_attn.out_proj.bias''')  )
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.weight''', F'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')  )
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.bias''', F'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')  )
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''model.decoder.layers.{i}.fc1.weight''')  )
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''model.decoder.layers.{i}.fc1.bias''')  )
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''model.decoder.layers.{i}.fc2.weight''')  )
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''model.decoder.layers.{i}.fc2.bias''')  )
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''model.decoder.layers.{i}.final_layer_norm.weight''')  )
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''model.decoder.layers.{i}.final_layer_norm.bias''')  )

				# fmt: on

				return rename_keys







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Optional[int]:
				UpperCamelCase_:							List[str]		=  dct.pop(lowerCamelCase  )
				UpperCamelCase_:							str		=  val







def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				UpperCamelCase_:							Dict		=  [int(backbone_config.embed_dim * 2**i  ) for i in range(len(backbone_config.depths  )  )]
				for i in range(len(backbone_config.depths  )  ):
								UpperCamelCase_:							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)
												UpperCamelCase_:							Any		=  state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'''  )
												UpperCamelCase_:							Optional[Any]		=  state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'''  )
												# next, add query, keys and values (in that order) to the state dict
												UpperCamelCase_:							Optional[Any]		=  in_proj_weight[:dim, :]
												UpperCamelCase_:							Union[str, Any]		=  in_proj_bias[: dim]
												UpperCamelCase_:							int		=  in_proj_weight[
												    dim : dim * 2, :
												]
												UpperCamelCase_:							int		=  in_proj_bias[
												    dim : dim * 2
												]
												UpperCamelCase_:							int		=  in_proj_weight[
												    -dim :, :
												]
												UpperCamelCase_:							Optional[int]		=  in_proj_bias[-dim :]
												# fmt: on







def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		List[str]:
				# transformer decoder self-attention layers
				UpperCamelCase_:							List[str]		=  config.d_model
				for i in range(config.decoder_layers  ):
								# read in weights + bias of input projection layer of self-attention
								UpperCamelCase_:							Union[str, Any]		=  state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''  )
								UpperCamelCase_:							Any		=  state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''  )
								# next, add query, keys and values (in that order) to the state dict
								UpperCamelCase_:							Tuple		=  in_proj_weight[:hidden_size, :]
								UpperCamelCase_:							Tuple		=  in_proj_bias[:hidden_size]
								UpperCamelCase_:							List[Any]		=  in_proj_weight[
								    hidden_size : hidden_size * 2, :
								]
								UpperCamelCase_:							List[Any]		=  in_proj_bias[hidden_size : hidden_size * 2]
								UpperCamelCase_:							Union[str, Any]		=  in_proj_weight[-hidden_size:, :]
								UpperCamelCase_:							Optional[Any]		=  in_proj_bias[-hidden_size:]







def     A__   (  )					->		Optional[Any]:
				UpperCamelCase_:							Tuple		=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
				UpperCamelCase_:							Any		=  Image.open(requests.get(lowerCamelCase       ,      stream=lowerCamelCase  ).raw  )

				return im







@torch.no_grad()
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Optional[Any]:
				UpperCamelCase_:							Optional[int]		=  get_deta_config(lowerCamelCase  )

				# load original state dict
				if model_name == "deta-swin-large":
								UpperCamelCase_:							List[str]		=  hf_hub_download(repo_id="""nielsr/deta-checkpoints"""       ,      filename="""adet_swin_ft.pth"""  )
				elif model_name == "deta-swin-large-o365":
								UpperCamelCase_:							Any		=  hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365"""       ,      filename="""deta_swin_pt_o365.pth"""  )
				else:
								raise ValueError(F'''Model name {model_name} not supported'''  )

				UpperCamelCase_:							Any		=  torch.load(lowerCamelCase       ,      map_location="""cpu"""  )["""model"""]

				# original state dict
				for name, param in state_dict.items():
								print(lowerCamelCase       ,      param.shape  )

				# rename keys
				UpperCamelCase_:							Union[str, Any]		=  create_rename_keys(lowerCamelCase  )
				for src, dest in rename_keys:
								rename_key(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )
				read_in_swin_q_k_v(lowerCamelCase       ,      config.backbone_config  )
				read_in_decoder_q_k_v(lowerCamelCase       ,      lowerCamelCase  )

				# fix some prefixes
				for key in state_dict.copy().keys():
								if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
												UpperCamelCase_:							List[Any]		=  state_dict.pop(lowerCamelCase  )
												UpperCamelCase_:							Any		=  val
								if "input_proj" in key:
												UpperCamelCase_:							Dict		=  state_dict.pop(lowerCamelCase  )
												UpperCamelCase_:							Optional[int]		=  val
								if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
												UpperCamelCase_:							List[str]		=  state_dict.pop(lowerCamelCase  )
												UpperCamelCase_:							Dict		=  val

    # finally, create HuggingFace model and load state dict
				UpperCamelCase_:							Tuple		=  DetaForObjectDetection(lowerCamelCase  )
				model.load_state_dict(lowerCamelCase  )
				model.eval()

				UpperCamelCase_:							Union[str, Any]		=  """cuda""" if torch.cuda.is_available() else """cpu"""
				model.to(lowerCamelCase  )

				# load image processor
				UpperCamelCase_:							Optional[Any]		=  DetaImageProcessor(format="""coco_detection"""  )

				# verify our conversion on image
				UpperCamelCase_:							Union[str, Any]		=  prepare_img()
				UpperCamelCase_:							Dict		=  processor(images=lowerCamelCase       ,      return_tensors="""pt"""  )
				UpperCamelCase_:							str		=  encoding["""pixel_values"""]
				UpperCamelCase_:							Tuple		=  model(pixel_values.to(lowerCamelCase  )  )

				# verify logits
				print("""Logits:"""       ,      outputs.logits[0, :3, :3]  )
				print("""Boxes:"""       ,      outputs.pred_boxes[0, :3, :3]  )
				if model_name == "deta-swin-large":
								UpperCamelCase_:							Optional[Any]		=  torch.tensor(
								    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]  )
								UpperCamelCase_:							Dict		=  torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]  )
				elif model_name == "deta-swin-large-o365":
								UpperCamelCase_:							Tuple		=  torch.tensor(
								    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]  )
								UpperCamelCase_:							str		=  torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]  )
				assert torch.allclose(outputs.logits[0, :3, :3]       ,      expected_logits.to(lowerCamelCase  )       ,      atol=1E-4  )
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,      expected_boxes.to(lowerCamelCase  )       ,      atol=1E-4  )
				print("""Everything ok!"""  )

				if pytorch_dump_folder_path:
								# Save model and processor
								logger.info(F'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''  )
								Path(lowerCamelCase  ).mkdir(exist_ok=lowerCamelCase  )
								model.save_pretrained(lowerCamelCase  )
								processor.save_pretrained(lowerCamelCase  )

				# Push to hub
				if push_to_hub:
								print("""Pushing model and processor to hub..."""  )
								model.push_to_hub(F'''jozhang97/{model_name}'''  )
								processor.push_to_hub(F'''jozhang97/{model_name}'''  )


if __name__ == "__main__":
							lowerCamelCase_  :	List[str] 				=					argparse.ArgumentParser()

							parser.add_argument(
							    """--model_name""",
							    type=str,
							    default="""deta-swin-large""",
							    choices=["""deta-swin-large""", """deta-swin-large-o365"""],
							    help="""Name of the 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 or not to push the converted model to the 🤗 hub."""
							)
							lowerCamelCase_  :	Dict 				=					parser.parse_args()
							convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching







def     A__   (  )					->		Union[str, Any]:
				import os as original_os
				from os import path as original_path
				from os import rename as original_rename
				from os.path import dirname as original_dirname
				from os.path import join as original_join

				assert _test_patching.os is original_os
				assert _test_patching.path is original_path
				assert _test_patching.join is original_join

				assert _test_patching.renamed_os is original_os
				assert _test_patching.renamed_path is original_path
				assert _test_patching.renamed_join is original_join

				UpperCamelCase_:							Optional[int]		=  """__test_patch_submodule_mock__"""
				with patch_submodule(_test_patching       ,      """os.path.join"""       ,      lowerCamelCase  ):
								# Every way to access os.path.join must be patched, and the rest must stay untouched

								# check os.path.join
								assert isinstance(_test_patching.os       ,      _PatchedModuleObj  )
								assert isinstance(_test_patching.os.path       ,      _PatchedModuleObj  )
								assert _test_patching.os.path.join is mock

								# check path.join
								assert isinstance(_test_patching.path       ,      _PatchedModuleObj  )
								assert _test_patching.path.join is mock

								# check join
								assert _test_patching.join is mock

								# check that the other attributes are untouched
								assert _test_patching.os.rename is original_rename
								assert _test_patching.path.dirname is original_dirname
								assert _test_patching.os.path.dirname is original_dirname

								# Even renamed modules or objects must be patched

								# check renamed_os.path.join
								assert isinstance(_test_patching.renamed_os       ,      _PatchedModuleObj  )
								assert isinstance(_test_patching.renamed_os.path       ,      _PatchedModuleObj  )
								assert _test_patching.renamed_os.path.join is mock

								# check renamed_path.join
								assert isinstance(_test_patching.renamed_path       ,      _PatchedModuleObj  )
								assert _test_patching.renamed_path.join is mock

								# check renamed_join
								assert _test_patching.renamed_join is mock

								# check that the other attributes are untouched
								assert _test_patching.renamed_os.rename is original_rename
								assert _test_patching.renamed_path.dirname is original_dirname
								assert _test_patching.renamed_os.path.dirname is original_dirname

				# check that everthing is back to normal when the patch is over

				assert _test_patching.os is original_os
				assert _test_patching.path is original_path
				assert _test_patching.join is original_join

				assert _test_patching.renamed_os is original_os
				assert _test_patching.renamed_path is original_path
				assert _test_patching.renamed_join is original_join







def     A__   (  )					->		Union[str, Any]:
				assert _test_patching.open is open

				UpperCamelCase_:							List[Any]		=  """__test_patch_submodule_builtin_mock__"""
				# _test_patching has "open" in its globals
				assert _test_patching.open is open
				with patch_submodule(_test_patching       ,      """open"""       ,      lowerCamelCase  ):
								assert _test_patching.open is mock

				# check that everthing is back to normal when the patch is over

				assert _test_patching.open is open







def     A__   (  )					->		Optional[Any]:
				# pandas.read_csv is not present in _test_patching
				UpperCamelCase_:							Optional[Any]		=  """__test_patch_submodule_missing_mock__"""
				with patch_submodule(_test_patching       ,      """pandas.read_csv"""       ,      lowerCamelCase  ):
								pass







def     A__   (  )					->		Any:
				# builtin should always be mocked even if they're not in the globals
				# in case they're loaded at one point
				UpperCamelCase_:							List[Any]		=  """__test_patch_submodule_missing_builtin_mock__"""
				# _test_patching doesn't have "len" in its globals
				assert getattr(_test_patching       ,      """len"""       ,      lowerCamelCase  ) is None
				with patch_submodule(_test_patching       ,      """len"""       ,      lowerCamelCase  ):
								assert _test_patching.len is mock
				assert _test_patching.len is len







def     A__   (  )					->		Any:
				UpperCamelCase_:							Dict		=  """__test_patch_submodule_start_and_stop_mock__"""
				UpperCamelCase_:							List[str]		=  patch_submodule(_test_patching       ,      """open"""       ,      lowerCamelCase  )
				assert _test_patching.open is open
				patch.start()
				assert _test_patching.open is mock
				patch.stop()
				assert _test_patching.open is open







def     A__   (  )					->		List[str]:
				from os import rename as original_rename
				from os.path import dirname as original_dirname
				from os.path import join as original_join

				UpperCamelCase_:							Optional[Any]		=  """__test_patch_submodule_successive_join__"""
				UpperCamelCase_:							Any		=  """__test_patch_submodule_successive_dirname__"""
				UpperCamelCase_:							Dict		=  """__test_patch_submodule_successive_rename__"""
				assert _test_patching.os.path.join is original_join
				assert _test_patching.os.path.dirname is original_dirname
				assert _test_patching.os.rename is original_rename

				with patch_submodule(_test_patching       ,      """os.path.join"""       ,      lowerCamelCase  ):
								with patch_submodule(_test_patching       ,      """os.rename"""       ,      lowerCamelCase  ):
												with patch_submodule(_test_patching       ,      """os.path.dirname"""       ,      lowerCamelCase  ):
																assert _test_patching.os.path.join is mock_join
																assert _test_patching.os.path.dirname is mock_dirname
																assert _test_patching.os.rename is mock_rename

    # try another order
				with patch_submodule(_test_patching       ,      """os.rename"""       ,      lowerCamelCase  ):
								with patch_submodule(_test_patching       ,      """os.path.join"""       ,      lowerCamelCase  ):
												with patch_submodule(_test_patching       ,      """os.path.dirname"""       ,      lowerCamelCase  ):
																assert _test_patching.os.path.join is mock_join
																assert _test_patching.os.path.dirname is mock_dirname
																assert _test_patching.os.rename is mock_rename

				assert _test_patching.os.path.join is original_join
				assert _test_patching.os.path.dirname is original_dirname
				assert _test_patching.os.rename is original_rename







def     A__   (  )					->		Union[str, Any]:
				UpperCamelCase_:							Dict		=  """__test_patch_submodule_doesnt_exist_mock__"""
				with patch_submodule(_test_patching       ,      """__module_that_doesn_exist__.__attribute_that_doesn_exist__"""       ,      lowerCamelCase  ):
								pass
				with patch_submodule(_test_patching       ,      """os.__attribute_that_doesn_exist__"""       ,      lowerCamelCase  ):
								pass






