import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


_lowerCamelCase   :				Any     			=						'''http://www.mocksite.com/file1.txt'''
_lowerCamelCase   :				Tuple     			=						'''"text": ["foo", "foo"]'''
_lowerCamelCase   :				List[str]     			=						'''6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'''


class   lowercase      :
							lowercase__  :    int									=							200
							lowercase__  :    Any									=							{"""Content-Length""": """100"""}
							lowercase__  :    Optional[int]									=							{}




							def 		__snake_case(  self				:   List[str]		,							**_UpperCamelCase				:   Dict )   ->			Dict:



												'''simple docstring'''

												return [bytes(_UpperCamelCase		,							"utf-8" )]





def 		__lowerCamelCase							(*UpperCAmelCase__       :							Optional[int] ,	**UpperCAmelCase__       :							Optional[Any]					):
					return MockResponse()





@pytest.mark.parametrize("urls_type" ,	[str, list, dict]					)
def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[Any]					):
					import requests

					monkeypatch.setattr(UpperCAmelCase__ ,	"request" ,	UpperCAmelCase__					)

					SCREAMING_SNAKE_CASE           =    URL
					if issubclass(UpperCAmelCase__ ,	UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    url
					elif issubclass(UpperCAmelCase__ ,	UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    [url]
					elif issubclass(UpperCAmelCase__ ,	UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    {"train": url}
					SCREAMING_SNAKE_CASE           =    "dummy"
					SCREAMING_SNAKE_CASE           =    "downloads"
					SCREAMING_SNAKE_CASE           =    tmp_path
					SCREAMING_SNAKE_CASE           =    DownloadConfig(
					    cache_dir=os.path.join(UpperCAmelCase__ ,	UpperCAmelCase__					) ,	use_etag=UpperCAmelCase__ ,	)
					SCREAMING_SNAKE_CASE           =    DownloadManager(dataset_name=UpperCAmelCase__ ,	download_config=UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    dl_manager.download(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    urls
					for downloaded_paths in [downloaded_paths]:
										if isinstance(UpperCAmelCase__ ,	UpperCAmelCase__					):
															SCREAMING_SNAKE_CASE           =    [downloaded_paths]
															SCREAMING_SNAKE_CASE           =    [urls]
										elif isinstance(UpperCAmelCase__ ,	UpperCAmelCase__					):
															assert "train" in downloaded_paths.keys()
															SCREAMING_SNAKE_CASE           =    downloaded_paths.values()
															SCREAMING_SNAKE_CASE           =    urls.values()
										assert downloaded_paths
										for downloaded_path, input_url in zip(UpperCAmelCase__ ,	UpperCAmelCase__					):
															assert downloaded_path == dl_manager.downloaded_paths[input_url]
															SCREAMING_SNAKE_CASE           =    Path(UpperCAmelCase__					)
															SCREAMING_SNAKE_CASE           =    downloaded_path.parts
															assert parts[-1] == HASH
															assert parts[-2] == cache_subdir
															assert downloaded_path.exists()
															SCREAMING_SNAKE_CASE           =    downloaded_path.read_text()
															assert content == CONTENT
															SCREAMING_SNAKE_CASE           =    downloaded_path.with_suffix(".json"					)
															assert metadata_downloaded_path.exists()
															SCREAMING_SNAKE_CASE           =    json.loads(metadata_downloaded_path.read_text()					)
															assert metadata_content == {"url": URL, "etag": None}





@pytest.mark.parametrize("paths_type" ,	[str, list, dict]					)
def 		__lowerCamelCase							(UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							List[Any]					):
					SCREAMING_SNAKE_CASE           =    str(UpperCAmelCase__					)
					if issubclass(UpperCAmelCase__ ,	UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    filename
					elif issubclass(UpperCAmelCase__ ,	UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    [filename]
					elif issubclass(UpperCAmelCase__ ,	UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    {"train": filename}
					SCREAMING_SNAKE_CASE           =    "dummy"
					SCREAMING_SNAKE_CASE           =    xz_file.parent
					SCREAMING_SNAKE_CASE           =    "extracted"
					SCREAMING_SNAKE_CASE           =    DownloadConfig(
					    cache_dir=UpperCAmelCase__ ,	use_etag=UpperCAmelCase__ ,	)
					SCREAMING_SNAKE_CASE           =    DownloadManager(dataset_name=UpperCAmelCase__ ,	download_config=UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    dl_manager.extract(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    paths
					for extracted_paths in [extracted_paths]:
										if isinstance(UpperCAmelCase__ ,	UpperCAmelCase__					):
															SCREAMING_SNAKE_CASE           =    [extracted_paths]
															SCREAMING_SNAKE_CASE           =    [paths]
										elif isinstance(UpperCAmelCase__ ,	UpperCAmelCase__					):
															assert "train" in extracted_paths.keys()
															SCREAMING_SNAKE_CASE           =    extracted_paths.values()
															SCREAMING_SNAKE_CASE           =    paths.values()
										assert extracted_paths
										for extracted_path, input_path in zip(UpperCAmelCase__ ,	UpperCAmelCase__					):
															assert extracted_path == dl_manager.extracted_paths[input_path]
															SCREAMING_SNAKE_CASE           =    Path(UpperCAmelCase__					)
															SCREAMING_SNAKE_CASE           =    extracted_path.parts
															assert parts[-1] == hash_url_to_filename(UpperCAmelCase__ ,	etag=UpperCAmelCase__					)
															assert parts[-2] == extracted_subdir
															assert extracted_path.exists()
															SCREAMING_SNAKE_CASE           =    extracted_path.read_text()
															SCREAMING_SNAKE_CASE           =    text_file.read_text()
															assert extracted_file_content == expected_file_content





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							Tuple					):
					assert path.endswith(".jsonl"					)
					for num_items, line in enumerate(UpperCAmelCase__ ,	start=1					):
										SCREAMING_SNAKE_CASE           =    json.loads(line.decode("utf-8"					)					)
										assert item.keys() == {"col_1", "col_2", "col_3"}
					assert num_items == 4





@pytest.mark.parametrize("archive_jsonl" ,	["tar_jsonl_path", "zip_jsonl_path"]					)
def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							str					):
					SCREAMING_SNAKE_CASE           =    request.getfixturevalue(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    DownloadManager()
					for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(UpperCAmelCase__					) ,	start=1					):
										_test_jsonl(UpperCAmelCase__ ,	UpperCAmelCase__					)
					assert num_jsonl == 2





@pytest.mark.parametrize("archive_nested_jsonl" ,	["tar_nested_jsonl_path", "zip_nested_jsonl_path"]					)
def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							Union[str, Any]					):
					SCREAMING_SNAKE_CASE           =    request.getfixturevalue(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    DownloadManager()
					for num_tar, (path, file) in enumerate(dl_manager.iter_archive(UpperCAmelCase__					) ,	start=1					):
										for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(UpperCAmelCase__					) ,	start=1					):
															_test_jsonl(UpperCAmelCase__ ,	UpperCAmelCase__					)
					assert num_tar == 1
					assert num_jsonl == 2





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[int]					):
					SCREAMING_SNAKE_CASE           =    DownloadManager()
					for num_file, file in enumerate(dl_manager.iter_files(UpperCAmelCase__					) ,	start=1					):
										assert os.path.basename(UpperCAmelCase__					) == ("test.txt" if num_file == 1 else "train.txt")
					assert num_file == 2





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   :				Any     			=						logging.get_logger(__name__)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Dict					):
					SCREAMING_SNAKE_CASE           =    SwinConfig(
					    embed_dim=1_9_2 ,	depths=(2, 2, 1_8, 2) ,	num_heads=(6, 1_2, 2_4, 4_8) ,	window_size=1_2 ,	out_features=["stage2", "stage3", "stage4"] ,	)

					SCREAMING_SNAKE_CASE           =    DetaConfig(
					    backbone_config=UpperCAmelCase__ ,	num_queries=9_0_0 ,	encoder_ffn_dim=2_0_4_8 ,	decoder_ffn_dim=2_0_4_8 ,	num_feature_levels=5 ,	assign_first_stage=UpperCAmelCase__ ,	with_box_refine=UpperCAmelCase__ ,	two_stage=UpperCAmelCase__ ,	)

					# set labels
					SCREAMING_SNAKE_CASE           =    "huggingface/label-files"
					if "o365" in model_name:
										SCREAMING_SNAKE_CASE           =    3_6_6
										SCREAMING_SNAKE_CASE           =    "object365-id2label.json"
					else:
										SCREAMING_SNAKE_CASE           =    9_1
										SCREAMING_SNAKE_CASE           =    "coco-detection-id2label.json"

					SCREAMING_SNAKE_CASE           =    num_labels
					SCREAMING_SNAKE_CASE           =    json.load(open(cached_download(hf_hub_url(UpperCAmelCase__ ,	UpperCAmelCase__ ,	repo_type="dataset"					)					) ,	"r"					)					)
					SCREAMING_SNAKE_CASE           =    {int(UpperCAmelCase__					): v for k, v in idalabel.items()}
					SCREAMING_SNAKE_CASE           =    idalabel
					SCREAMING_SNAKE_CASE           =    {v: k for k, v in idalabel.items()}

					return config





def 		__lowerCamelCase							(UpperCAmelCase__       :							Tuple					):
					SCREAMING_SNAKE_CASE           =    []

					# 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 		__lowerCamelCase							(UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							List[Any]					):
					SCREAMING_SNAKE_CASE           =    dct.pop(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    val





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							Tuple					):
					SCREAMING_SNAKE_CASE           =    [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           =    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           =    state_dict.pop(F"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"					)
															SCREAMING_SNAKE_CASE           =    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
															SCREAMING_SNAKE_CASE           =    in_proj_weight[:dim, :]
															SCREAMING_SNAKE_CASE           =    in_proj_bias[: dim]
															SCREAMING_SNAKE_CASE           =    in_proj_weight[
															    dim : dim * 2, :
															]
															SCREAMING_SNAKE_CASE           =    in_proj_bias[
															    dim : dim * 2
															]
															SCREAMING_SNAKE_CASE           =    in_proj_weight[
															    -dim :, :
															]
															SCREAMING_SNAKE_CASE           =    in_proj_bias[-dim :]
															# fmt: on





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							List[str]					):
					# transformer decoder self-attention layers
					SCREAMING_SNAKE_CASE           =    config.d_model
					for i in range(config.decoder_layers					):
										# read in weights + bias of input projection layer of self-attention
										SCREAMING_SNAKE_CASE           =    state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_weight"					)
										SCREAMING_SNAKE_CASE           =    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
										SCREAMING_SNAKE_CASE           =    in_proj_weight[:hidden_size, :]
										SCREAMING_SNAKE_CASE           =    in_proj_bias[:hidden_size]
										SCREAMING_SNAKE_CASE           =    in_proj_weight[
										    hidden_size : hidden_size * 2, :
										]
										SCREAMING_SNAKE_CASE           =    in_proj_bias[hidden_size : hidden_size * 2]
										SCREAMING_SNAKE_CASE           =    in_proj_weight[-hidden_size:, :]
										SCREAMING_SNAKE_CASE           =    in_proj_bias[-hidden_size:]





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE           =    "http://images.cocodataset.org/val2017/000000039769.jpg"
					SCREAMING_SNAKE_CASE           =    Image.open(requests.get(UpperCAmelCase__ ,	stream=UpperCAmelCase__					).raw					)

					return im





@torch.no_grad()
def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							List[Any]					):
					SCREAMING_SNAKE_CASE           =    get_deta_config(UpperCAmelCase__					)

					# load original state dict
					if model_name == "deta-swin-large":
										SCREAMING_SNAKE_CASE           =    hf_hub_download(repo_id="nielsr/deta-checkpoints" ,	filename="adet_swin_ft.pth"					)
					elif model_name == "deta-swin-large-o365":
										SCREAMING_SNAKE_CASE           =    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"					)

					SCREAMING_SNAKE_CASE           =    torch.load(UpperCAmelCase__ ,	map_location="cpu"					)["model"]

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

					# rename keys
					SCREAMING_SNAKE_CASE           =    create_rename_keys(UpperCAmelCase__					)
					for src, dest in rename_keys:
										rename_key(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
					read_in_swin_q_k_v(UpperCAmelCase__ ,	config.backbone_config					)
					read_in_decoder_q_k_v(UpperCAmelCase__ ,	UpperCAmelCase__					)

					# fix some prefixes
					for key in state_dict.copy().keys():
										if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
															SCREAMING_SNAKE_CASE           =    state_dict.pop(UpperCAmelCase__					)
															SCREAMING_SNAKE_CASE           =    val
										if "input_proj" in key:
															SCREAMING_SNAKE_CASE           =    state_dict.pop(UpperCAmelCase__					)
															SCREAMING_SNAKE_CASE           =    val
										if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
															SCREAMING_SNAKE_CASE           =    state_dict.pop(UpperCAmelCase__					)
															SCREAMING_SNAKE_CASE           =    val

    # finally, create HuggingFace model and load state dict
					SCREAMING_SNAKE_CASE           =    DetaForObjectDetection(UpperCAmelCase__					)
					model.load_state_dict(UpperCAmelCase__					)
					model.eval()

					SCREAMING_SNAKE_CASE           =    "cuda" if torch.cuda.is_available() else "cpu"
					model.to(UpperCAmelCase__					)

					# load image processor
					SCREAMING_SNAKE_CASE           =    DetaImageProcessor(format="coco_detection"					)

					# verify our conversion on image
					SCREAMING_SNAKE_CASE           =    prepare_img()
					SCREAMING_SNAKE_CASE           =    processor(images=UpperCAmelCase__ ,	return_tensors="pt"					)
					SCREAMING_SNAKE_CASE           =    encoding["pixel_values"]
					SCREAMING_SNAKE_CASE           =    model(pixel_values.to(UpperCAmelCase__					)					)

					# verify logits
					print("Logits:" ,	outputs.logits[0, :3, :3]					)
					print("Boxes:" ,	outputs.pred_boxes[0, :3, :3]					)
					if model_name == "deta-swin-large":
										SCREAMING_SNAKE_CASE           =    torch.tensor(
										    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]					)
										SCREAMING_SNAKE_CASE           =    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":
										SCREAMING_SNAKE_CASE           =    torch.tensor(
										    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]					)
										SCREAMING_SNAKE_CASE           =    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(UpperCAmelCase__					) ,	atol=1e-4					)
					assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,	expected_boxes.to(UpperCAmelCase__					) ,	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(UpperCAmelCase__					).mkdir(exist_ok=UpperCAmelCase__					)
										model.save_pretrained(UpperCAmelCase__					)
										processor.save_pretrained(UpperCAmelCase__					)

					# 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   :				Optional[Any]     			=						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   :				Any     			=						parser.parse_args()
							convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




