






"""simple docstring"""


import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
   from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
   _UpperCamelCase   :						int     						= None

if is_ray_available():
   import ray  # noqa: E402 # isort:skip
   from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
   _UpperCamelCase   :						Any     						= None
   _UpperCamelCase   :						Tuple     						= None
   _UpperCamelCase   :						List[Any]     						= None




def 				a (     UpperCamelCase_							:				Optional[int]		)  ->		Optional[int]:
 if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
  __A	:       List[Any]       =	unittest.skip('test requires Datasets, Faiss, psutil'		)(lowerCAmelCase__		)
 return test_case






@require_distributed_retrieval
class __a   (   snake_case__				):



  def 						A_				(self  :		Dict   )		->							int:
   __A	:       Optional[Any]       =	tempfile.mkdtemp()
   __A	:       Tuple       =	8

   # DPR tok
   __A	:       Any       =	[
       '[UNK]',
       '[CLS]',
       '[SEP]',
       '[PAD]',
       '[MASK]',
       'want',
       '##want',
       '##ed',
       'wa',
       'un',
       'runn',
       '##ing',
       ',',
       'low',
       'lowest',
   ]
   __A	:       List[str]       =	os.path.join(self.tmpdirname				,			'dpr_tokenizer'   )
   os.makedirs(lowercase_				,			exist_ok=lowercase_   )
   __A	:       int       =	os.path.join(lowercase_				,			DPR_VOCAB_FILES_NAMES['vocab_file']   )
   with open(self.vocab_file				,			'w'				,			encoding='utf-8'   ) as vocab_writer:
    vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]   )   )

   # BART tok
   __A	:       Optional[Any]       =	[
       'l',
       'o',
       'w',
       'e',
       'r',
       's',
       't',
       'i',
       'd',
       'n',
       '\u0120',
       '\u0120l',
       '\u0120n',
       '\u0120lo',
       '\u0120low',
       'er',
       '\u0120lowest',
       '\u0120newer',
       '\u0120wider',
       '<unk>',
   ]
   __A	:       Any       =	dict(zip(lowercase_				,			range(len(lowercase_   )   )   )   )
   __A	:       str       =	['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
   __A	:       Union[str, Any]       =	{'unk_token': '<unk>'}

   __A	:       int       =	os.path.join(self.tmpdirname				,			'bart_tokenizer'   )
   os.makedirs(lowercase_				,			exist_ok=lowercase_   )
   __A	:       List[Any]       =	os.path.join(lowercase_				,			BART_VOCAB_FILES_NAMES['vocab_file']   )
   __A	:       Tuple       =	os.path.join(lowercase_				,			BART_VOCAB_FILES_NAMES['merges_file']   )
   with open(self.vocab_file				,			'w'				,			encoding='utf-8'   ) as fp:
    fp.write(json.dumps(lowercase_   ) + '\n'   )
   with open(self.merges_file				,			'w'				,			encoding='utf-8'   ) as fp:
    fp.write('\n'.join(lowercase_   )   )



  def 						A_				(self  :		List[Any]   )		->							str:
   return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname				,			'dpr_tokenizer'   )   )



  def 						A_				(self  :		Union[str, Any]   )		->							Optional[Any]:
   return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname				,			'bart_tokenizer'   )   )



  def 						A_				(self  :		str   )		->							Optional[int]:
   shutil.rmtree(self.tmpdirname   )



  def 						A_				(self  :		Union[str, Any]   )		->							Dict:
   __A	:       Optional[int]       =	Dataset.from_dict(
       {
           'id': ['0', '1'],
           'text': ['foo', 'bar'],
           'title': ['Foo', 'Bar'],
           'embeddings': [np.ones(self.retrieval_vector_size   ), 2 * np.ones(self.retrieval_vector_size   )],
       }   )
   dataset.add_faiss_index('embeddings'				,			string_factory='Flat'				,			metric_type=faiss.METRIC_INNER_PRODUCT   )
   return dataset



  def 						A_				(self  :		Any				,			lowercase_  :		bool				,			lowercase_  :		Optional[Any]=12_345   )		->							int:
   __A	:       Optional[Any]       =	self.get_dummy_dataset()
   __A	:       Optional[int]       =	RagConfig(
       retrieval_vector_size=self.retrieval_vector_size				,			question_encoder=DPRConfig().to_dict()				,			generator=BartConfig().to_dict()				,			)
   with patch('transformers.models.rag.retrieval_rag.load_dataset'   ) as mock_load_dataset:
    __A	:       Optional[Any]       =	dataset
    __A	:       List[str]       =	RagPyTorchDistributedRetriever(
        lowercase_				,			question_encoder_tokenizer=self.get_dpr_tokenizer()				,			generator_tokenizer=self.get_bart_tokenizer()				,			)
    if init_retrieval:
     retriever.init_retrieval(lowercase_   )
   return retriever



  def 						A_				(self  :		Dict				,			lowercase_  :		bool   )		->							str:
   # Have to run in local mode because sys.path modifications at top of
   # file are not propogated to remote workers.
   # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
   ray.init(local_mode=lowercase_   )
   __A	:       Optional[int]       =	RagConfig(
       retrieval_vector_size=self.retrieval_vector_size				,			question_encoder=DPRConfig().to_dict()				,			generator=BartConfig().to_dict()				,			)
   __A	:       Any       =	ray.remote(lowercase_   )
   __A	:       int       =	[remote_cls.remote() for _ in range(1   )]
   with patch('transformers.models.rag.retrieval_rag.load_dataset'   ) as mock_load_dataset:
    __A	:       int       =	self.get_dummy_dataset()
    __A	:       Tuple       =	RagRayDistributedRetriever(
        lowercase_				,			question_encoder_tokenizer=self.get_dpr_tokenizer()				,			generator_tokenizer=self.get_bart_tokenizer()				,			retrieval_workers=lowercase_				,			)
    if init_retrieval:
     retriever.init_retrieval()
   return retriever



  def 						A_				(self  :		List[str]				,			lowercase_  :		bool				,			lowercase_  :		bool				,			lowercase_  :		List[Any]=12_345   )		->							Tuple:
   __A	:       int       =	self.get_dummy_dataset()
   __A	:       Any       =	RagConfig(
       retrieval_vector_size=self.retrieval_vector_size				,			question_encoder=DPRConfig().to_dict()				,			generator=BartConfig().to_dict()				,			index_name='custom'				,			)
   if from_disk:
    __A	:       Any       =	os.path.join(self.tmpdirname				,			'dataset'   )
    __A	:       str       =	os.path.join(self.tmpdirname				,			'index.faiss'   )
    dataset.get_index('embeddings'   ).save(os.path.join(self.tmpdirname				,			'index.faiss'   )   )
    dataset.drop_index('embeddings'   )
    dataset.save_to_disk(os.path.join(self.tmpdirname				,			'dataset'   )   )
    del dataset
    __A	:       str       =	RagPyTorchDistributedRetriever(
        lowercase_				,			question_encoder_tokenizer=self.get_dpr_tokenizer()				,			generator_tokenizer=self.get_bart_tokenizer()				,			)
   else:
    __A	:       List[Any]       =	RagPyTorchDistributedRetriever(
        lowercase_				,			question_encoder_tokenizer=self.get_dpr_tokenizer()				,			generator_tokenizer=self.get_bart_tokenizer()				,			index=CustomHFIndex(config.retrieval_vector_size				,			lowercase_   )				,			)
   if init_retrieval:
    retriever.init_retrieval(lowercase_   )
   return retriever



  def 						A_				(self  :		Dict				,			lowercase_  :		bool				,			lowercase_  :		bool   )		->							Any:
   # Have to run in local mode because sys.path modifications at top of
   # file are not propogated to remote workers.
   # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
   ray.init(local_mode=lowercase_   )
   __A	:       Dict       =	self.get_dummy_dataset()
   __A	:       Dict       =	RagConfig(
       retrieval_vector_size=self.retrieval_vector_size				,			question_encoder=DPRConfig().to_dict()				,			generator=BartConfig().to_dict()				,			index_name='custom'				,			)
   __A	:       Any       =	ray.remote(lowercase_   )
   __A	:       List[str]       =	[remote_cls.remote() for _ in range(1   )]
   if from_disk:
    __A	:       int       =	os.path.join(self.tmpdirname				,			'dataset'   )
    __A	:       Optional[int]       =	os.path.join(self.tmpdirname				,			'index.faiss'   )
    dataset.get_index('embeddings'   ).save(os.path.join(self.tmpdirname				,			'index.faiss'   )   )
    dataset.drop_index('embeddings'   )
    dataset.save_to_disk(os.path.join(self.tmpdirname				,			'dataset'   )   )
    del dataset
    __A	:       List[Any]       =	RagRayDistributedRetriever(
        lowercase_				,			question_encoder_tokenizer=self.get_dpr_tokenizer()				,			generator_tokenizer=self.get_bart_tokenizer()				,			retrieval_workers=lowercase_				,			index=CustomHFIndex.load_from_disk(
            vector_size=config.retrieval_vector_size				,			dataset_path=config.passages_path				,			index_path=config.index_path				,			)				,			)
   else:
    __A	:       List[str]       =	RagRayDistributedRetriever(
        lowercase_				,			question_encoder_tokenizer=self.get_dpr_tokenizer()				,			generator_tokenizer=self.get_bart_tokenizer()				,			retrieval_workers=lowercase_				,			index=CustomHFIndex(config.retrieval_vector_size				,			lowercase_   )				,			)
   if init_retrieval:
    retriever.init_retrieval()
   return retriever



  def 						A_				(self  :		Any				,			lowercase_  :		RagRetriever				,			lowercase_  :		np.array				,			lowercase_  :		int   )		->							int:
   __A	:       List[Any]       =	retriever.retrieve(lowercase_				,			n_docs=lowercase_   )
   self.assertEqual(retrieved_doc_embeds.shape				,			(2, n_docs, self.retrieval_vector_size)   )
   self.assertEqual(len(lowercase_   )				,			2   )
   self.assertEqual(sorted(doc_dicts[0]   )				,			['embeddings', 'id', 'text', 'title']   )
   self.assertEqual(len(doc_dicts[0]['id']   )				,			lowercase_   )
   self.assertEqual(doc_dicts[0]['id'][0]				,			'1'   )  # max inner product is reached with second doc
   self.assertEqual(doc_dicts[1]['id'][0]				,			'0'   )  # max inner product is reached with first doc
   self.assertListEqual(doc_ids.tolist()				,			[[1], [0]]   )



  def 						A_				(self  :		int   )		->							List[str]:
   __A	:       List[Any]       =	1
   __A	:       Union[str, Any]       =	np.array(
       [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]				,			dtype=np.floataa   )

   self.distributed_retriever_check(
       self.get_dummy_pytorch_distributed_retriever(init_retrieval=lowercase_   )				,			lowercase_				,			lowercase_   )



  def 						A_				(self  :		Optional[int]   )		->							Dict:
   __A	:       Optional[Any]       =	1
   __A	:       int       =	np.array(
       [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]				,			dtype=np.floataa   )

   self.distributed_retriever_check(
       self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowercase_				,			from_disk=lowercase_   )				,			lowercase_				,			lowercase_				,			)



  def 						A_				(self  :		List[Any]   )		->							Optional[int]:
   __A	:       List[str]       =	1
   __A	:       Union[str, Any]       =	np.array(
       [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]				,			dtype=np.floataa   )

   self.distributed_retriever_check(
       self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowercase_				,			from_disk=lowercase_   )				,			lowercase_				,			lowercase_				,			)



  @require_ray
  def 						A_				(self  :		Optional[Any]   )		->							Optional[int]:
   __A	:       Optional[int]       =	1
   __A	:       Tuple       =	np.array(
       [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]				,			dtype=np.floataa   )

   self.distributed_retriever_check(
       self.get_dummy_ray_distributed_retriever(init_retrieval=lowercase_   )				,			lowercase_				,			lowercase_   )
   ray.shutdown()



  @require_ray
  def 						A_				(self  :		List[str]   )		->							List[Any]:
   __A	:       str       =	1
   __A	:       Optional[int]       =	np.array(
       [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]				,			dtype=np.floataa   )
   with self.assertRaises(lowercase_   ):
    self.distributed_retriever_check(
        self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowercase_				,			from_disk=lowercase_   )				,			lowercase_				,			lowercase_				,			)
   ray.shutdown()



  @require_ray
  def 						A_				(self  :		List[Any]   )		->							Dict:
   __A	:       int       =	1
   __A	:       int       =	np.array(
       [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]				,			dtype=np.floataa   )

   self.distributed_retriever_check(
       self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowercase_				,			from_disk=lowercase_   )				,			lowercase_				,			lowercase_   )
   ray.shutdown()













"""simple docstring"""


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
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase   :						str     						= logging.get_logger(__name__)




def 				a (     )  ->		int:
 __A	:       Dict       =	DetaConfig(
     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
 __A	:       List[Any]       =	9_1
 __A	:       Any       =	'huggingface/label-files'
 __A	:       List[Any]       =	'coco-detection-id2label.json'
 __A	:       Union[str, Any]       =	json.load(open(cached_download(hf_hub_url(UpperCamelCase_     ,	UpperCamelCase_     ,	repo_type='dataset'		)		)     ,	'r'		)		)
 __A	:       int       =	{int(UpperCamelCase_		): v for k, v in idalabel.items()}
 __A	:       Optional[int]       =	idalabel
 __A	:       Optional[int]       =	{v: k for k, v in idalabel.items()}

 return config




def 				a (     UpperCamelCase_							:				Dict		)  ->		List[str]:
 __A	:       Optional[int]       =	[]

 # stem
 # fmt: off
 rename_keys.append(('backbone.0.body.conv1.weight', 'model.backbone.model.embedder.embedder.convolution.weight')		)
 rename_keys.append(('backbone.0.body.bn1.weight', 'model.backbone.model.embedder.embedder.normalization.weight')		)
 rename_keys.append(('backbone.0.body.bn1.bias', 'model.backbone.model.embedder.embedder.normalization.bias')		)
 rename_keys.append(('backbone.0.body.bn1.running_mean', 'model.backbone.model.embedder.embedder.normalization.running_mean')		)
 rename_keys.append(('backbone.0.body.bn1.running_var', 'model.backbone.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"model.backbone.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"model.backbone.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"model.backbone.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"model.backbone.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"model.backbone.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"model.backbone.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"model.backbone.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"model.backbone.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"model.backbone.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"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
        )		)
    # 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 (     UpperCamelCase_							:				Optional[Any]     ,	UpperCamelCase_							:				List[str]     ,	UpperCamelCase_							:				List[Any]		)  ->		Dict:
 __A	:       List[str]       =	dct.pop(UpperCamelCase_		)
 __A	:       Optional[Any]       =	val




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




def 				a (     )  ->		Dict:
 __A	:       str       =	'http://images.cocodataset.org/val2017/000000039769.jpg'
 __A	:       Union[str, Any]       =	Image.open(requests.get(UpperCamelCase_     ,	stream=UpperCamelCase_		).raw		)

 return im




@torch.no_grad()
def 				a (     UpperCamelCase_							:				Union[str, Any]     ,	UpperCamelCase_							:				Tuple     ,	UpperCamelCase_							:				Optional[int]		)  ->		Optional[int]:
 __A	:       Dict       =	get_deta_config()

 # load original state dict
 if model_name == "deta-resnet-50":
  __A	:       str       =	'adet_checkpoint0011.pth'
 elif model_name == "deta-resnet-50-24-epochs":
  __A	:       Dict       =	'adet_2x_checkpoint0023.pth'
 else:
  raise ValueError(F"Model name {model_name} not supported"		)
 __A	:       Tuple       =	hf_hub_download(repo_id='nielsr/deta-checkpoints'     ,	filename=UpperCamelCase_		)
 __A	:       Dict       =	torch.load(UpperCamelCase_     ,	map_location='cpu'		)['model']

 # rename keys
 __A	:       Optional[Any]       =	create_rename_keys(UpperCamelCase_		)
 for src, dest in rename_keys:
  rename_key(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_		)
 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:
   __A	:       Union[str, Any]       =	state_dict.pop(UpperCamelCase_		)
   __A	:       Dict       =	val
  if "input_proj" in key:
   __A	:       List[str]       =	state_dict.pop(UpperCamelCase_		)
   __A	:       Optional[Any]       =	val
  if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
   __A	:       List[Any]       =	state_dict.pop(UpperCamelCase_		)
   __A	:       Optional[Any]       =	val

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

 __A	:       Any       =	'cuda' if torch.cuda.is_available() else 'cpu'
 model.to(UpperCamelCase_		)

 # load image processor
 __A	:       Any       =	DetaImageProcessor(format='coco_detection'		)

 # verify our conversion on image
 __A	:       str       =	prepare_img()
 __A	:       int       =	processor(images=UpperCamelCase_     ,	return_tensors='pt'		)
 __A	:       str       =	encoding['pixel_values']
 __A	:       Optional[Any]       =	model(pixel_values.to(UpperCamelCase_		)		)

 # verify logits
 if model_name == "deta-resnet-50":
  __A	:       Union[str, Any]       =	torch.tensor(
      [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]		)
  __A	:       str       =	torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]		)
 elif model_name == "deta-resnet-50-24-epochs":
  __A	:       Tuple       =	torch.tensor(
      [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]		)
  __A	:       Optional[int]       =	torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]]		)

 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__":
   _UpperCamelCase   :						Optional[Any]     						= argparse.ArgumentParser()

   parser.add_argument(
       """--model_name""",
       type=str,
       default="""deta-resnet-50""",
       choices=["""deta-resnet-50""", """deta-resnet-50-24-epochs"""],
       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."""
   )
   _UpperCamelCase   :						Tuple     						= parser.parse_args()
   convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





