






"""simple docstring"""


import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class __a   (   UpperCAmelCase_ ,unittest.TestCase				):
  __lowercase    :    Optional[int]						=					GPTNeoXJapaneseTokenizer
  __lowercase    :    str						=					False
  __lowercase    :    Dict						=					{'do_clean_text': False, 'add_prefix_space': False}



  def 						A_				(self  :		Optional[Any]   )		->							Optional[Any]:
   super().setUp()

   __A	:       Tuple       =	[
       'こん',
       'こんに',
       'にちは',
       'ばんは',
       '世界,㔺界',
       '、',
       '。',
       '<BR>',
       '<SP>',
       '<TAB>',
       '<URL>',
       '<EMAIL>',
       '<TEL>',
       '<DATE>',
       '<PRICE>',
       '<BLOCK>',
       '<KIGOU>',
       '<U2000U2BFF>',
       '<|emoji1|>',
       '<unk>',
       '<|startoftext|>',
       '<|endoftext|>',
   ]
   __A	:       Tuple       =	{'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
   __A	:       Any       =	{'unk_token': '<unk>'}

   __A	:       Tuple       =	os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES['vocab_file']   )
   __A	:       Tuple       =	os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES['emoji_file']   )
   with open(self.vocab_file				,			'w'				,			encoding='utf-8'   ) as vocab_writer:
    vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]   )   )
   with open(self.emoji_file				,			'w'   ) as emoji_writer:
    emoji_writer.write(json.dumps(lowercase_   )   )



  def 						A_				(self  :		int				,			**lowercase_  :		List[str]   )		->							Tuple:
   kwargs.update(self.special_tokens_map   )
   return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname				,			**lowercase_   )



  def 						A_				(self  :		List[Any]				,			lowercase_  :		Any   )		->							Dict:
   __A	:       Optional[Any]       =	'こんにちは、世界。 \nこんばんは、㔺界。😀'
   __A	:       str       =	'こんにちは、世界。 \nこんばんは、世界。😀'
   return input_text, output_text



  def 						A_				(self  :		Any				,			lowercase_  :		Any   )		->							List[Any]:
   __A    ,					__A	:       Dict       =	self.get_input_output_texts(lowercase_   )
   __A	:       List[str]       =	tokenizer.encode(lowercase_				,			add_special_tokens=lowercase_   )
   __A	:       Any       =	tokenizer.decode(lowercase_				,			clean_up_tokenization_spaces=lowercase_   )
   return text, ids



  def 						A_				(self  :		str   )		->							Optional[int]:
   pass  # TODO add if relevant



  def 						A_				(self  :		int   )		->							Tuple:
   pass  # TODO add if relevant



  def 						A_				(self  :		Union[str, Any]   )		->							Optional[Any]:
   pass  # TODO add if relevant



  def 						A_				(self  :		int   )		->							int:
   __A	:       Union[str, Any]       =	self.get_tokenizer()

   # Testing tokenization
   __A	:       Dict       =	'こんにちは、世界。　こんばんは、㔺界。'
   __A	:       Tuple       =	['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
   __A	:       str       =	tokenizer.tokenize(lowercase_   )
   self.assertListEqual(lowercase_				,			lowercase_   )

   # Testing conversion to ids without special tokens
   __A	:       Tuple       =	[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
   __A	:       Optional[Any]       =	tokenizer.convert_tokens_to_ids(lowercase_   )
   self.assertListEqual(lowercase_				,			lowercase_   )

   # Testing conversion to ids with special tokens
   __A	:       int       =	tokens + [tokenizer.unk_token]
   __A	:       List[str]       =	[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
   __A	:       Optional[Any]       =	tokenizer.convert_tokens_to_ids(lowercase_   )
   self.assertListEqual(lowercase_				,			lowercase_   )



  @slow
  def 						A_				(self  :		List[str]   )		->							Optional[int]:
   __A	:       Any       =	self.tokenizer_class.from_pretrained('abeja/gpt-neox-japanese-2.7b'   )

   __A	:       List[str]       =	tokenizer.encode('ありがとう。'				,			add_special_tokens=lowercase_   )
   __A	:       Optional[int]       =	tokenizer.encode('どういたしまして。'				,			add_special_tokens=lowercase_   )

   __A	:       Any       =	tokenizer.build_inputs_with_special_tokens(lowercase_   )
   __A	:       List[Any]       =	tokenizer.build_inputs_with_special_tokens(lowercase_				,			lowercase_   )

   assert encoded_sentence == ids_a
   assert encoded_pair == ids_a + ids_a



  def 						A_				(self  :		Optional[Any]   )		->							Dict:
   # Intentionally convert some words to accommodate character fluctuations unique to Japanese
   pass



  def 						A_				(self  :		Any   )		->							List[str]:
   # tokenizer has no padding token
   pass













"""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)





