


import argparse
import json
from pathlib import Path

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

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE	:Optional[Any]  		=     logging.get_logger(__name__)



def 			_lowerCAmelCase						(							lowerCAmelCase_							:Dict							)->Optional[Any]:
   '''simple docstring'''






   if "backbone.0.body" in orig_key:
      snake_case_					    =					orig_key.replace("backbone.0.body"		,						"backbone.conv_encoder.model"							)
   if "transformer" in orig_key:
      snake_case_					    =					orig_key.replace("transformer."		,						""							)
   if "norm1" in orig_key:
      if "encoder" in orig_key:
         snake_case_					    =					orig_key.replace("norm1"		,						"self_attn_layer_norm"							)
      else:
         snake_case_					    =					orig_key.replace("norm1"		,						"encoder_attn_layer_norm"							)
   if "norm2" in orig_key:
      if "encoder" in orig_key:
         snake_case_					    =					orig_key.replace("norm2"		,						"final_layer_norm"							)
      else:
         snake_case_					    =					orig_key.replace("norm2"		,						"self_attn_layer_norm"							)
   if "norm3" in orig_key:
      snake_case_					    =					orig_key.replace("norm3"		,						"final_layer_norm"							)
   if "linear1" in orig_key:
      snake_case_					    =					orig_key.replace("linear1"		,						"fc1"							)
   if "linear2" in orig_key:
      snake_case_					    =					orig_key.replace("linear2"		,						"fc2"							)
   if "query_embed" in orig_key:
      snake_case_					    =					orig_key.replace("query_embed"		,						"query_position_embeddings"							)
   if "cross_attn" in orig_key:
      snake_case_					    =					orig_key.replace("cross_attn"		,						"encoder_attn"							)

   return orig_key



def 			_lowerCAmelCase						(							lowerCAmelCase_							:Dict							)->Optional[Any]:
   '''simple docstring'''






   for i in range(6							):
      # read in weights + bias of input projection layer of self-attention
      snake_case_					    =					state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''							)
      snake_case_					    =					state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_bias'''							)
      # next, add query, keys and values (in that order) to the state dict
      snake_case_					    =					in_proj_weight[:256, :]
      snake_case_					    =					in_proj_bias[:256]
      snake_case_					    =					in_proj_weight[256:512, :]
      snake_case_					    =					in_proj_bias[256:512]
      snake_case_					    =					in_proj_weight[-256:, :]
      snake_case_					    =					in_proj_bias[-256:]



def 			_lowerCAmelCase						(							)->Any:
   '''simple docstring'''






   snake_case_					    =					"http://images.cocodataset.org/val2017/000000039769.jpg"
   snake_case_					    =					Image.open(requests.get(a_		,						stream=a_							).raw							)

   return im



@torch.no_grad()
def 			_lowerCAmelCase						(							lowerCAmelCase_							:int		,						lowerCAmelCase_							:List[str]		,						lowerCAmelCase_							:List[Any]		,						lowerCAmelCase_							:int		,						lowerCAmelCase_							:Union[str, Any]		,						lowerCAmelCase_							:int		,						lowerCAmelCase_							:List[str]		,						)->Tuple:
   '''simple docstring'''






   snake_case_					    =					DeformableDetrConfig()
   # set config attributes
   if single_scale:
      snake_case_					    =					1
   snake_case_					    =					dilation
   snake_case_					    =					with_box_refine
   snake_case_					    =					two_stage
   # set labels
   snake_case_					    =					91
   snake_case_					    =					"huggingface/label-files"
   snake_case_					    =					"coco-detection-id2label.json"
   snake_case_					    =					json.load(open(cached_download(hf_hub_url(a_		,						a_		,						repo_type="dataset"							)							)		,						"r"							)							)
   snake_case_					    =					{int(a_							): v for k, v in idalabel.items()}
   snake_case_					    =					idalabel
   snake_case_					    =					{v: k for k, v in idalabel.items()}

   # load image processor
   snake_case_					    =					DeformableDetrImageProcessor(format="coco_detection"							)

   # prepare image
   snake_case_					    =					prepare_img()
   snake_case_					    =					image_processor(images=a_		,						return_tensors="pt"							)
   snake_case_					    =					encoding["pixel_values"]

   logger.info("Converting model..."							)

   # load original state dict
   snake_case_					    =					torch.load(a_		,						map_location="cpu"							)["model"]
   # rename keys
   for key in state_dict.copy().keys():
      snake_case_					    =					state_dict.pop(a_							)
      snake_case_					    =					val
   # query, key and value matrices need special treatment
   read_in_q_k_v(a_							)
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   snake_case_					    =					"model."
   for key in state_dict.copy().keys():
      if not key.startswith("class_embed"							) and not key.startswith("bbox_embed"							):
         snake_case_					    =					state_dict.pop(a_							)
         snake_case_					    =					val
    # finally, create HuggingFace model and load state dict
   snake_case_					    =					DeformableDetrForObjectDetection(a_							)
   model.load_state_dict(a_							)
   model.eval()

   snake_case_					    =					"cuda" if torch.cuda.is_available() else "cpu"
   model.to(a_							)
   # verify our conversion
   snake_case_					    =					model(pixel_values.to(a_							)							)

   snake_case_					    =					torch.tensor(
       [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-1_0.5_6_3_4, -5.3_3_7_9, -7.5_1_1_6]]							)
   snake_case_					    =					torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]]							)

   if single_scale:
      snake_case_					    =					torch.tensor(
          [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-1_0.0_6_6_5, -5.8_4_7_0, -7.7_0_0_3]]							)
      snake_case_					    =					torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]]							)

   if single_scale and dilation:
      snake_case_					    =					torch.tensor(
          [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-1_0.1_1_7_8, -4.5_2_7_5, -6.2_6_7_1]]							)
      snake_case_					    =					torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]]							)

   if with_box_refine:
      snake_case_					    =					torch.tensor(
          [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]]							)
      snake_case_					    =					torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]]							)

   if with_box_refine and two_stage:
      snake_case_					    =					torch.tensor(
          [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]]							)
      snake_case_					    =					torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]]							)

   print("Logits:"		,						outputs.logits[0, :3, :3]							)

   assert torch.allclose(outputs.logits[0, :3, :3]		,						expected_logits.to(a_							)		,						atol=1e-4							)
   assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,						expected_boxes.to(a_							)		,						atol=1e-4							)

   print("Everything ok!"							)

   # Save model and image processor
   logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''							)
   Path(a_							).mkdir(exist_ok=a_							)
   model.save_pretrained(a_							)
   image_processor.save_pretrained(a_							)

   # Push to hub
   if push_to_hub:
      snake_case_					    =					"deformable-detr"
      model_name += "-single-scale" if single_scale else ""
      model_name += "-dc5" if dilation else ""
      model_name += "-with-box-refine" if with_box_refine else ""
      model_name += "-two-stage" if two_stage else ""
      print("Pushing model to hub..."							)
      model.push_to_hub(repo_path_or_name=a_		,						organization="nielsr"		,						commit_message="Add model"							)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE	:Union[str, Any]  		=     argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_path''',
     type=str,
     default='''/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth''',
     help='''Path to Pytorch checkpoint (.pth file) you\'d like to convert.''',
 )
 parser.add_argument('''--single_scale''', action='''store_true''', help='''Whether to set config.num_features_levels = 1.''')
 parser.add_argument('''--dilation''', action='''store_true''', help='''Whether to set config.dilation=True.''')
 parser.add_argument('''--with_box_refine''', action='''store_true''', help='''Whether to set config.with_box_refine=True.''')
 parser.add_argument('''--two_stage''', action='''store_true''', help='''Whether to set config.two_stage=True.''')
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default=None,
     type=str,
     required=True,
     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.'''
 )
 SCREAMING_SNAKE_CASE	:Tuple  		=     parser.parse_args()
 convert_deformable_detr_checkpoint(
     args.checkpoint_path,
     args.single_scale,
     args.dilation,
     args.with_box_refine,
     args.two_stage,
     args.pytorch_dump_folder_path,
     args.push_to_hub,
 )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE	:List[Any]  		=     {
    '''configuration_speech_to_text_2''': ['''SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2Text2Config'''],
    '''processing_speech_to_text_2''': ['''Speech2Text2Processor'''],
    '''tokenization_speech_to_text_2''': ['''Speech2Text2Tokenizer'''],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE	:int  		=     [
       '''SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Speech2Text2ForCausalLM''',
       '''Speech2Text2PreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
   from .processing_speech_to_text_a import SpeechaTextaProcessor
   from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_speech_to_text_a import (
          SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          SpeechaTextaForCausalLM,
          SpeechaTextaPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE	:str  		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




