
"""simple docstring"""







from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowercase_						    =					logging.get_logger(__name__)

lowercase_						    =					{
    "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json",
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}



class 		UpperCAmelCase_	(lowerCamelCase_						):
 """simple docstring"""




 UpperCamelCase_						:     Union[str, Any]				   =    """m2m_100"""
 UpperCamelCase_						:     Optional[int]				   =    ["""past_key_values"""]
 UpperCamelCase_						:     Tuple				   =    {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




 def __init__( self       : Optional[Any]     ,   a_       : Optional[int]=12_81_12     ,   a_       : Optional[int]=10_24     ,   a_       : Optional[Any]=12     ,   a_       : Tuple=40_96     ,   a_       : str=16     ,   a_       : Union[str, Any]=12     ,   a_       : Optional[int]=40_96     ,   a_       : Any=16     ,   a_       : List[Any]=0.05     ,   a_       : Optional[Any]=0.05     ,   a_       : Union[str, Any]=True     ,   a_       : List[str]=True     ,   a_       : Any="relu"     ,   a_       : Tuple=10_24     ,   a_       : Union[str, Any]=0.1     ,   a_       : List[str]=0.1     ,   a_       : Optional[int]=0.0     ,   a_       : Union[str, Any]=0.02     ,   a_       : Union[str, Any]=2     ,   a_       : Any=True     ,   a_       : Dict=1     ,   a_       : List[str]=0     ,   a_       : Optional[Any]=2     ,   **a_       : Tuple     ,   )->					List[str]:



      """simple docstring"""

      UpperCAmelCase_						:    List[str]    					=  vocab_size
      UpperCAmelCase_						:    Union[str, Any]    					=  max_position_embeddings
      UpperCAmelCase_						:    List[Any]    					=  d_model
      UpperCAmelCase_						:    Optional[Any]    					=  encoder_ffn_dim
      UpperCAmelCase_						:    List[Any]    					=  encoder_layers
      UpperCAmelCase_						:    List[Any]    					=  encoder_attention_heads
      UpperCAmelCase_						:    Optional[int]    					=  decoder_ffn_dim
      UpperCAmelCase_						:    List[str]    					=  decoder_layers
      UpperCAmelCase_						:    str    					=  decoder_attention_heads
      UpperCAmelCase_						:    str    					=  dropout
      UpperCAmelCase_						:    Union[str, Any]    					=  attention_dropout
      UpperCAmelCase_						:    Any    					=  activation_dropout
      UpperCAmelCase_						:    Optional[int]    					=  activation_function
      UpperCAmelCase_						:    Any    					=  init_std
      UpperCAmelCase_						:    Any    					=  encoder_layerdrop
      UpperCAmelCase_						:    Union[str, Any]    					=  decoder_layerdrop
      UpperCAmelCase_						:    List[str]    					=  use_cache
      UpperCAmelCase_						:    Any    					=  encoder_layers
      UpperCAmelCase_						:    Tuple    					=  scale_embedding  # scale factor will be sqrt(d_model) if True

      super().__init__(
          pad_token_id=a_     ,   bos_token_id=a_     ,   eos_token_id=a_     ,   is_encoder_decoder=a_     ,   decoder_start_token_id=a_     ,   **a_     ,   )



class 		UpperCAmelCase_	(lowerCamelCase_						):
 """simple docstring"""




 @property
 def   a      ( self       : Optional[int] )->					Mapping[str, Mapping[int, str]]:



      """simple docstring"""

      UpperCAmelCase_						:    Union[str, Any]    					=  OrderedDict(
          [
              ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
              ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
          ] )

      if self.use_past:
           UpperCAmelCase_						:    Optional[Any]    					=  {0: """batch"""}
           UpperCAmelCase_						:    Dict    					=  {0: """batch""", 1: """past_decoder_sequence + sequence"""}
      else:
           UpperCAmelCase_						:    Union[str, Any]    					=  {0: """batch""", 1: """decoder_sequence"""}
           UpperCAmelCase_						:    List[Any]    					=  {0: """batch""", 1: """decoder_sequence"""}

      if self.use_past:
           self.fill_with_past_key_values_(a_     ,   direction="""inputs""" )
      return common_inputs




 def   a      ( self       : int     ,   a_       : PreTrainedTokenizer     ,   a_       : int = -1     ,   a_       : int = -1     ,   a_       : bool = False     ,   a_       : Optional[TensorType] = None     ,   )->					Mapping[str, Any]:



      """simple docstring"""

      UpperCAmelCase_						:    List[str]    					=  compute_effective_axis_dimension(
          a_     ,   fixed_dimension=OnnxConfig.default_fixed_batch     ,   num_token_to_add=0 )

      # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
      UpperCAmelCase_						:    Optional[int]    					=  tokenizer.num_special_tokens_to_add(a_ )
      UpperCAmelCase_						:    str    					=  compute_effective_axis_dimension(
          a_     ,   fixed_dimension=OnnxConfig.default_fixed_sequence     ,   num_token_to_add=a_ )

      # Generate dummy inputs according to compute batch and sequence
      UpperCAmelCase_						:    Optional[Any]    					=  [""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
      UpperCAmelCase_						:    List[str]    					=  dict(tokenizer(a_     ,   return_tensors=a_ ) )
      return common_inputs




 def   a      ( self       : Optional[int]     ,   a_       : PreTrainedTokenizer     ,   a_       : int = -1     ,   a_       : int = -1     ,   a_       : bool = False     ,   a_       : Optional[TensorType] = None     ,   )->					Mapping[str, Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Tuple    					=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          a_     ,   a_     ,   a_     ,   a_     ,   a_ )

      # Generate decoder inputs
      UpperCAmelCase_						:    List[Any]    					=  seq_length if not self.use_past else 1
      UpperCAmelCase_						:    Optional[Any]    					=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          a_     ,   a_     ,   a_     ,   a_     ,   a_ )
      UpperCAmelCase_						:    Dict    					=  {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
      UpperCAmelCase_						:    Tuple    					=  dict(**a_     ,   **a_ )

      if self.use_past:
           if not is_torch_available():
                raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
           else:
                import torch
           UpperCAmelCase_					,UpperCAmelCase_						:    Dict    					=  common_inputs["""input_ids"""].shape
           UpperCAmelCase_						:    Optional[int]    					=  common_inputs["""decoder_input_ids"""].shape[1]
           UpperCAmelCase_					,UpperCAmelCase_						:    Union[str, Any]    					=  self.num_attention_heads
           UpperCAmelCase_						:    Union[str, Any]    					=  (
               batch,
               num_encoder_attention_heads,
               encoder_seq_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           UpperCAmelCase_						:    int    					=  decoder_seq_length + 3
           UpperCAmelCase_						:    List[Any]    					=  (
               batch,
               num_decoder_attention_heads,
               decoder_past_length,
               self._config.hidden_size // num_decoder_attention_heads,
           )

           UpperCAmelCase_						:    List[str]    					=  torch.cat(
               [common_inputs["""decoder_attention_mask"""], torch.ones(a_     ,   a_ )]     ,   dim=1 )

           UpperCAmelCase_						:    List[Any]    					=  []
           # If the number of encoder and decoder layers are present in the model configuration, both are considered
           UpperCAmelCase_					,UpperCAmelCase_						:    Any    					=  self.num_layers
           UpperCAmelCase_						:    str    					=  min(a_     ,   a_ )
           UpperCAmelCase_						:    int    					=  max(a_     ,   a_ ) - min_num_layers
           UpperCAmelCase_						:    Dict    					=  """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

           for _ in range(a_ ):
                common_inputs["past_key_values"].append(
                    (
                        torch.zeros(a_ ),
                        torch.zeros(a_ ),
                        torch.zeros(a_ ),
                        torch.zeros(a_ ),
                    ) )
           # TODO: test this.
           UpperCAmelCase_						:    int    					=  encoder_shape if remaining_side_name == """encoder""" else decoder_shape
           for _ in range(a_     ,   a_ ):
                common_inputs["past_key_values"].append((torch.zeros(a_ ), torch.zeros(a_ )) )
      return common_inputs






 UpperCamelCase_						:     str				   =    _generate_dummy_inputs_for_default_and_seqaseq_lm



"""simple docstring"""







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()
lowercase_						    =					logging.get_logger(__name__)




def 			A_			(					lowercase      )     ->							List[Any]:


     """simple docstring"""







     if "backbone.0.body" in orig_key:
          UpperCAmelCase_						:    Dict    					=  orig_key.replace("""backbone.0.body"""						, """backbone.conv_encoder.model"""      )
     if "transformer" in orig_key:
          UpperCAmelCase_						:    List[Any]    					=  orig_key.replace("""transformer."""						, """"""      )
     if "norm1" in orig_key:
          if "encoder" in orig_key:
               UpperCAmelCase_						:    List[str]    					=  orig_key.replace("""norm1"""						, """self_attn_layer_norm"""      )
          else:
               UpperCAmelCase_						:    Any    					=  orig_key.replace("""norm1"""						, """encoder_attn_layer_norm"""      )
     if "norm2" in orig_key:
          if "encoder" in orig_key:
               UpperCAmelCase_						:    Any    					=  orig_key.replace("""norm2"""						, """final_layer_norm"""      )
          else:
               UpperCAmelCase_						:    Union[str, Any]    					=  orig_key.replace("""norm2"""						, """self_attn_layer_norm"""      )
     if "norm3" in orig_key:
          UpperCAmelCase_						:    Optional[Any]    					=  orig_key.replace("""norm3"""						, """final_layer_norm"""      )
     if "linear1" in orig_key:
          UpperCAmelCase_						:    Union[str, Any]    					=  orig_key.replace("""linear1"""						, """fc1"""      )
     if "linear2" in orig_key:
          UpperCAmelCase_						:    List[str]    					=  orig_key.replace("""linear2"""						, """fc2"""      )
     if "query_embed" in orig_key:
          UpperCAmelCase_						:    str    					=  orig_key.replace("""query_embed"""						, """query_position_embeddings"""      )
     if "cross_attn" in orig_key:
          UpperCAmelCase_						:    Optional[int]    					=  orig_key.replace("""cross_attn"""						, """encoder_attn"""      )

     return orig_key




def 			A_			(					lowercase      )     ->							Union[str, Any]:


     """simple docstring"""







     for i in range(6      ):
          # read in weights + bias of input projection layer of self-attention
          UpperCAmelCase_						:    int    					=  state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''      )
          UpperCAmelCase_						:    List[Any]    					=  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
          UpperCAmelCase_						:    List[Any]    					=  in_proj_weight[:256, :]
          UpperCAmelCase_						:    Union[str, Any]    					=  in_proj_bias[:256]
          UpperCAmelCase_						:    int    					=  in_proj_weight[256:512, :]
          UpperCAmelCase_						:    List[Any]    					=  in_proj_bias[256:512]
          UpperCAmelCase_						:    List[Any]    					=  in_proj_weight[-256:, :]
          UpperCAmelCase_						:    List[str]    					=  in_proj_bias[-256:]




def 			A_			(					)     ->							Any:


     """simple docstring"""







     UpperCAmelCase_						:    List[str]    					=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
     UpperCAmelCase_						:    Union[str, Any]    					=  Image.open(requests.get(lowercase						, stream=lowercase      ).raw      )

     return im




@torch.no_grad()
def 			A_			(					lowercase						, lowercase						, lowercase						, lowercase						, lowercase						, lowercase						, lowercase						, )     ->							List[Any]:


     """simple docstring"""







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

     # load image processor
     UpperCAmelCase_						:    Union[str, Any]    					=  DeformableDetrImageProcessor(format="""coco_detection"""      )

     # prepare image
     UpperCAmelCase_						:    Tuple    					=  prepare_img()
     UpperCAmelCase_						:    Optional[int]    					=  image_processor(images=lowercase						, return_tensors="""pt"""      )
     UpperCAmelCase_						:    List[str]    					=  encoding["""pixel_values"""]

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

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

     UpperCAmelCase_						:    Optional[Any]    					=  """cuda""" if torch.cuda.is_available() else """cpu"""
     model.to(lowercase      )
     # verify our conversion
     UpperCAmelCase_						:    Dict    					=  model(pixel_values.to(lowercase      )      )

     UpperCAmelCase_						:    Optional[int]    					=  torch.tensor(
         [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]      )
     UpperCAmelCase_						:    Dict    					=  torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]      )

     if single_scale:
          UpperCAmelCase_						:    Optional[Any]    					=  torch.tensor(
              [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]      )
          UpperCAmelCase_						:    Optional[int]    					=  torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]      )

     if single_scale and dilation:
          UpperCAmelCase_						:    Any    					=  torch.tensor(
              [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]      )
          UpperCAmelCase_						:    List[str]    					=  torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]      )

     if with_box_refine:
          UpperCAmelCase_						:    Optional[int]    					=  torch.tensor(
              [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]      )
          UpperCAmelCase_						:    List[str]    					=  torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]      )

     if with_box_refine and two_stage:
          UpperCAmelCase_						:    str    					=  torch.tensor(
              [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]      )
          UpperCAmelCase_						:    Optional[Any]    					=  torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]      )

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

     assert torch.allclose(outputs.logits[0, :3, :3]						, expected_logits.to(lowercase      )						, atol=1E-4      )
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]						, expected_boxes.to(lowercase      )						, 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(lowercase      ).mkdir(exist_ok=lowercase      )
     model.save_pretrained(lowercase      )
     image_processor.save_pretrained(lowercase      )

     # Push to hub
     if push_to_hub:
          UpperCAmelCase_						:    Any    					=  """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=lowercase						, organization="""nielsr"""						, commit_message="""Add model"""      )


if __name__ == "__main__":
       lowercase_						    =					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."
       )
       lowercase_						    =					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,
       )

