
import os

from ...utils import logging
from .utils import DataProcessor, InputExample


__lowerCamelCase							= logging.get_logger(__name__)






class 						UpperCAmelCase	(		A_				):



   def __init__(self	:      str					,		snake_case__	:      int					,		snake_case__	:      List[Any]=None				)	->			List[str]:


          '''simple docstring'''


          snake_case :     List[Any]           =   language
          snake_case :     int           =   train_language



   def        _SCREAMING_SNAKE_CASE     (self	:      Dict					,		snake_case__	:      List[str]				)	->			Optional[Any]:


          '''simple docstring'''


          snake_case :     int           =   self.language if self.train_language is None else self.train_language
          snake_case :     Any           =   self._read_tsv(os.path.join(snake_case__					,		f"""XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"""				)				)
          snake_case :     Tuple           =   []
          for i, line in enumerate(snake_case__				):
                 if i == 0:
                        continue
                 snake_case :     Tuple           =   f"""train-{i}"""
                 snake_case :     str           =   line[0]
                 snake_case :     int           =   line[1]
                 snake_case :     Union[str, Any]           =   "contradiction" if line[2] == "contradictory" else line[2]
                 if not isinstance(snake_case__					,		snake_case__				):
                        raise ValueError(f"""Training input {text_a} is not a string"""				)
                 if not isinstance(snake_case__					,		snake_case__				):
                        raise ValueError(f"""Training input {text_b} is not a string"""				)
                 if not isinstance(snake_case__					,		snake_case__				):
                        raise ValueError(f"""Training label {label} is not a string"""				)
                 examples.append(InputExample(guid=snake_case__					,		text_a=snake_case__					,		text_b=snake_case__					,		label=snake_case__				)				)
          return examples



   def        _SCREAMING_SNAKE_CASE     (self	:      str					,		snake_case__	:      str				)	->			Optional[int]:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   self._read_tsv(os.path.join(snake_case__					,		"XNLI-1.0/xnli.test.tsv"				)				)
          snake_case :     str           =   []
          for i, line in enumerate(snake_case__				):
                 if i == 0:
                        continue
                 snake_case :     Optional[Any]           =   line[0]
                 if language != self.language:
                        continue
                 snake_case :     Tuple           =   f"""test-{i}"""
                 snake_case :     Optional[Any]           =   line[6]
                 snake_case :     int           =   line[7]
                 snake_case :     Tuple           =   line[1]
                 if not isinstance(snake_case__					,		snake_case__				):
                        raise ValueError(f"""Training input {text_a} is not a string"""				)
                 if not isinstance(snake_case__					,		snake_case__				):
                        raise ValueError(f"""Training input {text_b} is not a string"""				)
                 if not isinstance(snake_case__					,		snake_case__				):
                        raise ValueError(f"""Training label {label} is not a string"""				)
                 examples.append(InputExample(guid=snake_case__					,		text_a=snake_case__					,		text_b=snake_case__					,		label=snake_case__				)				)
          return examples



   def        _SCREAMING_SNAKE_CASE     (self	:      Union[str, Any]				)	->			int:


          '''simple docstring'''


          return ["contradiction", "entailment", "neutral"]



__lowerCamelCase							= {
    """xnli""": XnliProcessor,
}

__lowerCamelCase							= {
    """xnli""": """classification""",
}

__lowerCamelCase							= {
    """xnli""": 3,
}







import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase							= logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCamelCase							= []
for i in range(6):
   # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
   rename_keys.append(
       (F'transformer.encoder.layers.{i}.self_attn.out_proj.weight', F'encoder.layers.{i}.self_attn.out_proj.weight')
   )
   rename_keys.append(
       (F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')
   )
   rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight'))
   rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias'))
   rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight'))
   rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias'))
   rename_keys.append(
       (F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')
   )
   rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias'))
   rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight'))
   rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias'))
   # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
   rename_keys.append(
       (F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'decoder.layers.{i}.self_attn.out_proj.weight')
   )
   rename_keys.append(
       (F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')
   )
   rename_keys.append(
       (
           F'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
           F'decoder.layers.{i}.encoder_attn.out_proj.weight',
       )
   )
   rename_keys.append(
       (
           F'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
           F'decoder.layers.{i}.encoder_attn.out_proj.bias',
       )
   )
   rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight'))
   rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias'))
   rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight'))
   rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias'))
   rename_keys.append(
       (F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')
   )
   rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias'))
   rename_keys.append(
       (F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')
   )
   rename_keys.append(
       (F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')
   )
   rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight'))
   rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias'))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
    ]
)


def     UpperCamelCase      (					__lowerCamelCase  :				Dict       ,      __lowerCamelCase  :				List[str]       ,      __lowerCamelCase  :				Any					):
       snake_case :     int           =   state_dict.pop(__lowerCamelCase					)
       snake_case :     int           =   val


def     UpperCamelCase      (					__lowerCamelCase  :				List[Any]					):
       snake_case :     Optional[int]           =   OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     snake_case :     List[str]           =   key.replace("backbone.0.body"       ,      "backbone.conv_encoder.model"					)
                     snake_case :     str           =   value
              else:
                     snake_case :     Any           =   value

       return new_state_dict


def     UpperCamelCase      (					__lowerCamelCase  :				List[str]       ,      __lowerCamelCase  :				Dict=False					):
       snake_case :     List[str]           =   ""
       if is_panoptic:
              snake_case :     Dict           =   "detr."

       # first: transformer encoder
       for i in range(6					):
              # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
              snake_case :     Optional[Any]           =   state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""					)
              snake_case :     int           =   state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""					)
              # next, add query, keys and values (in that order) to the state dict
              snake_case :     Optional[Any]           =   in_proj_weight[:256, :]
              snake_case :     List[str]           =   in_proj_bias[:256]
              snake_case :     Tuple           =   in_proj_weight[256:512, :]
              snake_case :     Dict           =   in_proj_bias[256:512]
              snake_case :     Optional[int]           =   in_proj_weight[-256:, :]
              snake_case :     Optional[int]           =   in_proj_bias[-256:]
       # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
       for i in range(6					):
              # read in weights + bias of input projection layer of self-attention
              snake_case :     int           =   state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""					)
              snake_case :     List[str]           =   state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""					)
              # next, add query, keys and values (in that order) to the state dict
              snake_case :     Dict           =   in_proj_weight[:256, :]
              snake_case :     Optional[int]           =   in_proj_bias[:256]
              snake_case :     Union[str, Any]           =   in_proj_weight[256:512, :]
              snake_case :     Optional[Any]           =   in_proj_bias[256:512]
              snake_case :     Dict           =   in_proj_weight[-256:, :]
              snake_case :     int           =   in_proj_bias[-256:]
              # read in weights + bias of input projection layer of cross-attention
              snake_case :     Dict           =   state_dict.pop(
                  f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""					)
              snake_case :     Dict           =   state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""					)
              # next, add query, keys and values (in that order) of cross-attention to the state dict
              snake_case :     Any           =   in_proj_weight_cross_attn[:256, :]
              snake_case :     Dict           =   in_proj_bias_cross_attn[:256]
              snake_case :     Any           =   in_proj_weight_cross_attn[256:512, :]
              snake_case :     Optional[int]           =   in_proj_bias_cross_attn[256:512]
              snake_case :     List[Any]           =   in_proj_weight_cross_attn[-256:, :]
              snake_case :     Optional[Any]           =   in_proj_bias_cross_attn[-256:]


def     UpperCamelCase      (					):
       snake_case :     Optional[Any]           =   "http://images.cocodataset.org/val2017/000000039769.jpg"
       snake_case :     List[str]           =   Image.open(requests.get(__lowerCamelCase       ,      stream=__lowerCamelCase					).raw					)

       return im


@torch.no_grad()
def     UpperCamelCase      (					__lowerCamelCase  :				Any       ,      __lowerCamelCase  :				List[Any]					):
       snake_case :     Tuple           =   DetrConfig()
       # set backbone and dilation attributes
       if "resnet101" in model_name:
              snake_case :     Optional[Any]           =   "resnet101"
       if "dc5" in model_name:
              snake_case :     int           =   True
       snake_case :     List[str]           =   "panoptic" in model_name
       if is_panoptic:
              snake_case :     Dict           =   250
       else:
              snake_case :     int           =   91
              snake_case :     List[Any]           =   "huggingface/label-files"
              snake_case :     Dict           =   "coco-detection-id2label.json"
              snake_case :     Dict           =   json.load(open(hf_hub_download(__lowerCamelCase       ,      __lowerCamelCase       ,      repo_type="dataset"					)       ,      "r"					)					)
              snake_case :     Union[str, Any]           =   {int(__lowerCamelCase					): v for k, v in idalabel.items()}
              snake_case :     int           =   idalabel
              snake_case :     str           =   {v: k for k, v in idalabel.items()}

       # load image processor
       snake_case :     Tuple           =   "coco_panoptic" if is_panoptic else "coco_detection"
       snake_case :     str           =   DetrImageProcessor(format=__lowerCamelCase					)

       # prepare image
       snake_case :     Optional[int]           =   prepare_img()
       snake_case :     Optional[Any]           =   image_processor(images=__lowerCamelCase       ,      return_tensors="pt"					)
       snake_case :     Optional[int]           =   encoding["pixel_values"]

       logger.info(f"""Converting model {model_name}..."""					)

       # load original model from torch hub
       snake_case :     Dict           =   torch.hub.load("facebookresearch/detr"       ,      __lowerCamelCase       ,      pretrained=__lowerCamelCase					).eval()
       snake_case :     Any           =   detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     snake_case :     List[str]           =   "detr." + src
              rename_key(__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					)
       snake_case :     str           =   rename_backbone_keys(__lowerCamelCase					)
       # query, key and value matrices need special treatment
       read_in_q_k_v(__lowerCamelCase       ,      is_panoptic=__lowerCamelCase					)
       # 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 :     List[Any]           =   "detr.model." if is_panoptic else "model."
       for key in state_dict.copy().keys():
              if is_panoptic:
                     if (
                         key.startswith("detr"					)
                         and not key.startswith("class_labels_classifier"					)
                         and not key.startswith("bbox_predictor"					)
                     ):
                            snake_case :     Tuple           =   state_dict.pop(__lowerCamelCase					)
                            snake_case :     Any           =   val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            snake_case :     Dict           =   state_dict.pop(__lowerCamelCase					)
                            snake_case :     Optional[Any]           =   val
                     elif key.startswith("bbox_attention"					) or key.startswith("mask_head"					):
                            continue
                     else:
                            snake_case :     Union[str, Any]           =   state_dict.pop(__lowerCamelCase					)
                            snake_case :     int           =   val
              else:
                     if not key.startswith("class_labels_classifier"					) and not key.startswith("bbox_predictor"					):
                            snake_case :     int           =   state_dict.pop(__lowerCamelCase					)
                            snake_case :     Optional[int]           =   val
    # finally, create HuggingFace model and load state dict
       snake_case :     Union[str, Any]           =   DetrForSegmentation(__lowerCamelCase					) if is_panoptic else DetrForObjectDetection(__lowerCamelCase					)
       model.load_state_dict(__lowerCamelCase					)
       model.eval()
       # verify our conversion
       snake_case :     List[Any]           =   detr(__lowerCamelCase					)
       snake_case :     Tuple           =   model(__lowerCamelCase					)
       assert torch.allclose(outputs.logits       ,      original_outputs["pred_logits"]       ,      atol=1E-4					)
       assert torch.allclose(outputs.pred_boxes       ,      original_outputs["pred_boxes"]       ,      atol=1E-4					)
       if is_panoptic:
              assert torch.allclose(outputs.pred_masks       ,      original_outputs["pred_masks"]       ,      atol=1E-4					)

       # Save model and image processor
       logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""					)
       Path(__lowerCamelCase					).mkdir(exist_ok=__lowerCamelCase					)
       model.save_pretrained(__lowerCamelCase					)
       image_processor.save_pretrained(__lowerCamelCase					)


if __name__ == "__main__":
   __lowerCamelCase							= argparse.ArgumentParser()

   parser.add_argument(
       """--model_name""", default="""detr_resnet50""", type=str, help="""Name of the DETR 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."""
   )
   __lowerCamelCase							= parser.parse_args()
   convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





