
'''simple docstring'''
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()
a_      =  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a_      =  []
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_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    snake_case_       :			Tuple   	=  state_dict.pop(__SCREAMING_SNAKE_CASE				)
    snake_case_       :			Union[str, Any]   	=  val

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    snake_case_       :			List[str]   	=  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_       :			int   	=  value
        else:
            snake_case_       :			Any   	=  value

    return new_state_dict

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE=False				):
    """simple docstring"""






    snake_case_       :			List[Any]   	=  ""
    if is_panoptic:
        snake_case_       :			Optional[int]   	=  "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_       :			Any   	=  state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'				)
        snake_case_       :			Any   	=  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_       :			Dict   	=  in_proj_weight[:2_5_6, :]
        snake_case_       :			Optional[int]   	=  in_proj_bias[:2_5_6]
        snake_case_       :			int   	=  in_proj_weight[2_5_6:5_1_2, :]
        snake_case_       :			Optional[Any]   	=  in_proj_bias[2_5_6:5_1_2]
        snake_case_       :			List[str]   	=  in_proj_weight[-2_5_6:, :]
        snake_case_       :			List[str]   	=  in_proj_bias[-2_5_6:]
    # 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_       :			str   	=  state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'				)
        snake_case_       :			Dict   	=  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_       :			Optional[int]   	=  in_proj_weight[:2_5_6, :]
        snake_case_       :			List[Any]   	=  in_proj_bias[:2_5_6]
        snake_case_       :			Union[str, Any]   	=  in_proj_weight[2_5_6:5_1_2, :]
        snake_case_       :			List[Any]   	=  in_proj_bias[2_5_6:5_1_2]
        snake_case_       :			List[str]   	=  in_proj_weight[-2_5_6:, :]
        snake_case_       :			List[str]   	=  in_proj_bias[-2_5_6:]
        # read in weights + bias of input projection layer of cross-attention
        snake_case_       :			Any   	=  state_dict.pop(
            f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'				)
        snake_case_       :			Optional[Any]   	=  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_       :			List[str]   	=  in_proj_weight_cross_attn[:2_5_6, :]
        snake_case_       :			List[str]   	=  in_proj_bias_cross_attn[:2_5_6]
        snake_case_       :			Tuple   	=  in_proj_weight_cross_attn[2_5_6:5_1_2, :]
        snake_case_       :			List[str]   	=  in_proj_bias_cross_attn[2_5_6:5_1_2]
        snake_case_       :			List[str]   	=  in_proj_weight_cross_attn[-2_5_6:, :]
        snake_case_       :			int   	=  in_proj_bias_cross_attn[-2_5_6:]

def   UpperCamelCase_   (		):
    """simple docstring"""






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

    return im

@torch.no_grad()
def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    snake_case_       :			List[Any]   	=  DetrConfig()
    # set backbone and dilation attributes
    if "resnet101" in model_name:
        snake_case_       :			Any   	=  "resnet101"
    if "dc5" in model_name:
        snake_case_       :			int   	=  True
    snake_case_       :			Tuple   	=  "panoptic" in model_name
    if is_panoptic:
        snake_case_       :			Optional[Any]   	=  2_5_0
    else:
        snake_case_       :			List[Any]   	=  9_1
        snake_case_       :			str   	=  "huggingface/label-files"
        snake_case_       :			Tuple   	=  "coco-detection-id2label.json"
        snake_case_       :			List[str]   	=  json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						repo_type="dataset"				),						"r"				)				)
        snake_case_       :			int   	=  {int(__SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
        snake_case_       :			Dict   	=  idalabel
        snake_case_       :			Optional[int]   	=  {v: k for k, v in idalabel.items()}

    # load image processor
    snake_case_       :			str   	=  "coco_panoptic" if is_panoptic else "coco_detection"
    snake_case_       :			int   	=  DetrImageProcessor(format=__SCREAMING_SNAKE_CASE				)

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

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

    # load original model from torch hub
    snake_case_       :			str   	=  torch.hub.load("facebookresearch/detr",						__SCREAMING_SNAKE_CASE,						pretrained=__SCREAMING_SNAKE_CASE				).eval()
    snake_case_       :			List[Any]   	=  detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
        if is_panoptic:
            snake_case_       :			Union[str, Any]   	=  "detr." + src
        rename_key(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				)
    snake_case_       :			List[Any]   	=  rename_backbone_keys(__SCREAMING_SNAKE_CASE				)
    # query, key and value matrices need special treatment
    read_in_q_k_v(__SCREAMING_SNAKE_CASE,						is_panoptic=__SCREAMING_SNAKE_CASE				)
    # 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_       :			Union[str, 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_       :			Optional[Any]   	=  state_dict.pop(__SCREAMING_SNAKE_CASE				)
                snake_case_       :			Optional[int]   	=  val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                snake_case_       :			Dict   	=  state_dict.pop(__SCREAMING_SNAKE_CASE				)
                snake_case_       :			Optional[Any]   	=  val
            elif key.startswith("bbox_attention"				) or key.startswith("mask_head"				):
                continue
            else:
                snake_case_       :			int   	=  state_dict.pop(__SCREAMING_SNAKE_CASE				)
                snake_case_       :			Any   	=  val
        else:
            if not key.startswith("class_labels_classifier"				) and not key.startswith("bbox_predictor"				):
                snake_case_       :			Union[str, Any]   	=  state_dict.pop(__SCREAMING_SNAKE_CASE				)
                snake_case_       :			str   	=  val
    # finally, create HuggingFace model and load state dict
    snake_case_       :			Tuple   	=  DetrForSegmentation(__SCREAMING_SNAKE_CASE				) if is_panoptic else DetrForObjectDetection(__SCREAMING_SNAKE_CASE				)
    model.load_state_dict(__SCREAMING_SNAKE_CASE				)
    model.eval()
    # verify our conversion
    snake_case_       :			Optional[int]   	=  detr(__SCREAMING_SNAKE_CASE				)
    snake_case_       :			int   	=  model(__SCREAMING_SNAKE_CASE				)
    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(__SCREAMING_SNAKE_CASE				).mkdir(exist_ok=__SCREAMING_SNAKE_CASE				)
    model.save_pretrained(__SCREAMING_SNAKE_CASE				)
    image_processor.save_pretrained(__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
  a_      =  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."
  )
  a_      =  parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase			=    {
    """configuration_big_bird""": ["""BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BigBirdConfig""", """BigBirdOnnxConfig"""],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase			=    ["""BigBirdTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase			=    ["""BigBirdTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase			=    [
       """BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BigBirdForCausalLM""",
       """BigBirdForMaskedLM""",
       """BigBirdForMultipleChoice""",
       """BigBirdForPreTraining""",
       """BigBirdForQuestionAnswering""",
       """BigBirdForSequenceClassification""",
       """BigBirdForTokenClassification""",
       """BigBirdLayer""",
       """BigBirdModel""",
       """BigBirdPreTrainedModel""",
       """load_tf_weights_in_big_bird""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase			=    [
       """FlaxBigBirdForCausalLM""",
       """FlaxBigBirdForMaskedLM""",
       """FlaxBigBirdForMultipleChoice""",
       """FlaxBigBirdForPreTraining""",
       """FlaxBigBirdForQuestionAnswering""",
       """FlaxBigBirdForSequenceClassification""",
       """FlaxBigBirdForTokenClassification""",
       """FlaxBigBirdModel""",
       """FlaxBigBirdPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_big_bird import BigBirdTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_big_bird_fast import BigBirdTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_big_bird import (
          BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
          BigBirdForCausalLM,
          BigBirdForMaskedLM,
          BigBirdForMultipleChoice,
          BigBirdForPreTraining,
          BigBirdForQuestionAnswering,
          BigBirdForSequenceClassification,
          BigBirdForTokenClassification,
          BigBirdLayer,
          BigBirdModel,
          BigBirdPreTrainedModel,
          load_tf_weights_in_big_bird,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_big_bird import (
          FlaxBigBirdForCausalLM,
          FlaxBigBirdForMaskedLM,
          FlaxBigBirdForMultipleChoice,
          FlaxBigBirdForPreTraining,
          FlaxBigBirdForQuestionAnswering,
          FlaxBigBirdForSequenceClassification,
          FlaxBigBirdForTokenClassification,
          FlaxBigBirdModel,
          FlaxBigBirdPreTrainedModel,
      )

else:
   import sys

   UpperCAmelCase			=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)