





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


class     __magic_name__     (							unittest.TestCase			, UpperCamelCase__							):





     """simple docstring"""



     def   SCREAMING_SNAKE_CASE						(							self				:Any     ):





        '''simple docstring'''




        A_    : Dict								=	load_tool("text-classification"     )
        self.tool.setup()
        A_    : List[str]								=	load_tool("text-classification"	,					remote=__a     )



     def   SCREAMING_SNAKE_CASE						(							self				:Optional[Any]     ):





        '''simple docstring'''




        A_    : Tuple								=	self.tool("That's quite cool"	,					["positive", "negative"]     )
        self.assertEqual(__a	,					"positive"     )



     def   SCREAMING_SNAKE_CASE						(							self				:List[Any]     ):





        '''simple docstring'''




        A_    : Tuple								=	self.remote_tool("That's quite cool"	,					["positive", "negative"]     )
        self.assertEqual(__a	,					"positive"     )



     def   SCREAMING_SNAKE_CASE						(							self				:Union[str, Any]     ):





        '''simple docstring'''




        A_    : List[str]								=	self.tool(text="That's quite cool"	,					labels=["positive", "negative"]     )
        self.assertEqual(__a	,					"positive"     )





     def   SCREAMING_SNAKE_CASE						(							self				:List[Any]     ):





        '''simple docstring'''




        A_    : str								=	self.remote_tool(text="That's quite cool"	,					labels=["positive", "negative"]     )
        self.assertEqual(__a	,					"positive"     )

'''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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:     Optional[int]  =logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
SCREAMING_SNAKE_CASE_:     Tuple  =[]
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}.cross_attn.out_proj.weight",
            f"decoder.layers.{i}.encoder_attn.out_proj.weight",
        )
    )
    rename_keys.append(
        (
            f"transformer.decoder.layers.{i}.cross_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"))

    # q, k, v projections in self/cross-attention in decoder for conditional DETR
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.sa_qcontent_proj.weight", f"decoder.layers.{i}.sa_qcontent_proj.weight")
    )
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.sa_kcontent_proj.weight", f"decoder.layers.{i}.sa_kcontent_proj.weight")
    )
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.sa_qpos_proj.weight", f"decoder.layers.{i}.sa_qpos_proj.weight")
    )
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.sa_kpos_proj.weight", f"decoder.layers.{i}.sa_kpos_proj.weight")
    )
    rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.weight", f"decoder.layers.{i}.sa_v_proj.weight"))
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.ca_qcontent_proj.weight", f"decoder.layers.{i}.ca_qcontent_proj.weight")
    )
    # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.ca_kcontent_proj.weight", f"decoder.layers.{i}.ca_kcontent_proj.weight")
    )
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.ca_kpos_proj.weight", f"decoder.layers.{i}.ca_kpos_proj.weight")
    )
    rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.weight", f"decoder.layers.{i}.ca_v_proj.weight"))
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight", f"decoder.layers.{i}.ca_qpos_sine_proj.weight")
    )

    rename_keys.append(
        (f"transformer.decoder.layers.{i}.sa_qcontent_proj.bias", f"decoder.layers.{i}.sa_qcontent_proj.bias")
    )
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.sa_kcontent_proj.bias", f"decoder.layers.{i}.sa_kcontent_proj.bias")
    )
    rename_keys.append((f"transformer.decoder.layers.{i}.sa_qpos_proj.bias", f"decoder.layers.{i}.sa_qpos_proj.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.sa_kpos_proj.bias", f"decoder.layers.{i}.sa_kpos_proj.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.bias", f"decoder.layers.{i}.sa_v_proj.bias"))
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.ca_qcontent_proj.bias", f"decoder.layers.{i}.ca_qcontent_proj.bias")
    )
    # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.ca_kcontent_proj.bias", f"decoder.layers.{i}.ca_kcontent_proj.bias")
    )
    rename_keys.append((f"transformer.decoder.layers.{i}.ca_kpos_proj.bias", f"decoder.layers.{i}.ca_kpos_proj.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.bias", f"decoder.layers.{i}.ca_v_proj.bias"))
    rename_keys.append(
        (f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias", f"decoder.layers.{i}.ca_qpos_sine_proj.bias")
    )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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'),
        ('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
        ('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
        ('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
        ('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
        ('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
        ('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
        ('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
        ('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
        ('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
        ('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
    ]
)





def 	lowerCAmelCase_				(					snake_case_       : Dict					,    snake_case_       : Any					,    snake_case_       : Optional[int]			)    ->      Dict:


     '''simple docstring'''




     UpperCAmelCase_           =   state_dict.pop(snake_case_			)
     UpperCAmelCase_           =   val





def 	lowerCAmelCase_				(					snake_case_       : int			)    ->      List[Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               UpperCAmelCase_           =   key.replace("backbone.0.body"					,    "backbone.conv_encoder.model"			)
               UpperCAmelCase_           =   value
          else:
               UpperCAmelCase_           =   value

     return new_state_dict





def 	lowerCAmelCase_				(					snake_case_       : List[Any]					,    snake_case_       : Dict=False			)    ->      List[str]:


     '''simple docstring'''




     UpperCAmelCase_           =   ""
     if is_panoptic:
          UpperCAmelCase_           =   "conditional_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)
          UpperCAmelCase_           =   state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""			)
          UpperCAmelCase_           =   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
          UpperCAmelCase_           =   in_proj_weight[:2_56, :]
          UpperCAmelCase_           =   in_proj_bias[:2_56]
          UpperCAmelCase_           =   in_proj_weight[2_56:5_12, :]
          UpperCAmelCase_           =   in_proj_bias[2_56:5_12]
          UpperCAmelCase_           =   in_proj_weight[-2_56:, :]
          UpperCAmelCase_           =   in_proj_bias[-2_56:]





def 	lowerCAmelCase_				(					)    ->      Dict:


     '''simple docstring'''




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

     return im





@torch.no_grad()
def 	lowerCAmelCase_				(					snake_case_       : Optional[int]					,    snake_case_       : Dict			)    ->      Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_           =   ConditionalDetrConfig()
     # set backbone and dilation attributes
     if "resnet101" in model_name:
          UpperCAmelCase_           =   "resnet101"
     if "dc5" in model_name:
          UpperCAmelCase_           =   True
     UpperCAmelCase_           =   "panoptic" in model_name
     if is_panoptic:
          UpperCAmelCase_           =   2_50
     else:
          UpperCAmelCase_           =   91
          UpperCAmelCase_           =   "huggingface/label-files"
          UpperCAmelCase_           =   "coco-detection-id2label.json"
          UpperCAmelCase_           =   json.load(open(hf_hub_download(snake_case_					,    snake_case_					,    repo_type="dataset"			)					,    "r"			)			)
          UpperCAmelCase_           =   {int(snake_case_			): v for k, v in idalabel.items()}
          UpperCAmelCase_           =   idalabel
          UpperCAmelCase_           =   {v: k for k, v in idalabel.items()}

     # load image processor
     UpperCAmelCase_           =   "coco_panoptic" if is_panoptic else "coco_detection"
     UpperCAmelCase_           =   ConditionalDetrImageProcessor(format=snake_case_			)

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

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

     # load original model from torch hub
     UpperCAmelCase_           =   torch.hub.load("DeppMeng/ConditionalDETR"					,    snake_case_					,    pretrained=snake_case_			).eval()
     UpperCAmelCase_           =   conditional_detr.state_dict()
     # rename keys
     for src, dest in rename_keys:
          if is_panoptic:
               UpperCAmelCase_           =   "conditional_detr." + src
          rename_key(snake_case_					,    snake_case_					,    snake_case_			)
     UpperCAmelCase_           =   rename_backbone_keys(snake_case_			)
     # query, key and value matrices need special treatment
     read_in_q_k_v(snake_case_					,    is_panoptic=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
     UpperCAmelCase_           =   "conditional_detr.model." if is_panoptic else "model."
     for key in state_dict.copy().keys():
          if is_panoptic:
               if (
                   key.startswith("conditional_detr"			)
                   and not key.startswith("class_labels_classifier"			)
                   and not key.startswith("bbox_predictor"			)
               ):
                    UpperCAmelCase_           =   state_dict.pop(snake_case_			)
                    UpperCAmelCase_           =   val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    UpperCAmelCase_           =   state_dict.pop(snake_case_			)
                    UpperCAmelCase_           =   val
               elif key.startswith("bbox_attention"			) or key.startswith("mask_head"			):
                    continue
               else:
                    UpperCAmelCase_           =   state_dict.pop(snake_case_			)
                    UpperCAmelCase_           =   val
          else:
               if not key.startswith("class_labels_classifier"			) and not key.startswith("bbox_predictor"			):
                    UpperCAmelCase_           =   state_dict.pop(snake_case_			)
                    UpperCAmelCase_           =   val
    # finally, create HuggingFace model and load state dict
     UpperCAmelCase_           =   ConditionalDetrForSegmentation(snake_case_			) if is_panoptic else ConditionalDetrForObjectDetection(snake_case_			)
     model.load_state_dict(snake_case_			)
     model.eval()
     model.push_to_hub(repo_id=snake_case_					,    organization="DepuMeng"					,    commit_message="Add model"			)
     # verify our conversion
     UpperCAmelCase_           =   conditional_detr(snake_case_			)
     UpperCAmelCase_           =   model(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(snake_case_			).mkdir(exist_ok=snake_case_			)
     model.save_pretrained(snake_case_			)
     image_processor.save_pretrained(snake_case_			)


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     List[str]  =argparse.ArgumentParser()

    parser.add_argument(
        '--model_name',
        default='conditional_detr_resnet50',
        type=str,
        help='Name of the CONDITIONAL_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.'
    )
    SCREAMING_SNAKE_CASE_:     int  =parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




