




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_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =state_dict.pop(__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__       =val



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

    return new_state_dict



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase=False     ):
    SCREAMING_SNAKE_CASE__       =""""""
    if is_panoptic:
        SCREAMING_SNAKE_CASE__       ="""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)
        SCREAMING_SNAKE_CASE__       =state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""     )
        SCREAMING_SNAKE_CASE__       =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
        SCREAMING_SNAKE_CASE__       =in_proj_weight[:256, :]
        SCREAMING_SNAKE_CASE__       =in_proj_bias[:256]
        SCREAMING_SNAKE_CASE__       =in_proj_weight[256:512, :]
        SCREAMING_SNAKE_CASE__       =in_proj_bias[256:512]
        SCREAMING_SNAKE_CASE__       =in_proj_weight[-256:, :]
        SCREAMING_SNAKE_CASE__       =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
        SCREAMING_SNAKE_CASE__       =state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""     )
        SCREAMING_SNAKE_CASE__       =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
        SCREAMING_SNAKE_CASE__       =in_proj_weight[:256, :]
        SCREAMING_SNAKE_CASE__       =in_proj_bias[:256]
        SCREAMING_SNAKE_CASE__       =in_proj_weight[256:512, :]
        SCREAMING_SNAKE_CASE__       =in_proj_bias[256:512]
        SCREAMING_SNAKE_CASE__       =in_proj_weight[-256:, :]
        SCREAMING_SNAKE_CASE__       =in_proj_bias[-256:]
        # read in weights + bias of input projection layer of cross-attention
        SCREAMING_SNAKE_CASE__       =state_dict.pop(
            f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""     )
        SCREAMING_SNAKE_CASE__       =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
        SCREAMING_SNAKE_CASE__       =in_proj_weight_cross_attn[:256, :]
        SCREAMING_SNAKE_CASE__       =in_proj_bias_cross_attn[:256]
        SCREAMING_SNAKE_CASE__       =in_proj_weight_cross_attn[256:512, :]
        SCREAMING_SNAKE_CASE__       =in_proj_bias_cross_attn[256:512]
        SCREAMING_SNAKE_CASE__       =in_proj_weight_cross_attn[-256:, :]
        SCREAMING_SNAKE_CASE__       =in_proj_bias_cross_attn[-256:]



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

    return im



@torch.no_grad()
def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =DetrConfig()
    # set backbone and dilation attributes
    if "resnet101" in model_name:
        SCREAMING_SNAKE_CASE__       ="""resnet101"""
    if "dc5" in model_name:
        SCREAMING_SNAKE_CASE__       =True
    SCREAMING_SNAKE_CASE__       ="""panoptic""" in model_name
    if is_panoptic:
        SCREAMING_SNAKE_CASE__       =250
    else:
        SCREAMING_SNAKE_CASE__       =91
        SCREAMING_SNAKE_CASE__       ="""huggingface/label-files"""
        SCREAMING_SNAKE_CASE__       ="""coco-detection-id2label.json"""
        SCREAMING_SNAKE_CASE__       =json.load(open(hf_hub_download(__UpperCamelCase,    __UpperCamelCase,    repo_type="""dataset"""     ),    """r"""     )     )
        SCREAMING_SNAKE_CASE__       ={int(__UpperCamelCase     ): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE__       =idalabel
        SCREAMING_SNAKE_CASE__       ={v: k for k, v in idalabel.items()}

    # load image processor
    SCREAMING_SNAKE_CASE__       ="""coco_panoptic""" if is_panoptic else """coco_detection"""
    SCREAMING_SNAKE_CASE__       =DetrImageProcessor(format=__UpperCamelCase     )

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

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

    # load original model from torch hub
    SCREAMING_SNAKE_CASE__       =torch.hub.load("""facebookresearch/detr""",    __UpperCamelCase,    pretrained=__UpperCamelCase     ).eval()
    SCREAMING_SNAKE_CASE__       =detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
        if is_panoptic:
            SCREAMING_SNAKE_CASE__       ="""detr.""" + src
        rename_key(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     )
    SCREAMING_SNAKE_CASE__       =rename_backbone_keys(__UpperCamelCase     )
    # query, key and value matrices need special treatment
    read_in_q_k_v(__UpperCamelCase,    is_panoptic=__UpperCamelCase     )
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    SCREAMING_SNAKE_CASE__       ="""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"""     )
            ):
                SCREAMING_SNAKE_CASE__       =state_dict.pop(__UpperCamelCase     )
                SCREAMING_SNAKE_CASE__       =val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                SCREAMING_SNAKE_CASE__       =state_dict.pop(__UpperCamelCase     )
                SCREAMING_SNAKE_CASE__       =val
            elif key.startswith("""bbox_attention"""     ) or key.startswith("""mask_head"""     ):
                continue
            else:
                SCREAMING_SNAKE_CASE__       =state_dict.pop(__UpperCamelCase     )
                SCREAMING_SNAKE_CASE__       =val
        else:
            if not key.startswith("""class_labels_classifier"""     ) and not key.startswith("""bbox_predictor"""     ):
                SCREAMING_SNAKE_CASE__       =state_dict.pop(__UpperCamelCase     )
                SCREAMING_SNAKE_CASE__       =val
    # finally, create HuggingFace model and load state dict
    SCREAMING_SNAKE_CASE__       =DetrForSegmentation(__UpperCamelCase     ) if is_panoptic else DetrForObjectDetection(__UpperCamelCase     )
    model.load_state_dict(__UpperCamelCase     )
    model.eval()
    # verify our conversion
    SCREAMING_SNAKE_CASE__       =detr(__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__       =model(__UpperCamelCase     )
    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(__UpperCamelCase     ).mkdir(exist_ok=__UpperCamelCase     )
    model.save_pretrained(__UpperCamelCase     )
    image_processor.save_pretrained(__UpperCamelCase     )


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)












def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =len(__UpperCamelCase     )
    # Create a stack and push index of fist element to it
    SCREAMING_SNAKE_CASE__       =[]
    st.append(0     )

    # Span value of first element is always 1
    SCREAMING_SNAKE_CASE__       =1

    # Calculate span values for rest of the elements
    for i in range(1,    __UpperCamelCase     ):
        # Pop elements from stack while stack is not
        # empty and top of stack is smaller than price[i]
        while len(__UpperCamelCase     ) > 0 and price[st[0]] <= price[i]:
            st.pop()

        # If stack becomes empty, then price[i] is greater
        # than all elements on left of it, i.e. price[0],
        # price[1], ..price[i-1]. Else the price[i]  is
        # greater than elements after top of stack
        SCREAMING_SNAKE_CASE__       =i + 1 if len(__UpperCamelCase     ) <= 0 else (i - st[0])

        # Push this element to stack
        st.append(__UpperCamelCase     )



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    for i in range(0,    __UpperCamelCase     ):
        print(arr[i],    end=""" """     )


# Driver program to test above function
lowerCamelCase_  						=       [10, 4, 5, 90, 120, 80]
lowerCamelCase_  						=       [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)




print_array(S, len(price))






