

import argparse
import json
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, ResNetConfig
from transformers.utils import logging


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







def 						lowerCamelCase__      ( __A    :Union[str, Any]		):


       """simple docstring"""




       if "resnet-50" in model_name:
              __snake_case       	=							ResNetConfig.from_pretrained("""microsoft/resnet-50"""		)
       elif "resnet-101" in model_name:
              __snake_case       	=							ResNetConfig.from_pretrained("""microsoft/resnet-101"""		)
       else:
              raise ValueError("""Model name should include either resnet50 or resnet101"""		)

       __snake_case       	=							DetrConfig(use_timm_backbone=__A	,backbone_config=__A		)

       # set label attributes
       __snake_case       	=							"""panoptic""" in model_name
       if is_panoptic:
              __snake_case       	=							2_5_0
       else:
              __snake_case       	=							9_1
              __snake_case       	=							"""huggingface/label-files"""
              __snake_case       	=							"""coco-detection-id2label.json"""
              __snake_case       	=							json.load(open(hf_hub_download(__A	,__A	,repo_type="""dataset"""		)	,"""r"""		)		)
              __snake_case       	=							{int(__A		): v for k, v in idalabel.items()}
              __snake_case       	=							idalabel
              __snake_case       	=							{v: k for k, v in idalabel.items()}

       return config, is_panoptic







def 						lowerCamelCase__      ( __A    :Any		):


       """simple docstring"""




       __snake_case       	=							[]

       # stem
       # fmt: off
       rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""")		)
       rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""")		)
       rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""")		)
       rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""")		)
       rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""")		)
       # stages
       for stage_idx in range(len(config.backbone_config.depths		)		):
              for layer_idx in range(config.backbone_config.depths[stage_idx]		):
                     # shortcut
                     if layer_idx == 0:
                            rename_keys.append(
                                (
                                    F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
                                    F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
                                )		)
                            rename_keys.append(
                                (
                                    F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
                                    F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
                                )		)
                            rename_keys.append(
                                (
                                    F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
                                    F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
                                )		)
                            rename_keys.append(
                                (
                                    F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
                                    F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
                                )		)
                            rename_keys.append(
                                (
                                    F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
                                    F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
                                )		)
                     # 3 convs
                     for i in range(3		):
                            rename_keys.append(
                                (
                                    F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
                                    F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
                                )		)
                            rename_keys.append(
                                (
                                    F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
                                    F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
                                )		)
                            rename_keys.append(
                                (
                                    F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
                                    F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
                                )		)
                            rename_keys.append(
                                (
                                    F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
                                    F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
                                )		)
                            rename_keys.append(
                                (
                                    F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
                                    F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
                                )		)
    # fmt: on

       for i in range(config.encoder_layers		):
              # 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"""),
           ]		)

       return rename_keys







def 						lowerCamelCase__      ( __A    :Dict	,__A    :Union[str, Any]	,__A    :Dict		):


       """simple docstring"""




       __snake_case       	=							state_dict.pop(__A		)
       __snake_case       	=							val







def 						lowerCamelCase__      ( __A    :List[Any]	,__A    :int=False		):


       """simple docstring"""




       __snake_case       	=							""""""
       if is_panoptic:
              __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)
              __snake_case       	=							state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'		)
              __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
              __snake_case       	=							in_proj_weight[:2_5_6, :]
              __snake_case       	=							in_proj_bias[:2_5_6]
              __snake_case       	=							in_proj_weight[2_5_6:5_1_2, :]
              __snake_case       	=							in_proj_bias[2_5_6:5_1_2]
              __snake_case       	=							in_proj_weight[-2_5_6:, :]
              __snake_case       	=							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       	=							state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'		)
              __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
              __snake_case       	=							in_proj_weight[:2_5_6, :]
              __snake_case       	=							in_proj_bias[:2_5_6]
              __snake_case       	=							in_proj_weight[2_5_6:5_1_2, :]
              __snake_case       	=							in_proj_bias[2_5_6:5_1_2]
              __snake_case       	=							in_proj_weight[-2_5_6:, :]
              __snake_case       	=							in_proj_bias[-2_5_6:]
              # read in weights + bias of input projection layer of cross-attention
              __snake_case       	=							state_dict.pop(
                  F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'		)
              __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
              __snake_case       	=							in_proj_weight_cross_attn[:2_5_6, :]
              __snake_case       	=							in_proj_bias_cross_attn[:2_5_6]
              __snake_case       	=							in_proj_weight_cross_attn[2_5_6:5_1_2, :]
              __snake_case       	=							in_proj_bias_cross_attn[2_5_6:5_1_2]
              __snake_case       	=							in_proj_weight_cross_attn[-2_5_6:, :]
              __snake_case       	=							in_proj_bias_cross_attn[-2_5_6:]







def 						lowerCamelCase__      ( ):


       """simple docstring"""




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

       return im







@torch.no_grad()
def 						lowerCamelCase__      ( __A    :Optional[Any]	,__A    :List[Any]=None	,__A    :Union[str, Any]=False		):


       """simple docstring"""




       __snake_case	,    __snake_case       	=							get_detr_config(__A		)

       # load original model from torch hub
       __snake_case       	=							{
           """detr-resnet-50""": """detr_resnet50""",
           """detr-resnet-101""": """detr_resnet101""",
       }
       logger.info(F'Converting model {model_name}...'		)
       __snake_case       	=							torch.hub.load("""facebookresearch/detr"""	,model_name_to_original_name[model_name]	,pretrained=__A		).eval()
       __snake_case       	=							detr.state_dict()
       # rename keys
       for src, dest in create_rename_keys(__A		):
              if is_panoptic:
                     __snake_case       	=							"""detr.""" + src
              rename_key(__A	,__A	,__A		)
       # query, key and value matrices need special treatment
       read_in_q_k_v(__A	,is_panoptic=__A		)
       # 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       	=							"""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       	=							state_dict.pop(__A		)
                            __snake_case       	=							val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            __snake_case       	=							state_dict.pop(__A		)
                            __snake_case       	=							val
                     elif key.startswith("""bbox_attention"""		) or key.startswith("""mask_head"""		):
                            continue
                     else:
                            __snake_case       	=							state_dict.pop(__A		)
                            __snake_case       	=							val
              else:
                     if not key.startswith("""class_labels_classifier"""		) and not key.startswith("""bbox_predictor"""		):
                            __snake_case       	=							state_dict.pop(__A		)
                            __snake_case       	=							val

    # finally, create HuggingFace model and load state dict
       __snake_case       	=							DetrForSegmentation(__A		) if is_panoptic else DetrForObjectDetection(__A		)
       model.load_state_dict(__A		)
       model.eval()

       # verify our conversion on an image
       __snake_case       	=							"""coco_panoptic""" if is_panoptic else """coco_detection"""
       __snake_case       	=							DetrImageProcessor(format=__A		)

       __snake_case       	=							processor(images=prepare_img()	,return_tensors="""pt"""		)
       __snake_case       	=							encoding["""pixel_values"""]

       __snake_case       	=							detr(__A		)
       __snake_case       	=							model(__A		)

       assert torch.allclose(outputs.logits	,original_outputs["""pred_logits"""]	,atol=1e-3		)
       assert torch.allclose(outputs.pred_boxes	,original_outputs["""pred_boxes"""]	,atol=1e-3		)
       if is_panoptic:
              assert torch.allclose(outputs.pred_masks	,original_outputs["""pred_masks"""]	,atol=1e-4		)
       print("""Looks ok!"""		)

       if pytorch_dump_folder_path is not None:
              # Save model and image processor
              logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'		)
              Path(__A		).mkdir(exist_ok=__A		)
              model.save_pretrained(__A		)
              processor.save_pretrained(__A		)

       if push_to_hub:
              # Upload model and image processor to the hub
              logger.info("""Uploading PyTorch model and image processor to the hub..."""		)
              model.push_to_hub(F'nielsr/{model_name}'		)
              processor.push_to_hub(F'nielsr/{model_name}'		)


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

    parser.add_argument(
        '''--model_name''',
        default='''detr-resnet-50''',
        type=str,
        choices=['''detr-resnet-50''', '''detr-resnet-101'''],
        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.'''
    )
    parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
    UpperCamelCase__		 =     parser.parse_args()
    convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




from __future__ import annotations







def 						lowerCamelCase__      ( __A    :list[float]	,__A    :Union[str, Any]		):


       """simple docstring"""




       print(F'Vertex\tShortest Distance from vertex {src}'		)
       for i, d in enumerate(__A		):
              print(F'{i}\t\t{d}'		)







def 						lowerCamelCase__      ( __A    :list[dict[str, int]]	,__A    :list[float]	,__A    :int		):


       """simple docstring"""




       for j in range(__A		):
              __snake_case	,    __snake_case	,    __snake_case       	=							(graph[j][k] for k in ["""src""", """dst""", """weight"""])
              if distance[u] != float("""inf"""		) and distance[u] + w < distance[v]:
                     return True
       return False







def 						lowerCamelCase__      ( __A    :list[dict[str, int]]	,__A    :int	,__A    :int	,__A    :int		):


       """simple docstring"""




       __snake_case       	=							[float("""inf"""		)] * vertex_count
       __snake_case       	=							0.0

       for _ in range(vertex_count - 1		):
              for j in range(__A		):
                     __snake_case	,    __snake_case	,    __snake_case       	=							(graph[j][k] for k in ["""src""", """dst""", """weight"""])

                     if distance[u] != float("""inf"""		) and distance[u] + w < distance[v]:
                            __snake_case       	=							distance[u] + w

       __snake_case       	=							check_negative_cycle(__A	,__A	,__A		)
       if negative_cycle_exists:
              raise Exception("""Negative cycle found"""		)

       return distance


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    UpperCamelCase__		 =     int(input('''Enter number of vertices: ''').strip())
    UpperCamelCase__		 =     int(input('''Enter number of edges: ''').strip())

    UpperCamelCase__		 =     [{} for _ in range(E)]

    for i in range(E):
        print('''Edge ''', i + 1)
        UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__		 =     (
            int(x)
            for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
        )
        UpperCamelCase__		 =     {'''src''': src, '''dst''': dest, '''weight''': weight}

    UpperCamelCase__		 =     int(input('''\nEnter shortest path source:''').strip())
    UpperCamelCase__		 =     bellman_ford(graph, V, E, source)
    print_distance(shortest_distance, 0)

