


'''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   :  Tuple       		=				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
A   :  List[Any]       		=				[]
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    lowerCAmelCase__ (       lowerCamelCase  :    Dict						,lowerCamelCase  :    Union[str, Any]						,lowerCamelCase  :    str     ):
       _A  :			str											=      state_dict.pop(lowerCamelCase     )
       _A  :			Optional[Any]											=      val






def    lowerCAmelCase__ (       lowerCamelCase  :    Any     ):
       _A  :			Optional[Any]											=      OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     _A  :			str											=      key.replace('backbone.0.body'						,'backbone.conv_encoder.model'     )
                     _A  :			List[Any]											=      value
              else:
                     _A  :			str											=      value

       return new_state_dict






def    lowerCAmelCase__ (       lowerCamelCase  :    Union[str, Any]						,lowerCamelCase  :    Tuple=False     ):
       _A  :			Dict											=      ''
       if is_panoptic:
              _A  :			Tuple											=      '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)
              _A  :			Optional[Any]											=      state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'     )
              _A  :			Optional[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
              _A  :			Any											=      in_proj_weight[:256, :]
              _A  :			List[str]											=      in_proj_bias[:256]
              _A  :			Union[str, Any]											=      in_proj_weight[256:512, :]
              _A  :			int											=      in_proj_bias[256:512]
              _A  :			Any											=      in_proj_weight[-256:, :]
              _A  :			str											=      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
              _A  :			Tuple											=      state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'     )
              _A  :			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
              _A  :			Any											=      in_proj_weight[:256, :]
              _A  :			Union[str, Any]											=      in_proj_bias[:256]
              _A  :			List[Any]											=      in_proj_weight[256:512, :]
              _A  :			Dict											=      in_proj_bias[256:512]
              _A  :			Union[str, Any]											=      in_proj_weight[-256:, :]
              _A  :			Optional[int]											=      in_proj_bias[-256:]
              # read in weights + bias of input projection layer of cross-attention
              _A  :			Dict											=      state_dict.pop(
                  F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'     )
              _A  :			Union[str, 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
              _A  :			Optional[Any]											=      in_proj_weight_cross_attn[:256, :]
              _A  :			Optional[Any]											=      in_proj_bias_cross_attn[:256]
              _A  :			Union[str, Any]											=      in_proj_weight_cross_attn[256:512, :]
              _A  :			Any											=      in_proj_bias_cross_attn[256:512]
              _A  :			Optional[Any]											=      in_proj_weight_cross_attn[-256:, :]
              _A  :			Dict											=      in_proj_bias_cross_attn[-256:]






def    lowerCAmelCase__ (       ):
       _A  :			Dict											=      'http://images.cocodataset.org/val2017/000000039769.jpg'
       _A  :			Any											=      Image.open(requests.get(lowerCamelCase						,stream=lowerCamelCase     ).raw     )

       return im






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

       # load image processor
       _A  :			int											=      'coco_panoptic' if is_panoptic else 'coco_detection'
       _A  :			Tuple											=      DetrImageProcessor(format=lowerCamelCase     )

       # prepare image
       _A  :			Tuple											=      prepare_img()
       _A  :			List[Any]											=      image_processor(images=lowerCamelCase						,return_tensors='pt'     )
       _A  :			Any											=      encoding['pixel_values']

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

       # load original model from torch hub
       _A  :			Union[str, Any]											=      torch.hub.load('facebookresearch/detr'						,lowerCamelCase						,pretrained=lowerCamelCase     ).eval()
       _A  :			Optional[Any]											=      detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     _A  :			Tuple											=      'detr.' + src
              rename_key(lowerCamelCase						,lowerCamelCase						,lowerCamelCase     )
       _A  :			Union[str, Any]											=      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
       _A  :			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'     )
                     ):
                            _A  :			List[str]											=      state_dict.pop(lowerCamelCase     )
                            _A  :			List[str]											=      val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            _A  :			Tuple											=      state_dict.pop(lowerCamelCase     )
                            _A  :			List[str]											=      val
                     elif key.startswith('bbox_attention'     ) or key.startswith('mask_head'     ):
                            continue
                     else:
                            _A  :			List[Any]											=      state_dict.pop(lowerCamelCase     )
                            _A  :			Optional[Any]											=      val
              else:
                     if not key.startswith('class_labels_classifier'     ) and not key.startswith('bbox_predictor'     ):
                            _A  :			Optional[Any]											=      state_dict.pop(lowerCamelCase     )
                            _A  :			Dict											=      val
    # finally, create HuggingFace model and load state dict
       _A  :			List[str]											=      DetrForSegmentation(lowerCamelCase     ) if is_panoptic else DetrForObjectDetection(lowerCamelCase     )
       model.load_state_dict(lowerCamelCase     )
       model.eval()
       # verify our conversion
       _A  :			List[str]											=      detr(lowerCamelCase     )
       _A  :			Optional[int]											=      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__":
   A   :  Dict       		=				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   :  Dict       		=				parser.parse_args()
   convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)










'''simple docstring'''







import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor






def    lowerCAmelCase__ (       ):
       _A  :			Union[str, Any]											=      set()
       # Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
       for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
              _A  :			Optional[Any]											=      getattr(transformers.models.auto						,lowerCamelCase						,lowerCamelCase     )
              if module is None:
                     continue
              # all mappings in a single auto modeling file
              _A  :			Any											=      [
                  x
                  for x in dir(lowerCamelCase     )
                  if x.endswith('_MAPPING_NAMES'     )
                  and (x.startswith('MODEL_'     ) or x.startswith('TF_MODEL_'     ) or x.startswith('FLAX_MODEL_'     ))
              ]
              for name in mapping_names:
                     _A  :			Tuple											=      getattr(lowerCamelCase						,lowerCamelCase     )
                     if mapping is not None:
                            for v in mapping.values():
                                   if isinstance(lowerCamelCase						,(list, tuple)     ):
                                          model_names.update(lowerCamelCase     )
                                   elif isinstance(lowerCamelCase						,lowerCamelCase     ):
                                          model_names.add(lowerCamelCase     )

       return sorted(lowerCamelCase     )






def    lowerCAmelCase__ (       ):
       # All model names defined in auto mappings
       _A  :			Union[str, Any]											=      set(get_all_model_names()     )

       with open('tests/utils/tiny_model_summary.json'     ) as fp:
              _A  :			Any											=      json.load(lowerCamelCase     )
       _A  :			Union[str, Any]											=      set()
       for model_base_name in tiny_model_info:
              tiny_models_names.update(tiny_model_info[model_base_name]['model_classes']     )

       # Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
       _A  :			List[Any]											=      model_names.difference(lowerCamelCase     )
       for model_name in copy.copy(lowerCamelCase     ):
              if not model_name.startswith('TF'     ) and F'TF{model_name}' in not_on_hub:
                     tiny_models_names.remove(lowerCamelCase     )
              elif model_name.startswith('TF'     ) and model_name[2:] in not_on_hub:
                     tiny_models_names.remove(lowerCamelCase     )

       return sorted(lowerCamelCase     )






def    lowerCAmelCase__ (       lowerCamelCase  :    Union[str, Any]     ):
       _A  :			Optional[int]											=      COMPOSITE_MODELS.values()

       # All tiny model base names on Hub
       _A  :			Optional[int]											=      get_all_model_names()
       _A  :			Tuple											=      hf_api.list_models(
           filter=ModelFilter(
               author='hf-internal-testing'						,)     )
       _A  :			Any											=      set()
       for x in models:
              _A  :			List[Any]											=      x.modelId
              _A						,				_A  :			Any											=      model.split('/'     )
              if not model.startswith('tiny-random-'     ):
                     continue
              _A  :			Dict											=      model.replace('tiny-random-'						,''     )
              if not model[0].isupper():
                     continue
              if model not in model_names and model not in special_models:
                     continue
              _models.add(lowerCamelCase     )

       _A  :			Dict											=      sorted(_models     )
       # All tiny model names on Hub
       _A  :			List[Any]											=      {}
       for model in models:
              _A  :			Optional[Any]											=      F'hf-internal-testing/tiny-random-{model}'
              _A  :			Optional[Any]											=      model.split('-'     )[0]
              try:
                     _A  :			Any											=      hf_api.repo_info(lowerCamelCase     )
                     _A  :			Any											=      {
                         'tokenizer_classes': set(),
                         'processor_classes': set(),
                         'model_classes': set(),
                         'sha': repo_info.sha,
                     }
              except Exception:
                     continue
              try:
                     time.sleep(1     )
                     _A  :			Optional[Any]											=      AutoTokenizer.from_pretrained(lowerCamelCase     )
                     content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__     )
              except Exception:
                     pass
              try:
                     time.sleep(1     )
                     _A  :			str											=      AutoTokenizer.from_pretrained(lowerCamelCase						,use_fast=lowerCamelCase     )
                     content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__     )
              except Exception:
                     pass
              try:
                     time.sleep(1     )
                     _A  :			Dict											=      AutoImageProcessor.from_pretrained(lowerCamelCase     )
                     content["processor_classes"].add(img_p.__class__.__name__     )
              except Exception:
                     pass
              try:
                     time.sleep(1     )
                     _A  :			Tuple											=      AutoFeatureExtractor.from_pretrained(lowerCamelCase     )
                     if not isinstance(lowerCamelCase						,lowerCamelCase     ):
                            content["processor_classes"].add(feat_p.__class__.__name__     )
              except Exception:
                     pass
              try:
                     time.sleep(1     )
                     _A  :			Optional[Any]											=      getattr(lowerCamelCase						,lowerCamelCase     )
                     _A  :			List[Any]											=      model_class.from_pretrained(lowerCamelCase     )
                     content["model_classes"].add(m.__class__.__name__     )
              except Exception:
                     pass
              try:
                     time.sleep(1     )
                     _A  :			Tuple											=      getattr(lowerCamelCase						,F'TF{model}'     )
                     _A  :			Optional[int]											=      model_class.from_pretrained(lowerCamelCase     )
                     content["model_classes"].add(m.__class__.__name__     )
              except Exception:
                     pass

              _A  :			Any											=      sorted(content['tokenizer_classes']     )
              _A  :			Tuple											=      sorted(content['processor_classes']     )
              _A  :			List[str]											=      sorted(content['model_classes']     )

              _A  :			int											=      content
              with open(os.path.join(lowerCamelCase						,'hub_tiny_model_summary.json'     )						,'w'     ) as fp:
                     json.dump(lowerCamelCase						,lowerCamelCase						,ensure_ascii=lowerCamelCase						,indent=4     )


if __name__ == "__main__":
   A   :  Dict       		=				argparse.ArgumentParser()
   parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
   A   :  Optional[int]       		=				parser.parse_args()

   # This has to be `spawn` to avoid hanging forever!
   multiprocessing.set_start_method('''spawn''')

   A   :  Any       		=				'''tiny_models'''
   A   :  Union[str, Any]       		=				True
   A   :  List[Any]       		=				None
   A   :  Any       		=				get_tiny_model_names_from_repo()
   A   :  List[str]       		=				True
   A   :  Union[str, Any]       		=				True
   A   :  Tuple       		=				'''hf-internal-testing'''

   create_tiny_models(
       output_path,
       all,
       model_types,
       models_to_skip,
       no_check,
       upload,
       organization,
       token=os.environ.get('''TOKEN''', None),
       num_workers=args.num_workers,
   )






