





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()
_UpperCAmelCase										=					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_UpperCAmelCase										=					[]
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_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Optional[Any]:
       UpperCamelCase_ 							=	state_dict.pop(UpperCamelCase_	)
       UpperCamelCase_ 							=	val




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		List[str]:
       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_			(				UpperCamelCase_       ,		UpperCamelCase_=False	)  ->		Optional[Any]:
       UpperCamelCase_ 							=	""
       if is_panoptic:
              UpperCamelCase_ 							=	"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[:256, :]
              UpperCamelCase_ 							=	in_proj_bias[:256]
              UpperCamelCase_ 							=	in_proj_weight[256:512, :]
              UpperCamelCase_ 							=	in_proj_bias[256:512]
              UpperCamelCase_ 							=	in_proj_weight[-256:, :]
              UpperCamelCase_ 							=	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
              UpperCamelCase_ 							=	state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''	)
              UpperCamelCase_ 							=	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
              UpperCamelCase_ 							=	in_proj_weight[:256, :]
              UpperCamelCase_ 							=	in_proj_bias[:256]
              UpperCamelCase_ 							=	in_proj_weight[256:512, :]
              UpperCamelCase_ 							=	in_proj_bias[256:512]
              UpperCamelCase_ 							=	in_proj_weight[-256:, :]
              UpperCamelCase_ 							=	in_proj_bias[-256:]
              # read in weights + bias of input projection layer of cross-attention
              UpperCamelCase_ 							=	state_dict.pop(
                  F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''	)
              UpperCamelCase_ 							=	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
              UpperCamelCase_ 							=	in_proj_weight_cross_attn[:256, :]
              UpperCamelCase_ 							=	in_proj_bias_cross_attn[:256]
              UpperCamelCase_ 							=	in_proj_weight_cross_attn[256:512, :]
              UpperCamelCase_ 							=	in_proj_bias_cross_attn[256:512]
              UpperCamelCase_ 							=	in_proj_weight_cross_attn[-256:, :]
              UpperCamelCase_ 							=	in_proj_bias_cross_attn[-256:]




def 			lowerCAmelCase_			(				)  ->		Optional[Any]:
       UpperCamelCase_ 							=	"http://images.cocodataset.org/val2017/000000039769.jpg"
       UpperCamelCase_ 							=	Image.open(requests.get(UpperCamelCase_       ,		stream=UpperCamelCase_	).raw	)

       return im




@torch.no_grad()
def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		int:
       UpperCamelCase_ 							=	DetrConfig()
       # 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_ 							=	250
       else:
              UpperCamelCase_ 							=	91
              UpperCamelCase_ 							=	"huggingface/label-files"
              UpperCamelCase_ 							=	"coco-detection-id2label.json"
              UpperCamelCase_ 							=	json.load(open(hf_hub_download(UpperCamelCase_       ,		UpperCamelCase_       ,		repo_type="dataset"	)       ,		"r"	)	)
              UpperCamelCase_ 							=	{int(UpperCamelCase_	): 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_ 							=	DetrImageProcessor(format=UpperCamelCase_	)

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

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

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




import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       from transformers.models.electra.modeling_flax_electra import (
           FlaxElectraForCausalLM,
           FlaxElectraForMaskedLM,
           FlaxElectraForMultipleChoice,
           FlaxElectraForPreTraining,
           FlaxElectraForQuestionAnswering,
           FlaxElectraForSequenceClassification,
           FlaxElectraForTokenClassification,
           FlaxElectraModel,
       )







class 		__magic_name__     (				unittest.TestCase				):

   '''simple docstring'''




   def __init__(							self,       lowercase_,       lowercase_=13,       lowercase_=7,       lowercase_=True,       lowercase_=True,       lowercase_=True,       lowercase_=True,       lowercase_=99,       lowercase_=24,       lowercase_=32,       lowercase_=5,       lowercase_=4,       lowercase_=37,       lowercase_="gelu",       lowercase_=0.1,       lowercase_=0.1,       lowercase_=512,       lowercase_=16,       lowercase_=2,       lowercase_=0.02,       lowercase_=4,       )				->       int:






     """simple docstring"""






     a__    =parent
     a__    =batch_size
     a__    =seq_length
     a__    =is_training
     a__    =use_attention_mask
     a__    =use_token_type_ids
     a__    =use_labels
     a__    =vocab_size
     a__    =hidden_size
     a__    =embedding_size
     a__    =num_hidden_layers
     a__    =num_attention_heads
     a__    =intermediate_size
     a__    =hidden_act
     a__    =hidden_dropout_prob
     a__    =attention_probs_dropout_prob
     a__    =max_position_embeddings
     a__    =type_vocab_size
     a__    =type_sequence_label_size
     a__    =initializer_range
     a__    =num_choices



   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     a__    =ids_tensor([self.batch_size, self.seq_length],       self.vocab_size			)

     a__    =None
     if self.use_attention_mask:
       a__    =random_attention_mask([self.batch_size, self.seq_length]			)

     a__    =None
     if self.use_token_type_ids:
       a__    =ids_tensor([self.batch_size, self.seq_length],       self.type_vocab_size			)

     a__    =ElectraConfig(
         vocab_size=self.vocab_size,       hidden_size=self.hidden_size,       embedding_size=self.embedding_size,       num_hidden_layers=self.num_hidden_layers,       num_attention_heads=self.num_attention_heads,       intermediate_size=self.intermediate_size,       hidden_act=self.hidden_act,       hidden_dropout_prob=self.hidden_dropout_prob,       attention_probs_dropout_prob=self.attention_probs_dropout_prob,       max_position_embeddings=self.max_position_embeddings,       type_vocab_size=self.type_vocab_size,       initializer_range=self.initializer_range,       )

     return config, input_ids, token_type_ids, attention_mask



   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     a__    =self.prepare_config_and_inputs()
     a__, a__, a__, a__    =config_and_inputs
     a__    ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
     return config, inputs_dict







@require_flax
class 		__magic_name__     (				lowerCamelCase__						,			unittest.TestCase				):

   '''simple docstring'''




   lowerCamelCase__					:      List[str]        =  True

   lowerCamelCase__					:      Tuple        =  (
       (
           FlaxElectraModel,
           FlaxElectraForCausalLM,
           FlaxElectraForMaskedLM,
           FlaxElectraForPreTraining,
           FlaxElectraForTokenClassification,
           FlaxElectraForQuestionAnswering,
           FlaxElectraForMultipleChoice,
           FlaxElectraForSequenceClassification,
       )
       if is_flax_available()
       else ()
   )



   def 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






     a__    =FlaxElectraModelTester(self			)



   @slow
   def 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






     for model_class_name in self.all_model_classes:
       if model_class_name == FlaxElectraForMaskedLM:
         a__    =model_class_name.from_pretrained('''google/electra-small-generator'''			)
       else:
         a__    =model_class_name.from_pretrained('''google/electra-small-discriminator'''			)
       a__    =model(np.ones((1, 1)			)			)
       self.assertIsNotNone(lowercase_			)
