
"""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       						=       logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__A       						=       []
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       )				-> Dict:
	_lowerCAmelCase								=state_dict.pop(__UpperCamelCase       )
	_lowerCAmelCase								=val



def     _lowerCamelCase(__UpperCamelCase       )				-> Dict:
	_lowerCAmelCase								=OrderedDict()
	for key, value in state_dict.items():
		if "backbone.0.body" in key:
			_lowerCAmelCase								=key.replace("""backbone.0.body"""    , """backbone.conv_encoder.model"""       )
			_lowerCAmelCase								=value
		else:
			_lowerCAmelCase								=value

	return new_state_dict



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



def     _lowerCamelCase()				-> Dict:
	_lowerCAmelCase								="""http://images.cocodataset.org/val2017/000000039769.jpg"""
	_lowerCAmelCase								=Image.open(requests.get(__UpperCamelCase    , stream=__UpperCamelCase       ).raw       )

	return im



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

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

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

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

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





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


__A       						=       {'configuration_bert_generation': ['BertGenerationConfig']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A       						=       ['BertGenerationTokenizer']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A       						=       [
			    'BertGenerationDecoder',
			    'BertGenerationEncoder',
			    'BertGenerationPreTrainedModel',
			    'load_tf_weights_in_bert_generation',
			]


if TYPE_CHECKING:
			from .configuration_bert_generation import BertGenerationConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bert_generation import BertGenerationTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bert_generation import (
						    BertGenerationDecoder,
						    BertGenerationEncoder,
						    BertGenerationPreTrainedModel,
						    load_tf_weights_in_bert_generation,
						)

else:
			import sys

			__A       						=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



