






"""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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
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}.cross_attn.out_proj.weight',
				        F'decoder.layers.{i}.encoder_attn.out_proj.weight',
				    )
				)
				rename_keys.append(
				    (
				        F'transformer.decoder.layers.{i}.cross_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'))

				# q, k, v projections in self/cross-attention in decoder for conditional DETR
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.sa_qcontent_proj.weight', F'decoder.layers.{i}.sa_qcontent_proj.weight')
				)
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.sa_kcontent_proj.weight', F'decoder.layers.{i}.sa_kcontent_proj.weight')
				)
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.sa_qpos_proj.weight', F'decoder.layers.{i}.sa_qpos_proj.weight')
				)
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.sa_kpos_proj.weight', F'decoder.layers.{i}.sa_kpos_proj.weight')
				)
				rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.weight', F'decoder.layers.{i}.sa_v_proj.weight'))
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.ca_qcontent_proj.weight', F'decoder.layers.{i}.ca_qcontent_proj.weight')
				)
				# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.ca_kcontent_proj.weight', F'decoder.layers.{i}.ca_kcontent_proj.weight')
				)
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.ca_kpos_proj.weight', F'decoder.layers.{i}.ca_kpos_proj.weight')
				)
				rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.weight', F'decoder.layers.{i}.ca_v_proj.weight'))
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight', F'decoder.layers.{i}.ca_qpos_sine_proj.weight')
				)

				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.sa_qcontent_proj.bias', F'decoder.layers.{i}.sa_qcontent_proj.bias')
				)
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.sa_kcontent_proj.bias', F'decoder.layers.{i}.sa_kcontent_proj.bias')
				)
				rename_keys.append((F'transformer.decoder.layers.{i}.sa_qpos_proj.bias', F'decoder.layers.{i}.sa_qpos_proj.bias'))
				rename_keys.append((F'transformer.decoder.layers.{i}.sa_kpos_proj.bias', F'decoder.layers.{i}.sa_kpos_proj.bias'))
				rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.bias', F'decoder.layers.{i}.sa_v_proj.bias'))
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.ca_qcontent_proj.bias', F'decoder.layers.{i}.ca_qcontent_proj.bias')
				)
				# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.ca_kcontent_proj.bias', F'decoder.layers.{i}.ca_kcontent_proj.bias')
				)
				rename_keys.append((F'transformer.decoder.layers.{i}.ca_kpos_proj.bias', F'decoder.layers.{i}.ca_kpos_proj.bias'))
				rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.bias', F'decoder.layers.{i}.ca_v_proj.bias'))
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias', F'decoder.layers.{i}.ca_qpos_sine_proj.bias')
				)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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'),
        ('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
        ('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
        ('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
        ('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
        ('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
        ('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
        ('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
        ('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
        ('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
        ('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
    ]
)


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Dict:
				lowercase    :      Optional[int]       = state_dict.pop(a_ )
				lowercase    :      str       = val


def      UpperCamelCase       ( _A )     ->       List[str]:
				lowercase    :      List[Any]       = OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												lowercase    :      int       = key.replace("""backbone.0.body"""  ,				"""backbone.conv_encoder.model""" )
												lowercase    :      Optional[int]       = value
								else:
												lowercase    :      Union[str, Any]       = value

				return new_state_dict


def      UpperCamelCase       ( _A  ,				_A=False )     ->       str:
				lowercase    :      Any       = """"""
				if is_panoptic:
								lowercase    :      Dict       = """conditional_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)
								lowercase    :      Dict       = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""" )
								lowercase    :      Dict       = 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
								lowercase    :      Optional[int]       = in_proj_weight[:256, :]
								lowercase    :      int       = in_proj_bias[:256]
								lowercase    :      Dict       = in_proj_weight[256:512, :]
								lowercase    :      Tuple       = in_proj_bias[256:512]
								lowercase    :      Tuple       = in_proj_weight[-256:, :]
								lowercase    :      Union[str, Any]       = in_proj_bias[-256:]


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

				return im


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

				# load image processor
				lowercase    :      List[str]       = """coco_panoptic""" if is_panoptic else """coco_detection"""
				lowercase    :      Optional[int]       = ConditionalDetrImageProcessor(format=a_ )

				# prepare image
				lowercase    :      Optional[int]       = prepare_img()
				lowercase    :      Optional[Any]       = image_processor(images=a_  ,				return_tensors="""pt""" )
				lowercase    :      List[str]       = encoding["""pixel_values"""]

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

				# load original model from torch hub
				lowercase    :      int       = torch.hub.load("""DeppMeng/ConditionalDETR"""  ,				a_  ,				pretrained=a_ ).eval()
				lowercase    :      Optional[Any]       = conditional_detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												lowercase    :      Any       = """conditional_detr.""" + src
								rename_key(a_  ,				a_  ,				a_ )
				lowercase    :      Union[str, Any]       = rename_backbone_keys(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
				lowercase    :      int       = """conditional_detr.model.""" if is_panoptic else """model."""
				for key in state_dict.copy().keys():
								if is_panoptic:
												if (
												    key.startswith("""conditional_detr""" )
												    and not key.startswith("""class_labels_classifier""" )
												    and not key.startswith("""bbox_predictor""" )
												):
																lowercase    :      List[str]       = state_dict.pop(a_ )
																lowercase    :      List[Any]       = val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																lowercase    :      Optional[Any]       = state_dict.pop(a_ )
																lowercase    :      Optional[int]       = val
												elif key.startswith("""bbox_attention""" ) or key.startswith("""mask_head""" ):
																continue
												else:
																lowercase    :      Optional[int]       = state_dict.pop(a_ )
																lowercase    :      str       = val
								else:
												if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
																lowercase    :      int       = state_dict.pop(a_ )
																lowercase    :      Any       = val
    # finally, create HuggingFace model and load state dict
				lowercase    :      List[str]       = ConditionalDetrForSegmentation(a_ ) if is_panoptic else ConditionalDetrForObjectDetection(a_ )
				model.load_state_dict(a_ )
				model.eval()
				model.push_to_hub(repo_id=a_  ,				organization="""DepuMeng"""  ,				commit_message="""Add model""" )
				# verify our conversion
				lowercase    :      Optional[Any]       = conditional_detr(a_ )
				lowercase    :      str       = model(a_ )
				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(a_ ).mkdir(exist_ok=a_ )
				model.save_pretrained(a_ )
				image_processor.save_pretrained(a_ )


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

				parser.add_argument(
				    '--model_name',
				    default='conditional_detr_resnet50',
				    type=str,
				    help='Name of the CONDITIONAL_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_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








'''simple docstring'''

import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


UpperCamelCase      					="\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n"




class      A     (   unittest.TestCase, SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				load_tool("""text-question-answering"""			)
       self.tool.setup()
       UpperCamelCase_					: List[str]            =				load_tool("""text-question-answering"""      ,  remote=__lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Union[str, Any]            =				self.tool(__lowerCAmelCase      ,  """What did Hugging Face do in April 2021?"""			)
       self.assertEqual(__lowerCAmelCase      ,  """launched the BigScience Research Workshop"""			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				self.remote_tool(__lowerCAmelCase      ,  """What did Hugging Face do in April 2021?"""			)
       self.assertEqual(__lowerCAmelCase      ,  """launched the BigScience Research Workshop"""			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[Any]            =				self.tool(text=__lowerCAmelCase      ,  question="""What did Hugging Face do in April 2021?"""			)
       self.assertEqual(__lowerCAmelCase      ,  """launched the BigScience Research Workshop"""			)





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Dict            =				self.remote_tool(text=__lowerCAmelCase      ,  question="""What did Hugging Face do in April 2021?"""			)
       self.assertEqual(__lowerCAmelCase      ,  """launched the BigScience Research Workshop"""			)






