



'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase  						=			logging.get_logger(__name__)
def 			_lowerCAmelCase(      UpperCAmelCase_      :	Dict						) ->							Any:






							if "backbone.0.body" in orig_key:
														lowerCAmelCase__  =   orig_key.replace("""backbone.0.body"""					, """backbone.conv_encoder.model"""						)
							if "transformer" in orig_key:
														lowerCAmelCase__  =   orig_key.replace("""transformer."""					, """"""						)
							if "norm1" in orig_key:
														if "encoder" in orig_key:
																					lowerCAmelCase__  =   orig_key.replace("""norm1"""					, """self_attn_layer_norm"""						)
														else:
																					lowerCAmelCase__  =   orig_key.replace("""norm1"""					, """encoder_attn_layer_norm"""						)
							if "norm2" in orig_key:
														if "encoder" in orig_key:
																					lowerCAmelCase__  =   orig_key.replace("""norm2"""					, """final_layer_norm"""						)
														else:
																					lowerCAmelCase__  =   orig_key.replace("""norm2"""					, """self_attn_layer_norm"""						)
							if "norm3" in orig_key:
														lowerCAmelCase__  =   orig_key.replace("""norm3"""					, """final_layer_norm"""						)
							if "linear1" in orig_key:
														lowerCAmelCase__  =   orig_key.replace("""linear1"""					, """fc1"""						)
							if "linear2" in orig_key:
														lowerCAmelCase__  =   orig_key.replace("""linear2"""					, """fc2"""						)
							if "query_embed" in orig_key:
														lowerCAmelCase__  =   orig_key.replace("""query_embed"""					, """query_position_embeddings"""						)
							if "cross_attn" in orig_key:
														lowerCAmelCase__  =   orig_key.replace("""cross_attn"""					, """encoder_attn"""						)

							return orig_key
def 			_lowerCAmelCase(      UpperCAmelCase_      :	Union[str, Any]						) ->							int:






							for i in range(6						):
														# read in weights + bias of input projection layer of self-attention
														lowerCAmelCase__  =   state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''						)
														lowerCAmelCase__  =   state_dict.pop(F'''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:]
def 			_lowerCAmelCase(      ) ->							int:
							lowerCAmelCase__  =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCAmelCase__  =   Image.open(requests.get(a__					, stream=a__						).raw						)

							return im
@torch.no_grad()
def 			_lowerCAmelCase(      UpperCAmelCase_      :	List[Any]					, UpperCAmelCase_      :	Union[str, Any]					, UpperCAmelCase_      :	List[str]					, UpperCAmelCase_      :	Optional[Any]					, UpperCAmelCase_      :	Union[str, Any]					, UpperCAmelCase_      :	List[str]					, UpperCAmelCase_      :	int					, ) ->							Union[str, Any]:
							lowerCAmelCase__  =   DeformableDetrConfig()
							# set config attributes
							if single_scale:
														lowerCAmelCase__  =   1
							lowerCAmelCase__  =   dilation
							lowerCAmelCase__  =   with_box_refine
							lowerCAmelCase__  =   two_stage
							# set labels
							lowerCAmelCase__  =   91
							lowerCAmelCase__  =   """huggingface/label-files"""
							lowerCAmelCase__  =   """coco-detection-id2label.json"""
							lowerCAmelCase__  =   json.load(open(cached_download(hf_hub_url(a__					, a__					, repo_type="""dataset"""						)						)					, """r"""						)						)
							lowerCAmelCase__  =   {int(a__						): v for k, v in idalabel.items()}
							lowerCAmelCase__  =   idalabel
							lowerCAmelCase__  =   {v: k for k, v in idalabel.items()}

							# load image processor
							lowerCAmelCase__  =   DeformableDetrImageProcessor(format="""coco_detection"""						)

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

							logger.info("""Converting model..."""						)

							# load original state dict
							lowerCAmelCase__  =   torch.load(a__					, map_location="""cpu"""						)["""model"""]
							# rename keys
							for key in state_dict.copy().keys():
														lowerCAmelCase__  =   state_dict.pop(a__						)
														lowerCAmelCase__  =   val
							# query, key and value matrices need special treatment
							read_in_q_k_v(a__						)
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							lowerCAmelCase__  =   """model."""
							for key in state_dict.copy().keys():
														if not key.startswith("""class_embed"""						) and not key.startswith("""bbox_embed"""						):
																					lowerCAmelCase__  =   state_dict.pop(a__						)
																					lowerCAmelCase__  =   val
    # finally, create HuggingFace model and load state dict
							lowerCAmelCase__  =   DeformableDetrForObjectDetection(a__						)
							model.load_state_dict(a__						)
							model.eval()

							lowerCAmelCase__  =   """cuda""" if torch.cuda.is_available() else """cpu"""
							model.to(a__						)
							# verify our conversion
							lowerCAmelCase__  =   model(pixel_values.to(a__						)						)

							lowerCAmelCase__  =   torch.tensor(
							    [[-9.66_45, -4.34_49, -5.87_05], [-9.70_35, -3.85_04, -5.07_24], [-10.56_34, -5.33_79, -7.51_16]]						)
							lowerCAmelCase__  =   torch.tensor([[0.86_93, 0.22_89, 0.24_92], [0.31_50, 0.54_89, 0.58_45], [0.55_63, 0.75_80, 0.85_18]]						)

							if single_scale:
														lowerCAmelCase__  =   torch.tensor(
														    [[-9.90_51, -4.25_41, -6.48_52], [-9.69_47, -4.08_54, -6.80_33], [-10.06_65, -5.84_70, -7.70_03]]						)
														lowerCAmelCase__  =   torch.tensor([[0.72_92, 0.49_91, 0.55_32], [0.79_59, 0.24_26, 0.42_36], [0.75_82, 0.35_18, 0.44_51]]						)

							if single_scale and dilation:
														lowerCAmelCase__  =   torch.tensor(
														    [[-8.96_52, -4.10_74, -5.66_35], [-9.05_96, -4.94_47, -6.60_75], [-10.11_78, -4.52_75, -6.26_71]]						)
														lowerCAmelCase__  =   torch.tensor([[0.76_65, 0.41_30, 0.47_69], [0.83_64, 0.18_41, 0.33_91], [0.62_61, 0.38_95, 0.79_78]]						)

							if with_box_refine:
														lowerCAmelCase__  =   torch.tensor(
														    [[-8.88_95, -5.41_87, -6.81_53], [-8.47_06, -6.16_68, -7.61_84], [-9.00_42, -5.53_59, -6.91_41]]						)
														lowerCAmelCase__  =   torch.tensor([[0.78_28, 0.22_08, 0.43_23], [0.08_92, 0.59_96, 0.13_19], [0.55_24, 0.63_89, 0.89_14]]						)

							if with_box_refine and two_stage:
														lowerCAmelCase__  =   torch.tensor(
														    [[-6.71_08, -4.32_13, -6.37_77], [-8.90_14, -6.17_99, -6.72_40], [-6.93_15, -4.47_35, -6.22_98]]						)
														lowerCAmelCase__  =   torch.tensor([[0.25_83, 0.54_99, 0.46_83], [0.76_52, 0.90_68, 0.48_82], [0.54_90, 0.27_63, 0.05_64]]						)

							print("""Logits:"""					, outputs.logits[0, :3, :3]						)

							assert torch.allclose(outputs.logits[0, :3, :3]					, expected_logits.to(a__						)					, atol=1E-4						)
							assert torch.allclose(outputs.pred_boxes[0, :3, :3]					, expected_boxes.to(a__						)					, atol=1E-4						)

							print("""Everything ok!"""						)

							# 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__						)

							# Push to hub
							if push_to_hub:
														lowerCAmelCase__  =   """deformable-detr"""
														model_name += "-single-scale" if single_scale else ""
														model_name += "-dc5" if dilation else ""
														model_name += "-with-box-refine" if with_box_refine else ""
														model_name += "-two-stage" if two_stage else ""
														print("""Pushing model to hub..."""						)
														model.push_to_hub(repo_path_or_name=a__					, organization="""nielsr"""					, commit_message="""Add model"""						)


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

		parser.add_argument(
		    """--checkpoint_path""",
		    type=str,
		    default="""/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth""",
		    help="""Path to Pytorch checkpoint (.pth file) you\'d like to convert.""",
		)
		parser.add_argument("""--single_scale""", action="""store_true""", help="""Whether to set config.num_features_levels = 1.""")
		parser.add_argument("""--dilation""", action="""store_true""", help="""Whether to set config.dilation=True.""")
		parser.add_argument("""--with_box_refine""", action="""store_true""", help="""Whether to set config.with_box_refine=True.""")
		parser.add_argument("""--two_stage""", action="""store_true""", help="""Whether to set config.two_stage=True.""")
		parser.add_argument(
		    """--pytorch_dump_folder_path""",
		    default=None,
		    type=str,
		    required=True,
		    help="""Path to the folder to output PyTorch model.""",
		)
		parser.add_argument(
		    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
		)
		_UpperCamelCase  						=			parser.parse_args()
		convert_deformable_detr_checkpoint(
		    args.checkpoint_path,
		    args.single_scale,
		    args.dilation,
		    args.with_box_refine,
		    args.two_stage,
		    args.pytorch_dump_folder_path,
		    args.push_to_hub,
		)






'''simple docstring'''

import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


lowercase_  		=			logging.get_logger(__name__)
class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   Any							=				'semantic'






		def __init__( self		,    __A=1_0000		,    __A=True		,    __A=768		,    __A=False		,    __A=False		,    __A=False		,    __A=False		,    __A=0.7		,    __A=True		,    __A=1_0048		,    __A=12_9595		,    __A=12_9599		,    __A=1_0000		,    __A=256		,    __A=49.9		,    **__A		,    )			->  Any:
								super().__init__(
								    temperature=__A		,    do_sample=__A		,    eos_token_id=__A		,    renormalize_logits=__A		,    max_new_tokens=__A		,    output_scores=__A		,    return_dict_in_generate=__A		,    output_hidden_states=__A		,    output_attentions=__A		,    **__A		,    )

								_lowerCAmelCase					    =text_encoding_offset
								_lowerCAmelCase					    =text_pad_token
								_lowerCAmelCase					    =eos_token_id
								_lowerCAmelCase					    =semantic_infer_token
								_lowerCAmelCase					    =semantic_vocab_size
								_lowerCAmelCase					    =max_input_semantic_length
								_lowerCAmelCase					    =semantic_rate_hz

class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   Any							=				'coarse_acoustics'






		def __init__( self		,    __A=True		,    __A=False		,    __A=False		,    __A=False		,    __A=False		,    __A=0.7		,    __A=True		,    __A=1_2048		,    __A=75		,    __A=2		,    __A=1_2050		,    __A=256		,    __A = 630		,    __A = 60		,    **__A		,    )			->  Union[str, Any]:
								super().__init__(
								    temperature=__A		,    do_sample=__A		,    renormalize_logits=__A		,    output_scores=__A		,    return_dict_in_generate=__A		,    output_hidden_states=__A		,    output_attentions=__A		,    **__A		,    )

								_lowerCAmelCase					    =coarse_semantic_pad_token
								_lowerCAmelCase					    =coarse_rate_hz
								_lowerCAmelCase					    =n_coarse_codebooks
								_lowerCAmelCase					    =coarse_infer_token
								_lowerCAmelCase					    =max_coarse_input_length
								_lowerCAmelCase					    =max_coarse_history
								_lowerCAmelCase					    =sliding_window_len

class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   str							=				'fine_acoustics'






		def __init__( self		,    __A=0.5		,    __A=512		,    __A=1024		,    __A=8		,    **__A		,    )			->  int:
								super().__init__(temperature=__A					)

								_lowerCAmelCase					    =max_fine_history_length
								_lowerCAmelCase					    =max_fine_input_length
								_lowerCAmelCase					    =n_fine_codebooks

class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   int							=				'bark'
		lowercase :   Optional[Any]							=				True






		def __init__( self		,    __A = None		,    __A = None		,    __A = None		,    __A=2_4000		,    __A=1024		,    **__A		,    )			->  Optional[int]:
								if semantic_config is None:
														_lowerCAmelCase					    ={}
														logger.info('semantic_config is None. initializing the semantic model with default values.'					)

								if coarse_acoustics_config is None:
														_lowerCAmelCase					    ={}
														logger.info('coarse_acoustics_config is None. initializing the coarse model with default values.'					)

								if fine_acoustics_config is None:
														_lowerCAmelCase					    ={}
														logger.info('fine_acoustics_config is None. initializing the fine model with default values.'					)

								_lowerCAmelCase					    =BarkSemanticGenerationConfig(**__A					)
								_lowerCAmelCase					    =BarkCoarseGenerationConfig(**__A					)
								_lowerCAmelCase					    =BarkFineGenerationConfig(**__A					)

								_lowerCAmelCase					    =sample_rate
								_lowerCAmelCase					    =codebook_size






		@classmethod
		def 		UpperCamelCase__							( cls		,    __A		,    __A		,    __A		,    **__A		,    )			->  List[Any]:
								return cls(
								    semantic_config=semantic_config.to_dict()		,    coarse_acoustics_config=coarse_acoustics_config.to_dict()		,    fine_acoustics_config=fine_acoustics_config.to_dict()		,    **__A		,    )






		def 		UpperCamelCase__							( self					)			->  Optional[int]:
								_lowerCAmelCase					    =copy.deepcopy(self.__dict__					)

								_lowerCAmelCase					    =self.semantic_config.to_dict()
								_lowerCAmelCase					    =self.coarse_acoustics_config.to_dict()
								_lowerCAmelCase					    =self.fine_acoustics_config.to_dict()

								_lowerCAmelCase					    =self.__class__.model_type
								return output





