
"""simple docstring"""
import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


lowercase		:    Union[str, Any]			=       logging.get_logger(__name__)

lowercase		:    Tuple			=       OrderedDict(
    [
        ('align', 'AlignProcessor'),
        ('altclip', 'AltCLIPProcessor'),
        ('bark', 'BarkProcessor'),
        ('blip', 'BlipProcessor'),
        ('blip-2', 'Blip2Processor'),
        ('bridgetower', 'BridgeTowerProcessor'),
        ('chinese_clip', 'ChineseCLIPProcessor'),
        ('clap', 'ClapProcessor'),
        ('clip', 'CLIPProcessor'),
        ('clipseg', 'CLIPSegProcessor'),
        ('flava', 'FlavaProcessor'),
        ('git', 'GitProcessor'),
        ('groupvit', 'CLIPProcessor'),
        ('hubert', 'Wav2Vec2Processor'),
        ('instructblip', 'InstructBlipProcessor'),
        ('layoutlmv2', 'LayoutLMv2Processor'),
        ('layoutlmv3', 'LayoutLMv3Processor'),
        ('markuplm', 'MarkupLMProcessor'),
        ('mctct', 'MCTCTProcessor'),
        ('mgp-str', 'MgpstrProcessor'),
        ('oneformer', 'OneFormerProcessor'),
        ('owlvit', 'OwlViTProcessor'),
        ('pix2struct', 'Pix2StructProcessor'),
        ('sam', 'SamProcessor'),
        ('sew', 'Wav2Vec2Processor'),
        ('sew-d', 'Wav2Vec2Processor'),
        ('speech_to_text', 'Speech2TextProcessor'),
        ('speech_to_text_2', 'Speech2Text2Processor'),
        ('speecht5', 'SpeechT5Processor'),
        ('trocr', 'TrOCRProcessor'),
        ('tvlt', 'TvltProcessor'),
        ('unispeech', 'Wav2Vec2Processor'),
        ('unispeech-sat', 'Wav2Vec2Processor'),
        ('vilt', 'ViltProcessor'),
        ('vision-text-dual-encoder', 'VisionTextDualEncoderProcessor'),
        ('wav2vec2', 'Wav2Vec2Processor'),
        ('wav2vec2-conformer', 'Wav2Vec2Processor'),
        ('wavlm', 'Wav2Vec2Processor'),
        ('whisper', 'WhisperProcessor'),
        ('xclip', 'XCLIPProcessor'),
    ]
)

lowercase		:    Optional[int]			=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)


def      lowerCamelCase_     (						lowerCamelCase_			)  ->      Optional[Any]:


					'''simple docstring'''
					for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
										if class_name in processors:
															__a:    List[str]		     =       model_type_to_module_name(lowerCamelCase_			)

															__a:    str		     =       importlib.import_module(f""".{module_name}"""						,					"""transformers.models"""			)
															try:
																				return getattr(lowerCamelCase_						,					lowerCamelCase_			)
															except AttributeError:
																				continue

					for processor in PROCESSOR_MAPPING._extra_content.values():
										if getattr(lowerCamelCase_						,					"""__name__"""						,					lowerCamelCase_			) == class_name:
															return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
					__a:    Any		     =       importlib.import_module("""transformers"""			)
					if hasattr(lowerCamelCase_						,					lowerCamelCase_			):
										return getattr(lowerCamelCase_						,					lowerCamelCase_			)

					return None




class  _snake_case	:






							def __init__(       self    :      int     ):






												"""simple docstring"""



												raise EnvironmentError(
												    """AutoProcessor is designed to be instantiated """
												    """using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method."""     )






							@classmethod
							@replace_list_option_in_docstrings(snake_case__     )
							def 				__lowercase	(       cls    :      List[Any]    ,			snake_case__    :      str    ,			**snake_case__    :      Optional[Any]     ):






												"""simple docstring"""



												__a:    List[Any]		     =       kwargs.pop("""config"""    ,			snake_case__     )
												__a:    str		     =       kwargs.pop("""trust_remote_code"""    ,			snake_case__     )
												__a:    List[str]		     =       True

												__a:    Tuple		     =       None
												__a:    List[Any]		     =       None

												# First, let's see if we have a preprocessor config.
												# Filter the kwargs for `get_file_from_repo`.
												__a:    List[str]		     =       {
												    key: kwargs[key] for key in inspect.signature(snake_case__     ).parameters.keys() if key in kwargs
												}
												# Let's start by checking whether the processor class is saved in an image processor
												__a:    List[str]		     =       get_file_from_repo(
												    snake_case__    ,			snake_case__    ,			**snake_case__     )
												if preprocessor_config_file is not None:
																	__a     ,					__a:    Optional[Any]		     =       ImageProcessingMixin.get_image_processor_dict(snake_case__    ,			**snake_case__     )
																	__a:    int		     =       config_dict.get("""processor_class"""    ,			snake_case__     )
																	if "AutoProcessor" in config_dict.get("""auto_map"""    ,			{}     ):
																						__a:    Optional[Any]		     =       config_dict["""auto_map"""]["""AutoProcessor"""]

        # If not found, let's check whether the processor class is saved in a feature extractor config
												if preprocessor_config_file is not None and processor_class is None:
																	__a     ,					__a:    int		     =       FeatureExtractionMixin.get_feature_extractor_dict(snake_case__    ,			**snake_case__     )
																	__a:    List[Any]		     =       config_dict.get("""processor_class"""    ,			snake_case__     )
																	if "AutoProcessor" in config_dict.get("""auto_map"""    ,			{}     ):
																						__a:    Any		     =       config_dict["""auto_map"""]["""AutoProcessor"""]

												if processor_class is None:
																	# Next, let's check whether the processor class is saved in a tokenizer
																	__a:    Optional[Any]		     =       get_file_from_repo(
																	    snake_case__    ,			snake_case__    ,			**snake_case__     )
																	if tokenizer_config_file is not None:
																						with open(snake_case__    ,			encoding="""utf-8"""     ) as reader:
																											__a:    Any		     =       json.load(snake_case__     )

																						__a:    Dict		     =       config_dict.get("""processor_class"""    ,			snake_case__     )
																						if "AutoProcessor" in config_dict.get("""auto_map"""    ,			{}     ):
																											__a:    Union[str, Any]		     =       config_dict["""auto_map"""]["""AutoProcessor"""]

												if processor_class is None:
																	# Otherwise, load config, if it can be loaded.
																	if not isinstance(snake_case__    ,			snake_case__     ):
																						__a:    str		     =       AutoConfig.from_pretrained(
																						    snake_case__    ,			trust_remote_code=snake_case__    ,			**snake_case__     )

																	# And check if the config contains the processor class.
																	__a:    Any		     =       getattr(snake_case__    ,			"""processor_class"""    ,			snake_case__     )
																	if hasattr(snake_case__    ,			"""auto_map"""     ) and "AutoProcessor" in config.auto_map:
																						__a:    int		     =       config.auto_map["""AutoProcessor"""]

												if processor_class is not None:
																	__a:    Optional[int]		     =       processor_class_from_name(snake_case__     )

												__a:    int		     =       processor_auto_map is not None
												__a:    List[Any]		     =       processor_class is not None or type(snake_case__     ) in PROCESSOR_MAPPING
												__a:    int		     =       resolve_trust_remote_code(
												    snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__     )

												if has_remote_code and trust_remote_code:
																	__a:    int		     =       get_class_from_dynamic_module(
																	    snake_case__    ,			snake_case__    ,			**snake_case__     )
																	__a:    Union[str, Any]		     =       kwargs.pop("""code_revision"""    ,			snake_case__     )
																	if os.path.isdir(snake_case__     ):
																						processor_class.register_for_auto_class()
																	return processor_class.from_pretrained(
																	    snake_case__    ,			trust_remote_code=snake_case__    ,			**snake_case__     )
												elif processor_class is not None:
																	return processor_class.from_pretrained(
																	    snake_case__    ,			trust_remote_code=snake_case__    ,			**snake_case__     )
												# Last try: we use the PROCESSOR_MAPPING.
												elif type(snake_case__     ) in PROCESSOR_MAPPING:
																	return PROCESSOR_MAPPING[type(snake_case__     )].from_pretrained(snake_case__    ,			**snake_case__     )

												# At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
												# tokenizer.
												try:
																	return AutoTokenizer.from_pretrained(
																	    snake_case__    ,			trust_remote_code=snake_case__    ,			**snake_case__     )
												except Exception:
																	try:
																						return AutoImageProcessor.from_pretrained(
																						    snake_case__    ,			trust_remote_code=snake_case__    ,			**snake_case__     )
																	except Exception:
																						pass

																	try:
																						return AutoFeatureExtractor.from_pretrained(
																						    snake_case__    ,			trust_remote_code=snake_case__    ,			**snake_case__     )
																	except Exception:
																						pass

												raise ValueError(
												    f"""Unrecognized processing class in {pretrained_model_name_or_path}. Can't instantiate a processor, a """
												    """tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains"""
												    """the files of at least one of those processing classes."""     )






							@staticmethod
							def 				__lowercase	(       snake_case__    :      Dict    ,			snake_case__    :      Union[str, Any]     ):






												"""simple docstring"""



												PROCESSOR_MAPPING.register(snake_case__    ,			snake_case__     )


"""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()
lowercase		:    List[str]			=       logging.get_logger(__name__)


def      lowerCamelCase_     (						lowerCamelCase_			)  ->      str:


					'''simple docstring'''
					if "backbone.0.body" in orig_key:
										__a:    Any		     =       orig_key.replace("""backbone.0.body"""						,					"""backbone.conv_encoder.model"""			)
					if "transformer" in orig_key:
										__a:    str		     =       orig_key.replace("""transformer."""						,					""""""			)
					if "norm1" in orig_key:
										if "encoder" in orig_key:
															__a:    List[str]		     =       orig_key.replace("""norm1"""						,					"""self_attn_layer_norm"""			)
										else:
															__a:    Optional[int]		     =       orig_key.replace("""norm1"""						,					"""encoder_attn_layer_norm"""			)
					if "norm2" in orig_key:
										if "encoder" in orig_key:
															__a:    Union[str, Any]		     =       orig_key.replace("""norm2"""						,					"""final_layer_norm"""			)
										else:
															__a:    str		     =       orig_key.replace("""norm2"""						,					"""self_attn_layer_norm"""			)
					if "norm3" in orig_key:
										__a:    int		     =       orig_key.replace("""norm3"""						,					"""final_layer_norm"""			)
					if "linear1" in orig_key:
										__a:    Dict		     =       orig_key.replace("""linear1"""						,					"""fc1"""			)
					if "linear2" in orig_key:
										__a:    Any		     =       orig_key.replace("""linear2"""						,					"""fc2"""			)
					if "query_embed" in orig_key:
										__a:    Dict		     =       orig_key.replace("""query_embed"""						,					"""query_position_embeddings"""			)
					if "cross_attn" in orig_key:
										__a:    Dict		     =       orig_key.replace("""cross_attn"""						,					"""encoder_attn"""			)

					return orig_key


def      lowerCamelCase_     (						lowerCamelCase_			)  ->      List[str]:


					'''simple docstring'''
					# transformer decoder self-attention layers
					for i in range(6			):
										# read in weights + bias of input projection layer of self-attention
										__a:    Optional[Any]		     =       state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_weight"""			)
										__a:    List[str]		     =       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
										__a:    Optional[int]		     =       in_proj_weight[:2_56, :]
										__a:    int		     =       in_proj_bias[:2_56]
										__a:    Tuple		     =       in_proj_weight[2_56:5_12, :]
										__a:    Union[str, Any]		     =       in_proj_bias[2_56:5_12]
										__a:    Optional[int]		     =       in_proj_weight[-2_56:, :]
										__a:    Any		     =       in_proj_bias[-2_56:]


def      lowerCamelCase_     (						)  ->      Union[str, Any]:


					'''simple docstring'''
					__a:    List[Any]		     =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
					__a:    Tuple		     =       Image.open(requests.get(lowerCamelCase_						,					stream=lowerCamelCase_			).raw			)

					return im


@torch.no_grad()
def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					)  ->      Union[str, Any]:


					'''simple docstring'''
					__a:    Optional[Any]		     =       DeformableDetrConfig()
					# set config attributes
					if single_scale:
										__a:    Optional[int]		     =       1
					__a:    Optional[Any]		     =       dilation
					__a:    Dict		     =       with_box_refine
					__a:    Any		     =       two_stage
					# set labels
					__a:    str		     =       91
					__a:    Dict		     =       """huggingface/label-files"""
					__a:    Tuple		     =       """coco-detection-id2label.json"""
					__a:    Optional[int]		     =       json.load(open(cached_download(hf_hub_url(lowerCamelCase_						,					lowerCamelCase_						,					repo_type="""dataset"""			)			)						,					"""r"""			)			)
					__a:    Union[str, Any]		     =       {int(lowerCamelCase_			): v for k, v in idalabel.items()}
					__a:    Dict		     =       idalabel
					__a:    List[Any]		     =       {v: k for k, v in idalabel.items()}

					# load image processor
					__a:    Tuple		     =       DeformableDetrImageProcessor(format="""coco_detection"""			)

					# prepare image
					__a:    Tuple		     =       prepare_img()
					__a:    Optional[int]		     =       image_processor(images=lowerCamelCase_						,					return_tensors="""pt"""			)
					__a:    Any		     =       encoding["""pixel_values"""]

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

					# load original state dict
					__a:    List[Any]		     =       torch.load(lowerCamelCase_						,					map_location="""cpu"""			)["""model"""]
					# rename keys
					for key in state_dict.copy().keys():
										__a:    Dict		     =       state_dict.pop(lowerCamelCase_			)
										__a:    List[str]		     =       val
					# query, key and value matrices need special treatment
					read_in_q_k_v(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:    Optional[int]		     =       """model."""
					for key in state_dict.copy().keys():
										if not key.startswith("""class_embed"""			) and not key.startswith("""bbox_embed"""			):
															__a:    List[Any]		     =       state_dict.pop(lowerCamelCase_			)
															__a:    Optional[int]		     =       val
    # finally, create HuggingFace model and load state dict
					__a:    List[str]		     =       DeformableDetrForObjectDetection(lowerCamelCase_			)
					model.load_state_dict(lowerCamelCase_			)
					model.eval()

					__a:    List[str]		     =       """cuda""" if torch.cuda.is_available() else """cpu"""
					model.to(lowerCamelCase_			)
					# verify our conversion
					__a:    str		     =       model(pixel_values.to(lowerCamelCase_			)			)

					__a:    Union[str, Any]		     =       torch.tensor(
					    [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]			)
					__a:    Union[str, Any]		     =       torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]			)

					if single_scale:
										__a:    Optional[Any]		     =       torch.tensor(
										    [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]			)
										__a:    Any		     =       torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]			)

					if single_scale and dilation:
										__a:    Dict		     =       torch.tensor(
										    [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]			)
										__a:    int		     =       torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]			)

					if with_box_refine:
										__a:    List[str]		     =       torch.tensor(
										    [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]			)
										__a:    Optional[int]		     =       torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]			)

					if with_box_refine and two_stage:
										__a:    List[Any]		     =       torch.tensor(
										    [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]			)
										__a:    Union[str, Any]		     =       torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]			)

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

					assert torch.allclose(outputs.logits[0, :3, :3]						,					expected_logits.to(lowerCamelCase_			)						,					atol=1e-4			)
					assert torch.allclose(outputs.pred_boxes[0, :3, :3]						,					expected_boxes.to(lowerCamelCase_			)						,					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(lowerCamelCase_			).mkdir(exist_ok=lowerCamelCase_			)
					model.save_pretrained(lowerCamelCase_			)
					image_processor.save_pretrained(lowerCamelCase_			)

					# Push to hub
					if push_to_hub:
										__a:    Dict		     =       """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=lowerCamelCase_						,					organization="""nielsr"""						,					commit_message="""Add model"""			)


if __name__ == "__main__":
			lowercase		:    int			=       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.'
			)
			lowercase		:    Union[str, Any]			=       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,
			)
