

import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase_			      =      '''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase_			      =      direct_transformers_import(PATH_TO_TRANSFORMERS)

lowerCAmelCase_			      =      transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowerCAmelCase_			      =      {
    # used to compute the property `self.chunk_length`
    '''EncodecConfig''': ['''overlap'''],
    # used as `self.bert_model = BertModel(config, ...)`
    '''DPRConfig''': True,
    # not used in modeling files, but it's an important information
    '''FSMTConfig''': ['''langs'''],
    # used internally in the configuration class file
    '''GPTNeoConfig''': ['''attention_types'''],
    # used internally in the configuration class file
    '''EsmConfig''': ['''is_folding_model'''],
    # used during training (despite we don't have training script for these models yet)
    '''Mask2FormerConfig''': ['''ignore_value'''],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    '''OneFormerConfig''': ['''ignore_value''', '''norm'''],
    # used during preprocessing and collation, see `collating_graphormer.py`
    '''GraphormerConfig''': ['''spatial_pos_max'''],
    # used internally in the configuration class file
    '''T5Config''': ['''feed_forward_proj'''],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    '''MT5Config''': ['''feed_forward_proj''', '''tokenizer_class'''],
    '''UMT5Config''': ['''feed_forward_proj''', '''tokenizer_class'''],
    # used internally in the configuration class file
    '''LongT5Config''': ['''feed_forward_proj'''],
    # used internally in the configuration class file
    '''SwitchTransformersConfig''': ['''feed_forward_proj'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''BioGptConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''GLPNConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''SegformerConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''CvtConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''PerceiverConfig''': ['''layer_norm_eps'''],
    # used internally to calculate the feature size
    '''InformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
    # used internally to calculate the feature size
    '''TimeSeriesTransformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
    # used internally to calculate the feature size
    '''AutoformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
    # used internally to calculate `mlp_dim`
    '''SamVisionConfig''': ['''mlp_ratio'''],
    # For (head) training, but so far not implemented
    '''ClapAudioConfig''': ['''num_classes'''],
    # Not used, but providing useful information to users
    '''SpeechT5HifiGanConfig''': ['''sampling_rate'''],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        '''CLIPSegConfig''': True,
        '''DeformableDetrConfig''': True,
        '''DetaConfig''': True,
        '''DinatConfig''': True,
        '''DonutSwinConfig''': True,
        '''EfficientFormerConfig''': True,
        '''FSMTConfig''': True,
        '''JukeboxConfig''': True,
        '''LayoutLMv2Config''': True,
        '''MaskFormerSwinConfig''': True,
        '''MT5Config''': True,
        '''NatConfig''': True,
        '''OneFormerConfig''': True,
        '''PerceiverConfig''': True,
        '''RagConfig''': True,
        '''SpeechT5Config''': True,
        '''SwinConfig''': True,
        '''Swin2SRConfig''': True,
        '''Swinv2Config''': True,
        '''SwitchTransformersConfig''': True,
        '''TableTransformerConfig''': True,
        '''TapasConfig''': True,
        '''TransfoXLConfig''': True,
        '''UniSpeechConfig''': True,
        '''UniSpeechSatConfig''': True,
        '''WavLMConfig''': True,
        '''WhisperConfig''': True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        '''JukeboxPriorConfig''': True,
        # TODO: @Younes (for `is_decoder`)
        '''Pix2StructTextConfig''': True,
    }
)


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						snake_case_  =      False
						for attribute in attributes:
												for modeling_source in source_strings:
																		# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
																		if (
																		    F'''config.{attribute}''' in modeling_source
																		    or F'''getattr(config, "{attribute}"''' in modeling_source
																		    or F'''getattr(self.config, "{attribute}"''' in modeling_source
																		):
																								snake_case_  =      True
																		# Deal with multi-line cases
																		elif (
																		    re.search(
																		        RF'''getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"'''    ,						SCREAMING_SNAKE_CASE__    ,						)
																		    is not None
																		):
																								snake_case_  =      True
																		# `SequenceSummary` is called with `SequenceSummary(config)`
																		elif attribute in [
																		    "summary_type",
																		    "summary_use_proj",
																		    "summary_activation",
																		    "summary_last_dropout",
																		    "summary_proj_to_labels",
																		    "summary_first_dropout",
																		]:
																								if "SequenceSummary" in modeling_source:
																														snake_case_  =      True
																		if attribute_used:
																								break
												if attribute_used:
																		break

    # common and important attributes, even if they do not always appear in the modeling files
						snake_case_  =      [
						    '''bos_index''',
						    '''eos_index''',
						    '''pad_index''',
						    '''unk_index''',
						    '''mask_index''',
						    '''image_size''',
						    '''use_cache''',
						    '''out_features''',
						    '''out_indices''',
						]
						snake_case_  =      ['''encoder_no_repeat_ngram_size''']

						# Special cases to be allowed
						snake_case_  =      True
						if not attribute_used:
												snake_case_  =      False
												for attribute in attributes:
																		# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
																		if attribute in ["is_encoder_decoder"] and default_value is True:
																								snake_case_  =      True
																		elif attribute in ["tie_word_embeddings"] and default_value is False:
																								snake_case_  =      True

																		# Allow cases without checking the default value in the configuration class
																		elif attribute in attributes_to_allow + attributes_used_in_generation:
																								snake_case_  =      True
																		elif attribute.endswith('''_token_id'''			):
																								snake_case_  =      True

																		# configuration class specific cases
																		if not case_allowed:
																								snake_case_  =      SPECIAL_CASES_TO_ALLOW.get(config_class.__name__    ,						[]			)
																								snake_case_  =      allowed_cases is True or attribute in allowed_cases

						return attribute_used or case_allowed


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      dict(inspect.signature(config_class.__init__			).parameters			)
						snake_case_  =      [x for x in list(signature.keys()			) if x not in ['''self''', '''kwargs''']]
						snake_case_  =      [signature[param].default for param in parameter_names]

						# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
						# as one variant is used, the test should pass
						snake_case_  =      {}
						if len(config_class.attribute_map			) > 0:
												snake_case_  =      {v: k for k, v in config_class.attribute_map.items()}

						# Get the path to modeling source files
						snake_case_  =      inspect.getsourcefile(SCREAMING_SNAKE_CASE__			)
						snake_case_  =      os.path.dirname(SCREAMING_SNAKE_CASE__			)
						# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
						snake_case_  =      [os.path.join(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			) for fn in os.listdir(SCREAMING_SNAKE_CASE__			) if fn.startswith('''modeling_'''			)]

						# Get the source code strings
						snake_case_  =      []
						for path in modeling_paths:
												if os.path.isfile(SCREAMING_SNAKE_CASE__			):
																		with open(SCREAMING_SNAKE_CASE__			) as fp:
																								modeling_sources.append(fp.read()			)

						snake_case_  =      []
						for config_param, default_value in zip(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
												# `attributes` here is all the variant names for `config_param`
												snake_case_  =      [config_param]
												# some configuration classes have non-empty `attribute_map`, and both names could be used in the
												# corresponding modeling files. As long as one of them appears, it is fine.
												if config_param in reversed_attribute_map:
																		attributes.append(reversed_attribute_map[config_param]			)

												if not check_attribute_being_used(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
																		unused_attributes.append(attributes[0]			)

						return sorted(SCREAMING_SNAKE_CASE__			)


def     __SCREAMING_SNAKE_CASE   ():
						snake_case_  =      {}
						for _config_class in list(CONFIG_MAPPING.values()			):
												# Skip deprecated models
												if "models.deprecated" in _config_class.__module__:
																		continue
												# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
												snake_case_  =      [
												    cls
												    for name, cls in inspect.getmembers(
												        inspect.getmodule(_config_class			)    ,						lambda SCREAMING_SNAKE_CASE__			: inspect.isclass(SCREAMING_SNAKE_CASE__			)
												        and issubclass(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)
												        and inspect.getmodule(SCREAMING_SNAKE_CASE__			) == inspect.getmodule(_config_class			)    ,						)
												]
												for config_class in config_classes_in_module:
																		snake_case_  =      check_config_attributes_being_used(SCREAMING_SNAKE_CASE__			)
																		if len(SCREAMING_SNAKE_CASE__			) > 0:
																								snake_case_  =      unused_attributes

						if len(SCREAMING_SNAKE_CASE__			) > 0:
												snake_case_  =      '''The following configuration classes contain unused attributes in the corresponding modeling files:\n'''
												for name, attributes in configs_with_unused_attributes.items():
																		error += F'''{name}: {attributes}\n'''

												raise ValueError(SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
					check_config_attributes()

"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_A     :			Optional[int]  						=			logging.get_logger(__name__)


def     __magic_name__      ( __snake_case							:			str    )					->							YolosConfig:
       lowercase       :							int									=				YolosConfig()

       # size of the architecture
       if "yolos_ti" in yolos_name:
              lowercase       :							Union[str, Any]									=				192
              lowercase       :							Tuple									=				768
              lowercase       :							Optional[Any]									=				12
              lowercase       :							List[str]									=				3
              lowercase       :							int									=				[800, 1333]
              lowercase       :							Union[str, Any]									=				False
       elif yolos_name == "yolos_s_dWr":
              lowercase       :							int									=				330
              lowercase       :							List[str]									=				14
              lowercase       :							Dict									=				6
              lowercase       :							Any									=				1320
       elif "yolos_s" in yolos_name:
              lowercase       :							str									=				384
              lowercase       :							int									=				1536
              lowercase       :							Any									=				12
              lowercase       :							int									=				6
       elif "yolos_b" in yolos_name:
              lowercase       :							str									=				[800, 1344]

       lowercase       :							Tuple									=				91
       lowercase       :							Tuple									=				"huggingface/label-files"
       lowercase       :							Any									=				"coco-detection-id2label.json"
       lowercase       :							str									=				json.load(open(hf_hub_download(__snake_case	,					__snake_case	,					repo_type="dataset"    )	,					"r"    )    )
       lowercase       :							List[str]									=				{int(__snake_case    ): v for k, v in idalabel.items()}
       lowercase       :							Union[str, Any]									=				idalabel
       lowercase       :							Union[str, Any]									=				{v: k for k, v in idalabel.items()}

       return config


def     __magic_name__      ( __snake_case							:			dict	,					__snake_case							:			YolosConfig	,					__snake_case							:			bool = False    )					->							int:
       for i in range(config.num_hidden_layers    ):
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              lowercase       :							Dict									=				state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""    )
              lowercase       :							int									=				state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""    )
              # next, add query, keys and values (in that order) to the state dict
              lowercase       :							Union[str, Any]									=				in_proj_weight[: config.hidden_size, :]
              lowercase       :							int									=				in_proj_bias[: config.hidden_size]
              lowercase       :							str									=				in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              lowercase       :							List[Any]									=				in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              lowercase       :							Dict									=				in_proj_weight[-config.hidden_size :, :]
              lowercase       :							List[Any]									=				in_proj_bias[-config.hidden_size :]


def     __magic_name__      ( __snake_case							:			str    )					->							str:
       if "backbone" in name:
              lowercase       :							Union[str, Any]									=				name.replace("backbone"	,					"vit"    )
       if "cls_token" in name:
              lowercase       :							Union[str, Any]									=				name.replace("cls_token"	,					"embeddings.cls_token"    )
       if "det_token" in name:
              lowercase       :							Dict									=				name.replace("det_token"	,					"embeddings.detection_tokens"    )
       if "mid_pos_embed" in name:
              lowercase       :							Optional[Any]									=				name.replace("mid_pos_embed"	,					"encoder.mid_position_embeddings"    )
       if "pos_embed" in name:
              lowercase       :							Any									=				name.replace("pos_embed"	,					"embeddings.position_embeddings"    )
       if "patch_embed.proj" in name:
              lowercase       :							Optional[int]									=				name.replace("patch_embed.proj"	,					"embeddings.patch_embeddings.projection"    )
       if "blocks" in name:
              lowercase       :							Union[str, Any]									=				name.replace("blocks"	,					"encoder.layer"    )
       if "attn.proj" in name:
              lowercase       :							Optional[int]									=				name.replace("attn.proj"	,					"attention.output.dense"    )
       if "attn" in name:
              lowercase       :							Dict									=				name.replace("attn"	,					"attention.self"    )
       if "norm1" in name:
              lowercase       :							List[str]									=				name.replace("norm1"	,					"layernorm_before"    )
       if "norm2" in name:
              lowercase       :							Union[str, Any]									=				name.replace("norm2"	,					"layernorm_after"    )
       if "mlp.fc1" in name:
              lowercase       :							Union[str, Any]									=				name.replace("mlp.fc1"	,					"intermediate.dense"    )
       if "mlp.fc2" in name:
              lowercase       :							List[str]									=				name.replace("mlp.fc2"	,					"output.dense"    )
       if "class_embed" in name:
              lowercase       :							List[Any]									=				name.replace("class_embed"	,					"class_labels_classifier"    )
       if "bbox_embed" in name:
              lowercase       :							Optional[Any]									=				name.replace("bbox_embed"	,					"bbox_predictor"    )
       if "vit.norm" in name:
              lowercase       :							Optional[int]									=				name.replace("vit.norm"	,					"vit.layernorm"    )

       return name


def     __magic_name__      ( __snake_case							:			dict	,					__snake_case							:			YolosForObjectDetection    )					->							dict:
       for key in orig_state_dict.copy().keys():
              lowercase       :							List[Any]									=				orig_state_dict.pop(__snake_case    )

              if "qkv" in key:
                     lowercase       :							Union[str, Any]									=				key.split("."    )
                     lowercase       :							List[Any]									=				int(key_split[2]    )
                     lowercase       :							Union[str, Any]									=				model.vit.encoder.layer[layer_num].attention.attention.all_head_size
                     if "weight" in key:
                            lowercase       :							int									=				val[:dim, :]
                            lowercase       :							List[Any]									=				val[
                                dim : dim * 2, :
                            ]
                            lowercase       :							Any									=				val[-dim:, :]
                     else:
                            lowercase       :							Dict									=				val[:dim]
                            lowercase       :							Dict									=				val[dim : dim * 2]
                            lowercase       :							str									=				val[-dim:]
              else:
                     lowercase       :							List[str]									=				val

       return orig_state_dict


def     __magic_name__      ( )					->							torch.Tensor:
       lowercase       :							Optional[int]									=				"http://images.cocodataset.org/val2017/000000039769.jpg"
       lowercase       :							Tuple									=				Image.open(requests.get(__snake_case	,					stream=__snake_case    ).raw    )
       return im


@torch.no_grad()
def     __magic_name__      ( __snake_case							:			str	,					__snake_case							:			str	,					__snake_case							:			str	,					__snake_case							:			bool = False    )					->							Dict:
       lowercase       :							Optional[int]									=				get_yolos_config(__snake_case    )

       # load original state_dict
       lowercase       :							str									=				torch.load(__snake_case	,					map_location="cpu"    )["model"]

       # load 🤗 model
       lowercase       :							str									=				YolosForObjectDetection(__snake_case    )
       model.eval()
       lowercase       :							Dict									=				convert_state_dict(__snake_case	,					__snake_case    )
       model.load_state_dict(__snake_case    )

       # Check outputs on an image, prepared by YolosImageProcessor
       lowercase       :							Any									=				800 if yolos_name != "yolos_ti" else 512
       lowercase       :							List[Any]									=				YolosImageProcessor(format="coco_detection"	,					size=__snake_case    )
       lowercase       :							Any									=				image_processor(images=prepare_img()	,					return_tensors="pt"    )
       lowercase       :							Any									=				model(**__snake_case    )
       lowercase						,       lowercase       :							Tuple									=				outputs.logits, outputs.pred_boxes

       lowercase						,       lowercase       :							int									=				None, None
       if yolos_name == "yolos_ti":
              lowercase       :							int									=				torch.tensor(
                  [[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]]    )
              lowercase       :							List[Any]									=				torch.tensor(
                  [[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]]    )
       elif yolos_name == "yolos_s_200_pre":
              lowercase       :							List[Any]									=				torch.tensor(
                  [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]    )
              lowercase       :							List[Any]									=				torch.tensor(
                  [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]    )
       elif yolos_name == "yolos_s_300_pre":
              lowercase       :							Optional[int]									=				torch.tensor(
                  [[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]]    )
              lowercase       :							Tuple									=				torch.tensor(
                  [[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]]    )
       elif yolos_name == "yolos_s_dWr":
              lowercase       :							Tuple									=				torch.tensor(
                  [[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]]    )
              lowercase       :							List[str]									=				torch.tensor(
                  [[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]]    )
       elif yolos_name == "yolos_base":
              lowercase       :							Optional[Any]									=				torch.tensor(
                  [[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]]    )
              lowercase       :							List[str]									=				torch.tensor(
                  [[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]]    )
       else:
              raise ValueError(f"""Unknown yolos_name: {yolos_name}"""    )

       assert torch.allclose(logits[0, :3, :3]	,					__snake_case	,					atol=1E-4    )
       assert torch.allclose(pred_boxes[0, :3, :3]	,					__snake_case	,					atol=1E-4    )

       Path(__snake_case    ).mkdir(exist_ok=__snake_case    )
       print(f"""Saving model {yolos_name} to {pytorch_dump_folder_path}"""    )
       model.save_pretrained(__snake_case    )
       print(f"""Saving image processor to {pytorch_dump_folder_path}"""    )
       image_processor.save_pretrained(__snake_case    )

       if push_to_hub:
              lowercase       :							Optional[int]									=				{
                  "yolos_ti": "yolos-tiny",
                  "yolos_s_200_pre": "yolos-small",
                  "yolos_s_300_pre": "yolos-small-300",
                  "yolos_s_dWr": "yolos-small-dwr",
                  "yolos_base": "yolos-base",
              }

              print("Pushing to the hub..."    )
              lowercase       :							Optional[Any]									=				model_mapping[yolos_name]
              image_processor.push_to_hub(__snake_case	,					organization="hustvl"    )
              model.push_to_hub(__snake_case	,					organization="hustvl"    )


if __name__ == "__main__":
   _A     :			List[Any]  						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--yolos_name""",
       default="""yolos_s_200_pre""",
       type=str,
       help=(
           """Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"""
           """ 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."""
       ),
   )
   parser.add_argument(
       """--checkpoint_path""", default=None, type=str, help="""Path to the original state dict (.pth file)."""
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
   )
   parser.add_argument(
       """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
   )

   _A     :			Any  						=			parser.parse_args()
   convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


