





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_a 						=      logging.get_logger(__name__)

_a 						=      {
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}






class 					__A    (    UpperCAmelCase__ ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""table-transformer"""
			lowerCAmelCase_							     =						["""past_key_values"""]
			lowerCAmelCase_							     =						{
			    """hidden_size""": """d_model""",
			    """num_attention_heads""": """encoder_attention_heads""",
			}





			def __init__(    self       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=3       ,  __lowerCAmelCase=1_0_0       ,  __lowerCAmelCase=6       ,  __lowerCAmelCase=2_0_4_8       ,  __lowerCAmelCase=8       ,  __lowerCAmelCase=6       ,  __lowerCAmelCase=2_0_4_8       ,  __lowerCAmelCase=8       ,  __lowerCAmelCase=0.0       ,  __lowerCAmelCase=0.0       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase="relu"       ,  __lowerCAmelCase=2_5_6       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=0.0       ,  __lowerCAmelCase=0.0       ,  __lowerCAmelCase=0.02       ,  __lowerCAmelCase=1.0       ,  __lowerCAmelCase=False       ,  __lowerCAmelCase="sine"       ,  __lowerCAmelCase="resnet50"       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=False       ,  __lowerCAmelCase=1       ,  __lowerCAmelCase=5       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase=1       ,  __lowerCAmelCase=1       ,  __lowerCAmelCase=5       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase=0.1       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				if backbone_config is not None and use_timm_backbone:
					raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''							)

				if not use_timm_backbone:
					if backbone_config is None:
						logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''							)
						lowerCamelCase__								=		CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']							)
					elif isinstance(_a       ,  _a							):
						lowerCamelCase__								=		backbone_config.get('''model_type'''							)
						lowerCamelCase__								=		CONFIG_MAPPING[backbone_model_type]
						lowerCamelCase__								=		config_class.from_dict(_a							)
					# set timm attributes to None
					lowerCamelCase__				,	lowerCamelCase__				,	lowerCamelCase__								=		None, None, None

				lowerCamelCase__								=		use_timm_backbone
				lowerCamelCase__								=		backbone_config
				lowerCamelCase__								=		num_channels
				lowerCamelCase__								=		num_queries
				lowerCamelCase__								=		d_model
				lowerCamelCase__								=		encoder_ffn_dim
				lowerCamelCase__								=		encoder_layers
				lowerCamelCase__								=		encoder_attention_heads
				lowerCamelCase__								=		decoder_ffn_dim
				lowerCamelCase__								=		decoder_layers
				lowerCamelCase__								=		decoder_attention_heads
				lowerCamelCase__								=		dropout
				lowerCamelCase__								=		attention_dropout
				lowerCamelCase__								=		activation_dropout
				lowerCamelCase__								=		activation_function
				lowerCamelCase__								=		init_std
				lowerCamelCase__								=		init_xavier_std
				lowerCamelCase__								=		encoder_layerdrop
				lowerCamelCase__								=		decoder_layerdrop
				lowerCamelCase__								=		encoder_layers
				lowerCamelCase__								=		auxiliary_loss
				lowerCamelCase__								=		position_embedding_type
				lowerCamelCase__								=		backbone
				lowerCamelCase__								=		use_pretrained_backbone
				lowerCamelCase__								=		dilation
				# Hungarian matcher
				lowerCamelCase__								=		class_cost
				lowerCamelCase__								=		bbox_cost
				lowerCamelCase__								=		giou_cost
				# Loss coefficients
				lowerCamelCase__								=		mask_loss_coefficient
				lowerCamelCase__								=		dice_loss_coefficient
				lowerCamelCase__								=		bbox_loss_coefficient
				lowerCamelCase__								=		giou_loss_coefficient
				lowerCamelCase__								=		eos_coefficient
				super().__init__(is_encoder_decoder=_a       ,  **_a							)





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return self.encoder_attention_heads





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return self.d_model






class 					__A    (    UpperCAmelCase__ ):

			'''simple docstring'''







			lowerCAmelCase_							     =						version.parse("""1.11""" )





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				        ('''pixel_mask''', {0: '''batch'''}),
				    ]							)





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return 1E-5





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return 1_2









"""simple docstring"""




import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase							:			Dict     							=							logging.get_logger(__name__)



def     a__ (				snake_case__				)						->					Dict:
			lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)
			if "model" in sd.keys():
						lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)["""model"""]

			# pop unnecessary weights
			lowerCamelCase			      =					[
			    """decoder.version""",
			    """decoder.output_projection.weight""",
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(snake_case__				)

			lowerCamelCase			      =					{
			    """decoder.project_in_dim.weight""": """decoder.project_in.weight""",
			    """decoder.project_out_dim.weight""": """decoder.project_out.weight""",
			    """decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
			    """decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									lowerCamelCase			      =					sd.pop(snake_case__				)

			lowerCamelCase			      =					list(sd.keys()				)
			for key in keys:
						if ".qkv_proj." in key:
									lowerCamelCase			      =					sd[key]
									# We split QKV in separate Q,K,V

									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.q_proj."""				)
									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.k_proj."""				)
									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.v_proj."""				)

									lowerCamelCase			      =					value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					torch.split(snake_case__       , depth // 3       , dim=0				)

									lowerCamelCase			      =					q
									lowerCamelCase			      =					k
									lowerCamelCase			      =					v
									del sd[key]

			return sd



@torch.no_grad()
def     a__ (				snake_case__       , snake_case__       , snake_case__=None				)						->					Tuple:
			lowerCamelCase			      =					load_checkpoint(snake_case__				)

			if config is not None:
						lowerCamelCase			      =					OPTConfig.from_pretrained(snake_case__				)
			else:
						lowerCamelCase			      =					OPTConfig()

			lowerCamelCase			      =					OPTModel(snake_case__				).half().eval()
			model.load_state_dict(snake_case__				)

			# Check results
			Path(snake_case__				).mkdir(exist_ok=snake_case__				)
			model.save_pretrained(snake_case__				)


if __name__ == "__main__":
					lowerCAmelCase							:			List[str]     							=							argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--fairseq_path""",
					    type=str,
					    help=(
					        """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
					        """ https://huggingface.co/models?other=opt_metasq"""
					    ),
					)
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
					lowerCAmelCase							:			Optional[Any]     							=							parser.parse_args()
					convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



