



'''simple docstring'''




import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
							from transformers import LlamaTokenizerFast
except ImportError as e:
							warnings.warn(e)
							warnings.warn(
							    """The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"""
							)
							a     :    Dict      						=					None
a     :    str      						=					{
    """7B""": 1_1_0_0_8,
    """13B""": 1_3_8_2_4,
    """30B""": 1_7_9_2_0,
    """65B""": 2_2_0_1_6,
    """70B""": 2_8_6_7_2,
}
a     :    Tuple      						=					{
    """7B""": 1,
    """7Bf""": 1,
    """13B""": 2,
    """13Bf""": 2,
    """30B""": 4,
    """65B""": 8,
    """70B""": 8,
    """70Bf""": 8,
}



def    __lowerCamelCase						(      _lowercase			,       _lowercase=1			,       _lowercase=2_5_6	)   ->    int:
							return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3	)	) + multiple_of - 1) // multiple_of)



def    __lowerCamelCase						(      _lowercase	)   ->    List[Any]:
							with open(_lowercase			,       """r"""	) as f:
														return json.load(_lowercase	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    Dict:
							with open(_lowercase			,       """w"""	) as f:
														json.dump(_lowercase			,       _lowercase	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase=True	)   ->    Dict:
							os.makedirs(_lowercase			,       exist_ok=_lowercase	)
							UpperCAmelCase  :	int							  =	os.path.join(_lowercase			,       """tmp"""	)
							os.makedirs(_lowercase			,       exist_ok=_lowercase	)

							UpperCAmelCase  :	Optional[int]							  =	read_json(os.path.join(_lowercase			,       """params.json"""	)	)
							UpperCAmelCase  :	Dict							  =	NUM_SHARDS[model_size]
							UpperCAmelCase  :	int							  =	params["""n_layers"""]
							UpperCAmelCase  :	str							  =	params["""n_heads"""]
							UpperCAmelCase  :	Optional[Any]							  =	n_heads // num_shards
							UpperCAmelCase  :	List[str]							  =	params["""dim"""]
							UpperCAmelCase  :	str							  =	dim // n_heads
							UpperCAmelCase  :	Optional[int]							  =	1_0000.0
							UpperCAmelCase  :	str							  =	1.0 / (base ** (torch.arange(0			,       _lowercase			,       2	).float() / dims_per_head))

							if "n_kv_heads" in params:
														UpperCAmelCase  :	List[Any]							  =	params["""n_kv_heads"""]  # for GQA / MQA
														UpperCAmelCase  :	str							  =	n_heads_per_shard // num_key_value_heads
														UpperCAmelCase  :	int							  =	dim // num_key_value_heads
							else:  # compatibility with other checkpoints
														UpperCAmelCase  :	Optional[int]							  =	n_heads
														UpperCAmelCase  :	Dict							  =	n_heads_per_shard
														UpperCAmelCase  :	Union[str, Any]							  =	dim

							# permute for sliced rotary
							def permute(_lowercase			,       _lowercase=n_heads			,       _lowercase=dim			,       _lowercase=dim	):
														return w.view(_lowercase			,       dima // n_heads // 2			,       2			,       _lowercase	).transpose(1			,       2	).reshape(_lowercase			,       _lowercase	)

							print(F'''Fetching all parameters from the checkpoint at {input_base_path}.'''	)
							# Load weights
							if model_size == "7B":
														# Not sharded
														# (The sharded implementation would also work, but this is simpler.)
														UpperCAmelCase  :	Tuple							  =	torch.load(os.path.join(_lowercase			,       """consolidated.00.pth"""	)			,       map_location="""cpu"""	)
							else:
														# Sharded
														UpperCAmelCase  :	Optional[Any]							  =	[
														    torch.load(os.path.join(_lowercase			,       F'''consolidated.{i:02d}.pth'''	)			,       map_location="""cpu"""	)
														    for i in range(_lowercase	)
														]
							UpperCAmelCase  :	Optional[int]							  =	0
							UpperCAmelCase  :	Any							  =	{"""weight_map""": {}}
							for layer_i in range(_lowercase	):
														UpperCAmelCase  :	Dict							  =	F'''pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'''
														if model_size == "7B":
																					# Unsharded
																					UpperCAmelCase  :	List[Any]							  =	{
																					    F'''model.layers.{layer_i}.self_attn.q_proj.weight''': permute(
																					        loaded[F'''layers.{layer_i}.attention.wq.weight''']	),
																					    F'''model.layers.{layer_i}.self_attn.k_proj.weight''': permute(
																					        loaded[F'''layers.{layer_i}.attention.wk.weight''']	),
																					    F'''model.layers.{layer_i}.self_attn.v_proj.weight''': loaded[F'''layers.{layer_i}.attention.wv.weight'''],
																					    F'''model.layers.{layer_i}.self_attn.o_proj.weight''': loaded[F'''layers.{layer_i}.attention.wo.weight'''],
																					    F'''model.layers.{layer_i}.mlp.gate_proj.weight''': loaded[F'''layers.{layer_i}.feed_forward.w1.weight'''],
																					    F'''model.layers.{layer_i}.mlp.down_proj.weight''': loaded[F'''layers.{layer_i}.feed_forward.w2.weight'''],
																					    F'''model.layers.{layer_i}.mlp.up_proj.weight''': loaded[F'''layers.{layer_i}.feed_forward.w3.weight'''],
																					    F'''model.layers.{layer_i}.input_layernorm.weight''': loaded[F'''layers.{layer_i}.attention_norm.weight'''],
																					    F'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[F'''layers.{layer_i}.ffn_norm.weight'''],
																					}
														else:
																					# Sharded
																					# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
																					# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
																					# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

																					UpperCAmelCase  :	List[str]							  =	{
																					    F'''model.layers.{layer_i}.input_layernorm.weight''': loaded[0][
																					        F'''layers.{layer_i}.attention_norm.weight'''
																					    ].clone(),
																					    F'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[0][
																					        F'''layers.{layer_i}.ffn_norm.weight'''
																					    ].clone(),
																					}
																					UpperCAmelCase  :	int							  =	permute(
																					    torch.cat(
																					        [
																					            loaded[i][F'''layers.{layer_i}.attention.wq.weight'''].view(_lowercase			,       _lowercase			,       _lowercase	)
																					            for i in range(_lowercase	)
																					        ]			,       dim=0			,       ).reshape(_lowercase			,       _lowercase	)	)
																					UpperCAmelCase  :	Optional[Any]							  =	permute(
																					    torch.cat(
																					        [
																					            loaded[i][F'''layers.{layer_i}.attention.wk.weight'''].view(
																					                _lowercase			,       _lowercase			,       _lowercase	)
																					            for i in range(_lowercase	)
																					        ]			,       dim=0			,       ).reshape(_lowercase			,       _lowercase	)			,       _lowercase			,       _lowercase			,       _lowercase			,       )
																					UpperCAmelCase  :	List[str]							  =	torch.cat(
																					    [
																					        loaded[i][F'''layers.{layer_i}.attention.wv.weight'''].view(
																					            _lowercase			,       _lowercase			,       _lowercase	)
																					        for i in range(_lowercase	)
																					    ]			,       dim=0			,       ).reshape(_lowercase			,       _lowercase	)

																					UpperCAmelCase  :	Tuple							  =	torch.cat(
																					    [loaded[i][F'''layers.{layer_i}.attention.wo.weight'''] for i in range(_lowercase	)]			,       dim=1	)
																					UpperCAmelCase  :	Optional[int]							  =	torch.cat(
																					    [loaded[i][F'''layers.{layer_i}.feed_forward.w1.weight'''] for i in range(_lowercase	)]			,       dim=0	)
																					UpperCAmelCase  :	Dict							  =	torch.cat(
																					    [loaded[i][F'''layers.{layer_i}.feed_forward.w2.weight'''] for i in range(_lowercase	)]			,       dim=1	)
																					UpperCAmelCase  :	Optional[int]							  =	torch.cat(
																					    [loaded[i][F'''layers.{layer_i}.feed_forward.w3.weight'''] for i in range(_lowercase	)]			,       dim=0	)

														UpperCAmelCase  :	Optional[int]							  =	inv_freq
														for k, v in state_dict.items():
																					UpperCAmelCase  :	List[str]							  =	filename
																					param_count += v.numel()
														torch.save(_lowercase			,       os.path.join(_lowercase			,       _lowercase	)	)

							UpperCAmelCase  :	Any							  =	F'''pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'''
							if model_size == "7B":
														# Unsharded
														UpperCAmelCase  :	Any							  =	{
														    """model.embed_tokens.weight""": loaded["""tok_embeddings.weight"""],
														    """model.norm.weight""": loaded["""norm.weight"""],
														    """lm_head.weight""": loaded["""output.weight"""],
														}
							else:
														UpperCAmelCase  :	Any							  =	{
														    """model.norm.weight""": loaded[0]["""norm.weight"""],
														    """model.embed_tokens.weight""": torch.cat(
														        [loaded[i]["""tok_embeddings.weight"""] for i in range(_lowercase	)]			,       dim=1	),
														    """lm_head.weight""": torch.cat([loaded[i]["""output.weight"""] for i in range(_lowercase	)]			,       dim=0	),
														}

							for k, v in state_dict.items():
														UpperCAmelCase  :	Any							  =	filename
														param_count += v.numel()
							torch.save(_lowercase			,       os.path.join(_lowercase			,       _lowercase	)	)

							# Write configs
							UpperCAmelCase  :	str							  =	{"""total_size""": param_count * 2}
							write_json(_lowercase			,       os.path.join(_lowercase			,       """pytorch_model.bin.index.json"""	)	)
							UpperCAmelCase  :	List[str]							  =	params["""ffn_dim_multiplier"""] if """ffn_dim_multiplier""" in params else 1
							UpperCAmelCase  :	Optional[int]							  =	params["""multiple_of"""] if """multiple_of""" in params else 2_5_6
							UpperCAmelCase  :	Any							  =	LlamaConfig(
							    hidden_size=_lowercase			,       intermediate_size=compute_intermediate_size(_lowercase			,       _lowercase			,       _lowercase	)			,       num_attention_heads=params["""n_heads"""]			,       num_hidden_layers=params["""n_layers"""]			,       rms_norm_eps=params["""norm_eps"""]			,       num_key_value_heads=_lowercase			,       )
							config.save_pretrained(_lowercase	)

							# Make space so we can load the model properly now.
							del state_dict
							del loaded
							gc.collect()

							print("""Loading the checkpoint in a Llama model."""	)
							UpperCAmelCase  :	Tuple							  =	LlamaForCausalLM.from_pretrained(_lowercase			,       torch_dtype=torch.floataa			,       low_cpu_mem_usage=_lowercase	)
							# Avoid saving this as part of the config.
							del model.config._name_or_path

							print("""Saving in the Transformers format."""	)
							model.save_pretrained(_lowercase			,       safe_serialization=_lowercase	)
							shutil.rmtree(_lowercase	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    Optional[Any]:
							# Initialize the tokenizer based on the `spm` model
							UpperCAmelCase  :	Union[str, Any]							  =	LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
							print(F'''Saving a {tokenizer_class.__name__} to {tokenizer_path}.'''	)
							UpperCAmelCase  :	List[str]							  =	tokenizer_class(_lowercase	)
							tokenizer.save_pretrained(_lowercase	)



def    __lowerCamelCase						(      )   ->    Optional[int]:
							UpperCAmelCase  :	Optional[Any]							  =	argparse.ArgumentParser()
							parser.add_argument(
							    """--input_dir"""			,       help="""Location of LLaMA weights, which contains tokenizer.model and model folders"""			,       )
							parser.add_argument(
							    """--model_size"""			,       choices=["""7B""", """7Bf""", """13B""", """13Bf""", """30B""", """65B""", """70B""", """70Bf""", """tokenizer_only"""]			,       )
							parser.add_argument(
							    """--output_dir"""			,       help="""Location to write HF model and tokenizer"""			,       )
							parser.add_argument("""--safe_serialization"""			,       type=_lowercase			,       help="""Whether or not to save using `safetensors`."""	)
							UpperCAmelCase  :	Tuple							  =	parser.parse_args()
							if args.model_size != "tokenizer_only":
														write_model(
														    model_path=args.output_dir			,       input_base_path=os.path.join(args.input_dir			,       args.model_size	)			,       model_size=args.model_size			,       safe_serialization=args.safe_serialization			,       )
							UpperCAmelCase  :	Optional[int]							  =	os.path.join(args.input_dir			,       """tokenizer.model"""	)
							write_tokenizer(args.output_dir			,       _lowercase	)


if __name__ == "__main__":
							main()






'''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()
a     :    List[Any]      						=					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a     :    List[str]      						=					[]
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    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    Optional[Any]:
							UpperCAmelCase  :	List[str]							  =	state_dict.pop(_lowercase	)
							UpperCAmelCase  :	List[str]							  =	val



def    __lowerCamelCase						(      _lowercase	)   ->    Any:
							UpperCAmelCase  :	Union[str, Any]							  =	OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					UpperCAmelCase  :	List[str]							  =	key.replace("""backbone.0.body"""			,       """backbone.conv_encoder.model"""	)
																					UpperCAmelCase  :	Dict							  =	value
														else:
																					UpperCAmelCase  :	List[Any]							  =	value

							return new_state_dict



def    __lowerCamelCase						(      _lowercase			,       _lowercase=False	)   ->    Optional[int]:
							UpperCAmelCase  :	Dict							  =	""""""
							if is_panoptic:
														UpperCAmelCase  :	Tuple							  =	"""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)
														UpperCAmelCase  :	List[Any]							  =	state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''	)
														UpperCAmelCase  :	List[Any]							  =	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
														UpperCAmelCase  :	Dict							  =	in_proj_weight[:2_5_6, :]
														UpperCAmelCase  :	Optional[Any]							  =	in_proj_bias[:2_5_6]
														UpperCAmelCase  :	List[Any]							  =	in_proj_weight[2_5_6:5_1_2, :]
														UpperCAmelCase  :	Tuple							  =	in_proj_bias[2_5_6:5_1_2]
														UpperCAmelCase  :	List[str]							  =	in_proj_weight[-2_5_6:, :]
														UpperCAmelCase  :	List[str]							  =	in_proj_bias[-2_5_6:]



def    __lowerCamelCase						(      )   ->    Dict:
							UpperCAmelCase  :	List[Any]							  =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase  :	Tuple							  =	Image.open(requests.get(_lowercase			,       stream=_lowercase	).raw	)

							return im



@torch.no_grad()
def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    str:
							UpperCAmelCase  :	str							  =	ConditionalDetrConfig()
							# set backbone and dilation attributes
							if "resnet101" in model_name:
														UpperCAmelCase  :	List[Any]							  =	"""resnet101"""
							if "dc5" in model_name:
														UpperCAmelCase  :	Optional[int]							  =	True
							UpperCAmelCase  :	List[Any]							  =	"""panoptic""" in model_name
							if is_panoptic:
														UpperCAmelCase  :	Union[str, Any]							  =	2_5_0
							else:
														UpperCAmelCase  :	int							  =	9_1
														UpperCAmelCase  :	Tuple							  =	"""huggingface/label-files"""
														UpperCAmelCase  :	List[Any]							  =	"""coco-detection-id2label.json"""
														UpperCAmelCase  :	Optional[int]							  =	json.load(open(hf_hub_download(_lowercase			,       _lowercase			,       repo_type="""dataset"""	)			,       """r"""	)	)
														UpperCAmelCase  :	Dict							  =	{int(_lowercase	): v for k, v in idalabel.items()}
														UpperCAmelCase  :	Optional[Any]							  =	idalabel
														UpperCAmelCase  :	List[Any]							  =	{v: k for k, v in idalabel.items()}

							# load image processor
							UpperCAmelCase  :	List[str]							  =	"""coco_panoptic""" if is_panoptic else """coco_detection"""
							UpperCAmelCase  :	List[Any]							  =	ConditionalDetrImageProcessor(format=_lowercase	)

							# prepare image
							UpperCAmelCase  :	Union[str, Any]							  =	prepare_img()
							UpperCAmelCase  :	Dict							  =	image_processor(images=_lowercase			,       return_tensors="""pt"""	)
							UpperCAmelCase  :	List[Any]							  =	encoding["""pixel_values"""]

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

							# load original model from torch hub
							UpperCAmelCase  :	int							  =	torch.hub.load("""DeppMeng/ConditionalDETR"""			,       _lowercase			,       pretrained=_lowercase	).eval()
							UpperCAmelCase  :	List[Any]							  =	conditional_detr.state_dict()
							# rename keys
							for src, dest in rename_keys:
														if is_panoptic:
																					UpperCAmelCase  :	List[Any]							  =	"""conditional_detr.""" + src
														rename_key(_lowercase			,       _lowercase			,       _lowercase	)
							UpperCAmelCase  :	List[Any]							  =	rename_backbone_keys(_lowercase	)
							# query, key and value matrices need special treatment
							read_in_q_k_v(_lowercase			,       is_panoptic=_lowercase	)
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							UpperCAmelCase  :	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"""	)
																					):
																												UpperCAmelCase  :	Union[str, Any]							  =	state_dict.pop(_lowercase	)
																												UpperCAmelCase  :	int							  =	val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												UpperCAmelCase  :	Any							  =	state_dict.pop(_lowercase	)
																												UpperCAmelCase  :	Optional[Any]							  =	val
																					elif key.startswith("""bbox_attention"""	) or key.startswith("""mask_head"""	):
																												continue
																					else:
																												UpperCAmelCase  :	List[Any]							  =	state_dict.pop(_lowercase	)
																												UpperCAmelCase  :	str							  =	val
														else:
																					if not key.startswith("""class_labels_classifier"""	) and not key.startswith("""bbox_predictor"""	):
																												UpperCAmelCase  :	Optional[int]							  =	state_dict.pop(_lowercase	)
																												UpperCAmelCase  :	Union[str, Any]							  =	val
    # finally, create HuggingFace model and load state dict
							UpperCAmelCase  :	List[Any]							  =	ConditionalDetrForSegmentation(_lowercase	) if is_panoptic else ConditionalDetrForObjectDetection(_lowercase	)
							model.load_state_dict(_lowercase	)
							model.eval()
							model.push_to_hub(repo_id=_lowercase			,       organization="""DepuMeng"""			,       commit_message="""Add model"""	)
							# verify our conversion
							UpperCAmelCase  :	Union[str, Any]							  =	conditional_detr(_lowercase	)
							UpperCAmelCase  :	int							  =	model(_lowercase	)
							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(_lowercase	).mkdir(exist_ok=_lowercase	)
							model.save_pretrained(_lowercase	)
							image_processor.save_pretrained(_lowercase	)


if __name__ == "__main__":
							a     :    Tuple      						=					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."""
							)
							a     :    Optional[Any]      						=					parser.parse_args()
							convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

