
import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


__A				=logging.get_logger(__name__)





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			nn.ModuleList([src_layers[i] for i in layers_to_copy]    )
				assert len(lowerCamelCase__    ) == len(lowerCamelCase__    ), F'{len(lowerCamelCase__    )} != {len(lowerCamelCase__    )}'
				dest_layers.load_state_dict(layers_to_copy.state_dict()    )


__A				={
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    1_2: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 1_1],
        4: [0, 4, 8, 1_1],
        6: [0, 2, 4, 7, 9, 1_1],
        9: [0, 1, 2, 4, 5, 7, 9, 1_0, 1_1],
        1_2: list(range(1_2)),
    },
    1_6: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 1_5],
        3: [0, 8, 1_5],
        4: [0, 5, 1_0, 1_5],
        6: [0, 3, 6, 9, 1_2, 1_5],
        8: [0, 2, 4, 6, 8, 1_0, 1_2, 1_5],
        9: [0, 1, 3, 5, 7, 9, 1_1, 1_3, 1_5],
        1_2: [0, 1, 2, 3, 4, 5, 6, 7, 9, 1_1, 1_3, 1_5],
        1_6: list(range(1_6)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
__A				={
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    1_2: {1: [1_1], 2: [5, 1_1], 3: [3, 7, 1_1], 6: [1, 3, 5, 8, 1_0, 1_1]},
    1_6: {1: [1_5], 4: [4, 9, 1_2, 1_5], 8: [1, 3, 5, 7, 9, 1_1, 1_3, 1_5]},
}





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				try:
								lowerCamelCase_						=			LAYERS_TO_COPY[n_teacher][n_student]
								return val
				except KeyError:
								if n_student != n_teacher:
												warnings.warn(
												    F'no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'
												    F' {n_student}'    )
								return list(range(lowerCamelCase__    )    )





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				if n_student > n_teacher:
								raise ValueError(F'Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}'    )
				elif n_teacher == n_student:
								return list(range(lowerCamelCase__    )    )
				elif n_student == 1:
								return [n_teacher - 1]
				else:
								return LAYERS_TO_SUPERVISE[n_teacher][n_student]





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__ = "student"	, lowerCamelCase__ = None	, lowerCamelCase__ = None	, lowerCamelCase__=False	, lowerCamelCase__=None	, lowerCamelCase__=None	, **lowerCamelCase__	, ):
				lowerCamelCase_						=			"encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."
				assert (e is not None) or (d is not None), _msg
				if isinstance(lowerCamelCase__	, lowerCamelCase__    ):
								AutoTokenizer.from_pretrained(lowerCamelCase__    ).save_pretrained(lowerCamelCase__    )  # purely for convenience
								lowerCamelCase_						=			AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__    ).eval()
				else:
								assert isinstance(lowerCamelCase__	, lowerCamelCase__    ), F'teacher must be a model or string got type {type(lowerCamelCase__    )}'
				lowerCamelCase_						=			teacher.config.to_diff_dict()

				try:
								lowerCamelCase_	,    lowerCamelCase_						=			teacher.config.encoder_layers, teacher.config.decoder_layers
								if e is None:
												lowerCamelCase_						=			teacher_e
								if d is None:
												lowerCamelCase_						=			teacher_d
								init_kwargs.update({"encoder_layers": e, "decoder_layers": d}    )
				except AttributeError:  # T5
								if hasattr(teacher.config	, "num_encoder_layers"    ):
												lowerCamelCase_	,    lowerCamelCase_						=			teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
								else:
												lowerCamelCase_	,    lowerCamelCase_						=			teacher.config.num_layers, teacher.config.num_decoder_layers
								if e is None:
												lowerCamelCase_						=			teacher_e
								if d is None:
												lowerCamelCase_						=			teacher_d
								if hasattr(teacher.config	, "num_encoder_layers"    ):
												init_kwargs.update({"num_encoder_layers": e, "num_decoder_layers": d}    )
								else:
												init_kwargs.update({"num_layers": e, "num_decoder_layers": d}    )

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
				init_kwargs.update(lowerCamelCase__    )

				# Copy weights
				lowerCamelCase_						=			teacher.config_class(**lowerCamelCase__    )
				lowerCamelCase_						=			AutoModelForSeqaSeqLM.from_config(lowerCamelCase__    )
				# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
				lowerCamelCase_						=			student.load_state_dict(teacher.state_dict()	, strict=lowerCamelCase__    )
				assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

				if copy_first_teacher_layers:  # Our copying is done. We just log and save
								lowerCamelCase_	,    lowerCamelCase_						=			list(range(lowerCamelCase__    )    ), list(range(lowerCamelCase__    )    )
								logger.info(
								    F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'
								    F' {save_path}'    )
								student.save_pretrained(lowerCamelCase__    )
								return student, e_layers_to_copy, d_layers_to_copy

				# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
				if e_layers_to_copy is None:
								lowerCamelCase_						=			pick_layers_to_copy(lowerCamelCase__	, lowerCamelCase__    )
				if d_layers_to_copy is None:
								lowerCamelCase_						=			pick_layers_to_copy(lowerCamelCase__	, lowerCamelCase__    )

				try:
								if hasattr(
								    lowerCamelCase__	, "prophetnet"    ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
												copy_layers(teacher.prophetnet.encoder.layers	, student.prophetnet.encoder.layers	, lowerCamelCase__    )
												copy_layers(teacher.prophetnet.decoder.layers	, student.prophetnet.decoder.layers	, lowerCamelCase__    )
								else:
												copy_layers(teacher.model.encoder.layers	, student.model.encoder.layers	, lowerCamelCase__    )
												copy_layers(teacher.model.decoder.layers	, student.model.decoder.layers	, lowerCamelCase__    )
				except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
								copy_layers(teacher.encoder.block	, student.encoder.block	, lowerCamelCase__    )
								copy_layers(teacher.decoder.block	, student.decoder.block	, lowerCamelCase__    )
				logger.info(
				    F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'    )
				lowerCamelCase_						=			{
				    "teacher_type": teacher.config.model_type,
				    "copied_encoder_layers": e_layers_to_copy,
				    "copied_decoder_layers": d_layers_to_copy,
				}
				student.save_pretrained(lowerCamelCase__    )
				# Save information about copying for easier reproducibility

				return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
			fire.Fire(create_student_by_copying_alternating_layers)




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				=logging.get_logger(__name__)





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			YolosConfig()

				# size of the architecture
				if "yolos_ti" in yolos_name:
								lowerCamelCase_						=			1_9_2
								lowerCamelCase_						=			7_6_8
								lowerCamelCase_						=			1_2
								lowerCamelCase_						=			3
								lowerCamelCase_						=			[8_0_0, 1_3_3_3]
								lowerCamelCase_						=			False
				elif yolos_name == "yolos_s_dWr":
								lowerCamelCase_						=			3_3_0
								lowerCamelCase_						=			1_4
								lowerCamelCase_						=			6
								lowerCamelCase_						=			1_3_2_0
				elif "yolos_s" in yolos_name:
								lowerCamelCase_						=			3_8_4
								lowerCamelCase_						=			1_5_3_6
								lowerCamelCase_						=			1_2
								lowerCamelCase_						=			6
				elif "yolos_b" in yolos_name:
								lowerCamelCase_						=			[8_0_0, 1_3_4_4]

				lowerCamelCase_						=			9_1
				lowerCamelCase_						=			"huggingface/label-files"
				lowerCamelCase_						=			"coco-detection-id2label.json"
				lowerCamelCase_						=			json.load(open(hf_hub_download(lowerCamelCase__	, lowerCamelCase__	, repo_type="dataset"    )	, "r"    )    )
				lowerCamelCase_						=			{int(lowerCamelCase__    ): v for k, v in idalabel.items()}
				lowerCamelCase_						=			idalabel
				lowerCamelCase_						=			{v: k for k, v in idalabel.items()}

				return config





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__ = False    ):
				for i in range(config.num_hidden_layers    ):
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								lowerCamelCase_						=			state_dict.pop(F'blocks.{i}.attn.qkv.weight'    )
								lowerCamelCase_						=			state_dict.pop(F'blocks.{i}.attn.qkv.bias'    )
								# next, add query, keys and values (in that order) to the state dict
								lowerCamelCase_						=			in_proj_weight[: config.hidden_size, :]
								lowerCamelCase_						=			in_proj_bias[: config.hidden_size]
								lowerCamelCase_						=			in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								lowerCamelCase_						=			in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								lowerCamelCase_						=			in_proj_weight[-config.hidden_size :, :]
								lowerCamelCase_						=			in_proj_bias[-config.hidden_size :]





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				if "backbone" in name:
								lowerCamelCase_						=			name.replace("backbone"	, "vit"    )
				if "cls_token" in name:
								lowerCamelCase_						=			name.replace("cls_token"	, "embeddings.cls_token"    )
				if "det_token" in name:
								lowerCamelCase_						=			name.replace("det_token"	, "embeddings.detection_tokens"    )
				if "mid_pos_embed" in name:
								lowerCamelCase_						=			name.replace("mid_pos_embed"	, "encoder.mid_position_embeddings"    )
				if "pos_embed" in name:
								lowerCamelCase_						=			name.replace("pos_embed"	, "embeddings.position_embeddings"    )
				if "patch_embed.proj" in name:
								lowerCamelCase_						=			name.replace("patch_embed.proj"	, "embeddings.patch_embeddings.projection"    )
				if "blocks" in name:
								lowerCamelCase_						=			name.replace("blocks"	, "encoder.layer"    )
				if "attn.proj" in name:
								lowerCamelCase_						=			name.replace("attn.proj"	, "attention.output.dense"    )
				if "attn" in name:
								lowerCamelCase_						=			name.replace("attn"	, "attention.self"    )
				if "norm1" in name:
								lowerCamelCase_						=			name.replace("norm1"	, "layernorm_before"    )
				if "norm2" in name:
								lowerCamelCase_						=			name.replace("norm2"	, "layernorm_after"    )
				if "mlp.fc1" in name:
								lowerCamelCase_						=			name.replace("mlp.fc1"	, "intermediate.dense"    )
				if "mlp.fc2" in name:
								lowerCamelCase_						=			name.replace("mlp.fc2"	, "output.dense"    )
				if "class_embed" in name:
								lowerCamelCase_						=			name.replace("class_embed"	, "class_labels_classifier"    )
				if "bbox_embed" in name:
								lowerCamelCase_						=			name.replace("bbox_embed"	, "bbox_predictor"    )
				if "vit.norm" in name:
								lowerCamelCase_						=			name.replace("vit.norm"	, "vit.layernorm"    )

				return name





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				for key in orig_state_dict.copy().keys():
								lowerCamelCase_						=			orig_state_dict.pop(lowerCamelCase__    )

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

				return orig_state_dict





def 			lowerCamelCase_ (				):
				lowerCamelCase_						=			"http://images.cocodataset.org/val2017/000000039769.jpg"
				lowerCamelCase_						=			Image.open(requests.get(lowerCamelCase__	, stream=lowerCamelCase__    ).raw    )
				return im





@torch.no_grad()
def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__ = False    ):
				lowerCamelCase_						=			get_yolos_config(lowerCamelCase__    )

				# load original state_dict
				lowerCamelCase_						=			torch.load(lowerCamelCase__	, map_location="cpu"    )["model"]

				# load 🤗 model
				lowerCamelCase_						=			YolosForObjectDetection(lowerCamelCase__    )
				model.eval()
				lowerCamelCase_						=			convert_state_dict(lowerCamelCase__	, lowerCamelCase__    )
				model.load_state_dict(lowerCamelCase__    )

				# Check outputs on an image, prepared by YolosImageProcessor
				lowerCamelCase_						=			8_0_0 if yolos_name != "yolos_ti" else 5_1_2
				lowerCamelCase_						=			YolosImageProcessor(format="coco_detection"	, size=lowerCamelCase__    )
				lowerCamelCase_						=			image_processor(images=prepare_img()	, return_tensors="pt"    )
				lowerCamelCase_						=			model(**lowerCamelCase__    )
				lowerCamelCase_	,    lowerCamelCase_						=			outputs.logits, outputs.pred_boxes

				lowerCamelCase_	,    lowerCamelCase_						=			None, None
				if yolos_name == "yolos_ti":
								lowerCamelCase_						=			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]]    )
								lowerCamelCase_						=			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":
								lowerCamelCase_						=			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]]    )
								lowerCamelCase_						=			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":
								lowerCamelCase_						=			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]]    )
								lowerCamelCase_						=			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":
								lowerCamelCase_						=			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]]    )
								lowerCamelCase_						=			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":
								lowerCamelCase_						=			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]]    )
								lowerCamelCase_						=			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]	, lowerCamelCase__	, atol=1e-4    )
				assert torch.allclose(pred_boxes[0, :3, :3]	, lowerCamelCase__	, atol=1e-4    )

				Path(lowerCamelCase__    ).mkdir(exist_ok=lowerCamelCase__    )
				print(F'Saving model {yolos_name} to {pytorch_dump_folder_path}'    )
				model.save_pretrained(lowerCamelCase__    )
				print(F'Saving image processor to {pytorch_dump_folder_path}'    )
				image_processor.save_pretrained(lowerCamelCase__    )

				if push_to_hub:
								lowerCamelCase_						=			{
								    "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..."    )
								lowerCamelCase_						=			model_mapping[yolos_name]
								image_processor.push_to_hub(lowerCamelCase__	, organization="hustvl"    )
								model.push_to_hub(lowerCamelCase__	, organization="hustvl"    )


if __name__ == "__main__":
			__A				=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				=parser.parse_args()
			convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


