

import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_								=     logging.get_logger(__name__)



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=False  ):
			lowercase__					  =				[]
			for i in range(config.num_hidden_layers  ):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''deit.encoder.layer.{i}.layernorm_before.weight''')  )
						rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''deit.encoder.layer.{i}.layernorm_before.bias''')  )
						rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''deit.encoder.layer.{i}.attention.output.dense.weight''')  )
						rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''deit.encoder.layer.{i}.attention.output.dense.bias''')  )
						rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''deit.encoder.layer.{i}.layernorm_after.weight''')  )
						rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''deit.encoder.layer.{i}.layernorm_after.bias''')  )
						rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''deit.encoder.layer.{i}.intermediate.dense.weight''')  )
						rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''deit.encoder.layer.{i}.intermediate.dense.bias''')  )
						rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''deit.encoder.layer.{i}.output.dense.weight''')  )
						rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''deit.encoder.layer.{i}.output.dense.bias''')  )

			# projection layer + position embeddings
			rename_keys.extend(
			    [
			        ("cls_token", "deit.embeddings.cls_token"),
			        ("dist_token", "deit.embeddings.distillation_token"),
			        ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
			        ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
			        ("pos_embed", "deit.embeddings.position_embeddings"),
			    ]  )

			if base_model:
						# layernorm + pooler
						rename_keys.extend(
						    [
						        ("norm.weight", "layernorm.weight"),
						        ("norm.bias", "layernorm.bias"),
						        ("pre_logits.fc.weight", "pooler.dense.weight"),
						        ("pre_logits.fc.bias", "pooler.dense.bias"),
						    ]  )

						# if just the base model, we should remove "deit" from all keys that start with "deit"
						lowercase__					  =				[(pair[0], pair[1][4:]) if pair[1].startswith("deit"  ) else pair for pair in rename_keys]
			else:
						# layernorm + classification heads
						rename_keys.extend(
						    [
						        ("norm.weight", "deit.layernorm.weight"),
						        ("norm.bias", "deit.layernorm.bias"),
						        ("head.weight", "cls_classifier.weight"),
						        ("head.bias", "cls_classifier.bias"),
						        ("head_dist.weight", "distillation_classifier.weight"),
						        ("head_dist.bias", "distillation_classifier.bias"),
						    ]  )

			return rename_keys



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



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				dct.pop(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				val



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



@torch.no_grad()
def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				DeiTConfig()
			# all deit models have fine-tuned heads
			lowercase__					  =				False
			# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
			lowercase__					  =				1000
			lowercase__					  =				"huggingface/label-files"
			lowercase__					  =				"imagenet-1k-id2label.json"
			lowercase__					  =				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  repo_type="dataset"  )						,  "r"  )  )
			lowercase__					  =				{int(SCREAMING_SNAKE_CASE_  ): v for k, v in idalabel.items()}
			lowercase__					  =				idalabel
			lowercase__					  =				{v: k for k, v in idalabel.items()}
			lowercase__					  =				int(deit_name[-6:-4]  )
			lowercase__					  =				int(deit_name[-3:]  )
			# size of the architecture
			if deit_name[9:].startswith("tiny"  ):
						lowercase__					  =				192
						lowercase__					  =				768
						lowercase__					  =				12
						lowercase__					  =				3
			elif deit_name[9:].startswith("small"  ):
						lowercase__					  =				384
						lowercase__					  =				1536
						lowercase__					  =				12
						lowercase__					  =				6
			if deit_name[9:].startswith("base"  ):
						pass
			elif deit_name[4:].startswith("large"  ):
						lowercase__					  =				1024
						lowercase__					  =				4096
						lowercase__					  =				24
						lowercase__					  =				16

			# load original model from timm
			lowercase__					  =				timm.create_model(SCREAMING_SNAKE_CASE_						,  pretrained=SCREAMING_SNAKE_CASE_  )
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			lowercase__					  =				timm_model.state_dict()
			lowercase__					  =				create_rename_keys(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			for src, dest in rename_keys:
						rename_key(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			read_in_q_k_v(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )

			# load HuggingFace model
			lowercase__					  =				DeiTForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE_  ).eval()
			model.load_state_dict(SCREAMING_SNAKE_CASE_  )

			# Check outputs on an image, prepared by DeiTImageProcessor
			lowercase__					  =				int(
			    (256 / 224) * config.image_size  )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
			lowercase__					  =				DeiTImageProcessor(size=SCREAMING_SNAKE_CASE_						,  crop_size=config.image_size  )
			lowercase__					  =				image_processor(images=prepare_img()						,  return_tensors="pt"  )
			lowercase__					  =				encoding["pixel_values"]
			lowercase__					  =				model(SCREAMING_SNAKE_CASE_  )

			lowercase__					  =				timm_model(SCREAMING_SNAKE_CASE_  )
			assert timm_logits.shape == outputs.logits.shape
			assert torch.allclose(SCREAMING_SNAKE_CASE_						,  outputs.logits						,  atol=1e-3  )

			Path(SCREAMING_SNAKE_CASE_  ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_  )
			print(f'''Saving model {deit_name} to {pytorch_dump_folder_path}'''  )
			model.save_pretrained(SCREAMING_SNAKE_CASE_  )
			print(f'''Saving image processor to {pytorch_dump_folder_path}'''  )
			image_processor.save_pretrained(SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
							lowercase_								=     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--deit_name""",
							    default="""vit_deit_base_distilled_patch16_224""",
							    type=str,
							    help="""Name of the DeiT timm model you'd like to convert.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
							)

							lowercase_								=     parser.parse_args()
							convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_								=     {
    """configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TimesformerModel""",
							    """TimesformerForVideoClassification""",
							    """TimesformerPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_timesformer import (
														    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TimesformerForVideoClassification,
														    TimesformerModel,
														    TimesformerPreTrainedModel,
														)

else:
							import sys

							lowercase_								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




