



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()
UpperCAmelCase							 =					logging.get_logger(__name__)

def 			A_      (	__a     :				Union[str, Any] ,		__a     :				str=False		):

				"""simple docstring"""
				a__				      =		[]
				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"
								a__				      =		[(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 			A_      (	__a     :				Optional[int] ,		__a     :				Dict ,		__a     :				Tuple=False		):

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

def 			A_      (	__a     :				str ,		__a     :				List[Any] ,		__a     :				str		):

				"""simple docstring"""
				a__				      =		dct.pop(__a		)
				a__				      =		val

def 			A_      (	):

				"""simple docstring"""
				a__				      =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
				a__				      =		Image.open(requests.get(__a ,		stream=__a		).raw		)
				return im

@torch.no_grad()
def 			A_      (	__a     :				Optional[int] ,		__a     :				int		):

				"""simple docstring"""
				a__				      =		DeiTConfig()
				# all deit models have fine-tuned heads
				a__				      =		False
				# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
				a__				      =		1_000
				a__				      =		"""huggingface/label-files"""
				a__				      =		"""imagenet-1k-id2label.json"""
				a__				      =		json.load(open(hf_hub_download(__a ,		__a ,		repo_type="""dataset"""		) ,		"""r"""		)		)
				a__				      =		{int(__a		): v for k, v in idalabel.items()}
				a__				      =		idalabel
				a__				      =		{v: k for k, v in idalabel.items()}
				a__				      =		int(deit_name[-6:-4]		)
				a__				      =		int(deit_name[-3:]		)
				# size of the architecture
				if deit_name[9:].startswith("""tiny"""		):
								a__				      =		192
								a__				      =		768
								a__				      =		12
								a__				      =		3
				elif deit_name[9:].startswith("""small"""		):
								a__				      =		384
								a__				      =		1_536
								a__				      =		12
								a__				      =		6
				if deit_name[9:].startswith("""base"""		):
								pass
				elif deit_name[4:].startswith("""large"""		):
								a__				      =		1_024
								a__				      =		4_096
								a__				      =		24
								a__				      =		16

				# load original model from timm
				a__				      =		timm.create_model(__a ,		pretrained=__a		)
				timm_model.eval()

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

				# load HuggingFace model
				a__				      =		DeiTForImageClassificationWithTeacher(__a		).eval()
				model.load_state_dict(__a		)

				# Check outputs on an image, prepared by DeiTImageProcessor
				a__				      =		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
				a__				      =		DeiTImageProcessor(size=__a ,		crop_size=config.image_size		)
				a__				      =		image_processor(images=prepare_img() ,		return_tensors="""pt"""		)
				a__				      =		encoding["""pixel_values"""]
				a__				      =		model(__a		)

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

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


if __name__ == "__main__":
	UpperCAmelCase							 =					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."""
	)

	UpperCAmelCase							 =					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,
)


UpperCAmelCase							 =					{
    """configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							 =					[
	    """FALCON_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """FalconForCausalLM""",
	    """FalconModel""",
	    """FalconPreTrainedModel""",
	    """FalconForSequenceClassification""",
	    """FalconForTokenClassification""",
	    """FalconForQuestionAnswering""",
	]


if TYPE_CHECKING:
	from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_falcon import (
		    FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FalconForCausalLM,
		    FalconForQuestionAnswering,
		    FalconForSequenceClassification,
		    FalconForTokenClassification,
		    FalconModel,
		    FalconPreTrainedModel,
		)


else:
	import sys

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

