






'''simple docstring'''


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()
lowerCamelCase_				: Optional[Any]    	=							logging.get_logger(__name__)







def 	__magic_name__( _A						,      _A=False					):
						'''simple docstring'''





						UpperCamelCase__         = []
						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"
												UpperCamelCase__         = [(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 	__magic_name__( _A						,      _A						,      _A=False					):
						'''simple docstring'''





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







def 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = dct.pop(__snake_case					)
						UpperCamelCase__         = val







def 	__magic_name__( ):
						'''simple docstring'''





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







@torch.no_grad()
def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





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

						# load original model from timm
						UpperCamelCase__         = timm.create_model(__snake_case						,      pretrained=__snake_case					)
						timm_model.eval()

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

						# load HuggingFace model
						UpperCamelCase__         = DeiTForImageClassificationWithTeacher(__snake_case					).eval()
						model.load_state_dict(__snake_case					)

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

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

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


if __name__ == "__main__":
					lowerCamelCase_				: Dict    	=							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.'''
					)

					lowerCamelCase_				: Optional[int]    	=							parser.parse_args()
					convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase_				: Tuple    	=							{
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_				: Tuple    	=							['''LayoutLMv3TokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_				: Union[str, Any]    	=							[
					    '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''LayoutLMv3ForQuestionAnswering''',
					    '''LayoutLMv3ForSequenceClassification''',
					    '''LayoutLMv3ForTokenClassification''',
					    '''LayoutLMv3Model''',
					    '''LayoutLMv3PreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_				: Optional[Any]    	=							[
					    '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFLayoutLMv3ForQuestionAnswering''',
					    '''TFLayoutLMv3ForSequenceClassification''',
					    '''TFLayoutLMv3ForTokenClassification''',
					    '''TFLayoutLMv3Model''',
					    '''TFLayoutLMv3PreTrainedModel''',
					]

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_				: Optional[Any]    	=							['''LayoutLMv3FeatureExtractor''']
					lowerCamelCase_				: Dict    	=							['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
					from .configuration_layoutlmva import (
					    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    LayoutLMvaConfig,
					    LayoutLMvaOnnxConfig,
					)
					from .processing_layoutlmva import LayoutLMvaProcessor
					from .tokenization_layoutlmva import LayoutLMvaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_layoutlmva import (
										    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LayoutLMvaForQuestionAnswering,
										    LayoutLMvaForSequenceClassification,
										    LayoutLMvaForTokenClassification,
										    LayoutLMvaModel,
										    LayoutLMvaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_layoutlmva import (
										    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFLayoutLMvaForQuestionAnswering,
										    TFLayoutLMvaForSequenceClassification,
										    TFLayoutLMvaForTokenClassification,
										    TFLayoutLMvaModel,
										    TFLayoutLMvaPreTrainedModel,
										)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
										from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
					import sys

					lowerCamelCase_				: Optional[Any]    	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
