






'''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						:    Union[str, Any]     				=  logging.get_logger(__name__)



def        __snake_case						(__UpperCAmelCase       ,	__UpperCAmelCase=False    ):





	"""simple docstring"""
	lowerCamelCase_					:  List[str]						=    []
	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"
		lowerCamelCase_					:  str						=    [(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        __snake_case						(__UpperCAmelCase       ,	__UpperCAmelCase       ,	__UpperCAmelCase=False    ):





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



def        __snake_case						(__UpperCAmelCase       ,	__UpperCAmelCase       ,	__UpperCAmelCase    ):





	"""simple docstring"""
	lowerCamelCase_					:  Optional[Any]						=    dct.pop(__lowerCAmelCase    )
	lowerCamelCase_					:  Any						=    val



def        __snake_case						():





	"""simple docstring"""
	lowerCamelCase_					:  List[Any]						=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
	lowerCamelCase_					:  Optional[int]						=    Image.open(requests.get(__lowerCAmelCase       ,	stream=__lowerCAmelCase    ).raw    )
	return im



@torch.no_grad()
def        __snake_case						(__UpperCAmelCase       ,	__UpperCAmelCase    ):





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

	# load original model from timm
	lowerCamelCase_					:  int						=    timm.create_model(__lowerCAmelCase       ,	pretrained=__lowerCAmelCase    )
	timm_model.eval()

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

	# load HuggingFace model
	lowerCamelCase_					:  Any						=    DeiTForImageClassificationWithTeacher(__lowerCAmelCase    ).eval()
	model.load_state_dict(__lowerCAmelCase    )

	# Check outputs on an image, prepared by DeiTImageProcessor
	lowerCamelCase_					:  Union[str, Any]						=    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
	lowerCamelCase_					:  Any						=    DeiTImageProcessor(size=__lowerCAmelCase       ,	crop_size=config.image_size    )
	lowerCamelCase_					:  List[Any]						=    image_processor(images=prepare_img()       ,	return_tensors='''pt'''    )
	lowerCamelCase_					:  List[str]						=    encoding['''pixel_values''']
	lowerCamelCase_					:  List[str]						=    model(__lowerCAmelCase    )

	lowerCamelCase_					:  Optional[int]						=    timm_model(__lowerCAmelCase    )
	assert timm_logits.shape == outputs.logits.shape
	assert torch.allclose(__lowerCAmelCase       ,	outputs.logits       ,	atol=1E-3    )

	Path(__lowerCAmelCase    ).mkdir(exist_ok=__lowerCAmelCase    )
	print(F"""Saving model {deit_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 __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						:    Union[str, Any]     				=  parser.parse_args()
			convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)








import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Optional[int]     						=  ['image_processor', 'tokenizer']
      _A    :		List[Any]     						=  'LayoutLMv2ImageProcessor'
      _A    :		Dict     						=  ('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')
      def __init__(      self		,						lowerCamelCase=None		,						lowerCamelCase=None		,						**lowerCamelCase			):
          if "feature_extractor" in kwargs:
              warnings.warn(
                  "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                  " instead."		,						lowerCamelCase		,						)
              snake_case__    =					kwargs.pop("feature_extractor"			)

          snake_case__    =					image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
              raise ValueError("You need to specify an `image_processor`."			)
          if tokenizer is None:
              raise ValueError("You need to specify a `tokenizer`."			)

          super().__init__(lowerCamelCase		,						lowerCamelCase			)
      def __call__(      self		,						lowerCamelCase		,						lowerCamelCase = None		,						lowerCamelCase = None		,						lowerCamelCase = None		,						lowerCamelCase = None		,						lowerCamelCase = True		,						lowerCamelCase = False		,						lowerCamelCase = None		,						lowerCamelCase = None		,						lowerCamelCase = 0		,						lowerCamelCase = None		,						lowerCamelCase = None		,						lowerCamelCase = None		,						lowerCamelCase = False		,						lowerCamelCase = False		,						lowerCamelCase = False		,						lowerCamelCase = False		,						lowerCamelCase = True		,						lowerCamelCase = None		,						**lowerCamelCase		,						):
          # verify input
          if self.image_processor.apply_ocr and (boxes is not None):
              raise ValueError(
                  "You cannot provide bounding boxes "
                  "if you initialized the image processor with apply_ocr set to True."			)

          if self.image_processor.apply_ocr and (word_labels is not None):
              raise ValueError(
                  "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."			)

          if return_overflowing_tokens is True and return_offsets_mapping is False:
              raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."			)

          # first, apply the image processor
          snake_case__    =					self.image_processor(images=lowerCamelCase		,						return_tensors=lowerCamelCase			)

          # second, apply the tokenizer
          if text is not None and self.image_processor.apply_ocr and text_pair is None:
              if isinstance(lowerCamelCase		,						lowerCamelCase			):
                  snake_case__    =					[text]  # add batch dimension (as the image processor always adds a batch dimension)
              snake_case__    =					features["words"]

          snake_case__    =					self.tokenizer(
              text=text if text is not None else features["words"]		,						text_pair=text_pair if text_pair is not None else None		,						boxes=boxes if boxes is not None else features["boxes"]		,						word_labels=lowerCamelCase		,						add_special_tokens=lowerCamelCase		,						padding=lowerCamelCase		,						truncation=lowerCamelCase		,						max_length=lowerCamelCase		,						stride=lowerCamelCase		,						pad_to_multiple_of=lowerCamelCase		,						return_token_type_ids=lowerCamelCase		,						return_attention_mask=lowerCamelCase		,						return_overflowing_tokens=lowerCamelCase		,						return_special_tokens_mask=lowerCamelCase		,						return_offsets_mapping=lowerCamelCase		,						return_length=lowerCamelCase		,						verbose=lowerCamelCase		,						return_tensors=lowerCamelCase		,						**lowerCamelCase		,						)

          # add pixel values
          snake_case__    =					features.pop("pixel_values"			)
          if return_overflowing_tokens is True:
              snake_case__    =					self.get_overflowing_images(lowerCamelCase		,						encoded_inputs["overflow_to_sample_mapping"]			)
          snake_case__    =					images

          return encoded_inputs
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase			):
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          snake_case__    =					[]
          for sample_idx in overflow_to_sample_mapping:
              images_with_overflow.append(images[sample_idx]			)

          if len(lowerCamelCase			) != len(lowerCamelCase			):
              raise ValueError(
                  "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
                  F""" {len(lowerCamelCase			)} and {len(lowerCamelCase			)}"""			)

          return images_with_overflow
      def A_      (      self		,						*lowerCamelCase		,						**lowerCamelCase			):
          return self.tokenizer.batch_decode(*lowerCamelCase		,						**lowerCamelCase			)
      def A_      (      self		,						*lowerCamelCase		,						**lowerCamelCase			):
          return self.tokenizer.decode(*lowerCamelCase		,						**lowerCamelCase			)
      @property
      def A_      (      self			):
          return ["input_ids", "bbox", "attention_mask", "image"]
      @property
      def A_      (      self			):
          warnings.warn(
              "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,						lowerCamelCase		,						)
          return self.image_processor_class



      @property
      def A_      (      self			):
          warnings.warn(
              "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,						lowerCamelCase		,						)
          return self.image_processor



