
'''simple docstring'''





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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
a__						:      List[Any]   				=				logging.get_logger(__name__)





def 				snake_case							(						UpperCAmelCase					,     UpperCAmelCase=False		)->	str:







      """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'vit.encoder.layer.{i}.layernorm_before.weight')		)
            rename_keys.append((f'blocks.{i}.norm1.bias', f'vit.encoder.layer.{i}.layernorm_before.bias')		)
            rename_keys.append((f'blocks.{i}.attn.proj.weight', f'vit.encoder.layer.{i}.attention.output.dense.weight')		)
            rename_keys.append((f'blocks.{i}.attn.proj.bias', f'vit.encoder.layer.{i}.attention.output.dense.bias')		)
            rename_keys.append((f'blocks.{i}.norm2.weight', f'vit.encoder.layer.{i}.layernorm_after.weight')		)
            rename_keys.append((f'blocks.{i}.norm2.bias', f'vit.encoder.layer.{i}.layernorm_after.bias')		)
            rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'vit.encoder.layer.{i}.intermediate.dense.weight')		)
            rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'vit.encoder.layer.{i}.intermediate.dense.bias')		)
            rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'vit.encoder.layer.{i}.output.dense.weight')		)
            rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'vit.encoder.layer.{i}.output.dense.bias')		)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ('cls_token', 'vit.embeddings.cls_token'),
              ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
              ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
              ('pos_embed', 'vit.embeddings.position_embeddings'),
          ]		)

      if base_model:
            # layernorm + pooler
            rename_keys.extend(
                [
                    ('norm.weight', 'layernorm.weight'),
                    ('norm.bias', 'layernorm.bias'),
                ]		)

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

      return rename_keys





def 				snake_case							(						UpperCAmelCase					,     UpperCAmelCase					,     UpperCAmelCase=False		)->	Optional[int]:







      """simple docstring"""
      for i in range(config.num_hidden_layers		):
            if base_model:
                  __A						=	""""""
            else:
                  __A						=	"""vit."""
            # 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 				snake_case							(						UpperCAmelCase		)->	Dict:







      """simple docstring"""
      __A						=	["""head.weight""", """head.bias"""]
      for k in ignore_keys:
            state_dict.pop(lowerCAmelCase_					,     lowerCAmelCase_		)





def 				snake_case							(						UpperCAmelCase					,     UpperCAmelCase					,     UpperCAmelCase		)->	List[str]:







      """simple docstring"""
      __A						=	dct.pop(lowerCAmelCase_		)
      __A						=	val





def 				snake_case							(						)->	Optional[Any]:







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





@torch.no_grad()
def 				snake_case							(						UpperCAmelCase					,     UpperCAmelCase					,     UpperCAmelCase=True		)->	Union[str, Any]:







      """simple docstring"""
      __A						=	ViTConfig()
      # patch_size
      if model_name[-1] == "8":
            __A						=	8
      # set labels if required
      if not base_model:
            __A						=	1_0_0_0
            __A						=	"""huggingface/label-files"""
            __A						=	"""imagenet-1k-id2label.json"""
            __A						=	json.load(open(hf_hub_download(lowerCAmelCase_					,     lowerCAmelCase_					,     repo_type='dataset'		)					,     'r'		)		)
            __A						=	{int(lowerCAmelCase_		): v for k, v in idalabel.items()}
            __A						=	idalabel
            __A						=	{v: k for k, v in idalabel.items()}
      # size of the architecture
      if model_name in ["dino_vits8", "dino_vits16"]:
            __A						=	3_8_4
            __A						=	1_5_3_6
            __A						=	1_2
            __A						=	6

      # load original model from torch hub
      __A						=	torch.hub.load('facebookresearch/dino:main'					,     lowerCAmelCase_		)
      original_model.eval()

      # load state_dict of original model, remove and rename some keys
      __A						=	original_model.state_dict()
      if base_model:
            remove_classification_head_(lowerCAmelCase_		)
      __A						=	create_rename_keys(lowerCAmelCase_					,     base_model=lowerCAmelCase_		)
      for src, dest in rename_keys:
            rename_key(lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_		)
      read_in_q_k_v(lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_		)

      # load HuggingFace model
      if base_model:
            __A						=	ViTModel(lowerCAmelCase_					,     add_pooling_layer=lowerCAmelCase_		).eval()
      else:
            __A						=	ViTForImageClassification(lowerCAmelCase_		).eval()
      model.load_state_dict(lowerCAmelCase_		)

      # Check outputs on an image, prepared by ViTImageProcessor
      __A						=	ViTImageProcessor()
      __A						=	image_processor(images=prepare_img()					,     return_tensors='pt'		)
      __A						=	encoding["""pixel_values"""]
      __A						=	model(lowerCAmelCase_		)

      if base_model:
            __A						=	original_model(lowerCAmelCase_		)
            assert torch.allclose(lowerCAmelCase_					,     outputs.last_hidden_state[:, 0, :]					,     atol=1e-1		)
      else:
            __A						=	original_model(lowerCAmelCase_		)
            assert logits.shape == outputs.logits.shape
            assert torch.allclose(lowerCAmelCase_					,     outputs.logits					,     atol=1e-3		)

      Path(lowerCAmelCase_		).mkdir(exist_ok=lowerCAmelCase_		)
      print(f'Saving model {model_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__":
 a__						:      Optional[int]   				=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="dino_vitb16",
     type=str,
     help="Name of the model trained with DINO you\'d like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )
 parser.add_argument(
     "--base_model",
     action="store_true",
     help="Whether to only convert the base model (no projection head weights).",
 )

 parser.set_defaults(base_model=True)
 a__						:      int   				=				parser.parse_args()
 convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)














import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def 					snake_case_						(   lowerCAmelCase_				:  List[Any]				,				lowerCAmelCase_				:  Dict				,				lowerCAmelCase_				:  Any				,				lowerCAmelCase_				:  Tuple				,				lowerCAmelCase_				:  int					):
							# Initialise PyTorch model.
							# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
							# TapasConfig to False.

							# initialize configuration from json file
							__lowercase					:							Optional[int]			=   TapasConfig.from_json_file(lowerCAmelCase_					)
							# set absolute/relative position embeddings parameter
							__lowercase					:							Optional[Any]			=   reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														__lowercase					:							Union[str, Any]			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "WTQ":
														# run_task_main.py hparams
														__lowercase					:							List[Any]			=   4
														__lowercase					:							Union[str, Any]			=   True
														# hparam_utils.py hparams
														__lowercase					:							Any			=   0.664_694
														__lowercase					:							Tuple			=   0.207_951
														__lowercase					:							Dict			=   0.121_194
														__lowercase					:							List[str]			=   True
														__lowercase					:							str			=   True
														__lowercase					:							Dict			=   False
														__lowercase					:							Tuple			=   0.0_352_513

														__lowercase					:							List[Any]			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														__lowercase					:							Optional[int]			=   4
														__lowercase					:							int			=   False
														# hparam_utils.py hparams
														__lowercase					:							Tuple			=   36.4_519
														__lowercase					:							str			=   0.903_421
														__lowercase					:							List[Any]			=   222.088
														__lowercase					:							Union[str, Any]			=   True
														__lowercase					:							Tuple			=   True
														__lowercase					:							Union[str, Any]			=   True
														__lowercase					:							Optional[Any]			=   0.763_141

														__lowercase					:							str			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "TABFACT":
														__lowercase					:							List[Any]			=   TapasForSequenceClassification(config=lowerCAmelCase_					)
							elif task == "MLM":
														__lowercase					:							Optional[int]			=   TapasForMaskedLM(config=lowerCAmelCase_					)
							elif task == "INTERMEDIATE_PRETRAINING":
														__lowercase					:							Dict			=   TapasModel(config=lowerCAmelCase_					)
							else:
														raise ValueError(F"Task {task} not supported."					)

							print(F"Building PyTorch model from configuration: {config}"					)
							# Load weights from tf checkpoint
							load_tf_weights_in_tapas(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_					)

							# Save pytorch-model (weights and configuration)
							print(F"Save PyTorch model to {pytorch_dump_path}"					)
							model.save_pretrained(lowerCAmelCase_					)

							# Save tokenizer files
							print(F"Save tokenizer files to {pytorch_dump_path}"					)
							__lowercase					:							Any			=   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""				,				model_max_length=512					)
							tokenizer.save_pretrained(lowerCAmelCase_					)

							print("""Used relative position embeddings:"""				,				model.config.reset_position_index_per_cell					)


if __name__ == "__main__":
	lowerCamelCase       :       List[Any]        =				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
	)
	parser.add_argument(
	    '''--reset_position_index_per_cell''',
	    default=False,
	    action='''store_true''',
	    help='''Whether to use relative position embeddings or not. Defaults to True.''',
	)
	parser.add_argument(
	    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
	)
	parser.add_argument(
	    '''--tapas_config_file''',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        '''The config json file corresponding to the pre-trained TAPAS model. \n'''
	        '''This specifies the model architecture.'''
	    ),
	)
	parser.add_argument(
	    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	lowerCamelCase       :       int        =				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(
	    args.task,
	    args.reset_position_index_per_cell,
	    args.tf_checkpoint_path,
	    args.tapas_config_file,
	    args.pytorch_dump_path,
	)