


"""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()
_lowerCAmelCase							:    str					   =	logging.get_logger(__name__)







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase=False       )    ->     List[Any]:
   '''simple docstring'''





   _lowerCamelCase   :				int							=     []
   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"
      _lowerCamelCase   :				List[str]							=     [(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        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase=False       )    ->     Optional[int]:
   '''simple docstring'''





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







def        lowerCamelCase_(		_lowerCamelCase       )    ->     int:
   '''simple docstring'''





   _lowerCamelCase   :				Union[str, Any]							=     ["head.weight", "head.bias"]
   for k in ignore_keys:
      state_dict.pop(_lowerCamelCase			,		_lowerCamelCase       )







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     Any:
   '''simple docstring'''





   _lowerCamelCase   :				Any							=     dct.pop(_lowerCamelCase       )
   _lowerCamelCase   :				Dict							=     val







def        lowerCamelCase_(		)    ->     Optional[int]:
   '''simple docstring'''





   _lowerCamelCase   :				Optional[int]							=     "http://images.cocodataset.org/val2017/000000039769.jpg"
   _lowerCamelCase   :				List[str]							=     Image.open(requests.get(_lowerCamelCase			,		stream=_lowerCamelCase       ).raw       )
   return im







@torch.no_grad()
def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase=True       )    ->     str:
   '''simple docstring'''





   _lowerCamelCase   :				Union[str, Any]							=     ViTConfig()
   # patch_size
   if model_name[-1] == "8":
      _lowerCamelCase   :				str							=     8
   # set labels if required
   if not base_model:
      _lowerCamelCase   :				str							=     1000
      _lowerCamelCase   :				Any							=     "huggingface/label-files"
      _lowerCamelCase   :				Union[str, Any]							=     "imagenet-1k-id2label.json"
      _lowerCamelCase   :				Optional[int]							=     json.load(open(hf_hub_download(_lowerCamelCase			,		_lowerCamelCase			,		repo_type="dataset"       )			,		"r"       )       )
      _lowerCamelCase   :				str							=     {int(_lowerCamelCase       ): v for k, v in idalabel.items()}
      _lowerCamelCase   :				Optional[Any]							=     idalabel
      _lowerCamelCase   :				Optional[Any]							=     {v: k for k, v in idalabel.items()}
   # size of the architecture
   if model_name in ["dino_vits8", "dino_vits16"]:
      _lowerCamelCase   :				int							=     384
      _lowerCamelCase   :				str							=     1536
      _lowerCamelCase   :				List[str]							=     12
      _lowerCamelCase   :				Optional[int]							=     6

   # load original model from torch hub
   _lowerCamelCase   :				Union[str, Any]							=     torch.hub.load("facebookresearch/dino:main"			,		_lowerCamelCase       )
   original_model.eval()

   # load state_dict of original model, remove and rename some keys
   _lowerCamelCase   :				List[str]							=     original_model.state_dict()
   if base_model:
      remove_classification_head_(_lowerCamelCase       )
   _lowerCamelCase   :				Tuple							=     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:
      _lowerCamelCase   :				Optional[Any]							=     ViTModel(_lowerCamelCase			,		add_pooling_layer=_lowerCamelCase       ).eval()
   else:
      _lowerCamelCase   :				Union[str, Any]							=     ViTForImageClassification(_lowerCamelCase       ).eval()
   model.load_state_dict(_lowerCamelCase       )

   # Check outputs on an image, prepared by ViTImageProcessor
   _lowerCamelCase   :				Tuple							=     ViTImageProcessor()
   _lowerCamelCase   :				List[Any]							=     image_processor(images=prepare_img()			,		return_tensors="pt"       )
   _lowerCamelCase   :				Dict							=     encoding["pixel_values"]
   _lowerCamelCase   :				int							=     model(_lowerCamelCase       )

   if base_model:
      _lowerCamelCase   :				List[str]							=     original_model(_lowerCamelCase       )
      assert torch.allclose(_lowerCamelCase			,		outputs.last_hidden_state[:, 0, :]			,		atol=1e-1       )
   else:
      _lowerCamelCase   :				Tuple							=     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__":
  _lowerCAmelCase							:    List[str]					   =	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)
  _lowerCAmelCase							:    List[Any]					   =	parser.parse_args()
  convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)




'''simple docstring'''

import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)







def a_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    List[str]      							=checkpoints.load_tax_checkpoint(__UpperCAmelCase					)
			snake_case:    str      							=flatten_dict(__UpperCAmelCase					)
			return flax_params







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    int      							={}

			snake_case:    Any      							={
			    'token_embedder': 'embeddings',
			    'encoder_norm': 'layernorm',
			    'kernel': 'weight',
			    '.out': '.output',
			    'scale': 'weight',
			    'embedders_0.pos_embedding': 'row_embedder.weight',
			    'embedders_1.pos_embedding': 'column_embedder.weight',
			}

			snake_case:    Union[str, Any]      							={
			    'query': 'attention.query',
			    'key': 'attention.key',
			    'value': 'attention.value',
			    'output.dense': 'output',
			    'encoder_decoder_attention.o': 'encoder_decoder_attention.attention.o',
			    'pre_self_attention_layer_norm': 'self_attention.layer_norm',
			    'pre_cross_attention_layer_norm': 'encoder_decoder_attention.layer_norm',
			    'mlp.': 'mlp.DenseReluDense.',
			    'pre_mlp_layer_norm': 'mlp.layer_norm',
			    'self_attention.o': 'self_attention.attention.o',
			    'decoder.embeddings.embedding': 'decoder.embed_tokens.weight',
			    'decoder.relpos_bias.rel_embedding': 'decoder.layer.0.self_attention.attention.relative_attention_bias.weight',
			    'decoder.decoder_norm.weight': 'decoder.final_layer_norm.weight',
			    'decoder.logits_dense.weight': 'decoder.lm_head.weight',
			}

			for key in flax_dict.keys():
						if "target" in key:
									# remove the first prefix from the key
									snake_case:    Dict      							='.'.join(key[1:]					)

									# rename the key
									for old, new in CONVERSION_MAPPING.items():
												snake_case:    Union[str, Any]      							=new_key.replace(__UpperCAmelCase				,							__UpperCAmelCase					)

									if "decoder" in new_key:
												for old, new in DECODER_CONVERSION_MAPPING.items():
															snake_case:    Optional[Any]      							=new_key.replace(__UpperCAmelCase				,							__UpperCAmelCase					)

									if "layers" in new_key and "decoder" not in new_key:
												# use regex to replace the layer number
												snake_case:    Any      							=re.sub(R'layers_(\d+)'				,							R'layer.\1'				,							__UpperCAmelCase					)
												snake_case:    Optional[int]      							=new_key.replace('encoder'				,							'encoder.encoder'					)

									elif "layers" in new_key and "decoder" in new_key:
												# use regex to replace the layer number
												snake_case:    Optional[Any]      							=re.sub(R'layers_(\d+)'				,							R'layer.\1'				,							__UpperCAmelCase					)

									snake_case:    Optional[int]      							=flax_dict[key]

			snake_case:    Optional[int]      							={}
			# convert converted_dict into torch format
			for key in converted_dict.keys():
						if ("embed_tokens" not in key) and ("embedder" not in key):
									snake_case:    Union[str, Any]      							=torch.from_numpy(converted_dict[key].T					)
						else:
									snake_case:    Union[str, Any]      							=torch.from_numpy(converted_dict[key]					)

			return converted_torch_dict







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=False				,							__UpperCAmelCase=False					)				->					Tuple:





			"""simple docstring"""







			snake_case:    Any      							=get_flax_param(__UpperCAmelCase					)

			if not use_large:
						snake_case:    Optional[int]      							=PixaStructVisionConfig()
						snake_case:    int      							=PixaStructTextConfig()
			else:
						snake_case:    Tuple      							=PixaStructVisionConfig(
						    hidden_size=15_36				,							d_ff=39_68				,							num_attention_heads=24				,							num_hidden_layers=18					)
						snake_case:    List[Any]      							=PixaStructTextConfig(hidden_size=15_36				,							d_ff=39_68				,							num_heads=24				,							num_layers=18					)
			snake_case:    Any      							=PixaStructConfig(
			    vision_config=encoder_config.to_dict()				,							text_config=decoder_config.to_dict()				,							is_vqa=__UpperCAmelCase					)

			snake_case:    List[Any]      							=PixaStructForConditionalGeneration(__UpperCAmelCase					)

			snake_case:    str      							=rename_and_convert_flax_params(__UpperCAmelCase					)
			model.load_state_dict(__UpperCAmelCase					)

			snake_case:    List[Any]      							=AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'					)
			snake_case:    Optional[Any]      							=PixaStructImageProcessor()
			snake_case:    Optional[int]      							=PixaStructProcessor(image_processor=__UpperCAmelCase				,							tokenizer=__UpperCAmelCase					)

			if use_large:
						snake_case:    Optional[Any]      							=40_96

			snake_case:    str      							=True

			# mkdir if needed
			os.makedirs(__UpperCAmelCase				,							exist_ok=__UpperCAmelCase					)

			model.save_pretrained(__UpperCAmelCase					)
			processor.save_pretrained(__UpperCAmelCase					)

			print('Model saved in {}'.format(__UpperCAmelCase					)					)


if __name__ == "__main__":
	a							   =				argparse.ArgumentParser()
	parser.add_argument('--t5x_checkpoint_path', default=None, type=str, help='Path to the original T5x checkpoint.')
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument('--use_large', action='store_true', help='Use large model.')
	parser.add_argument('--is_vqa', action='store_true', help='Use large model.')
	a							   =				parser.parse_args()

	convert_pixastruct_original_pytorch_checkpoint_to_hf(
	    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
	)




