




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()
_snake_case										=       logging.get_logger(__name__)







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







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Optional[int]			=						["""head.weight""", """head.bias"""]
      for k in ignore_keys:
            state_dict.pop(snake_case_,snake_case_     )







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     Any			=						dct.pop(snake_case_     )
      _A							:     str			=						val







def 				lowerCAmelCase_	(     ):
      _A							:     List[str]			=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      _A							:     List[Any]			=						Image.open(requests.get(snake_case_,stream=snake_case_     ).raw     )
      return im







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

      # load original model from torch hub
      _A							:     str			=						torch.hub.load("""facebookresearch/dino:main""",snake_case_     )
      original_model.eval()

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

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

      if base_model:
            _A							:     Union[str, Any]			=						original_model(snake_case_     )
            assert torch.allclose(snake_case_,outputs.last_hidden_state[:, 0, :],atol=1e-1     )
      else:
            _A							:     Any			=						original_model(snake_case_     )
            assert 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 {model_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__":
  _snake_case										=       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)
  _snake_case										=       parser.parse_args()
  convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)





def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				List[Any]		     =					current_set.copy()
  for row_index, row in enumerate(SCREAMING_SNAKE_CASE       ):
    A_      :				List[str]		     =					row[0]
    for column_index, column in enumerate(SCREAMING_SNAKE_CASE       ):
      if magnitude == 0:
        A_      :				Union[str, Any]		     =					column
        continue
      A_      :				Dict		     =					column / magnitude
    # Subtract to cancel term
  A_      :				Union[str, Any]		     =					current_set[0]
  A_      :				Tuple		     =					[first_row]
  A_      :				int		     =					current_set[1::]
  for row in current_set:
    A_      :				Tuple		     =					[]
    # If first term is 0, it is already in form we want, so we preserve it
    if row[0] == 0:
      final_set.append(SCREAMING_SNAKE_CASE       )
      continue
    for column_index in range(len(SCREAMING_SNAKE_CASE       )       ):
      temp_row.append(first_row[column_index] - row[column_index]       )
    final_set.append(SCREAMING_SNAKE_CASE       )
  # Create next recursion iteration set
  if len(final_set[0]       ) != 3:
    A_      :				Optional[Any]		     =					final_set[0]
    A_      :				Any		     =					[]
    A_      :				str		     =					[]
    for row in final_set[1::]:
      current_first_column.append(row[0]       )
      next_iteration.append(row[1::]       )
    A_      :				Optional[Any]		     =					simplify(SCREAMING_SNAKE_CASE       )
    for i in range(len(SCREAMING_SNAKE_CASE       )       ):
      resultant[i].insert(0 ,			current_first_column[i]       )
    resultant.insert(0 ,			SCREAMING_SNAKE_CASE       )
    A_      :				List[Any]		     =					resultant
  return final_set


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if len(SCREAMING_SNAKE_CASE       ) == 0:
    raise IndexError('''solve_simultaneous() requires n lists of length n+1'''       )
  A_      :				str		     =					len(SCREAMING_SNAKE_CASE       ) + 1
  if any(len(SCREAMING_SNAKE_CASE       ) != _length for item in equations       ):
    raise IndexError('''solve_simultaneous() requires n lists of length n+1'''       )
  for row in equations:
    if any(not isinstance(SCREAMING_SNAKE_CASE ,			(int, float)       ) for column in row       ):
      raise ValueError('''solve_simultaneous() requires lists of integers'''       )
  if len(SCREAMING_SNAKE_CASE       ) == 1:
    return [equations[0][-1] / equations[0][0]]
  A_      :				Dict		     =					equations.copy()
  if any(0 in row for row in data_set       ):
    A_      :				Tuple		     =					data_set.copy()
    A_      :				Optional[Any]		     =					[]
    for row_index, row in enumerate(SCREAMING_SNAKE_CASE       ):
      if 0 not in row:
        A_      :				str		     =					data_set.pop(SCREAMING_SNAKE_CASE       )
        break
    if not full_row:
      raise ValueError('''solve_simultaneous() requires at least 1 full equation'''       )
    data_set.insert(0 ,			SCREAMING_SNAKE_CASE       )
  A_      :				int		     =					data_set.copy()
  A_      :				Dict		     =					simplify(SCREAMING_SNAKE_CASE       )
  A_      :				Dict		     =					simplified[::-1]
  A_      :				list		     =					[]
  for row in simplified:
    A_      :				Union[str, Any]		     =					row[-1]
    if not solutions:
      if row[-2] == 0:
        solutions.append(0       )
        continue
      solutions.append(current_solution / row[-2]       )
      continue
    A_      :				Optional[Any]		     =					row.copy()[: len(SCREAMING_SNAKE_CASE       ) - 1 :]
    while temp_row[0] == 0:
      temp_row.pop(0       )
    if len(SCREAMING_SNAKE_CASE       ) == 0:
      solutions.append(0       )
      continue
    A_      :				int		     =					temp_row[1::]
    A_      :				int		     =					temp_row[::-1]
    for column_index, column in enumerate(SCREAMING_SNAKE_CASE       ):
      current_solution -= column * solutions[column_index]
    solutions.append(SCREAMING_SNAKE_CASE       )
  A_      :				Union[str, Any]		     =					[]
  for item in solutions:
    final.append(float(round(SCREAMING_SNAKE_CASE ,			5       )       )       )
  return final[::-1]


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     UpperCamelCase								=	[
         [2, 1, 1, 1, 1, 4],
         [1, 2, 1, 1, 1, 5],
         [1, 1, 2, 1, 1, 6],
         [1, 1, 1, 2, 1, 7],
         [1, 1, 1, 1, 2, 8],
     ]
     print(solve_simultaneous(eq))
     print(solve_simultaneous([[4, 2]]))




