


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


logging.set_verbosity_info()
snake_case__			 =						logging.get_logger(__name__)


def 				lowerCamelCase__	(      a	:   Union[str, Any]	,  a	:   Union[str, Any]=False				)    ->	Optional[int]:





   """simple docstring"""
   a__ :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"),
              ("pre_logits.fc.weight", "pooler.dense.weight"),
              ("pre_logits.fc.bias", "pooler.dense.bias"),
          ]				)

      # if just the base model, we should remove "vit" from all keys that start with "vit"
      a__ :Union[str, Any]						  =    [(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__	(      a	:   Dict	,  a	:   Dict	,  a	:   Tuple=False				)    ->	Union[str, Any]:





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


def 				lowerCamelCase__	(      a	:   List[str]				)    ->	Any:





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


def 				lowerCamelCase__	(      a	:   List[Any]	,  a	:   List[str]	,  a	:   Dict				)    ->	Optional[int]:





   """simple docstring"""
   a__ :Tuple						  =    dct.pop(a				)
   a__ :Any						  =    val


def 				lowerCamelCase__	(      )    ->	Union[str, Any]:





   """simple docstring"""
   a__ :Union[str, Any]						  =    "http://images.cocodataset.org/val2017/000000039769.jpg"
   a__ :Optional[int]						  =    Image.open(requests.get(a	,  stream=a				).raw				)
   return im


@torch.no_grad()
def 				lowerCamelCase__	(      a	:   List[Any]	,  a	:   List[Any]				)    ->	List[str]:





   """simple docstring"""
   a__ :str						  =    ViTConfig()
   a__ :int						  =    False
   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   if vit_name[-5:] == "in21k":
      a__ :Optional[Any]						  =    True
      a__ :List[Any]						  =    int(vit_name[-12:-10]				)
      a__ :Tuple						  =    int(vit_name[-9:-6]				)
   else:
      a__ :int						  =    1_000
      a__ :str						  =    "huggingface/label-files"
      a__ :Optional[int]						  =    "imagenet-1k-id2label.json"
      a__ :Optional[Any]						  =    json.load(open(hf_hub_download(a	,  a	,  repo_type="dataset"				)	,  "r"				)				)
      a__ :Optional[int]						  =    {int(a				): v for k, v in idalabel.items()}
      a__ :Tuple						  =    idalabel
      a__ :Optional[int]						  =    {v: k for k, v in idalabel.items()}
      a__ :Optional[Any]						  =    int(vit_name[-6:-4]				)
      a__ :Dict						  =    int(vit_name[-3:]				)
   # size of the architecture
   if "deit" in vit_name:
      if vit_name[9:].startswith("tiny"				):
         a__ :List[str]						  =    192
         a__ :List[Any]						  =    768
         a__ :Optional[Any]						  =    12
         a__ :Any						  =    3
      elif vit_name[9:].startswith("small"				):
         a__ :Tuple						  =    384
         a__ :str						  =    1_536
         a__ :Tuple						  =    12
         a__ :List[Any]						  =    6
      else:
         pass
   else:
      if vit_name[4:].startswith("small"				):
         a__ :Optional[int]						  =    768
         a__ :Optional[int]						  =    2_304
         a__ :int						  =    8
         a__ :Tuple						  =    8
      elif vit_name[4:].startswith("base"				):
         pass
      elif vit_name[4:].startswith("large"				):
         a__ :List[str]						  =    1_024
         a__ :str						  =    4_096
         a__ :Optional[int]						  =    24
         a__ :str						  =    16
      elif vit_name[4:].startswith("huge"				):
         a__ :Optional[Any]						  =    1_280
         a__ :Dict						  =    5_120
         a__ :List[str]						  =    32
         a__ :Tuple						  =    16

    # load original model from timm
   a__ :str						  =    timm.create_model(a	,  pretrained=a				)
   timm_model.eval()

   # load state_dict of original model, remove and rename some keys
   a__ :Tuple						  =    timm_model.state_dict()
   if base_model:
      remove_classification_head_(a				)
   a__ :List[str]						  =    create_rename_keys(a	,  a				)
   for src, dest in rename_keys:
      rename_key(a	,  a	,  a				)
   read_in_q_k_v(a	,  a	,  a				)

   # load HuggingFace model
   if vit_name[-5:] == "in21k":
      a__ :Optional[int]						  =    ViTModel(a				).eval()
   else:
      a__ :Any						  =    ViTForImageClassification(a				).eval()
   model.load_state_dict(a				)

   # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
   if "deit" in vit_name:
      a__ :List[str]						  =    DeiTImageProcessor(size=config.image_size				)
   else:
      a__ :Union[str, Any]						  =    ViTImageProcessor(size=config.image_size				)
   a__ :Optional[int]						  =    image_processor(images=prepare_img()	,  return_tensors="pt"				)
   a__ :Any						  =    encoding["pixel_values"]
   a__ :Union[str, Any]						  =    model(a				)

   if base_model:
      a__ :int						  =    timm_model.forward_features(a				)
      assert timm_pooled_output.shape == outputs.pooler_output.shape
      assert torch.allclose(a	,  outputs.pooler_output	,  atol=1e-3				)
   else:
      a__ :str						  =    timm_model(a				)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(a	,  outputs.logits	,  atol=1e-3				)

   Path(a				).mkdir(exist_ok=a				)
   print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''				)
   model.save_pretrained(a				)
   print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
   image_processor.save_pretrained(a				)


if __name__ == "__main__":
  snake_case__			 =						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--vit_name''',
      default='''vit_base_patch16_224''',
      type=str,
      help='''Name of the ViT 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.'''
  )

  snake_case__			 =						parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)






def 				lowerCamelCase__	(      a	:   list[list]				)    ->	list[list]:





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


def 				lowerCamelCase__	(      a	:   list[list]				)    ->	list:





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


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		snake_case__			 =						[
		    [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]]))


