'''simple docstring'''



import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


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

@dataclass
class 			SCREAMING_SNAKE_CASE					:






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				nn.Module
      SCREAMING_SNAKE_CASE__ :				List[nn.Module]    =				field(default_factory=lowercase_					)
      SCREAMING_SNAKE_CASE__ :				list    =				field(default_factory=lowercase_					)




      def 	__UpperCAmelCase			(					self :		Optional[int]     ,					snake_case :		Optional[Any]     ,					snake_case :		Tensor     ,					snake_case :		Tensor   ):



          """simple docstring"""





          _snake_case : int												=		len(list(m.modules()   )   ) == 1 or isinstance(snake_case     ,					nn.Convad   ) or isinstance(snake_case     ,					nn.BatchNormad   )
          if has_not_submodules:
              if not isinstance(snake_case     ,					snake_case   ):
                  self.traced.append(snake_case   )




      def __call__(					self :		Optional[int]     ,					snake_case :		Tensor   ):



          """simple docstring"""





          for m in self.module.modules():
              self.handles.append(m.register_forward_hook(self._forward_hook   )   )
          self.module(snake_case   )
          [x.remove() for x in self.handles]
          return self




      @property
      def 	__UpperCAmelCase			(					self :		Dict   ):



          """simple docstring"""





          return list(filter(lambda snake_case   : len(list(x.state_dict().keys()   )   ) > 0     ,					self.traced   )   )



@dataclass
class 			SCREAMING_SNAKE_CASE					:






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				nn.Module
      SCREAMING_SNAKE_CASE__ :				nn.Module
      SCREAMING_SNAKE_CASE__ :				int    =				0
      SCREAMING_SNAKE_CASE__ :				List    =				field(default_factory=lowercase_					)
      SCREAMING_SNAKE_CASE__ :				List    =				field(default_factory=lowercase_					)




      def __call__(					self :		int     ,					snake_case :		Tensor   ):



          """simple docstring"""





          _snake_case : Any												=		Tracker(self.dest   )(snake_case   ).parametrized
          _snake_case : Tuple												=		Tracker(self.src   )(snake_case   ).parametrized

          _snake_case : int												=		list(filter(lambda snake_case   : type(snake_case   ) not in self.src_skip     ,					snake_case   )   )
          _snake_case : Optional[int]												=		list(filter(lambda snake_case   : type(snake_case   ) not in self.dest_skip     ,					snake_case   )   )

          if len(snake_case   ) != len(snake_case   ):
              raise Exception(
                  F"""Numbers of operations are different. Source module has {len(snake_case   )} operations while"""
                  F""" destination module has {len(snake_case   )}."""   )

          for dest_m, src_m in zip(snake_case     ,					snake_case   ):
              dest_m.load_state_dict(src_m.state_dict()   )
              if self.verbose == 1:
                  print(F"""Transfered from={src_m} to={dest_m}"""   )



def   lowerCamelCase__       (      a__			,      a__)		->    nn.Module:



    """simple docstring"""







    _snake_case : Optional[Any]												=		from_model.state_dict()
    _snake_case : Any												=		our_model.state_dict()
    _snake_case : List[str]												=		our_model.config
    _snake_case : Optional[int]												=		[]
    for stage_idx in range(len(config.hidden_sizes)):
        for block_id in range(config.depths[stage_idx]):
            _snake_case : Dict												=		F"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
            _snake_case : Any												=		F"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

            all_keys.append((from_key, to_key))
            _snake_case : List[Any]												=		F"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
            _snake_case : List[Any]												=		F"""van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"""

            all_keys.append((from_key, to_key))

    for from_key, to_key in all_keys:
        _snake_case : str												=		from_state_dict.pop(a__)

    our_model.load_state_dict(a__)
    return our_model

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



    """simple docstring"""







    print(F"""Downloading weights for {name}...""")
    _snake_case : int												=		cached_download(a__)
    print(F"""Converting {name}...""")
    _snake_case : int												=		torch.load(a__)['state_dict']
    from_model.load_state_dict(a__)
    from_model.eval()
    with torch.no_grad():
        _snake_case : str												=		VanForImageClassification(a__).eval()
        _snake_case : Tuple												=		ModuleTransfer(src=a__			,      dest=a__)
        _snake_case : Optional[Any]												=		torch.randn((1, 3, 2_2_4, 2_2_4))
        module_transfer(a__)
        _snake_case : List[Any]												=		copy_parameters(a__			,      a__)

    if not torch.allclose(from_model(a__)			,      our_model(a__).logits):
        raise ValueError('The model logits don\'t match the original one.')

    _snake_case : Dict												=		name
    print(a__)

    if push_to_hub:
        our_model.push_to_hub(
            repo_path_or_name=save_directory / checkpoint_name			,      commit_message='Add model'			,      use_temp_dir=a__			,      )

        # we can use the convnext one
        _snake_case : Tuple												=		AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k')
        image_processor.push_to_hub(
            repo_path_or_name=save_directory / checkpoint_name			,      commit_message='Add image processor'			,      use_temp_dir=a__			,      )

        print(F"""Pushed {checkpoint_name}""")

def   lowerCamelCase__       (      a__			,      a__ = None			,      a__ = True)		->    Tuple:



    """simple docstring"""







    _snake_case : Any												=		'imagenet-1k-id2label.json'
    _snake_case : str												=		1_0_0_0

    _snake_case : int												=		'huggingface/label-files'
    _snake_case : List[Any]												=		num_labels
    _snake_case : Union[str, Any]												=		json.load(open(hf_hub_download(a__			,      a__			,      repo_type='dataset')			,      'r'))
    _snake_case : Optional[int]												=		{int(a__): v for k, v in idalabel.items()}

    _snake_case : Union[str, Any]												=		idalabel
    _snake_case : List[Any]												=		{v: k for k, v in idalabel.items()}

    _snake_case : Optional[Any]												=		partial(a__			,      num_labels=a__			,      idalabel=a__			,      labelaid=a__)

    _snake_case : Optional[int]												=		{
        'van-tiny': ImageNetPreTrainedConfig(
            hidden_sizes=[3_2, 6_4, 1_6_0, 2_5_6]			,      depths=[3, 3, 5, 2]			,      mlp_ratios=[8, 8, 4, 4]			,      ),
        'van-small': ImageNetPreTrainedConfig(
            hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]			,      depths=[2, 2, 4, 2]			,      mlp_ratios=[8, 8, 4, 4]			,      ),
        'van-base': ImageNetPreTrainedConfig(
            hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]			,      depths=[3, 3, 1_2, 3]			,      mlp_ratios=[8, 8, 4, 4]			,      ),
        'van-large': ImageNetPreTrainedConfig(
            hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]			,      depths=[3, 5, 2_7, 3]			,      mlp_ratios=[8, 8, 4, 4]			,      ),
    }

    _snake_case : str												=		{
        'van-tiny': van_tiny,
        'van-small': van_small,
        'van-base': van_base,
        'van-large': van_large,
    }

    _snake_case : Optional[Any]												=		{
        'van-tiny': (
            'https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'
        ),
        'van-small': (
            'https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'
        ),
        'van-base': (
            'https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'
        ),
        'van-large': (
            'https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'
        ),
    }

    if model_name:
        convert_weight_and_push(
            a__			,      names_to_config[model_name]			,      checkpoint=names_to_original_checkpoints[model_name]			,      from_model=names_to_original_models[model_name]()			,      save_directory=a__			,      push_to_hub=a__			,      )
    else:
        for model_name, config in names_to_config.items():
            convert_weight_and_push(
                a__			,      a__			,      checkpoint=names_to_original_checkpoints[model_name]			,      from_model=names_to_original_models[model_name]()			,      save_directory=a__			,      push_to_hub=a__			,      )


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_    =   argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--model-name",
       default=None,
       type=str,
       help=(
           "The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
           " currently: van-tiny/small/base/large. If `None`, all of them will the converted."
       ),
   )
   parser.add_argument(
       "--pytorch_dump_folder_path",
       default=None,
       type=Path,
       required=True,
       help="Path to the output PyTorch model directory.",
   )
   parser.add_argument(
       "--van_dir",
       required=True,
       type=Path,
       help=(
           "A path to VAN\'s original implementation directory. You can download from here:"
           " https://github.com/Visual-Attention-Network/VAN-Classification"
       ),
   )
   parser.add_argument(
       "--push_to_hub",
       default=True,
       type=bool,
       required=False,
       help="If True, push model and image processor to the hub.",
   )

   SCREAMING_SNAKE_CASE_    =   parser.parse_args()
   SCREAMING_SNAKE_CASE_    =   args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   SCREAMING_SNAKE_CASE_    =   args.van_dir
   # append the path to the parents to maskformer dir
   sys.path.append(str(van_dir.parent))
   from van.models.van import van_base, van_large, van_small, van_tiny

   convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)






'''simple docstring'''



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
   from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
   SCREAMING_SNAKE_CASE_    =   False

if is_vision_available():
   from PIL import Image

   from transformers import (
       AutoProcessor,
       PixaStructImageProcessor,
       PixaStructProcessor,
       PreTrainedTokenizerFast,
       TaTokenizer,
   )

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11      ,reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''      ,)
@require_vision
@require_torch
class 			SCREAMING_SNAKE_CASE					(							unittest.TestCase					):






      '''simple docstring'''






      def 	__UpperCAmelCase			(					self :		Union[str, Any]   ):



          """simple docstring"""





          _snake_case : int												=		tempfile.mkdtemp()

          _snake_case : Optional[int]												=		PixaStructImageProcessor()
          _snake_case : Dict												=		TaTokenizer.from_pretrained('t5-small'   )

          _snake_case : Optional[Any]												=		PixaStructProcessor(snake_case     ,					snake_case   )

          processor.save_pretrained(self.tmpdirname   )




      def 	__UpperCAmelCase			(					self :		Optional[Any]     ,					**snake_case :		Any   ):



          """simple docstring"""





          return AutoProcessor.from_pretrained(self.tmpdirname     ,					**snake_case   ).tokenizer




      def 	__UpperCAmelCase			(					self :		int     ,					**snake_case :		Optional[int]   ):



          """simple docstring"""





          return AutoProcessor.from_pretrained(self.tmpdirname     ,					**snake_case   ).image_processor




      def 	__UpperCAmelCase			(					self :		str   ):



          """simple docstring"""





          shutil.rmtree(self.tmpdirname   )




      def 	__UpperCAmelCase			(					self :		Tuple   ):



          """simple docstring"""





          _snake_case : List[Any]												=		[np.random.randint(255     ,					size=(3, 30, 400)     ,					dtype=np.uinta   )]

          _snake_case : List[str]												=		[Image.fromarray(np.moveaxis(snake_case     ,					0     ,					-1   )   ) for x in image_inputs]

          return image_inputs




      def 	__UpperCAmelCase			(					self :		Any   ):



          """simple docstring"""





          _snake_case : Optional[Any]												=		PixaStructProcessor(tokenizer=self.get_tokenizer()     ,					image_processor=self.get_image_processor()   )
          processor.save_pretrained(self.tmpdirname   )

          _snake_case : Tuple												=		self.get_tokenizer(bos_token='(BOS)'     ,					eos_token='(EOS)'   )
          _snake_case : Dict												=		self.get_image_processor(do_normalize=snake_case     ,					padding_value=1.0   )

          _snake_case : int												=		PixaStructProcessor.from_pretrained(
              self.tmpdirname     ,					bos_token='(BOS)'     ,					eos_token='(EOS)'     ,					do_normalize=snake_case     ,					padding_value=1.0   )

          self.assertEqual(processor.tokenizer.get_vocab()     ,					tokenizer_add_kwargs.get_vocab()   )
          self.assertIsInstance(processor.tokenizer     ,					snake_case   )

          self.assertEqual(processor.image_processor.to_json_string()     ,					image_processor_add_kwargs.to_json_string()   )
          self.assertIsInstance(processor.image_processor     ,					snake_case   )




      def 	__UpperCAmelCase			(					self :		Any   ):



          """simple docstring"""





          _snake_case : Tuple												=		self.get_image_processor()
          _snake_case : Optional[Any]												=		self.get_tokenizer()

          _snake_case : Optional[int]												=		PixaStructProcessor(tokenizer=snake_case     ,					image_processor=snake_case   )

          _snake_case : List[str]												=		self.prepare_image_inputs()

          _snake_case : Optional[Any]												=		image_processor(snake_case     ,					return_tensors='np'   )
          _snake_case : Tuple												=		processor(images=snake_case     ,					return_tensors='np'   )

          for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()     ,					input_processor[key].sum()     ,					delta=1e-2   )




      def 	__UpperCAmelCase			(					self :		Dict   ):



          """simple docstring"""





          _snake_case : Optional[Any]												=		self.get_image_processor()
          _snake_case : Union[str, Any]												=		self.get_tokenizer()

          _snake_case : str												=		PixaStructProcessor(tokenizer=snake_case     ,					image_processor=snake_case   )

          _snake_case : List[str]												=		'lower newer'

          _snake_case : Dict												=		processor(text=snake_case   )

          _snake_case : Any												=		tokenizer(snake_case     ,					return_token_type_ids=snake_case     ,					add_special_tokens=snake_case   )

          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]     ,					encoded_processor[key]   )




      def 	__UpperCAmelCase			(					self :		Union[str, Any]   ):



          """simple docstring"""





          _snake_case : Tuple												=		self.get_image_processor()
          _snake_case : List[str]												=		self.get_tokenizer()

          _snake_case : int												=		PixaStructProcessor(tokenizer=snake_case     ,					image_processor=snake_case   )

          _snake_case : Optional[Any]												=		'lower newer'
          _snake_case : Any												=		self.prepare_image_inputs()

          _snake_case : Any												=		processor(text=snake_case     ,					images=snake_case   )

          self.assertListEqual(
              list(inputs.keys()   )     ,					['flattened_patches', 'attention_mask', 'decoder_attention_mask', 'decoder_input_ids']   )

          # test if it raises when no input is passed
          with pytest.raises(snake_case   ):
              processor()




      def 	__UpperCAmelCase			(					self :		Any   ):



          """simple docstring"""





          _snake_case : int												=		self.get_image_processor()
          _snake_case : List[str]												=		self.get_tokenizer()

          _snake_case : Optional[Any]												=		PixaStructProcessor(tokenizer=snake_case     ,					image_processor=snake_case   )

          _snake_case : Optional[Any]												=		'lower newer'
          _snake_case : str												=		self.prepare_image_inputs()

          _snake_case : Optional[Any]												=		processor(text=snake_case     ,					images=snake_case   )

          _snake_case : Tuple												=		[512, 1024, 2048, 4096]
          _snake_case : str												=		[770, 770, 770, 770]
          # with text
          for i, max_patch in enumerate(snake_case   ):
              _snake_case : List[Any]												=		processor(text=snake_case     ,					images=snake_case     ,					max_patches=snake_case   )
              self.assertEqual(inputs['flattened_patches'][0].shape[0]     ,					snake_case   )
              self.assertEqual(inputs['flattened_patches'][0].shape[1]     ,					expected_hidden_size[i]   )

          # without text input
          for i, max_patch in enumerate(snake_case   ):
              _snake_case : Union[str, Any]												=		processor(images=snake_case     ,					max_patches=snake_case   )
              self.assertEqual(inputs['flattened_patches'][0].shape[0]     ,					snake_case   )
              self.assertEqual(inputs['flattened_patches'][0].shape[1]     ,					expected_hidden_size[i]   )




      def 	__UpperCAmelCase			(					self :		int   ):



          """simple docstring"""





          _snake_case : Union[str, Any]												=		self.get_image_processor()
          _snake_case : List[Any]												=		self.get_tokenizer()

          _snake_case : List[Any]												=		PixaStructProcessor(tokenizer=snake_case     ,					image_processor=snake_case   )

          _snake_case : List[Any]												=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          _snake_case : Dict												=		processor.batch_decode(snake_case   )
          _snake_case : List[Any]												=		tokenizer.batch_decode(snake_case   )

          self.assertListEqual(snake_case     ,					snake_case   )




      def 	__UpperCAmelCase			(					self :		Dict   ):



          """simple docstring"""





          _snake_case : str												=		self.get_image_processor()
          _snake_case : Optional[int]												=		self.get_tokenizer()

          _snake_case : List[str]												=		PixaStructProcessor(tokenizer=snake_case     ,					image_processor=snake_case   )

          _snake_case : Tuple												=		'lower newer'
          _snake_case : List[Any]												=		self.prepare_image_inputs()

          _snake_case : Tuple												=		processor(text=snake_case     ,					images=snake_case   )

          # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
          self.assertListEqual(
              list(inputs.keys()   )     ,					['flattened_patches', 'attention_mask', 'decoder_attention_mask', 'decoder_input_ids']   )

          _snake_case : str												=		processor(text=snake_case   )

          # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
          self.assertListEqual(list(inputs.keys()   )     ,					['input_ids', 'attention_mask']   )



