

"""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()
lowerCAmelCase__						=	logging.get_logger(__name__)

@dataclass
class    a__  :
			"""simple docstring"""







			__lowerCamelCase	    =    42
			__lowerCamelCase	    =    field(default_factory=snake_case     )
			__lowerCamelCase	    =    field(default_factory=snake_case     )


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase    )					->  Optional[Any]:
							'''simple docstring'''






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


			def __call__(      self , lowercase    )					->  Union[str, Any]:
							'''simple docstring'''






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


			@property
			def        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






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

@dataclass
class    a__  :
			"""simple docstring"""







			__lowerCamelCase	    =    42
			__lowerCamelCase	    =    42
			__lowerCamelCase	    =    0
			__lowerCamelCase	    =    field(default_factory=snake_case     )
			__lowerCamelCase	    =    field(default_factory=snake_case     )


			def __call__(      self , lowercase    )					->  List[Any]:
							'''simple docstring'''






							A__            =     Tracker(self.dest    )(lowercase__    ).parametrized
							A__            =     Tracker(self.src    )(lowercase__    ).parametrized

							A__            =     list(filter(lambda lowercase    : type(lowercase__    ) not in self.src_skip , lowercase__    )    )
							A__            =     list(filter(lambda lowercase    : type(lowercase__    ) not in self.dest_skip , lowercase__    )    )

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

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



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str		,    SCREAMING_SNAKE_CASE_:							List[str] )     -> Tuple:




				'''simple docstring'''





				A__            =     from_model.state_dict()
				A__            =     our_model.state_dict()
				A__            =     our_model.config
				A__            =     []
				for stage_idx in range(len(config.hidden_sizes ) ):
								for block_id in range(config.depths[stage_idx] ):
												A__            =     F'block{stage_idx + 1}.{block_id}.layer_scale_1'
												A__            =     F'van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'

												all_keys.append((from_key, to_key) )
												A__            =     F'block{stage_idx + 1}.{block_id}.layer_scale_2'
												A__            =     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:
								A__            =     from_state_dict.pop(SCREAMING_SNAKE_CASE_ )

				our_model.load_state_dict(SCREAMING_SNAKE_CASE_ )
				return our_model



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Union[str, Any]		,    SCREAMING_SNAKE_CASE_:							Optional[int]		,    SCREAMING_SNAKE_CASE_:							str		,    SCREAMING_SNAKE_CASE_:							List[str]		,    SCREAMING_SNAKE_CASE_:							int		,    SCREAMING_SNAKE_CASE_:							str = True		,    )     -> str:




				'''simple docstring'''





				print(F'Downloading weights for {name}...' )
				A__            =     cached_download(SCREAMING_SNAKE_CASE_ )
				print(F'Converting {name}...' )
				A__            =     torch.load(SCREAMING_SNAKE_CASE_ )["state_dict"]
				from_model.load_state_dict(SCREAMING_SNAKE_CASE_ )
				from_model.eval()
				with torch.no_grad():
								A__            =     VanForImageClassification(SCREAMING_SNAKE_CASE_ ).eval()
								A__            =     ModuleTransfer(src=SCREAMING_SNAKE_CASE_		,    dest=SCREAMING_SNAKE_CASE_ )
								A__            =     torch.randn((1, 3, 2_2_4, 2_2_4) )
								module_transfer(SCREAMING_SNAKE_CASE_ )
								A__            =     copy_parameters(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_ )

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

				A__            =     name
				print(SCREAMING_SNAKE_CASE_ )

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

								# we can use the convnext one
								A__            =     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=SCREAMING_SNAKE_CASE_		,    )

								print(F'Pushed {checkpoint_name}' )



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Tuple		,    SCREAMING_SNAKE_CASE_:							Tuple = None		,    SCREAMING_SNAKE_CASE_:							Union[str, Any] = True )     -> List[Any]:




				'''simple docstring'''





				A__            =     "imagenet-1k-id2label.json"
				A__            =     1_0_0_0

				A__            =     "huggingface/label-files"
				A__            =     num_labels
				A__            =     json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    repo_type="dataset" )		,    "r" ) )
				A__            =     {int(SCREAMING_SNAKE_CASE_ ): v for k, v in idalabel.items()}

				A__            =     idalabel
				A__            =     {v: k for k, v in idalabel.items()}

				A__            =     partial(SCREAMING_SNAKE_CASE_		,    num_labels=SCREAMING_SNAKE_CASE_		,    idalabel=SCREAMING_SNAKE_CASE_		,    labelaid=SCREAMING_SNAKE_CASE_ )

				A__            =     {
				    "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]		,    ),
				}

				A__            =     {
				    "van-tiny": van_tiny,
				    "van-small": van_small,
				    "van-base": van_base,
				    "van-large": van_large,
				}

				A__            =     {
				    "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(
								    SCREAMING_SNAKE_CASE_		,    names_to_config[model_name]		,    checkpoint=names_to_original_checkpoints[model_name]		,    from_model=names_to_original_models[model_name]()		,    save_directory=SCREAMING_SNAKE_CASE_		,    push_to_hub=SCREAMING_SNAKE_CASE_		,    )
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(
												    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    checkpoint=names_to_original_checkpoints[model_name]		,    from_model=names_to_original_models[model_name]()		,    save_directory=SCREAMING_SNAKE_CASE_		,    push_to_hub=SCREAMING_SNAKE_CASE_		,    )


if __name__ == "__main__":
	lowerCAmelCase__						=	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.""",
	)

	lowerCAmelCase__						=	parser.parse_args()
	lowerCAmelCase__						=	args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	lowerCAmelCase__						=	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)




import unittest

from cer import CER


lowerCAmelCase__						=	CER()

class    a__  (   unittest.TestCase     ):
			"""simple docstring"""







			def        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






							A__            =     ["White House"]
							A__            =     ["white house"]
							# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.18_1818_1818    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  Any:
							'''simple docstring'''






							A__            =     ["were wolf"]
							A__            =     ["werewolf"]
							# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.111_1111    ) < 1e-6    )

							A__            =     ["werewolf"]
							A__            =     ["weae     wolf"]
							# S = 1, D = 1, I = 0, N = 8, CER = 0.25
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.25    ) < 1e-6    )

							# consecutive whitespaces case 1
							A__            =     ["were wolf"]
							A__            =     ["were               wolf"]
							# S = 0, D = 0, I = 0, N = 9, CER = 0
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.0    ) < 1e-6    )

							# consecutive whitespaces case 2
							A__            =     ["were   wolf"]
							A__            =     ["were               wolf"]
							# S = 0, D = 0, I = 0, N = 9, CER = 0
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.0    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






							A__            =     ["werewolf"]
							A__            =     ["weaewolf"]
							# S = 1, D = 0, I = 0, N = 8, CER = 0.125
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






							A__            =     ["werewolf"]
							A__            =     ["wereawolf"]
							# S = 0, D = 1, I = 0, N = 8, CER = 0.125
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






							A__            =     ["werewolf"]
							A__            =     ["wereolf"]
							# S = 0, D = 0, I = 1, N = 8, CER = 0.125
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






							A__            =     ["werewolf"]
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertEqual(lowercase , 0.0    )


			def        UpperCamelCase						(      self    )					->  Any:
							'''simple docstring'''






							A__            =     ["werewolf", "I am your father"]
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertEqual(lowercase , 0.0    )

							A__            =     ["werewolf", "I am your father", "doge"]
							A__            =     ["werxwolf", "I       am your father", "doge"]
							# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.0357_1428    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






							A__            =     ["My hovercraft", "is full of eels"]
							A__            =     ["My hovercraft is full", " of eels"]
							# S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
							# whitespace at the front of " of eels" will be strip during preporcessing
							# so need to insert 2 whitespaces
							A__            =     cer.compute(predictions=lowercase , references=lowercase , concatenate_texts=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.07_1428    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  List[str]:
							'''simple docstring'''






							A__            =     ["我能吞下玻璃而不伤身体"]
							A__            =     [" 能吞虾玻璃而 不霜身体啦"]
							# S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.45_4545_4545    ) < 1e-6    )

							A__            =     ["我能吞下玻璃", "而不伤身体"]
							A__            =     ["我    能 吞 下 玻 璃", "而不伤身体"]
							# S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.4545_4545_4545    ) < 1e-6    )

							A__            =     ["我能吞下玻璃而不伤身体"]
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertFalse(lowercase , 0.0    )


			def        UpperCamelCase						(      self    )					->  Optional[Any]:
							'''simple docstring'''






							A__            =     [""]
							A__            =     ["Hypothesis"]
							with self.assertRaises(lowercase    ):
											cer.compute(predictions=lowercase , references=lowercase    )


if __name__ == "__main__":
	unittest.main()

