




'''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()
a__         =     logging.get_logger(__name__)





@dataclass
class __magic_name__:
						UpperCAmelCase_		:					nn.Module
						UpperCAmelCase_		:					List[nn.Module]    =  field(default_factory=lowerCamelCase_      )
						UpperCAmelCase_		:					list    =  field(default_factory=lowerCamelCase_      )






						def       __lowerCAmelCase(   self						:						Dict					,   __UpperCamelCase						:						Union[str, Any]					,   __UpperCamelCase						:						Tuple					,   __UpperCamelCase						:						Dict       ):



									'''simple docstring'''






									snake_case__          =						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						:						Any					,   __UpperCamelCase						:						Optional[Any]       ):



									'''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       __lowerCAmelCase(   self						:						List[str]       ):



									'''simple docstring'''






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







@dataclass
class __magic_name__:
						UpperCAmelCase_		:					nn.Module
						UpperCAmelCase_		:					nn.Module
						UpperCAmelCase_		:					int    =  0
						UpperCAmelCase_		:					List    =  field(default_factory=lowerCamelCase_      )
						UpperCAmelCase_		:					List    =  field(default_factory=lowerCamelCase_      )






						def __call__(   self						:						List[str]					,   __UpperCamelCase						:						int       ):



									'''simple docstring'''






									snake_case__          =						Tracker(self.dest       )(snake_case_       ).parametrized
									snake_case__          =						Tracker(self.src       )(snake_case_       ).parametrized

									snake_case__          =						list(filter(lambda __UpperCamelCase       : type(snake_case_       ) not in self.src_skip					,   snake_case_       )       )
									snake_case__          =						list(filter(lambda __UpperCamelCase       : 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    snake_case__     (  a       ,      a	)			->       Any:





			'''simple docstring'''






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

									all_keys.append((from_key, to_key)	)
									snake_case__          =						F"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
									snake_case__          =						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__          =						from_state_dict.pop(lowerCamelCase_	)

			our_model.load_state_dict(lowerCamelCase_	)
			return our_model




def    snake_case__     (  a       ,      a       ,      a       ,      a       ,      a       ,      a = True       ,      )			->       int:





			'''simple docstring'''






			print(F"""Downloading weights for {name}..."""	)
			snake_case__          =						cached_download(lowerCamelCase_	)
			print(F"""Converting {name}..."""	)
			snake_case__          =						torch.load(lowerCamelCase_	)['state_dict']
			from_model.load_state_dict(lowerCamelCase_	)
			from_model.eval()
			with torch.no_grad():
						snake_case__          =						VanForImageClassification(lowerCamelCase_	).eval()
						snake_case__          =						ModuleTransfer(src=lowerCamelCase_       ,      dest=lowerCamelCase_	)
						snake_case__          =						torch.randn((1, 3, 224, 224)	)
						module_transfer(lowerCamelCase_	)
						snake_case__          =						copy_parameters(lowerCamelCase_       ,      lowerCamelCase_	)

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

			snake_case__          =						name
			print(lowerCamelCase_	)

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

						# we can use the convnext one
						snake_case__          =						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=lowerCamelCase_       ,      )

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




def    snake_case__     (  a       ,      a = None       ,      a = True	)			->       int:





			'''simple docstring'''






			snake_case__          =						'imagenet-1k-id2label.json'
			snake_case__          =						1000

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

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

			snake_case__          =						partial(lowerCamelCase_       ,      num_labels=lowerCamelCase_       ,      idalabel=lowerCamelCase_       ,      labelaid=lowerCamelCase_	)

			snake_case__          =						{
			    'van-tiny': ImageNetPreTrainedConfig(
			        hidden_sizes=[32, 64, 160, 256]       ,      depths=[3, 3, 5, 2]       ,      mlp_ratios=[8, 8, 4, 4]       ,      ),
			    'van-small': ImageNetPreTrainedConfig(
			        hidden_sizes=[64, 128, 320, 512]       ,      depths=[2, 2, 4, 2]       ,      mlp_ratios=[8, 8, 4, 4]       ,      ),
			    'van-base': ImageNetPreTrainedConfig(
			        hidden_sizes=[64, 128, 320, 512]       ,      depths=[3, 3, 12, 3]       ,      mlp_ratios=[8, 8, 4, 4]       ,      ),
			    'van-large': ImageNetPreTrainedConfig(
			        hidden_sizes=[64, 128, 320, 512]       ,      depths=[3, 5, 27, 3]       ,      mlp_ratios=[8, 8, 4, 4]       ,      ),
			}

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

			snake_case__          =						{
			    '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(
						    lowerCamelCase_       ,      names_to_config[model_name]       ,      checkpoint=names_to_original_checkpoints[model_name]       ,      from_model=names_to_original_models[model_name]()       ,      save_directory=lowerCamelCase_       ,      push_to_hub=lowerCamelCase_       ,      )
			else:
						for model_name, config in names_to_config.items():
									convert_weight_and_push(
									    lowerCamelCase_       ,      lowerCamelCase_       ,      checkpoint=names_to_original_checkpoints[model_name]       ,      from_model=names_to_original_models[model_name]()       ,      save_directory=lowerCamelCase_       ,      push_to_hub=lowerCamelCase_       ,      )


if __name__ == "__main__":
		a__         =     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.''',
		)

		a__         =     parser.parse_args()
		a__         =     args.pytorch_dump_folder_path
		pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
		a__         =     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 io
import json
import subprocess


snake_case__	:     Any      =    [
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

snake_case__	:     Tuple      =    8


def    _lowerCamelCase       ( lowerCamelCase_   :				Tuple      ,	lowerCamelCase_   :				Union[str, Any]  ):



     """simple docstring"""





     UpperCAmelCase_				:  List[Any]  = {}
     for src, tgt in pairs:
          UpperCAmelCase_				:  str  = F'''{src}-{tgt}'''
          UpperCAmelCase_				:  List[str]  = F'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
          UpperCAmelCase_				:  Any  = subprocess.run(lowerCamelCase_      ,	stdout=subprocess.PIPE  ).stdout.decode('utf-8'  ).splitlines()
          UpperCAmelCase_				:  Any  = F'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
          UpperCAmelCase_				:  List[str]  = subprocess.run(lowerCamelCase_      ,	stdout=subprocess.PIPE  ).stdout.decode('utf-8'  ).splitlines()
          UpperCAmelCase_				:  str  = {'src': src_lines[:n_objs], 'tgt': tgt_lines[:n_objs]}
     return text


snake_case__	:     Union[str, Any]      =    get_all_data(pairs, n_objs)
snake_case__	:     Dict      =    '''./fsmt_val_data.json'''
with io.open(filename, '''w''', encoding='''utf-8''') as f:
 snake_case__	:     List[str]      =    json.dump(text, f, indent=2, ensure_ascii=False)






