
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     :   Dict							 =      logging.get_logger(__name__)







@dataclass
class 			UpperCamelCase__		:







	'''simple docstring'''
	_lowerCAmelCase											=					42
	_lowerCAmelCase											=					field(default_factory=snake_case_ )
	_lowerCAmelCase											=					field(default_factory=snake_case_ )





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__		):
					A__	:						Union[str, Any]								=				len(list(m.modules()		)		) == 1 or isinstance(_A					,							nn.Convad		) or isinstance(_A					,							nn.BatchNormad		)
					if has_not_submodules:
									if not isinstance(_A					,							_A		):
													self.traced.append(_A		)





	def __call__(      self					,							UpperCamelCase__		):


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





	@property
	def 	__snake_case					(      self		):
					return list(filter(lambda UpperCamelCase__		: len(list(x.state_dict().keys()		)		) > 0					,							self.traced		)		)







@dataclass
class 			UpperCamelCase__		:







	'''simple docstring'''
	_lowerCAmelCase											=					42
	_lowerCAmelCase											=					42
	_lowerCAmelCase											=					0
	_lowerCAmelCase											=					field(default_factory=snake_case_ )
	_lowerCAmelCase											=					field(default_factory=snake_case_ )





	def __call__(      self					,							UpperCamelCase__		):
					A__	:						List[Any]								=				Tracker(self.dest		)(_A		).parametrized
					A__	:						List[str]								=				Tracker(self.src		)(_A		).parametrized

					A__	:						List[str]								=				list(filter(lambda UpperCamelCase__		: type(_A		) not in self.src_skip					,							_A		)		)
					A__	:						Union[str, Any]								=				list(filter(lambda UpperCamelCase__		: type(_A		) not in self.dest_skip					,							_A		)		)

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

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




def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    List[Any]				,    __UpperCamelCase			:    Dict							)			->							List[Any]:

				"""simple docstring"""







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

												all_keys.append((from_key, to_key)							)
												A__	:						str								=				F"block{stage_idx + 1}.{block_id}.layer_scale_2"
												A__	:						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:
								A__	:						Union[str, Any]								=				from_state_dict.pop(__a							)

				our_model.load_state_dict(__a							)
				return our_model

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

				"""simple docstring"""







				print(F"Downloading weights for {name}..."							)
				A__	:						Union[str, Any]								=				cached_download(__a							)
				print(F"Converting {name}..."							)
				A__	:						List[Any]								=				torch.load(__a							)['state_dict']
				from_model.load_state_dict(__a							)
				from_model.eval()
				with torch.no_grad():
								A__	:						Optional[Any]								=				VanForImageClassification(__a							).eval()
								A__	:						Dict								=				ModuleTransfer(src=__a				,    dest=__a							)
								A__	:						Any								=				torch.randn((1, 3, 2_24, 2_24)							)
								module_transfer(__a							)
								A__	:						Union[str, 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.'''							)

				A__	:						Tuple								=				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
								A__	:						Any								=				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 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    List[Any]				,    __UpperCamelCase			:    List[str] = None				,    __UpperCamelCase			:    Optional[Any] = True							)			->							Dict:

				"""simple docstring"""







				A__	:						int								=				'imagenet-1k-id2label.json'
				A__	:						Optional[int]								=				10_00

				A__	:						Any								=				'huggingface/label-files'
				A__	:						str								=				num_labels
				A__	:						Tuple								=				json.load(open(hf_hub_download(__a				,    __a				,    repo_type='''dataset'''							)				,    '''r'''							)							)
				A__	:						Dict								=				{int(__a							): v for k, v in idalabel.items()}

				A__	:						str								=				idalabel
				A__	:						Optional[Any]								=				{v: k for k, v in idalabel.items()}

				A__	:						Union[str, Any]								=				partial(__a				,    num_labels=__a				,    idalabel=__a				,    labelaid=__a							)

				A__	:						Tuple								=				{
				    'van-tiny': ImageNetPreTrainedConfig(
				        hidden_sizes=[32, 64, 1_60, 2_56]				,    depths=[3, 3, 5, 2]				,    mlp_ratios=[8, 8, 4, 4]				,    ),
				    'van-small': ImageNetPreTrainedConfig(
				        hidden_sizes=[64, 1_28, 3_20, 5_12]				,    depths=[2, 2, 4, 2]				,    mlp_ratios=[8, 8, 4, 4]				,    ),
				    'van-base': ImageNetPreTrainedConfig(
				        hidden_sizes=[64, 1_28, 3_20, 5_12]				,    depths=[3, 3, 12, 3]				,    mlp_ratios=[8, 8, 4, 4]				,    ),
				    'van-large': ImageNetPreTrainedConfig(
				        hidden_sizes=[64, 1_28, 3_20, 5_12]				,    depths=[3, 5, 27, 3]				,    mlp_ratios=[8, 8, 4, 4]				,    ),
				}

				A__	:						List[Any]								=				{
				    'van-tiny': van_tiny,
				    'van-small': van_small,
				    'van-base': van_base,
				    'van-large': van_large,
				}

				A__	:						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     :   Tuple							 =      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     :   Union[str, Any]							 =      parser.parse_args()
					_SCREAMING_SNAKE_CASE     :   Union[str, Any]							 =      args.pytorch_dump_folder_path
					pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
					_SCREAMING_SNAKE_CASE     :   int							 =      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 sacrebleu as scb
from packaging import version

import datasets


_SCREAMING_SNAKE_CASE   						=		"""\
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

_SCREAMING_SNAKE_CASE   						=		"""\
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
It also knows all the standard test sets and handles downloading, processing, and tokenization for you.

See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
"""

_SCREAMING_SNAKE_CASE   						=		"""
Produces BLEU scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).
    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
        - `'none'`: no smoothing
        - `'floor'`: increment zero counts
        - `'add-k'`: increment num/denom by k for n>1
        - `'exp'`: exponential decay
    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).
    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:
        - `'none'`: No tokenization.
        - `'zh'`: Chinese tokenization.
        - `'13a'`: mimics the `mteval-v13a` script from Moses.
        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
        - `'char'`: Language-agnostic character-level tokenization.
        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.

Returns:
    'score': BLEU score,
    'counts': Counts,
    'totals': Totals,
    'precisions': Precisions,
    'bp': Brevity penalty,
    'sys_len': predictions length,
    'ref_len': reference length,

Examples:

    Example 1:
        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions, references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        100.0

    Example 2:
        >>> predictions = [\"hello there general kenobi\",
        ...                 \"on our way to ankh morpork\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"],
        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions,
        ...                             references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        39.8
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,					_KWARGS_DESCRIPTION       )
class SCREAMING_SNAKE_CASE_    (       datasets.Metric       ):






						def UpperCAmelCase_					(   self   :   str						)				->     List[str]:







							"""simple docstring"""


							if version.parse(scb.__version__						) < version.parse('1.4.12'						):
								raise ImportWarning(
								    'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
								    'You can install it with `pip install "sacrebleu>=1.4.12"`.'						)
							return datasets.MetricInfo(
							    description=_DESCRIPTION		, citation=_CITATION		, homepage='https://github.com/mjpost/sacreBLEU'		, inputs_description=_KWARGS_DESCRIPTION		, features=datasets.Features(
							        {
							            'predictions': datasets.Value('string'		, id='sequence'						),
							            'references': datasets.Sequence(datasets.Value('string'		, id='sequence'						)		, id='references'						),
							        }						)		, codebase_urls=['https://github.com/mjpost/sacreBLEU']		, reference_urls=[
							        'https://github.com/mjpost/sacreBLEU',
							        'https://en.wikipedia.org/wiki/BLEU',
							        'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
							    ]		, )






						def UpperCAmelCase_					(   self   :   List[Any]		, _A   :   List[str]		, _A   :   Optional[Any]		, _A   :   Tuple="exp"		, _A   :   Tuple=None		, _A   :   Optional[Any]=False		, _A   :   Dict=False		, _A   :   List[str]=None		, _A   :   Dict=False		, )				->     Optional[Any]:







							"""simple docstring"""


							snake_case_		:      List[str]           =						len(references[0]						)
							if any(len(_A						) != references_per_prediction for refs in references						):
								raise ValueError('Sacrebleu requires the same number of references for each prediction'						)
							snake_case_		:      Optional[Any]           =						[[refs[i] for refs in references] for i in range(_A						)]
							snake_case_		:      Any           =						scb.corpus_bleu(
							    _A		, _A		, smooth_method=_A		, smooth_value=_A		, force=_A		, lowercase=_A		, use_effective_order=_A		, **({'tokenize': tokenize} if tokenize else {})		, )
							snake_case_		:      int           =						{
							    'score': output.score,
							    'counts': output.counts,
							    'totals': output.totals,
							    'precisions': output.precisions,
							    'bp': output.bp,
							    'sys_len': output.sys_len,
							    'ref_len': output.ref_len,
							}
							return output_dict

