



'''simple docstring'''

from sklearn.metrics import fa_score

import datasets


__lowerCAmelCase						      =       '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


__lowerCAmelCase						      =       '''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.

        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


__lowerCAmelCase						      =       '''
@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}
'''

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,						_KWARGS_DESCRIPTION       )
class      __magic_name__						(   datasets.Metric       ):







							def 							__lowercase      (							self :	Optional[int]		):
									return datasets.MetricInfo(
									    description=_DESCRIPTION     ,citation=_CITATION     ,inputs_description=_KWARGS_DESCRIPTION     ,features=datasets.Features(
									        {
									            'predictions': datasets.Sequence(datasets.Value('int32'		)		),
									            'references': datasets.Sequence(datasets.Value('int32'		)		),
									        }
									        if self.config_name == 'multilabel'
									        else {
									            'predictions': datasets.Value('int32'		),
									            'references': datasets.Value('int32'		),
									        }		)     ,reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html']     ,)







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	int=None     ,_UpperCAmelCase :	List[Any]=1     ,_UpperCAmelCase :	int="binary"     ,_UpperCAmelCase :	List[Any]=None		):
									_a					:				Any				=							fa_score(
									    snake_case__     ,snake_case__     ,labels=snake_case__     ,pos_label=snake_case__     ,average=snake_case__     ,sample_weight=snake_case__		)
									return {"f1": float(snake_case__		) if score.size == 1 else score}









'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_=False						,      lowerCAmelCase_=False  )      ->				Any:
		_a					:				Dict				=							'backbone.' if is_semantic else ''

		_a					:				Optional[int]				=							[]
		for i in range(config.num_hidden_layers  ):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append((f"""{prefix}blocks.{i}.norm1.weight""", f"""beit.encoder.layer.{i}.layernorm_before.weight""")  )
				rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""beit.encoder.layer.{i}.layernorm_before.bias""")  )
				rename_keys.append(
				    (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""beit.encoder.layer.{i}.attention.output.dense.weight""")  )
				rename_keys.append(
				    (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""beit.encoder.layer.{i}.attention.output.dense.bias""")  )
				rename_keys.append((f"""{prefix}blocks.{i}.norm2.weight""", f"""beit.encoder.layer.{i}.layernorm_after.weight""")  )
				rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""beit.encoder.layer.{i}.layernorm_after.bias""")  )
				rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""beit.encoder.layer.{i}.intermediate.dense.weight""")  )
				rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""beit.encoder.layer.{i}.intermediate.dense.bias""")  )
				rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""beit.encoder.layer.{i}.output.dense.weight""")  )
				rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""beit.encoder.layer.{i}.output.dense.bias""")  )

		# projection layer + position embeddings
		rename_keys.extend(
		    [
		        (f"""{prefix}cls_token""", 'beit.embeddings.cls_token'),
		        (f"""{prefix}patch_embed.proj.weight""", 'beit.embeddings.patch_embeddings.projection.weight'),
		        (f"""{prefix}patch_embed.proj.bias""", 'beit.embeddings.patch_embeddings.projection.bias'),
		        (f"""{prefix}pos_embed""", 'beit.embeddings.position_embeddings'),
		    ]  )

		if has_lm_head:
				# mask token + layernorm
				rename_keys.extend(
				    [
				        ('mask_token', 'beit.embeddings.mask_token'),
				        ('norm.weight', 'layernorm.weight'),
				        ('norm.bias', 'layernorm.bias'),
				    ]  )
		else:
				# layernorm + classification head
				rename_keys.extend(
				    [
				        ('fc_norm.weight', 'beit.pooler.layernorm.weight'),
				        ('fc_norm.bias', 'beit.pooler.layernorm.bias'),
				        ('head.weight', 'classifier.weight'),
				        ('head.bias', 'classifier.bias'),
				    ]  )

		return rename_keys



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=False						,      lowerCAmelCase_=False  )      ->				int:
		for i in range(config.num_hidden_layers  ):
				_a					:				Union[str, Any]				=							'backbone.' if is_semantic else ''
				# queries, keys and values
				_a					:				Any				=							state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""  )
				_a					:				str				=							state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""  )
				_a					:				str				=							state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""  )

				_a					:				int				=							in_proj_weight[
				    : config.hidden_size, :
				]
				_a					:				str				=							q_bias
				_a					:				List[Any]				=							in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				_a					:				List[str]				=							in_proj_weight[
				    -config.hidden_size :, :
				]
				_a					:				Any				=							v_bias

				# gamma_1 and gamma_2
				# we call them lambda because otherwise they are renamed when using .from_pretrained
				_a					:				int				=							state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""  )
				_a					:				Tuple				=							state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""  )

				_a					:				Tuple				=							gamma_a
				_a					:				Optional[Any]				=							gamma_a



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Dict:
		_a					:				List[Any]				=							dct.pop(lowerCAmelCase_  )
		_a					:				List[Any]				=							val



def 					__lowerCamelCase					(				)      ->				Dict:
		_a					:				Optional[Any]				=							'http://images.cocodataset.org/val2017/000000039769.jpg'
		_a					:				Dict				=							Image.open(requests.get(lowerCAmelCase_						,      stream=lowerCAmelCase_  ).raw  )
		return im



@torch.no_grad()
def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=False  )      ->				Union[str, Any]:
		_a					:				Optional[Any]				=							False if 'rvlcdip' in checkpoint_url else True
		_a					:				List[Any]				=							BeitConfig(use_absolute_position_embeddings=lowerCAmelCase_						,      use_mask_token=lowerCAmelCase_  )

		# size of the architecture
		if "large" in checkpoint_url or "dit-l" in checkpoint_url:
				_a					:				Optional[Any]				=							1024
				_a					:				Tuple				=							4096
				_a					:				Any				=							24
				_a					:				Optional[int]				=							16

		# labels
		if "rvlcdip" in checkpoint_url:
				_a					:				Any				=							16
				_a					:				Tuple				=							'huggingface/label-files'
				_a					:				List[Any]				=							'rvlcdip-id2label.json'
				_a					:				Optional[int]				=							json.load(open(hf_hub_download(lowerCAmelCase_						,      lowerCAmelCase_						,      repo_type='dataset'  )						,      'r'  )  )
				_a					:				int				=							{int(lowerCAmelCase_  ): v for k, v in idalabel.items()}
				_a					:				int				=							idalabel
				_a					:				int				=							{v: k for k, v in idalabel.items()}

		# load state_dict of original model, remove and rename some keys
		_a					:				List[Any]				=							torch.hub.load_state_dict_from_url(lowerCAmelCase_						,      map_location='cpu'  )['model']

		_a					:				Dict				=							create_rename_keys(lowerCAmelCase_						,      has_lm_head=lowerCAmelCase_  )
		for src, dest in rename_keys:
				rename_key(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )
		read_in_q_k_v(lowerCAmelCase_						,      lowerCAmelCase_						,      has_lm_head=lowerCAmelCase_  )

		# load HuggingFace model
		_a					:				Dict				=							BeitForMaskedImageModeling(lowerCAmelCase_  ) if has_lm_head else BeitForImageClassification(lowerCAmelCase_  )
		model.eval()
		model.load_state_dict(lowerCAmelCase_  )

		# Check outputs on an image
		_a					:				Dict				=							BeitImageProcessor(
		    size=config.image_size						,      resample=PILImageResampling.BILINEAR						,      do_center_crop=lowerCAmelCase_  )
		_a					:				Dict				=							prepare_img()

		_a					:				str				=							image_processor(images=lowerCAmelCase_						,      return_tensors='pt'  )
		_a					:				Optional[Any]				=							encoding['pixel_values']

		_a					:				Optional[Any]				=							model(lowerCAmelCase_  )
		_a					:				Optional[Any]				=							outputs.logits

		# verify logits
		_a					:				int				=							[1, 16] if 'rvlcdip' in checkpoint_url else [1, 196, 8192]
		assert logits.shape == torch.Size(lowerCAmelCase_  ), "Shape of logits not as expected"

		Path(lowerCAmelCase_  ).mkdir(exist_ok=lowerCAmelCase_  )
		print(f"""Saving model to {pytorch_dump_folder_path}"""  )
		model.save_pretrained(lowerCAmelCase_  )
		print(f"""Saving image processor to {pytorch_dump_folder_path}"""  )
		image_processor.save_pretrained(lowerCAmelCase_  )

		if push_to_hub:
				if has_lm_head:
						_a					:				Tuple				=							'dit-base' if 'base' in checkpoint_url else 'dit-large'
				else:
						_a					:				int				=							'dit-base-finetuned-rvlcdip' if 'dit-b' in checkpoint_url else 'dit-large-finetuned-rvlcdip'
				image_processor.push_to_hub(
				    repo_path_or_name=Path(lowerCAmelCase_						,      lowerCAmelCase_  )						,      organization='nielsr'						,      commit_message='Add image processor'						,      use_temp_dir=lowerCAmelCase_						,      )
				model.push_to_hub(
				    repo_path_or_name=Path(lowerCAmelCase_						,      lowerCAmelCase_  )						,      organization='nielsr'						,      commit_message='Add model'						,      use_temp_dir=lowerCAmelCase_						,      )


if __name__ == "__main__":
							__lowerCAmelCase						      =       argparse.ArgumentParser()

							parser.add_argument(
							    '''--checkpoint_url''',
							    default='''https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth''',
							    type=str,
							    help='''URL to the original PyTorch checkpoint (.pth file).''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
							)
							parser.add_argument(
							    '''--push_to_hub''',
							    action='''store_true''',
							)
							__lowerCAmelCase						      =       parser.parse_args()
							convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




