


import argparse
import json

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

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
		lowercase__            =      True if '''large''' in model_name or '''huge''' in model_name else False
		lowercase__            =      True if '''large''' in model_name or '''huge''' in model_name else False
		lowercase__            =      True if '''large''' in model_name or '''huge''' in model_name else False

		if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
				if "fl3" in model_name:
						lowercase__            =      [3, 3, 3, 3]
						lowercase__            =      [5, 5, 5, 5]
				elif "fl4" in model_name:
						lowercase__            =      [4, 4, 4, 4]
						lowercase__            =      [3, 3, 3, 3]

		if "tiny" in model_name or "small" in model_name or "base" in model_name:
				lowercase__            =      [3, 3, 3, 3]
				if "lrf" in model_name:
						lowercase__            =      [3, 3, 3, 3]
				else:
						lowercase__            =      [2, 2, 2, 2]

		if "tiny" in model_name:
				lowercase__            =      96
		elif "small" in model_name:
				lowercase__            =      96
		elif "base" in model_name:
				lowercase__            =      1_28
		elif "large" in model_name:
				lowercase__            =      1_92
		elif "xlarge" in model_name:
				lowercase__            =      2_56
		elif "huge" in model_name:
				lowercase__            =      3_52

		# set label information
		lowercase__            =      '''huggingface/label-files'''
		if "large" in model_name or "huge" in model_name:
				lowercase__            =      '''imagenet-22k-id2label.json'''
		else:
				lowercase__            =      '''imagenet-1k-id2label.json'''

		lowercase__            =      json.load(open(hf_hub_download(snake_case__						,				snake_case__						,				repo_type='''dataset'''     )						,				'''r'''     )     )
		lowercase__            =      {int(snake_case__     ): v for k, v in idalabel.items()}
		lowercase__            =      {v: k for k, v in idalabel.items()}

		lowercase__            =      FocalNetConfig(
		    embed_dim=snake_case__						,				depths=snake_case__						,				focal_levels=snake_case__						,				focal_windows=snake_case__						,				use_conv_embed=snake_case__						,				idalabel=snake_case__						,				labelaid=snake_case__						,				use_post_layernorm=snake_case__						,				use_layerscale=snake_case__						,				)

		return config
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		if "patch_embed.proj" in name:
				lowercase__            =      name.replace('''patch_embed.proj'''						,				'''embeddings.patch_embeddings.projection'''     )
		if "patch_embed.norm" in name:
				lowercase__            =      name.replace('''patch_embed.norm'''						,				'''embeddings.norm'''     )
		if "layers" in name:
				lowercase__            =      '''encoder.''' + name
		if "encoder.layers" in name:
				lowercase__            =      name.replace('''encoder.layers'''						,				'''encoder.stages'''     )
		if "downsample.proj" in name:
				lowercase__            =      name.replace('''downsample.proj'''						,				'''downsample.projection'''     )
		if "blocks" in name:
				lowercase__            =      name.replace('''blocks'''						,				'''layers'''     )
		if "modulation.f.weight" in name or "modulation.f.bias" in name:
				lowercase__            =      name.replace('''modulation.f'''						,				'''modulation.projection_in'''     )
		if "modulation.h.weight" in name or "modulation.h.bias" in name:
				lowercase__            =      name.replace('''modulation.h'''						,				'''modulation.projection_context'''     )
		if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
				lowercase__            =      name.replace('''modulation.proj'''						,				'''modulation.projection_out'''     )

		if name == "norm.weight":
				lowercase__            =      '''layernorm.weight'''
		if name == "norm.bias":
				lowercase__            =      '''layernorm.bias'''

		if "head" in name:
				lowercase__            =      name.replace('''head'''						,				'''classifier'''     )
		else:
				lowercase__            =      '''focalnet.''' + name

		return name
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE=False     ):


		"""simple docstring"""




		lowercase__            =      {
		    '''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
		    '''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
		    '''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
		    '''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
		    '''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
		    '''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
		    '''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
		    '''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
		    '''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
		    '''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
		}
		# fmt: on

		lowercase__            =      model_name_to_url[model_name]
		print('''Checkpoint URL: '''						,				snake_case__     )
		lowercase__            =      torch.hub.load_state_dict_from_url(snake_case__						,				map_location='''cpu'''     )['''model''']

		# rename keys
		for key in state_dict.copy().keys():
				lowercase__            =      state_dict.pop(snake_case__     )
				lowercase__            =      val

		lowercase__            =      get_focalnet_config(snake_case__     )
		lowercase__            =      FocalNetForImageClassification(snake_case__     )
		model.eval()

		# load state dict
		model.load_state_dict(snake_case__     )

		# verify conversion
		lowercase__            =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''

		lowercase__            =      BitImageProcessor(
		    do_resize=snake_case__						,				size={'''shortest_edge''': 2_56}						,				resample=PILImageResampling.BILINEAR						,				do_center_crop=snake_case__						,				crop_size=2_24						,				do_normalize=snake_case__						,				image_mean=snake_case__						,				image_std=snake_case__						,				)
		lowercase__            =      Image.open(requests.get(snake_case__						,				stream=snake_case__     ).raw     )
		lowercase__            =      processor(images=snake_case__						,				return_tensors='''pt'''     )

		lowercase__            =      transforms.Compose(
		    [
		        transforms.Resize(2_56     ),
		        transforms.CenterCrop(2_24     ),
		        transforms.ToTensor(),
		        transforms.Normalize(mean=[0.485, 0.456, 0.406]						,				std=[0.229, 0.224, 0.225]     ),
		    ]     )

		lowercase__            =      image_transforms(snake_case__     ).unsqueeze(0     )

		# verify pixel_values
		assert torch.allclose(inputs.pixel_values						,				snake_case__						,				atol=1E-4     )

		lowercase__            =      model(**snake_case__     )

		lowercase__            =      outputs.logits.argmax(-1     ).item()
		print('''Predicted class:'''						,				model.config.idalabel[predicted_class_idx]     )

		print('''First values of logits:'''						,				outputs.logits[0, :3]     )

		if model_name == "focalnet-tiny":
				lowercase__            =      torch.tensor([0.2_166, -0.4_368, 0.2_191]     )
		elif model_name == "focalnet-tiny-lrf":
				lowercase__            =      torch.tensor([1.1_669, 0.0_125, -0.1_695]     )
		elif model_name == "focalnet-small":
				lowercase__            =      torch.tensor([0.4_917, -0.0_430, 0.1_341]     )
		elif model_name == "focalnet-small-lrf":
				lowercase__            =      torch.tensor([-0.2_588, -0.5_342, -0.2_331]     )
		elif model_name == "focalnet-base":
				lowercase__            =      torch.tensor([-0.1_655, -0.4_090, -0.1_730]     )
		elif model_name == "focalnet-base-lrf":
				lowercase__            =      torch.tensor([0.5_306, -0.0_483, -0.3_928]     )
		assert torch.allclose(outputs.logits[0, :3]						,				snake_case__						,				atol=1E-4     )
		print('''Looks ok!'''     )

		if pytorch_dump_folder_path is not None:
				print(f'Saving model and processor of {model_name} to {pytorch_dump_folder_path}'     )
				model.save_pretrained(snake_case__     )
				processor.save_pretrained(snake_case__     )

		if push_to_hub:
				print(f'Pushing model and processor of {model_name} to the hub...'     )
				model.push_to_hub(f'{model_name}'     )
				processor.push_to_hub(f'{model_name}'     )


if __name__ == "__main__":
			lowerCAmelCase       =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--model_name',
			    default='focalnet-tiny',
			    type=str,
			    help='Name of the FocalNet model you\'d like to convert.',
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
			)
			parser.add_argument(
			    '--push_to_hub',
			    action='store_true',
			    help='Whether to push the model and processor to the hub.',
			)

			lowerCAmelCase       =							parser.parse_args()
			convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""




from sklearn.metrics import recall_score

import datasets


lowerCAmelCase							:			Any     							=							"""
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""


lowerCAmelCase							:			Any     							=							"""
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while 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 y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If 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 target labels and predictions 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. Note that it 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`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
    - `0`: If there is a zero division, the return value is `0`.
    - `1`: If there is a zero division, the return value is `1`.

Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.

Examples:

    Example 1-A simple example with some errors
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
        >>> print(results)
        {'recall': 0.6666666666666666}

    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
        >>> print(results)
        {'recall': 0.5}

    Example 3-The same example as Example 1, but with `sample_weight` included.
        >>> recall_metric = datasets.load_metric('recall')
        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
        >>> print(results)
        {'recall': 0.55}

    Example 4-A multiclass example, using different averages.
        >>> recall_metric = datasets.load_metric('recall')
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'recall': array([1., 0., 0.])}
"""


lowerCAmelCase							:			Any     							=							"""
@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				):






				'''simple docstring'''





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							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.recall_score.html"""]		, )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a=None		, _a=1		, _a="binary"		, _a=None		, _a="warn"		, ):





							"""simple docstring"""
							lowerCamelCase			      =					recall_score(
							    _a		, _a		, labels=_a		, pos_label=_a		, average=_a		, sample_weight=_a		, zero_division=_a		, )
							return {"recall": float(_a ) if score.size == 1 else score}



