"""simple docstring"""

import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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






def      lowercase		(						a__	:		Any ,  a__	:		List[str]=False     )						->   Any:
					_UpperCamelCase      						=	[]
					for i in range(config.num_hidden_layers     ):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''deit.encoder.layer.{i}.layernorm_before.weight''')     )
										rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''')     )
										rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''')     )
										rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''')     )
										rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''')     )
										rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''')     )
										rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''')     )
										rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''')     )
										rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''')     )
										rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''')     )

					# projection layer + position embeddings
					rename_keys.extend(
					    [
					        ('''cls_token''', '''deit.embeddings.cls_token'''),
					        ('''dist_token''', '''deit.embeddings.distillation_token'''),
					        ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
					        ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
					        ('''pos_embed''', '''deit.embeddings.position_embeddings'''),
					    ]     )

					if base_model:
										# layernorm + pooler
										rename_keys.extend(
										    [
										        ('''norm.weight''', '''layernorm.weight'''),
										        ('''norm.bias''', '''layernorm.bias'''),
										        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
										        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
										    ]     )

										# if just the base model, we should remove "deit" from all keys that start with "deit"
										_UpperCamelCase      						=	[(pair[0], pair[1][4:]) if pair[1].startswith('''deit'''     ) else pair for pair in rename_keys]
					else:
										# layernorm + classification heads
										rename_keys.extend(
										    [
										        ('''norm.weight''', '''deit.layernorm.weight'''),
										        ('''norm.bias''', '''deit.layernorm.bias'''),
										        ('''head.weight''', '''cls_classifier.weight'''),
										        ('''head.bias''', '''cls_classifier.bias'''),
										        ('''head_dist.weight''', '''distillation_classifier.weight'''),
										        ('''head_dist.bias''', '''distillation_classifier.bias'''),
										    ]     )

					return rename_keys






def      lowercase		(						a__	:		Dict ,  a__	:		Union[str, Any] ,  a__	:		Optional[int]=False     )						->   Union[str, Any]:
					for i in range(config.num_hidden_layers     ):
										if base_model:
															_UpperCamelCase      						=	""
										else:
															_UpperCamelCase      						=	"deit."
										# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
										_UpperCamelCase      						=	state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''     )
										_UpperCamelCase      						=	state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''     )
										# next, add query, keys and values (in that order) to the state dict
										_UpperCamelCase      						=	in_proj_weight[
										    : config.hidden_size, :
										]
										_UpperCamelCase      						=	in_proj_bias[: config.hidden_size]
										_UpperCamelCase      						=	in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										_UpperCamelCase      						=	in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										_UpperCamelCase      						=	in_proj_weight[
										    -config.hidden_size :, :
										]
										_UpperCamelCase      						=	in_proj_bias[-config.hidden_size :]






def      lowercase		(						a__	:		List[str] ,  a__	:		Tuple ,  a__	:		Optional[int]     )						->   int:
					_UpperCamelCase      						=	dct.pop(_lowerCAmelCase     )
					_UpperCamelCase      						=	val






def      lowercase		(						)						->   Optional[Any]:
					_UpperCamelCase      						=	"http://images.cocodataset.org/val2017/000000039769.jpg"
					_UpperCamelCase      						=	Image.open(requests.get(_lowerCAmelCase ,  stream=_lowerCAmelCase     ).raw     )
					return im






@torch.no_grad()
def      lowercase		(						a__	:		Optional[int] ,  a__	:		str     )						->   List[str]:
					_UpperCamelCase      						=	DeiTConfig()
					# all deit models have fine-tuned heads
					_UpperCamelCase      						=	False
					# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
					_UpperCamelCase      						=	1000
					_UpperCamelCase      						=	"huggingface/label-files"
					_UpperCamelCase      						=	"imagenet-1k-id2label.json"
					_UpperCamelCase      						=	json.load(open(hf_hub_download(_lowerCAmelCase ,  _lowerCAmelCase ,  repo_type='''dataset'''     ) ,  '''r'''     )     )
					_UpperCamelCase      						=	{int(_lowerCAmelCase     ): v for k, v in idalabel.items()}
					_UpperCamelCase      						=	idalabel
					_UpperCamelCase      						=	{v: k for k, v in idalabel.items()}
					_UpperCamelCase      						=	int(deit_name[-6:-4]     )
					_UpperCamelCase      						=	int(deit_name[-3:]     )
					# size of the architecture
					if deit_name[9:].startswith('''tiny'''     ):
										_UpperCamelCase      						=	192
										_UpperCamelCase      						=	768
										_UpperCamelCase      						=	12
										_UpperCamelCase      						=	3
					elif deit_name[9:].startswith('''small'''     ):
										_UpperCamelCase      						=	384
										_UpperCamelCase      						=	1536
										_UpperCamelCase      						=	12
										_UpperCamelCase      						=	6
					if deit_name[9:].startswith('''base'''     ):
										pass
					elif deit_name[4:].startswith('''large'''     ):
										_UpperCamelCase      						=	1024
										_UpperCamelCase      						=	4096
										_UpperCamelCase      						=	24
										_UpperCamelCase      						=	16

					# load original model from timm
					_UpperCamelCase      						=	timm.create_model(_lowerCAmelCase ,  pretrained=_lowerCAmelCase     )
					timm_model.eval()

					# load state_dict of original model, remove and rename some keys
					_UpperCamelCase      						=	timm_model.state_dict()
					_UpperCamelCase      						=	create_rename_keys(_lowerCAmelCase ,  _lowerCAmelCase     )
					for src, dest in rename_keys:
										rename_key(_lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase     )
					read_in_q_k_v(_lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase     )

					# load HuggingFace model
					_UpperCamelCase      						=	DeiTForImageClassificationWithTeacher(_lowerCAmelCase     ).eval()
					model.load_state_dict(_lowerCAmelCase     )

					# Check outputs on an image, prepared by DeiTImageProcessor
					_UpperCamelCase      						=	int(
					    (256 / 224) * config.image_size     )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
					_UpperCamelCase      						=	DeiTImageProcessor(size=_lowerCAmelCase ,  crop_size=config.image_size     )
					_UpperCamelCase      						=	image_processor(images=prepare_img() ,  return_tensors='''pt'''     )
					_UpperCamelCase      						=	encoding["pixel_values"]
					_UpperCamelCase      						=	model(_lowerCAmelCase     )

					_UpperCamelCase      						=	timm_model(_lowerCAmelCase     )
					assert timm_logits.shape == outputs.logits.shape
					assert torch.allclose(_lowerCAmelCase ,  outputs.logits ,  atol=1e-3     )

					Path(_lowerCAmelCase     ).mkdir(exist_ok=_lowerCAmelCase     )
					print(F'''Saving model {deit_name} 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 __name__ == "__main__":
		UpperCAmelCase       =  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--deit_name""",
		    default="""vit_deit_base_distilled_patch16_224""",
		    type=str,
		    help="""Name of the DeiT timm 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."""
		)

		UpperCAmelCase       =  parser.parse_args()
		convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)







from sklearn.metrics import fa_score

import datasets


__lowerCamelCase       :     List[Any]   =     """
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       :     List[Any]   =     """
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       :     str   =     """
@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 					A__   (       datasets.Metric			):





      def        __UpperCamelCase( 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.f1_score.html"]       ,							)





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_=None       ,							A_=1       ,							A_="binary"       ,							A_=None   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   fa_score(
           A_       ,							A_       ,							labels=A_       ,							pos_label=A_       ,							average=A_       ,							sample_weight=A_   )
       return {"f1": float(A_   ) if score.size == 1 else score}
