



"""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 ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


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





def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	Tuple            =ConvNextConfig()

   if "tiny" in checkpoint_url:
      SCREAMING_SNAKE_CASE_:	int            =[3, 3, 9, 3]
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =[96, 192, 384, 768]
   if "small" in checkpoint_url:
      SCREAMING_SNAKE_CASE_:	Any            =[3, 3, 27, 3]
      SCREAMING_SNAKE_CASE_:	int            =[96, 192, 384, 768]
   if "base" in checkpoint_url:
      SCREAMING_SNAKE_CASE_:	Optional[int]            =[3, 3, 27, 3]
      SCREAMING_SNAKE_CASE_:	Optional[int]            =[128, 256, 512, 1024]
   if "large" in checkpoint_url:
      SCREAMING_SNAKE_CASE_:	int            =[3, 3, 27, 3]
      SCREAMING_SNAKE_CASE_:	Tuple            =[192, 384, 768, 1536]
   if "xlarge" in checkpoint_url:
      SCREAMING_SNAKE_CASE_:	List[str]            =[3, 3, 27, 3]
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =[256, 512, 1024, 2048]

   if "1k" in checkpoint_url:
      SCREAMING_SNAKE_CASE_:	str            =1000
      SCREAMING_SNAKE_CASE_:	Tuple            ="""imagenet-1k-id2label.json"""
      SCREAMING_SNAKE_CASE_:	Dict            =(1, 1000)
   else:
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =2_1841
      SCREAMING_SNAKE_CASE_:	List[str]            ="""imagenet-22k-id2label.json"""
      SCREAMING_SNAKE_CASE_:	Tuple            =(1, 2_1841)

   SCREAMING_SNAKE_CASE_:	Any            ="""huggingface/label-files"""
   SCREAMING_SNAKE_CASE_:	int            =num_labels
   SCREAMING_SNAKE_CASE_:	str            =json.load(open(hf_hub_download(lowercase							,     lowercase							,     repo_type="""dataset"""	)							,     """r"""	)	)
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            ={int(lowercase	): v for k, v in idalabel.items()}
   if "1k" not in checkpoint_url:
      # this dataset contains 21843 labels but the model only has 21841
      # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
      del idalabel[9205]
      del idalabel[1_5027]
   SCREAMING_SNAKE_CASE_:	Tuple            =idalabel
   SCREAMING_SNAKE_CASE_:	str            ={v: k for k, v in idalabel.items()}
   SCREAMING_SNAKE_CASE_:	int            =hidden_sizes
   SCREAMING_SNAKE_CASE_:	Any            =depths

   return config, expected_shape





def        __magic_name__			(							lowercase	):
   if "downsample_layers.0.0" in name:
      SCREAMING_SNAKE_CASE_:	str            =name.replace("""downsample_layers.0.0"""							,     """embeddings.patch_embeddings"""	)
   if "downsample_layers.0.1" in name:
      SCREAMING_SNAKE_CASE_:	List[Any]            =name.replace("""downsample_layers.0.1"""							,     """embeddings.norm"""	)  # we rename to layernorm later on
   if "downsample_layers.1.0" in name:
      SCREAMING_SNAKE_CASE_:	List[str]            =name.replace("""downsample_layers.1.0"""							,     """stages.1.downsampling_layer.0"""	)
   if "downsample_layers.1.1" in name:
      SCREAMING_SNAKE_CASE_:	Dict            =name.replace("""downsample_layers.1.1"""							,     """stages.1.downsampling_layer.1"""	)
   if "downsample_layers.2.0" in name:
      SCREAMING_SNAKE_CASE_:	List[str]            =name.replace("""downsample_layers.2.0"""							,     """stages.2.downsampling_layer.0"""	)
   if "downsample_layers.2.1" in name:
      SCREAMING_SNAKE_CASE_:	List[Any]            =name.replace("""downsample_layers.2.1"""							,     """stages.2.downsampling_layer.1"""	)
   if "downsample_layers.3.0" in name:
      SCREAMING_SNAKE_CASE_:	List[Any]            =name.replace("""downsample_layers.3.0"""							,     """stages.3.downsampling_layer.0"""	)
   if "downsample_layers.3.1" in name:
      SCREAMING_SNAKE_CASE_:	List[Any]            =name.replace("""downsample_layers.3.1"""							,     """stages.3.downsampling_layer.1"""	)
   if "stages" in name and "downsampling_layer" not in name:
      # stages.0.0. for instance should be renamed to stages.0.layers.0.
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =name[: len("""stages.0"""	)] + """.layers""" + name[len("""stages.0"""	) :]
   if "stages" in name:
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =name.replace("""stages"""							,     """encoder.stages"""	)
   if "norm" in name:
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =name.replace("""norm"""							,     """layernorm"""	)
   if "gamma" in name:
      SCREAMING_SNAKE_CASE_:	Dict            =name.replace("""gamma"""							,     """layer_scale_parameter"""	)
   if "head" in name:
      SCREAMING_SNAKE_CASE_:	List[Any]            =name.replace("""head"""							,     """classifier"""	)

   return name





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





@torch.no_grad()
def        __magic_name__			(							lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Dict            =get_convnext_config(lowercase	)
   # load original state_dict from URL
   SCREAMING_SNAKE_CASE_:	int            =torch.hub.load_state_dict_from_url(lowercase	)["""model"""]
   # rename keys
   for key in state_dict.copy().keys():
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =state_dict.pop(lowercase	)
      SCREAMING_SNAKE_CASE_:	Optional[int]            =val
   # add prefix to all keys expect classifier head
   for key in state_dict.copy().keys():
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =state_dict.pop(lowercase	)
      if not key.startswith("""classifier"""	):
         SCREAMING_SNAKE_CASE_:	str            ="""convnext.""" + key
      SCREAMING_SNAKE_CASE_:	Any            =val

   # load HuggingFace model
   SCREAMING_SNAKE_CASE_:	int            =ConvNextForImageClassification(lowercase	)
   model.load_state_dict(lowercase	)
   model.eval()

   # Check outputs on an image, prepared by ConvNextImageProcessor
   SCREAMING_SNAKE_CASE_:	Optional[int]            =224 if """224""" in checkpoint_url else 384
   SCREAMING_SNAKE_CASE_:	Any            =ConvNextImageProcessor(size=lowercase	)
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =image_processor(images=prepare_img()							,     return_tensors="""pt"""	).pixel_values

   SCREAMING_SNAKE_CASE_:	str            =model(lowercase	).logits

   # note: the logits below were obtained without center cropping
   if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
      SCREAMING_SNAKE_CASE_:	Tuple            =torch.tensor([-0.1_210, -0.6_605, 0.1_918]	)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
      SCREAMING_SNAKE_CASE_:	Any            =torch.tensor([-0.4_473, -0.1_847, -0.6_365]	)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
      SCREAMING_SNAKE_CASE_:	Dict            =torch.tensor([0.4_525, 0.7_539, 0.0_308]	)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
      SCREAMING_SNAKE_CASE_:	int            =torch.tensor([0.3_561, 0.6_350, -0.0_384]	)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
      SCREAMING_SNAKE_CASE_:	Optional[int]            =torch.tensor([0.4_174, -0.0_989, 0.1_489]	)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
      SCREAMING_SNAKE_CASE_:	List[Any]            =torch.tensor([0.2_513, -0.1_349, -0.1_613]	)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
      SCREAMING_SNAKE_CASE_:	str            =torch.tensor([1.2_980, 0.3_631, -0.1_198]	)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =torch.tensor([1.2_963, 0.1_227, 0.1_723]	)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
      SCREAMING_SNAKE_CASE_:	str            =torch.tensor([1.7_956, 0.8_390, 0.2_820]	)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
      SCREAMING_SNAKE_CASE_:	List[Any]            =torch.tensor([-0.2_822, -0.0_502, -0.0_878]	)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
      SCREAMING_SNAKE_CASE_:	Union[str, Any]            =torch.tensor([-0.5_672, -0.0_730, -0.4_348]	)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
      SCREAMING_SNAKE_CASE_:	Any            =torch.tensor([0.2_681, 0.2_365, 0.6_246]	)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
      SCREAMING_SNAKE_CASE_:	int            =torch.tensor([-0.2_642, 0.3_931, 0.5_116]	)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
      SCREAMING_SNAKE_CASE_:	Union[str, Any]            =torch.tensor([-0.6_677, -0.1_873, -0.8_379]	)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
      SCREAMING_SNAKE_CASE_:	Optional[int]            =torch.tensor([-0.7_749, -0.2_967, -0.6_444]	)
   else:
      raise ValueError(f'''Unknown URL: {checkpoint_url}'''	)

   assert torch.allclose(logits[0, :3]							,     lowercase							,     atol=1e-3	)
   assert logits.shape == expected_shape

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

   print("""Pushing model to the hub..."""	)
   SCREAMING_SNAKE_CASE_:	Tuple            ="""convnext"""
   if "tiny" in checkpoint_url:
      model_name += "-tiny"
   elif "small" in checkpoint_url:
      model_name += "-small"
   elif "base" in checkpoint_url:
      model_name += "-base"
   elif "xlarge" in checkpoint_url:
      model_name += "-xlarge"
   elif "large" in checkpoint_url:
      model_name += "-large"
   if "224" in checkpoint_url:
      model_name += "-224"
   elif "384" in checkpoint_url:
      model_name += "-384"
   if "22k" in checkpoint_url and "1k" not in checkpoint_url:
      model_name += "-22k"
   if "22k" in checkpoint_url and "1k" in checkpoint_url:
      model_name += "-22k-1k"

   model.push_to_hub(
       repo_path_or_name=Path(lowercase							,     lowercase	)							,     organization="""nielsr"""							,     commit_message="""Add model"""							,     )


if __name__ == "__main__":
   _UpperCAmelCase       =     argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--checkpoint_url""",
       default="""https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth""",
       type=str,
       help="""URL of the original ConvNeXT checkpoint you'd like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default=None,
       type=str,
       required=True,
       help="""Path to the output PyTorch model directory.""",
   )

   _UpperCAmelCase       =     parser.parse_args()
   convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








"""simple docstring"""
from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase	):
   seed(1	)
   return [gauss(lowercase							,     lowercase	) for _ in range(lowercase	)]





def        __magic_name__			(							lowercase							,     lowercase	):
   return [k for k in range(lowercase	) for _ in range(instance_count[k]	)]





def        __magic_name__			(							lowercase							,     lowercase	):
   return sum(lowercase	) / instance_count





def        __magic_name__			(							lowercase							,     lowercase	):
   return instance_count / total_count





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =[]  # An empty list to store all squared differences
   # iterate over number of elements in items
   for i in range(len(lowercase	)	):
      # for loop iterates over number of elements in inner layer of items
      for j in range(len(items[i]	)	):
         # appending squared differences to 'squared_diff' list
         squared_diff.append((items[i][j] - means[i]) ** 2	)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =len(lowercase	)  # Number of classes in dataset
   return 1 / (total_count - n_classes) * sum(lowercase	)





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =[]
   # for loop iterates over number of elements in list
   for i in range(len(lowercase	)	):
      # for loop iterates over number of inner items of each element
      for j in range(len(x_items[i]	)	):
         SCREAMING_SNAKE_CASE_:	Optional[int]            =[]  # to store all discriminant values of each item as a list
         # for loop iterates over number of classes we have in our dataset
         for k in range(len(lowercase	)	):
            # appending values of discriminants for each class to 'temp' list
            temp.append(
                x_items[i][j] * (means[k] / variance)
                - (means[k] ** 2 / (2 * variance))
                + log(probabilities[k]	)	)
         # appending discriminant values of each item to 'results' list
         results.append(lowercase	)

   return [result.index(max(lowercase	)	) for result in results]





def        __magic_name__			(							lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Dict            =sum(1 for i, j in zip(lowercase							,     lowercase	) if i == j	)
   # percentage of accuracy equals to number of correct predictions divided by number
   # of all data and multiplied by 100
   return (correct / len(lowercase	)) * 100


_UpperCAmelCase       =     TypeVar("""num""")





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase = lambda lowercase	: True							,     lowercase = None							,     ):
   while True:
      try:
         SCREAMING_SNAKE_CASE_:	List[Any]            =input_type(input(lowercase	).strip() or default	)
         if condition(lowercase	):
            return user_input
         else:
            print(f'''{user_input}: {err_msg}'''	)
            continue
      except ValueError:
         print(
             f'''{user_input}: Incorrect input type, expected {input_type.__name__!r}'''	)





def        __magic_name__			(							):
   while True:
      print(""" Linear Discriminant Analysis """.center(50							,     """*"""	)	)
      print("""*""" * 50							,     """\n"""	)
      print("""First of all we should specify the number of classes that"""	)
      print("""we want to generate as training dataset"""	)
      # Trying to get number of classes
      SCREAMING_SNAKE_CASE_:	List[Any]            =valid_input(
          input_type=lowercase							,     condition=lambda lowercase	: x > 0							,     input_msg="""Enter the number of classes (Data Groupings): """							,     err_msg="""Number of classes should be positive!"""							,     )

      print("""-""" * 100	)

      # Trying to get the value of standard deviation
      SCREAMING_SNAKE_CASE_:	Union[str, Any]            =valid_input(
          input_type=lowercase							,     condition=lambda lowercase	: x >= 0							,     input_msg=(
              """Enter the value of standard deviation"""
              """(Default value is 1.0 for all classes): """
          )							,     err_msg="""Standard deviation should not be negative!"""							,     default="""1.0"""							,     )

      print("""-""" * 100	)

      # Trying to get number of instances in classes and theirs means to generate
      # dataset
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =[]  # An empty list to store instance counts of classes in dataset
      for i in range(lowercase	):
         SCREAMING_SNAKE_CASE_:	int            =valid_input(
             input_type=lowercase							,     condition=lambda lowercase	: x > 0							,     input_msg=(f'''Enter The number of instances for class_{i+1}: ''')							,     err_msg="""Number of instances should be positive!"""							,     )
         counts.append(lowercase	)
      print("""-""" * 100	)

      # An empty list to store values of user-entered means of classes
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =[]
      for a in range(lowercase	):
         SCREAMING_SNAKE_CASE_:	Tuple            =valid_input(
             input_type=lowercase							,     input_msg=(f'''Enter the value of mean for class_{a+1}: ''')							,     err_msg="""This is an invalid value."""							,     )
         user_means.append(lowercase	)
      print("""-""" * 100	)

      print("""Standard deviation: """							,     lowercase	)
      # print out the number of instances in classes in separated line
      for i, count in enumerate(lowercase							,     1	):
         print(f'''Number of instances in class_{i} is: {count}'''	)
      print("""-""" * 100	)

      # print out mean values of classes separated line
      for i, user_mean in enumerate(lowercase							,     1	):
         print(f'''Mean of class_{i} is: {user_mean}'''	)
      print("""-""" * 100	)

      # Generating training dataset drawn from gaussian distribution
      SCREAMING_SNAKE_CASE_:	Tuple            =[
          gaussian_distribution(user_means[j]							,     lowercase							,     counts[j]	)
          for j in range(lowercase	)
      ]
      print("""Generated Normal Distribution: \n"""							,     lowercase	)
      print("""-""" * 100	)

      # Generating Ys to detecting corresponding classes
      SCREAMING_SNAKE_CASE_:	int            =y_generator(lowercase							,     lowercase	)
      print("""Generated Corresponding Ys: \n"""							,     lowercase	)
      print("""-""" * 100	)

      # Calculating the value of actual mean for each class
      SCREAMING_SNAKE_CASE_:	str            =[calculate_mean(counts[k]							,     x[k]	) for k in range(lowercase	)]
      # for loop iterates over number of elements in 'actual_means' list and print
      # out them in separated line
      for i, actual_mean in enumerate(lowercase							,     1	):
         print(f'''Actual(Real) mean of class_{i} is: {actual_mean}'''	)
      print("""-""" * 100	)

      # Calculating the value of probabilities for each class
      SCREAMING_SNAKE_CASE_:	List[Any]            =[
          calculate_probabilities(counts[i]							,     sum(lowercase	)	) for i in range(lowercase	)
      ]

      # for loop iterates over number of elements in 'probabilities' list and print
      # out them in separated line
      for i, probability in enumerate(lowercase							,     1	):
         print(f'''Probability of class_{i} is: {probability}'''	)
      print("""-""" * 100	)

      # Calculating the values of variance for each class
      SCREAMING_SNAKE_CASE_:	str            =calculate_variance(lowercase							,     lowercase							,     sum(lowercase	)	)
      print("""Variance: """							,     lowercase	)
      print("""-""" * 100	)

      # Predicting Y values
      # storing predicted Y values in 'pre_indexes' variable
      SCREAMING_SNAKE_CASE_:	Dict            =predict_y_values(lowercase							,     lowercase							,     lowercase							,     lowercase	)
      print("""-""" * 100	)

      # Calculating Accuracy of the model
      print(f'''Accuracy: {accuracy(lowercase							,     lowercase	)}'''	)
      print("""-""" * 100	)
      print(""" DONE """.center(100							,     """+"""	)	)

      if input("""Press any key to restart or 'q' for quit: """	).strip().lower() == "q":
         print("""\n""" + """GoodBye!""".center(100							,     """-"""	) + """\n"""	)
         break
      system("""cls""" if name == """nt""" else """clear"""	)  # noqa: S605


if __name__ == "__main__":
   main()



