





'''simple docstring'''

import argparse
import json
import os

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

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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





def lowerCamelCase						(					__lowerCamelCase			:   Union[str, Any]   )						->Optional[int]:
    _SCREAMING_SNAKE_CASE     =      ConvNextVaConfig()

    if "atto" in checkpoint_url:
        _SCREAMING_SNAKE_CASE     =      [2, 2, 6, 2]
        _SCREAMING_SNAKE_CASE     =      [40, 80, 160, 320]
    if "femto" in checkpoint_url:
        _SCREAMING_SNAKE_CASE     =      [2, 2, 6, 2]
        _SCREAMING_SNAKE_CASE     =      [48, 96, 192, 384]
    if "pico" in checkpoint_url:
        _SCREAMING_SNAKE_CASE     =      [2, 2, 6, 2]
        _SCREAMING_SNAKE_CASE     =      [64, 128, 256, 512]
    if "nano" in checkpoint_url:
        _SCREAMING_SNAKE_CASE     =      [2, 2, 8, 2]
        _SCREAMING_SNAKE_CASE     =      [80, 160, 320, 640]
    if "tiny" in checkpoint_url:
        _SCREAMING_SNAKE_CASE     =      [3, 3, 9, 3]
        _SCREAMING_SNAKE_CASE     =      [96, 192, 384, 768]
    if "base" in checkpoint_url:
        _SCREAMING_SNAKE_CASE     =      [3, 3, 27, 3]
        _SCREAMING_SNAKE_CASE     =      [128, 256, 512, 1024]
    if "large" in checkpoint_url:
        _SCREAMING_SNAKE_CASE     =      [3, 3, 27, 3]
        _SCREAMING_SNAKE_CASE     =      [192, 384, 768, 1536]
    if "huge" in checkpoint_url:
        _SCREAMING_SNAKE_CASE     =      [3, 3, 27, 3]
        _SCREAMING_SNAKE_CASE     =      [352, 704, 1408, 2816]

    _SCREAMING_SNAKE_CASE     =      1000
    _SCREAMING_SNAKE_CASE     =      "imagenet-1k-id2label.json"
    _SCREAMING_SNAKE_CASE     =      (1, 1000)

    _SCREAMING_SNAKE_CASE     =      "huggingface/label-files"
    _SCREAMING_SNAKE_CASE     =      num_labels
    _SCREAMING_SNAKE_CASE     =      json.load(open(hf_hub_download(_a ,	_a ,	repo_type="""dataset"""   ) ,	"""r"""   )   )
    _SCREAMING_SNAKE_CASE     =      {int(_a   ): v for k, v in idalabel.items()}

    _SCREAMING_SNAKE_CASE     =      idalabel
    _SCREAMING_SNAKE_CASE     =      {v: k for k, v in idalabel.items()}
    _SCREAMING_SNAKE_CASE     =      hidden_sizes
    _SCREAMING_SNAKE_CASE     =      depths

    return config, expected_shape





def lowerCamelCase						(					__lowerCamelCase			:   Optional[Any]   )						->Dict:
    if "downsample_layers.0.0" in name:
        _SCREAMING_SNAKE_CASE     =      name.replace("""downsample_layers.0.0""" ,	"""embeddings.patch_embeddings"""   )
    if "downsample_layers.0.1" in name:
        _SCREAMING_SNAKE_CASE     =      name.replace("""downsample_layers.0.1""" ,	"""embeddings.norm"""   )  # we rename to layernorm later on
    if "downsample_layers.1.0" in name:
        _SCREAMING_SNAKE_CASE     =      name.replace("""downsample_layers.1.0""" ,	"""stages.1.downsampling_layer.0"""   )
    if "downsample_layers.1.1" in name:
        _SCREAMING_SNAKE_CASE     =      name.replace("""downsample_layers.1.1""" ,	"""stages.1.downsampling_layer.1"""   )
    if "downsample_layers.2.0" in name:
        _SCREAMING_SNAKE_CASE     =      name.replace("""downsample_layers.2.0""" ,	"""stages.2.downsampling_layer.0"""   )
    if "downsample_layers.2.1" in name:
        _SCREAMING_SNAKE_CASE     =      name.replace("""downsample_layers.2.1""" ,	"""stages.2.downsampling_layer.1"""   )
    if "downsample_layers.3.0" in name:
        _SCREAMING_SNAKE_CASE     =      name.replace("""downsample_layers.3.0""" ,	"""stages.3.downsampling_layer.0"""   )
    if "downsample_layers.3.1" in name:
        _SCREAMING_SNAKE_CASE     =      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     =      name[: len("""stages.0"""   )] + ".layers" + name[len("""stages.0"""   ) :]
    if "gamma" in name:
        _SCREAMING_SNAKE_CASE     =      name.replace("""gamma""" ,	"""weight"""   )
    if "beta" in name:
        _SCREAMING_SNAKE_CASE     =      name.replace("""beta""" ,	"""bias"""   )
    if "stages" in name:
        _SCREAMING_SNAKE_CASE     =      name.replace("""stages""" ,	"""encoder.stages"""   )
    if "norm" in name:
        _SCREAMING_SNAKE_CASE     =      name.replace("""norm""" ,	"""layernorm"""   )
    if "head" in name:
        _SCREAMING_SNAKE_CASE     =      name.replace("""head""" ,	"""classifier"""   )

    return name





def lowerCamelCase						(					)						->Dict:
    _SCREAMING_SNAKE_CASE     =      "http://images.cocodataset.org/val2017/000000039769.jpg"
    _SCREAMING_SNAKE_CASE     =      Image.open(requests.get(_a ,	stream=_a   ).raw   )
    return im





def lowerCamelCase						(					__lowerCamelCase			:   int   )						->int:
    if "224" in checkpoint_url:
        _SCREAMING_SNAKE_CASE     =      224
        _SCREAMING_SNAKE_CASE     =      224 / 256
    elif "384" in checkpoint_url:
        _SCREAMING_SNAKE_CASE     =      384
        _SCREAMING_SNAKE_CASE     =      None
    else:
        _SCREAMING_SNAKE_CASE     =      512
        _SCREAMING_SNAKE_CASE     =      None

    return ConvNextImageProcessor(
        size=_a ,	crop_pct=_a ,	image_mean=[0.485, 0.456, 0.406] ,	image_std=[0.229, 0.224, 0.225] ,	resample=PILImageResampling.BICUBIC ,	)





@torch.no_grad()
def lowerCamelCase						(					__lowerCamelCase			:   List[Any] ,	__lowerCamelCase			:   Tuple ,	__lowerCamelCase			:   Any ,	__lowerCamelCase			:   List[Any]   )						->List[str]:
    print("""Downloading original model from checkpoint..."""   )
    # define ConvNeXTV2 configuration based on URL
    _SCREAMING_SNAKE_CASE     =      get_convnextva_config(_a   )
    # load original state_dict from URL
    _SCREAMING_SNAKE_CASE     =      torch.hub.load_state_dict_from_url(_a   )["model"]

    print("""Converting model parameters..."""   )
    # rename keys
    for key in state_dict.copy().keys():
        _SCREAMING_SNAKE_CASE     =      state_dict.pop(_a   )
        _SCREAMING_SNAKE_CASE     =      val
    # add prefix to all keys expect classifier head
    for key in state_dict.copy().keys():
        _SCREAMING_SNAKE_CASE     =      state_dict.pop(_a   )
        if not key.startswith("""classifier"""   ):
            _SCREAMING_SNAKE_CASE     =      "convnextv2." + key
        _SCREAMING_SNAKE_CASE     =      val

    # load HuggingFace model
    _SCREAMING_SNAKE_CASE     =      ConvNextVaForImageClassification(_a   )
    model.load_state_dict(_a   )
    model.eval()

    # Check outputs on an image, prepared by ConvNextImageProcessor
    _SCREAMING_SNAKE_CASE     =      convert_preprocessor(_a   )
    _SCREAMING_SNAKE_CASE     =      preprocessor(images=prepare_img() ,	return_tensors="""pt"""   )
    _SCREAMING_SNAKE_CASE     =      model(**_a   ).logits

    # note: the logits below were obtained without center cropping
    if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
        _SCREAMING_SNAKE_CASE     =      torch.tensor([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
        _SCREAMING_SNAKE_CASE     =      torch.tensor([-0.1727, -0.5341, -0.7818, -0.4745, -0.6566]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
        _SCREAMING_SNAKE_CASE     =      torch.tensor([-0.0333, 0.1563, -0.9137, 0.1054, 0.0381]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
        _SCREAMING_SNAKE_CASE     =      torch.tensor([-0.1744, -0.1555, -0.0713, 0.0950, -0.1431]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
        _SCREAMING_SNAKE_CASE     =      torch.tensor([0.9996, 0.1966, -0.4386, -0.3472, 0.6661]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
        _SCREAMING_SNAKE_CASE     =      torch.tensor([-0.2553, -0.6708, -0.1359, 0.2518, -0.2488]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
        _SCREAMING_SNAKE_CASE     =      torch.tensor([-0.0673, -0.5627, -0.3753, -0.2722, 0.0178]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
        _SCREAMING_SNAKE_CASE     =      torch.tensor([-0.6377, -0.7458, -0.2150, 0.1184, -0.0597]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
        _SCREAMING_SNAKE_CASE     =      torch.tensor([1.0799, 0.2322, -0.8860, 1.0219, 0.6231]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
        _SCREAMING_SNAKE_CASE     =      torch.tensor([0.3766, 0.4917, -1.1426, 0.9942, 0.6024]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
        _SCREAMING_SNAKE_CASE     =      torch.tensor([0.4220, -0.6919, -0.4317, -0.2881, -0.6609]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
        _SCREAMING_SNAKE_CASE     =      torch.tensor([0.1082, -0.8286, -0.5095, 0.4681, -0.8085]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
        _SCREAMING_SNAKE_CASE     =      torch.tensor([-0.2419, -0.6221, 0.2176, -0.0980, -0.7527]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
        _SCREAMING_SNAKE_CASE     =      torch.tensor([0.0391, -0.4371, 0.3786, 0.1251, -0.2784]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
        _SCREAMING_SNAKE_CASE     =      torch.tensor([-0.0504, 0.5636, -0.1729, -0.6507, -0.3949]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
        _SCREAMING_SNAKE_CASE     =      torch.tensor([0.3560, 0.9486, 0.3149, -0.2667, -0.5138]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
        _SCREAMING_SNAKE_CASE     =      torch.tensor([-0.2469, -0.4550, -0.5853, -0.0810, 0.0309]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
        _SCREAMING_SNAKE_CASE     =      torch.tensor([-0.3090, 0.0802, -0.0682, -0.1979, -0.2826]   )
    else:
        raise ValueError(F'Unknown URL: {checkpoint_url}'   )

    assert torch.allclose(logits[0, :5] ,	_a ,	atol=1e-3   )
    assert logits.shape == expected_shape
    print("""Model outputs match the original results!"""   )

    if save_model:
        print("""Saving model to local..."""   )
        # Create folder to save model
        if not os.path.isdir(_a   ):
            os.mkdir(_a   )

        model.save_pretrained(_a   )
        preprocessor.save_pretrained(_a   )

    _SCREAMING_SNAKE_CASE     =      "convnextv2"
    if "atto" in checkpoint_url:
        model_name += "-atto"
    if "femto" in checkpoint_url:
        model_name += "-femto"
    if "pico" in checkpoint_url:
        model_name += "-pico"
    if "nano" in checkpoint_url:
        model_name += "-nano"
    elif "tiny" in checkpoint_url:
        model_name += "-tiny"
    elif "base" in checkpoint_url:
        model_name += "-base"
    elif "large" in checkpoint_url:
        model_name += "-large"
    elif "huge" in checkpoint_url:
        model_name += "-huge"
    if "22k" in checkpoint_url and "1k" not in checkpoint_url:
        model_name += "-22k"
    elif "22k" in checkpoint_url and "1k" in checkpoint_url:
        model_name += "-22k-1k"
    elif "1k" in checkpoint_url:
        model_name += "-1k"
    if "224" in checkpoint_url:
        model_name += "-224"
    elif "384" in checkpoint_url:
        model_name += "-384"
    elif "512" in checkpoint_url:
        model_name += "-512"

    if push_to_hub:
        print(F'Pushing {model_name} to the hub...'   )
        model.push_to_hub(_a   )
        preprocessor.push_to_hub(_a   )


if __name__ == "__main__":
      lowercase_         =			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--checkpoint_url""",
          default="""https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt""",
          type=str,
          help="""URL of the original ConvNeXTV2 checkpoint you\'d like to convert.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default="""model""",
          type=str,
          help="""Path to the output PyTorch model directory.""",
      )
      parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
      parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image preprocessor to the hub""")

      lowercase_         =			parser.parse_args()
      convert_convnextva_checkpoint(
          args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
      )










'''simple docstring'''

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			(LMSDiscreteScheduler,)
    UpperCamelCase			   =			10


    def       snake_case_(		self			,		**A	)		->       str:
        _SCREAMING_SNAKE_CASE     =      {
            """num_train_timesteps""": 1100,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
        }

        config.update(**A	)
        return config


    def       snake_case_(		self	)		->       str:
        for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=A	)


    def       snake_case_(		self	)		->       Optional[Any]:
        for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]			,		[0.0002, 0.002, 0.02]	):
            self.check_over_configs(beta_start=A			,		beta_end=A	)


    def       snake_case_(		self	)		->       Tuple:
        for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=A	)


    def       snake_case_(		self	)		->       List[str]:
        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=A	)


    def       snake_case_(		self	)		->       Optional[int]:
        for t in [0, 500, 800]:
            self.check_over_forward(time_step=A	)


    def       snake_case_(		self	)		->       str:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps	):
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 1006.388	) < 1e-2
        assert abs(result_mean.item() - 1.31	) < 1e-3


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config(prediction_type="""v_prediction"""	)
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps	):
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 0.0017	) < 1e-2
        assert abs(result_mean.item() - 2.26_76e-06	) < 1e-3


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps			,		device=A	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        _SCREAMING_SNAKE_CASE     =      sample.to(A	)

        for i, t in enumerate(scheduler.timesteps	):
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 1006.388	) < 1e-2
        assert abs(result_mean.item() - 1.31	) < 1e-3





    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A			,		use_karras_sigmas=A	)

        scheduler.set_timesteps(self.num_inference_steps			,		device=A	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter.to(A	) * scheduler.init_noise_sigma
        _SCREAMING_SNAKE_CASE     =      sample.to(A	)

        for t in scheduler.timesteps:
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 3812.9927	) < 2e-2
        assert abs(result_mean.item() - 4.9648	) < 1e-3



