
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 (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


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

_UpperCAmelCase      :				Tuple          = torch.device("cpu")


def    A (      )     ->						Dict:
		'''simple docstring'''

		UpperCamelCase						=  'http://images.cocodataset.org/val2017/000000039769.jpg'
		UpperCamelCase						=  Image.open(requests.get(lowercase					, stream=lowercase				).raw				)
		return im


def    A (      lowercase				)     ->						List[Any]:
		'''simple docstring'''

		if swiftformer_name == "swiftformer_xs":
				return torch.tensor([-2.17_03e00, 2.11_07e00, -2.08_11e00, 8.86_85e-01, 2.43_60e-01]				)

		elif swiftformer_name == "swiftformer_s":
				return torch.tensor([3.96_36e-01, 2.34_78e-01, -1.69_63e00, -1.73_81e00, -8.63_37e-01]				)

		elif swiftformer_name == "swiftformer_l1":
				return torch.tensor([-4.27_68e-01, -4.74_29e-01, -1.08_97e00, -1.02_48e00, 3.55_23e-02]				)

		elif swiftformer_name == "swiftformer_l3":
				return torch.tensor([-2.53_30e-01, 2.42_11e-01, -6.01_85e-01, -8.27_89e-01, -6.04_46e-02]				)


def    A (      lowercase					, lowercase					, lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  dct.pop(lowercase				)
		UpperCamelCase						=  val


def    A (      lowercase				)     ->						Tuple:
		'''simple docstring'''

		UpperCamelCase						=  []
		for k in state_dict.keys():
				UpperCamelCase						=  k
				if ".pwconv" in k:
						UpperCamelCase						=  k_new.replace('.pwconv'					, '.point_wise_conv'				)
				if ".dwconv" in k:
						UpperCamelCase						=  k_new.replace('.dwconv'					, '.depth_wise_conv'				)
				if ".Proj." in k:
						UpperCamelCase						=  k_new.replace('.Proj.'					, '.proj.'				)
				if "patch_embed" in k_new:
						UpperCamelCase						=  k_new.replace('patch_embed'					, 'swiftformer.patch_embed.patch_embedding'				)
				if "network" in k_new:
						UpperCamelCase						=  k_new.split('.'				)
						if ls[2].isdigit():
								UpperCamelCase						=  'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]				)
						else:
								UpperCamelCase						=  k_new.replace('network'					, 'swiftformer.encoder.network'				)
				rename_keys.append((k, k_new)				)
		return rename_keys


@torch.no_grad()
def    A (      lowercase					, lowercase					, lowercase				)     ->						Optional[Any]:
		'''simple docstring'''

		UpperCamelCase						=  SwiftFormerConfig()

		# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
		UpperCamelCase						=  1_000
		UpperCamelCase						=  'huggingface/label-files'
		UpperCamelCase						=  'imagenet-1k-id2label.json'
		UpperCamelCase						=  json.load(open(hf_hub_download(lowercase					, lowercase					, repo_type='dataset'				)					, 'r'				)				)
		UpperCamelCase						=  {int(lowercase				): v for k, v in idalabel.items()}
		UpperCamelCase						=  idalabel
		UpperCamelCase						=  {v: k for k, v in idalabel.items()}

		# size of the architecture
		if swiftformer_name == "swiftformer_xs":
				UpperCamelCase						=  [3, 3, 6, 4]
				UpperCamelCase						=  [48, 56, 112, 220]

		elif swiftformer_name == "swiftformer_s":
				UpperCamelCase						=  [3, 3, 9, 6]
				UpperCamelCase						=  [48, 64, 168, 224]

		elif swiftformer_name == "swiftformer_l1":
				UpperCamelCase						=  [4, 3, 10, 5]
				UpperCamelCase						=  [48, 96, 192, 384]

		elif swiftformer_name == "swiftformer_l3":
				UpperCamelCase						=  [4, 4, 12, 6]
				UpperCamelCase						=  [64, 128, 320, 512]

		# load state_dict of original model, remove and rename some keys
		if original_ckpt:
				if original_ckpt.startswith('https'				):
						UpperCamelCase						=  torch.hub.load_state_dict_from_url(lowercase					, map_location='cpu'					, check_hash=lowercase				)
				else:
						UpperCamelCase						=  torch.load(lowercase					, map_location='cpu'				)
		UpperCamelCase						=  checkpoint

		UpperCamelCase						=  create_rename_keys(lowercase				)
		for rename_key_src, rename_key_dest in rename_keys:
				rename_key(lowercase					, lowercase					, lowercase				)

		# load HuggingFace model
		UpperCamelCase						=  SwiftFormerForImageClassification(lowercase				).eval()
		hf_model.load_state_dict(lowercase				)

		# prepare test inputs
		UpperCamelCase						=  prepare_img()
		UpperCamelCase						=  ViTImageProcessor.from_pretrained('preprocessor_config'				)
		UpperCamelCase						=  processor(images=lowercase					, return_tensors='pt'				)

		# compare outputs from both models
		UpperCamelCase						=  get_expected_output(lowercase				)
		UpperCamelCase						=  hf_model(inputs['pixel_values']				).logits

		assert hf_logits.shape == torch.Size([1, 1_000]				)
		assert torch.allclose(hf_logits[0, 0:5]					, lowercase					, atol=1e-3				)

		Path(lowercase				).mkdir(exist_ok=lowercase				)
		print(f'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''				)
		hf_model.save_pretrained(lowercase				)


if __name__ == "__main__":
					_UpperCAmelCase      :				int          = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--swiftformer_name",
					    default="swiftformer_xs",
					    choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
					    type=str,
					    help="Name of the SwiftFormer model you'd like to convert.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path",
					    default="./converted_outputs/",
					    type=str,
					    help="Path to the output PyTorch model directory.",
					)
					parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")

					_UpperCAmelCase      :				List[Any]          = parser.parse_args()
					convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)


import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


_UpperCAmelCase      :				str          = "scheduler_config.json"


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Tuple			  =       1
		__lowercase : int			  =       2
		__lowercase : List[Any]			  =       3
		__lowercase : str			  =       4
		__lowercase : Optional[Any]			  =       5






@dataclass
class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : jnp.ndarray






class        lowercase      :
		__lowercase : Union[str, Any]			  =       SCHEDULER_CONFIG_NAME
		__lowercase : Dict			  =       ["dtype"]
		__lowercase : List[Any]			  =       []
		__lowercase : Dict			  =       True



		@classmethod
		def __UpperCamelCase				(				cls       ,   A_ = None       ,   A_ = None       ,   A_=False       ,   **A_       ,   )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  cls.load_config(
				    pretrained_model_name_or_path=A_       ,   subfolder=A_       ,   return_unused_kwargs=A_       ,   **A_       ,   )
				UpperCamelCase			,						UpperCamelCase						=  cls.from_config(A_       ,   return_unused_kwargs=A_       ,   **A_ )

				if hasattr(A_       ,   'create_state' ) and getattr(A_       ,   'has_state'       ,   A_ ):
						UpperCamelCase						=  scheduler.create_state()

				if return_unused_kwargs:
						return scheduler, state, unused_kwargs

				return scheduler, state



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = False       ,   **A_ )						->      str:
				"""simple docstring"""
				self.save_config(save_directory=A_       ,   push_to_hub=A_       ,   **A_ )



		@property
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				return self._get_compatibles()



		@classmethod
		def __UpperCamelCase				(				cls )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  list(set([cls.__name__] + cls._compatibles ) )
				UpperCamelCase						=  importlib.import_module(__name__.split('.' )[0] )
				UpperCamelCase						=  [
				    getattr(A_       ,   A_ ) for c in compatible_classes_str if hasattr(A_       ,   A_ )
				]
				return compatible_classes






def    A (      lowercase					, lowercase				)     ->						jnp.ndarray:
		'''simple docstring'''

		assert len(lowercase				) >= x.ndim
		return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowercase				) - x.ndim)				)					, lowercase				)


def    A (      lowercase					, lowercase=0.9_9_9					, lowercase=jnp.floataa				)     ->						jnp.ndarray:
		'''simple docstring'''

		def alpha_bar(lowercase				):
				return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

		UpperCamelCase						=  []
		for i in range(lowercase				):
				UpperCamelCase						=  i / num_diffusion_timesteps
				UpperCamelCase						=  (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar(lowercase				) / alpha_bar(lowercase				)					, lowercase				)				)
		return jnp.array(lowercase					, dtype=lowercase				)


@flax.struct.dataclass
class        lowercase      :
		__lowercase : jnp.ndarray
		__lowercase : jnp.ndarray
		__lowercase : jnp.ndarray



		@classmethod
		def __UpperCamelCase				(				cls       ,   A_ )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  scheduler.config

				if config.trained_betas is not None:
						UpperCamelCase						=  jnp.asarray(config.trained_betas       ,   dtype=scheduler.dtype )
				elif config.beta_schedule == "linear":
						UpperCamelCase						=  jnp.linspace(config.beta_start       ,   config.beta_end       ,   config.num_train_timesteps       ,   dtype=scheduler.dtype )
				elif config.beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						UpperCamelCase						=  (
						    jnp.linspace(
						        config.beta_start**0.5       ,   config.beta_end**0.5       ,   config.num_train_timesteps       ,   dtype=scheduler.dtype )
						    ** 2
						)
				elif config.beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						UpperCamelCase						=  betas_for_alpha_bar(config.num_train_timesteps       ,   dtype=scheduler.dtype )
				else:
						raise NotImplementedError(
						    F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''' )

				UpperCamelCase						=  1.0 - betas

				UpperCamelCase						=  jnp.cumprod(A_       ,   axis=0 )

				return cls(
				    alphas=A_       ,   betas=A_       ,   alphas_cumprod=A_       ,   )






def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  state.alphas_cumprod

		UpperCamelCase						=  alphas_cumprod[timesteps] ** 0.5
		UpperCamelCase						=  sqrt_alpha_prod.flatten()
		UpperCamelCase						=  broadcast_to_shape_from_left(lowercase					, original_samples.shape				)

		UpperCamelCase						=  (1 - alphas_cumprod[timesteps]) ** 0.5
		UpperCamelCase						=  sqrt_one_minus_alpha_prod.flatten()
		UpperCamelCase						=  broadcast_to_shape_from_left(lowercase					, original_samples.shape				)

		return sqrt_alpha_prod, sqrt_one_minus_alpha_prod


def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						Dict:
		'''simple docstring'''

		UpperCamelCase			,						UpperCamelCase						=  get_sqrt_alpha_prod(lowercase					, lowercase					, lowercase					, lowercase				)
		UpperCamelCase						=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
		return noisy_samples


def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						int:
		'''simple docstring'''

		UpperCamelCase			,						UpperCamelCase						=  get_sqrt_alpha_prod(lowercase					, lowercase					, lowercase					, lowercase				)
		UpperCamelCase						=  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
		return velocity
