


"""simple docstring"""




from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)







@flax.struct.dataclass
class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   jnp.ndarray
					UpperCAmelCase__ :   jnp.ndarray







class 	snake_case_       (nn.Module				):
					UpperCAmelCase__ :   int
					UpperCAmelCase__ :   Tuple[int]   				=      (1_6, 3_2, 9_6, 2_5_6)
					UpperCAmelCase__ :   jnp.dtype   				=      jnp.floataa
					def 			lowerCamelCase__(				self       :List[str]				) ->   int:
												a__					=					nn.Conv(
												    self.block_out_channels[0]      ,kernel_size=(3, 3)      ,padding=((1, 1), (1, 1))      ,dtype=self.dtype      ,)

												a__					=					[]
												for i in range(len(self.block_out_channels				) - 1				):
																			a__					=					self.block_out_channels[i]
																			a__					=					self.block_out_channels[i + 1]
																			a__					=					nn.Conv(
																			    __snake_case      ,kernel_size=(3, 3)      ,padding=((1, 1), (1, 1))      ,dtype=self.dtype      ,)
																			blocks.append(__snake_case				)
																			a__					=					nn.Conv(
																			    __snake_case      ,kernel_size=(3, 3)      ,strides=(2, 2)      ,padding=((1, 1), (1, 1))      ,dtype=self.dtype      ,)
																			blocks.append(__snake_case				)
												a__					=					blocks

												a__					=					nn.Conv(
												    self.conditioning_embedding_channels      ,kernel_size=(3, 3)      ,padding=((1, 1), (1, 1))      ,kernel_init=nn.initializers.zeros_init()      ,bias_init=nn.initializers.zeros_init()      ,dtype=self.dtype      ,)



					def __call__(				self       :Tuple      ,__snake_case       :List[str]				) ->   Tuple:
												a__					=					self.conv_in(__snake_case				)
												a__					=					nn.silu(__snake_case				)

												for block in self.blocks:
																			a__					=					block(__snake_case				)
																			a__					=					nn.silu(__snake_case				)

												a__					=					self.conv_out(__snake_case				)

												return embedding







@flax_register_to_config
class 	snake_case_       (nn.Module     ,						lowerCamelCase_     ,						lowerCamelCase_				):
					UpperCAmelCase__ :   int   				=      3_2
					UpperCAmelCase__ :   int   				=      4
					UpperCAmelCase__ :   Tuple[str]   				=      (
					    "CrossAttnDownBlock2D",
					    "CrossAttnDownBlock2D",
					    "CrossAttnDownBlock2D",
					    "DownBlock2D",
					)
					UpperCAmelCase__ :   Union[bool, Tuple[bool]]   				=      False
					UpperCAmelCase__ :   Tuple[int]   				=      (3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
					UpperCAmelCase__ :   int   				=      2
					UpperCAmelCase__ :   Union[int, Tuple[int]]   				=      8
					UpperCAmelCase__ :   Optional[Union[int, Tuple[int]]]   				=      None
					UpperCAmelCase__ :   int   				=      1_2_8_0
					UpperCAmelCase__ :   float   				=      0.0
					UpperCAmelCase__ :   bool   				=      False
					UpperCAmelCase__ :   jnp.dtype   				=      jnp.floataa
					UpperCAmelCase__ :   bool   				=      True
					UpperCAmelCase__ :   int   				=      0
					UpperCAmelCase__ :   str   				=      "rgb"
					UpperCAmelCase__ :   Tuple[int]   				=      (1_6, 3_2, 9_6, 2_5_6)
					def 			lowerCamelCase__(				self       :Any      ,__snake_case       :jax.random.KeyArray				) ->   FrozenDict:
												# init input tensors
												a__					=					(1, self.in_channels, self.sample_size, self.sample_size)
												a__					=					jnp.zeros(__snake_case      ,dtype=jnp.floataa				)
												a__					=					jnp.ones((1,)      ,dtype=jnp.intaa				)
												a__					=					jnp.zeros((1, 1, self.cross_attention_dim)      ,dtype=jnp.floataa				)
												a__					=					(1, 3, self.sample_size * 8, self.sample_size * 8)
												a__					=					jnp.zeros(__snake_case      ,dtype=jnp.floataa				)

												a__				, a__					=					jax.random.split(__snake_case				)
												a__					=					{'params': params_rng, 'dropout': dropout_rng}

												return self.init(__snake_case      ,__snake_case      ,__snake_case      ,__snake_case      ,__snake_case				)["params"]
					def 			lowerCamelCase__(				self       :Dict				) ->   Union[str, Any]:
												a__					=					self.block_out_channels
												a__					=					block_out_channels[0] * 4

												# If `num_attention_heads` is not defined (which is the case for most models)
												# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
												# The reason for this behavior is to correct for incorrectly named variables that were introduced
												# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
												# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
												# which is why we correct for the naming here.
												a__					=					self.num_attention_heads or self.attention_head_dim

												# input
												a__					=					nn.Conv(
												    block_out_channels[0]      ,kernel_size=(3, 3)      ,strides=(1, 1)      ,padding=((1, 1), (1, 1))      ,dtype=self.dtype      ,)

												# time
												a__					=					FlaxTimesteps(
												    block_out_channels[0]      ,flip_sin_to_cos=self.flip_sin_to_cos      ,freq_shift=self.config.freq_shift				)
												a__					=					FlaxTimestepEmbedding(__snake_case      ,dtype=self.dtype				)

												a__					=					FlaxControlNetConditioningEmbedding(
												    conditioning_embedding_channels=block_out_channels[0]      ,block_out_channels=self.conditioning_embedding_out_channels      ,)

												a__					=					self.only_cross_attention
												if isinstance(__snake_case      ,__snake_case				):
																			a__					=					(only_cross_attention,) * len(self.down_block_types				)

												if isinstance(__snake_case      ,__snake_case				):
																			a__					=					(num_attention_heads,) * len(self.down_block_types				)

												# down
												a__					=					[]
												a__					=					[]

												a__					=					block_out_channels[0]

												a__					=					nn.Conv(
												    __snake_case      ,kernel_size=(1, 1)      ,padding='VALID'      ,kernel_init=nn.initializers.zeros_init()      ,bias_init=nn.initializers.zeros_init()      ,dtype=self.dtype      ,)
												controlnet_down_blocks.append(__snake_case				)

												for i, down_block_type in enumerate(self.down_block_types				):
																			a__					=					output_channel
																			a__					=					block_out_channels[i]
																			a__					=					i == len(__snake_case				) - 1

																			if down_block_type == "CrossAttnDownBlock2D":
																										a__					=					FlaxCrossAttnDownBlockaD(
																										    in_channels=__snake_case      ,out_channels=__snake_case      ,dropout=self.dropout      ,num_layers=self.layers_per_block      ,num_attention_heads=num_attention_heads[i]      ,add_downsample=not is_final_block      ,use_linear_projection=self.use_linear_projection      ,only_cross_attention=only_cross_attention[i]      ,dtype=self.dtype      ,)
																			else:
																										a__					=					FlaxDownBlockaD(
																										    in_channels=__snake_case      ,out_channels=__snake_case      ,dropout=self.dropout      ,num_layers=self.layers_per_block      ,add_downsample=not is_final_block      ,dtype=self.dtype      ,)

																			down_blocks.append(__snake_case				)

																			for _ in range(self.layers_per_block				):
																										a__					=					nn.Conv(
																										    __snake_case      ,kernel_size=(1, 1)      ,padding='VALID'      ,kernel_init=nn.initializers.zeros_init()      ,bias_init=nn.initializers.zeros_init()      ,dtype=self.dtype      ,)
																										controlnet_down_blocks.append(__snake_case				)

																			if not is_final_block:
																										a__					=					nn.Conv(
																										    __snake_case      ,kernel_size=(1, 1)      ,padding='VALID'      ,kernel_init=nn.initializers.zeros_init()      ,bias_init=nn.initializers.zeros_init()      ,dtype=self.dtype      ,)
																										controlnet_down_blocks.append(__snake_case				)

												a__					=					down_blocks
												a__					=					controlnet_down_blocks

												# mid
												a__					=					block_out_channels[-1]
												a__					=					FlaxUNetMidBlockaDCrossAttn(
												    in_channels=__snake_case      ,dropout=self.dropout      ,num_attention_heads=num_attention_heads[-1]      ,use_linear_projection=self.use_linear_projection      ,dtype=self.dtype      ,)

												a__					=					nn.Conv(
												    __snake_case      ,kernel_size=(1, 1)      ,padding='VALID'      ,kernel_init=nn.initializers.zeros_init()      ,bias_init=nn.initializers.zeros_init()      ,dtype=self.dtype      ,)



					def __call__(				self       :Optional[int]      ,__snake_case       :Union[str, Any]      ,__snake_case       :List[str]      ,__snake_case       :str      ,__snake_case       :Tuple      ,__snake_case       :float = 1.0      ,__snake_case       :bool = True      ,__snake_case       :bool = False      ,) ->   Union[FlaxControlNetOutput, Tuple]:
												a__					=					self.controlnet_conditioning_channel_order
												if channel_order == "bgr":
																			a__					=					jnp.flip(__snake_case      ,axis=1				)

												# 1. time
												if not isinstance(__snake_case      ,jnp.ndarray				):
																			a__					=					jnp.array([timesteps]      ,dtype=jnp.intaa				)
												elif isinstance(__snake_case      ,jnp.ndarray				) and len(timesteps.shape				) == 0:
																			a__					=					timesteps.astype(dtype=jnp.floataa				)
																			a__					=					jnp.expand_dims(__snake_case      ,0				)

												a__					=					self.time_proj(__snake_case				)
												a__					=					self.time_embedding(__snake_case				)

												# 2. pre-process
												a__					=					jnp.transpose(__snake_case      ,(0, 2, 3, 1)				)
												a__					=					self.conv_in(__snake_case				)

												a__					=					jnp.transpose(__snake_case      ,(0, 2, 3, 1)				)
												a__					=					self.controlnet_cond_embedding(__snake_case				)
												sample += controlnet_cond

												# 3. down
												a__					=					(sample,)
												for down_block in self.down_blocks:
																			if isinstance(__snake_case      ,__snake_case				):
																										a__				, a__					=					down_block(__snake_case      ,__snake_case      ,__snake_case      ,deterministic=not train				)
																			else:
																										a__				, a__					=					down_block(__snake_case      ,__snake_case      ,deterministic=not train				)
																			down_block_res_samples += res_samples

												# 4. mid
												a__					=					self.mid_block(__snake_case      ,__snake_case      ,__snake_case      ,deterministic=not train				)

												# 5. contronet blocks
												a__					=					()
												for down_block_res_sample, controlnet_block in zip(__snake_case      ,self.controlnet_down_blocks				):
																			a__					=					controlnet_block(__snake_case				)
																			controlnet_down_block_res_samples += (down_block_res_sample,)

												a__					=					controlnet_down_block_res_samples

												a__					=					self.controlnet_mid_block(__snake_case				)

												# 6. scaling
												a__					=					[sample * conditioning_scale for sample in down_block_res_samples]
												mid_block_res_sample *= conditioning_scale

												if not return_dict:
																			return (down_block_res_samples, mid_block_res_sample)

												return FlaxControlNetOutput(
												    down_block_res_samples=__snake_case      ,mid_block_res_sample=__snake_case				)








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()
snake_case							:	Union[str, Any]					      =				logging.get_logger(__name__)

snake_case							:	Tuple					      =				torch.device('''cpu''')






def 							__lowercase       (			):
							a__					=					'http://images.cocodataset.org/val2017/000000039769.jpg'
							a__					=					Image.open(requests.get(__lowerCAmelCase ,   stream=__lowerCAmelCase       ).raw       )
							return im






def 							__lowercase       (			__lowerCAmelCase							:    List[str]       ):
							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1]       )

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1]       )

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2]       )

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2]       )






def 							__lowercase       (			__lowerCAmelCase							:    List[Any] ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    str       ):
							a__					=					dct.pop(__lowerCAmelCase       )
							a__					=					val






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






@torch.no_grad()
def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    str       ):
							a__					=					SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							a__					=					1_0_0_0
							a__					=					'huggingface/label-files'
							a__					=					'imagenet-1k-id2label.json'
							a__					=					json.load(open(hf_hub_download(__lowerCAmelCase ,   __lowerCAmelCase ,   repo_type='dataset'       ) ,   'r'       )       )
							a__					=					{int(__lowerCAmelCase       ): v for k, v in idalabel.items()}
							a__					=					idalabel
							a__					=					{v: k for k, v in idalabel.items()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														a__					=					[3, 3, 6, 4]
														a__					=					[4_8, 5_6, 1_1_2, 2_2_0]

							elif swiftformer_name == "swiftformer_s":
														a__					=					[3, 3, 9, 6]
														a__					=					[4_8, 6_4, 1_6_8, 2_2_4]

							elif swiftformer_name == "swiftformer_l1":
														a__					=					[4, 3, 1_0, 5]
														a__					=					[4_8, 9_6, 1_9_2, 3_8_4]

							elif swiftformer_name == "swiftformer_l3":
														a__					=					[4, 4, 1_2, 6]
														a__					=					[6_4, 1_2_8, 3_2_0, 5_1_2]

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

							a__					=					create_rename_keys(__lowerCAmelCase       )
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )

							# load HuggingFace model
							a__					=					SwiftFormerForImageClassification(__lowerCAmelCase       ).eval()
							hf_model.load_state_dict(__lowerCAmelCase       )

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

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

							assert hf_logits.shape == torch.Size([1, 1_0_0_0]       )
							assert torch.allclose(hf_logits[0, 0:5] ,   __lowerCAmelCase ,   atol=1E-3       )

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


if __name__ == "__main__":
						snake_case							:	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.''')

						snake_case							:	Optional[int]					      =				parser.parse_args()
						convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)




