





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()
__A				:    Tuple       =   logging.get_logger(__name__)

__A				:    str       =   torch.device("cpu")





def 						__SCREAMING_SNAKE_CASE  (				)  ->				str:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				int:







			'''simple docstring'''

			if swiftformer_name == "swiftformer_xs":
						return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01] )

			elif swiftformer_name == "swiftformer_s":
						return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01] )

			elif swiftformer_name == "swiftformer_l1":
						return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02] )

			elif swiftformer_name == "swiftformer_l3":
						return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02] )





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase   		=				dct.pop(UpperCamelCase__ )
			UpperCAmelCase   		=				val





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				int:







			'''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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				Tuple:







			'''simple docstring'''

			UpperCAmelCase   		=				SwiftFormerConfig()

			# dataset (ImageNet-21k only or also 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(UpperCamelCase__			,			UpperCamelCase__			,			repo_type='''dataset''' )			,			'''r''' ) )
			UpperCAmelCase   		=				{int(UpperCamelCase__ ): 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(UpperCamelCase__			,			map_location='''cpu'''			,			check_hash=UpperCamelCase__ )
						else:
									UpperCAmelCase   		=				torch.load(UpperCamelCase__			,			map_location='''cpu''' )
			UpperCAmelCase   		=				checkpoint

			UpperCAmelCase   		=				create_rename_keys(UpperCamelCase__ )
			for rename_key_src, rename_key_dest in rename_keys:
						rename_key(UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )

			# load HuggingFace model
			UpperCAmelCase   		=				SwiftFormerForImageClassification(UpperCamelCase__ ).eval()
			hf_model.load_state_dict(UpperCamelCase__ )

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

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

			assert hf_logits.shape == torch.Size([1, 1000] )
			assert torch.allclose(hf_logits[0, 0:5]			,			UpperCamelCase__			,			atol=1E-3 )

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


if __name__ == "__main__":
				__A				:    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.")

				__A				:    Dict       =   parser.parse_args()
				convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)










import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
__A				:    int       =   {
    "/attention/": "/0/SelfAttention/",
    "/self_attention/": "/0/SelfAttention/",
    "/encoder_decoder_attention/": "/1/EncDecAttention/",
    "value": "v",
    "query": "q",
    "key": "k",
    "out": "o",
    "pre_self_attention_layer_norm": "0/layer_norm",
    "pre_cross_attention_layer_norm": "1/layer_norm",
    "pre_attention_layer_norm": "0/layer_norm",  # previously 1, but seems wrong
    "token_embedder": "shared",
    "encoder_norm": "final_layer_norm",
    "decoder_norm": "final_layer_norm",
    "relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
    "router/router_weights/w/": "router/classifier/",
    "roer/roer_weights/w/": "router/classifier/",
    "logits_dense": "lm_head",
}





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				List[str]:







			'''simple docstring'''

			UpperCAmelCase   		=				list(s_dict.keys() )
			for key in keys:
						UpperCAmelCase   		=				R'''.*/layers_(\d+)'''
						UpperCAmelCase   		=				key
						if re.match(UpperCamelCase__			,			UpperCamelCase__ ):
									UpperCAmelCase   		=				re.sub(R'''layers_(\d+)'''			,			R'''block/\1/layer'''			,			UpperCamelCase__ )

						UpperCAmelCase   		=				R'''(encoder|decoder)\/'''

						if re.match(UpperCamelCase__			,			UpperCamelCase__ ):
									UpperCAmelCase   		=				re.match(UpperCamelCase__			,			UpperCamelCase__ ).groups()
									if groups[0] == "encoder":
												UpperCAmelCase   		=				re.sub(R'''/mlp/'''			,			R'''/1/mlp/'''			,			UpperCamelCase__ )
												UpperCAmelCase   		=				re.sub(R'''/pre_mlp_layer_norm/'''			,			R'''/1/layer_norm/'''			,			UpperCamelCase__ )

									elif groups[0] == "decoder":
												UpperCAmelCase   		=				re.sub(R'''/mlp/'''			,			R'''/2/mlp/'''			,			UpperCamelCase__ )
												UpperCAmelCase   		=				re.sub(R'''/pre_mlp_layer_norm/'''			,			R'''/2/layer_norm/'''			,			UpperCamelCase__ )

        # 2. Convert other classic mappings
						for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
									if old_key in new_key:
												UpperCAmelCase   		=				new_key.replace(UpperCamelCase__			,			UpperCamelCase__ )

						print(F"""{key} -> {new_key}""" )
						UpperCAmelCase   		=				s_dict.pop(UpperCamelCase__ )

			if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
						UpperCAmelCase   		=				s_dict[
						    '''encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
						].T
			if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
						UpperCAmelCase   		=				s_dict[
						    '''decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
						].T

			# 3. Take extra care of the EXPERTS layer
			for key in list(s_dict.keys() ):
						if "expert" in key:
									UpperCAmelCase   		=				s_dict[key].shape[0]
									UpperCAmelCase   		=				s_dict[key]
									for idx in range(UpperCamelCase__ ):
												UpperCAmelCase   		=				expert_weihts[idx]
												print(F"""{key} -> {key.replace("expert/"			,			"nested fstring" )}""" )

									s_dict.pop(UpperCamelCase__ )

			return s_dict


__A				:    Optional[int]       =   {
    "NUM_ENCODER_LAYERS": "num_layers",
    "NUM_DECODER_LAYERS": "num_decoder_layers",
    "NUM_HEADS": "num_heads",
    "HEAD_DIM": "d_kv",
    "EMBED_DIM": "d_model",
    "MLP_DIM": "d_ff",
    "NUM_SELECTED_EXPERTS": "num_selected_experts",
    "NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
    "NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
    "dense.MlpBlock.activations": "feed_forward_proj",
}





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				List[str]:







			'''simple docstring'''

			import regex as re

			with open(UpperCamelCase__			,			'''r''' ) as f:
						UpperCAmelCase   		=				f.read()

			UpperCAmelCase   		=				re.findall(R'''(.*) = ([0-9.]*)'''			,			UpperCamelCase__ )
			UpperCAmelCase   		=				{}
			for param, value in regex_match:
						if param in GIN_TO_CONFIG_MAPPING and value != "":
									UpperCAmelCase   		=				float(UpperCamelCase__ ) if '''.''' in value else int(UpperCamelCase__ )

			UpperCAmelCase   		=				re.findall(R'''(.*activations) = \(\'(.*)\',\)'''			,			UpperCamelCase__ )[0]
			UpperCAmelCase   		=				str(activation[1] )

			UpperCAmelCase   		=				num_experts
			UpperCAmelCase   		=				SwitchTransformersConfig(**UpperCamelCase__ )
			return config





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__=None			,			UpperCamelCase__="./"			,			UpperCamelCase__=8 )  ->				List[Any]:







			'''simple docstring'''

			print(F"""Loading flax weights from : {flax_checkpoint_path}""" )
			UpperCAmelCase   		=				checkpoints.load_tax_checkpoint(UpperCamelCase__ )

			if gin_file is not None:
						UpperCAmelCase   		=				convert_gin_to_config(UpperCamelCase__			,			UpperCamelCase__ )
			else:
						UpperCAmelCase   		=				SwitchTransformersConfig.from_pretrained(UpperCamelCase__ )

			UpperCAmelCase   		=				SwitchTransformersForConditionalGeneration(UpperCamelCase__ )

			UpperCAmelCase   		=				flax_params['''target''']
			UpperCAmelCase   		=				flatten_dict(UpperCamelCase__			,			sep='''/''' )
			UpperCAmelCase   		=				rename_keys(UpperCamelCase__ )
			UpperCAmelCase   		=				unflatten_dict(UpperCamelCase__			,			sep='''/''' )

			# Load the flax params in the PT model
			load_flax_weights_in_pytorch_model(UpperCamelCase__			,			UpperCamelCase__ )

			print(F"""Save PyTorch model to {pytorch_dump_path}""" )
			pt_model.save_pretrained(UpperCamelCase__ )


if __name__ == "__main__":
				__A				:    Tuple       =   argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--switch_t5x_checkpoint_path",
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        "The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
				        " model architecture. If not provided, a `gin_file` has to be provided."
				    ),
				)
				parser.add_argument(
				    "--gin_file",
				    default=None,
				    type=str,
				    required=False,
				    help="Path to the gin config file. If not provided, a `config_file` has to be passed   ",
				)
				parser.add_argument(
				    "--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
				)
				parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
				__A				:    Tuple       =   parser.parse_args()
				convert_flax_checkpoint_to_pytorch(
				    args.switch_tax_checkpoint_path,
				    args.config_name,
				    args.gin_file,
				    args.pytorch_dump_folder_path,
				    args.num_experts,
				)



