"""simple docstring"""







import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__SCREAMING_SNAKE_CASE             =data_utils.TransfoXLTokenizer
__SCREAMING_SNAKE_CASE             =data_utils.TransfoXLCorpus
__SCREAMING_SNAKE_CASE             =data_utils
__SCREAMING_SNAKE_CASE             =data_utils



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Optional[int]	,     __SCREAMING_SNAKE_CASE				:	Optional[Any]	,     __SCREAMING_SNAKE_CASE				:	Optional[int]	,     __SCREAMING_SNAKE_CASE				:	str ):
     if transfo_xl_dataset_file:
          # Convert a pre-processed corpus (see original TensorFlow repo)
          with open(A__	,     'rb' ) as fp:
               lowercase_      :							List[str]						=      pickle.load(A__	,     encoding='latin1' )
          # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
          lowercase_      :							Dict						=      pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
          print(F'''Save vocabulary to {pytorch_vocab_dump_path}''' )
          lowercase_      :							Tuple						=      corpus.vocab.__dict__
          torch.save(A__	,     A__ )

          lowercase_      :							Union[str, Any]						=      corpus.__dict__
          corpus_dict_no_vocab.pop('vocab'	,     A__ )
          lowercase_      :							List[Any]						=      pytorch_dump_folder_path + "/" + CORPUS_NAME
          print(F'''Save dataset to {pytorch_dataset_dump_path}''' )
          torch.save(A__	,     A__ )

     if tf_checkpoint_path:
          # Convert a pre-trained TensorFlow model
          lowercase_      :							Optional[Any]						=      os.path.abspath(A__ )
          lowercase_      :							List[Any]						=      os.path.abspath(A__ )

          print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.''' )
          # Initialise PyTorch model
          if transfo_xl_config_file == "":
               lowercase_      :							Optional[Any]						=      TransfoXLConfig()
          else:
               lowercase_      :							Dict						=      TransfoXLConfig.from_json_file(A__ )
          print(F'''Building PyTorch model from configuration: {config}''' )
          lowercase_      :							Union[str, Any]						=      TransfoXLLMHeadModel(A__ )

          lowercase_      :							Union[str, Any]						=      load_tf_weights_in_transfo_xl(A__	,     A__	,     A__ )
          # Save pytorch-model
          lowercase_      :							Any						=      os.path.join(A__	,     A__ )
          lowercase_      :							Union[str, Any]						=      os.path.join(A__	,     A__ )
          print(F'''Save PyTorch model to {os.path.abspath(A__ )}''' )
          torch.save(model.state_dict()	,     A__ )
          print(F'''Save configuration file to {os.path.abspath(A__ )}''' )
          with open(A__	,     'w'	,     encoding='utf-8' ) as f:
               f.write(config.to_json_string() )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE             =argparse.ArgumentParser()
       parser.add_argument(
           "--pytorch_dump_folder_path",
           default=None,
           type=str,
           required=True,
           help="Path to the folder to store the PyTorch model or dataset/vocab.",
       )
       parser.add_argument(
           "--tf_checkpoint_path",
           default="",
           type=str,
           help="An optional path to a TensorFlow checkpoint path to be converted.",
       )
       parser.add_argument(
           "--transfo_xl_config_file",
           default="",
           type=str,
           help=(
               "An optional config json file corresponding to the pre-trained BERT model. \n"
               "This specifies the model architecture."
           ),
       )
       parser.add_argument(
           "--transfo_xl_dataset_file",
           default="",
           type=str,
           help="An optional dataset file to be converted in a vocabulary.",
       )
       __SCREAMING_SNAKE_CASE             =parser.parse_args()
       convert_transfo_xl_checkpoint_to_pytorch(
           args.tf_checkpoint_path,
           args.transfo_xl_config_file,
           args.pytorch_dump_folder_path,
           args.transfo_xl_dataset_file,
       )









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


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

lowerCamelCase_							      = torch.device('''cpu''')

def 							snake_case (  ):
			UpperCAmelCase_	:					str     	=   "http://images.cocodataset.org/val2017/000000039769.jpg"
			UpperCAmelCase_	:					str     	=   Image.open(requests.get(A__      ,stream=A__		).raw		)
			return im

def 							snake_case (  A__		):
			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 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Tuple     	=   dct.pop(A__		)
			UpperCAmelCase_	:					Optional[Any]     	=   val

def 							snake_case (  A__		):
			UpperCAmelCase_	:					List[str]     	=   []
			for k in state_dict.keys():
						UpperCAmelCase_	:					Union[str, Any]     	=   k
						if ".pwconv" in k:
									UpperCAmelCase_	:					Dict     	=   k_new.replace(".pwconv"      ,".point_wise_conv"		)
						if ".dwconv" in k:
									UpperCAmelCase_	:					Any     	=   k_new.replace(".dwconv"      ,".depth_wise_conv"		)
						if ".Proj." in k:
									UpperCAmelCase_	:					Dict     	=   k_new.replace(".Proj."      ,".proj."		)
						if "patch_embed" in k_new:
									UpperCAmelCase_	:					Tuple     	=   k_new.replace("patch_embed"      ,"swiftformer.patch_embed.patch_embedding"		)
						if "network" in k_new:
									UpperCAmelCase_	:					List[Any]     	=   k_new.split("."		)
									if ls[2].isdigit():
												UpperCAmelCase_	:					Tuple     	=   "swiftformer.encoder.network." + ls[1] + ".blocks." + ls[2] + "." + ".".join(ls[3:]		)
									else:
												UpperCAmelCase_	:					Optional[Any]     	=   k_new.replace("network"      ,"swiftformer.encoder.network"		)
						rename_keys.append((k, k_new)		)
			return rename_keys

@torch.no_grad()
def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Optional[int]     	=   SwiftFormerConfig()

			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			UpperCAmelCase_	:					Optional[Any]     	=   10_00
			UpperCAmelCase_	:					str     	=   "huggingface/label-files"
			UpperCAmelCase_	:					str     	=   "imagenet-1k-id2label.json"
			UpperCAmelCase_	:					List[str]     	=   json.load(open(hf_hub_download(A__      ,A__      ,repo_type="dataset"		)      ,"r"		)		)
			UpperCAmelCase_	:					Tuple     	=   {int(A__		): v for k, v in idalabel.items()}
			UpperCAmelCase_	:					List[Any]     	=   idalabel
			UpperCAmelCase_	:					Optional[Any]     	=   {v: k for k, v in idalabel.items()}

			# size of the architecture
			if swiftformer_name == "swiftformer_xs":
						UpperCAmelCase_	:					Tuple     	=   [3, 3, 6, 4]
						UpperCAmelCase_	:					str     	=   [48, 56, 1_12, 2_20]

			elif swiftformer_name == "swiftformer_s":
						UpperCAmelCase_	:					Optional[Any]     	=   [3, 3, 9, 6]
						UpperCAmelCase_	:					Optional[Any]     	=   [48, 64, 1_68, 2_24]

			elif swiftformer_name == "swiftformer_l1":
						UpperCAmelCase_	:					int     	=   [4, 3, 10, 5]
						UpperCAmelCase_	:					Union[str, Any]     	=   [48, 96, 1_92, 3_84]

			elif swiftformer_name == "swiftformer_l3":
						UpperCAmelCase_	:					Dict     	=   [4, 4, 12, 6]
						UpperCAmelCase_	:					Optional[int]     	=   [64, 1_28, 3_20, 5_12]

			# load state_dict of original model, remove and rename some keys
			if original_ckpt:
						if original_ckpt.startswith("https"		):
									UpperCAmelCase_	:					List[Any]     	=   torch.hub.load_state_dict_from_url(A__      ,map_location="cpu"      ,check_hash=A__		)
						else:
									UpperCAmelCase_	:					Any     	=   torch.load(A__      ,map_location="cpu"		)
			UpperCAmelCase_	:					List[str]     	=   checkpoint

			UpperCAmelCase_	:					Dict     	=   create_rename_keys(A__		)
			for rename_key_src, rename_key_dest in rename_keys:
						rename_key(A__      ,A__      ,A__		)

			# load HuggingFace model
			UpperCAmelCase_	:					Optional[int]     	=   SwiftFormerForImageClassification(A__		).eval()
			hf_model.load_state_dict(A__		)

			# prepare test inputs
			UpperCAmelCase_	:					Tuple     	=   prepare_img()
			UpperCAmelCase_	:					int     	=   ViTImageProcessor.from_pretrained("preprocessor_config"		)
			UpperCAmelCase_	:					int     	=   processor(images=A__      ,return_tensors="pt"		)

			# compare outputs from both models
			UpperCAmelCase_	:					List[Any]     	=   get_expected_output(A__		)
			UpperCAmelCase_	:					int     	=   hf_model(inputs["pixel_values"]		).logits

			assert hf_logits.shape == torch.Size([1, 10_00]		)
			assert torch.allclose(hf_logits[0, 0:5]      ,A__      ,atol=1e-3		)

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


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

			lowerCamelCase_							      = parser.parse_args()
			convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)



