


'''simple docstring'''




import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


_a   :   str												=   logging.getLogger(__name__)




class 			_lowercase (	__lowercase							):
   def __init__(						self       :      int       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=-1      ) ->							List[Any]:
        # in NER datasets, the last column is usually reserved for NER label
        __snake_case           =						label_idx
   def a  (						self       :      Any       ,   SCREAMING_SNAKE_CASE_       :      Dict       ,   SCREAMING_SNAKE_CASE_       :      Union[Split, str]      ) ->							List[InputExample]:
        if isinstance(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      ):
             __snake_case           =						mode.value
        __snake_case           =						os.path.join(SCREAMING_SNAKE_CASE_       ,   f'{mode}.txt'      )
        __snake_case           =						1
        __snake_case           =						[]
        with open(SCREAMING_SNAKE_CASE_       ,   encoding='utf-8'      ) as f:
             __snake_case           =						[]
             __snake_case           =						[]
             for line in f:
                  if line.startswith('-DOCSTART-'      ) or line == "" or line == "\n":
                       if words:
                            examples.append(InputExample(guid=f'{mode}-{guid_index}'       ,   words=SCREAMING_SNAKE_CASE_       ,   labels=SCREAMING_SNAKE_CASE_      )      )
                            guid_index += 1
                            __snake_case           =						[]
                            __snake_case           =						[]
                  else:
                       __snake_case           =						line.split(' '      )
                       words.append(splits[0]      )
                       if len(SCREAMING_SNAKE_CASE_      ) > 1:
                            labels.append(splits[self.label_idx].replace('\n'       ,   ''      )      )
                       else:
                            # Examples could have no label for mode = "test"
                            labels.append('O'      )
             if words:
                  examples.append(InputExample(guid=f'{mode}-{guid_index}'       ,   words=SCREAMING_SNAKE_CASE_       ,   labels=SCREAMING_SNAKE_CASE_      )      )
        return examples
   def a  (						self       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      TextIO       ,   SCREAMING_SNAKE_CASE_       :      TextIO       ,   SCREAMING_SNAKE_CASE_       :      List      ) ->							List[Any]:
        __snake_case           =						0
        for line in test_input_reader:
             if line.startswith('-DOCSTART-'      ) or line == "" or line == "\n":
                  writer.write(SCREAMING_SNAKE_CASE_      )
                  if not preds_list[example_id]:
                       example_id += 1
             elif preds_list[example_id]:
                  __snake_case           =						line.split()[0] + ' ' + preds_list[example_id].pop(0      ) + '\n'
                  writer.write(SCREAMING_SNAKE_CASE_      )
             else:
                  logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.'       ,   line.split()[0]      )





   def a  (						self       :      int       ,   SCREAMING_SNAKE_CASE_       :      str      ) ->							List[str]:
        if path:
             with open(SCREAMING_SNAKE_CASE_       ,   'r'      ) as f:
                  __snake_case           =						f.read().splitlines()
             if "O" not in labels:
                  __snake_case           =						['O'] + labels
             return labels
        else:
             return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]




class 			_lowercase (	__lowercase							):
   def __init__(						self       :      int      ) ->							List[str]:
        # in CONLL2003 dataset chunk column is second-to-last
        super().__init__(label_idx=-2      )





   def a  (						self       :      List[Any]       ,   SCREAMING_SNAKE_CASE_       :      str      ) ->							List[str]:
        if path:
             with open(SCREAMING_SNAKE_CASE_       ,   'r'      ) as f:
                  __snake_case           =						f.read().splitlines()
             if "O" not in labels:
                  __snake_case           =						['O'] + labels
             return labels
        else:
             return [
                 "O",
                 "B-ADVP",
                 "B-INTJ",
                 "B-LST",
                 "B-PRT",
                 "B-NP",
                 "B-SBAR",
                 "B-VP",
                 "B-ADJP",
                 "B-CONJP",
                 "B-PP",
                 "I-ADVP",
                 "I-INTJ",
                 "I-LST",
                 "I-PRT",
                 "I-NP",
                 "I-SBAR",
                 "I-VP",
                 "I-ADJP",
                 "I-CONJP",
                 "I-PP",
             ]




class 			_lowercase (	__lowercase							):
   def a  (						self       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      Any       ,   SCREAMING_SNAKE_CASE_       :      Union[Split, str]      ) ->							List[InputExample]:
        if isinstance(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      ):
             __snake_case           =						mode.value
        __snake_case           =						os.path.join(SCREAMING_SNAKE_CASE_       ,   f'{mode}.txt'      )
        __snake_case           =						1
        __snake_case           =						[]

        with open(SCREAMING_SNAKE_CASE_       ,   encoding='utf-8'      ) as f:
             for sentence in parse_incr(SCREAMING_SNAKE_CASE_      ):
                  __snake_case           =						[]
                  __snake_case           =						[]
                  for token in sentence:
                       words.append(token['form']      )
                       labels.append(token['upos']      )
                  assert len(SCREAMING_SNAKE_CASE_      ) == len(SCREAMING_SNAKE_CASE_      )
                  if words:
                       examples.append(InputExample(guid=f'{mode}-{guid_index}'       ,   words=SCREAMING_SNAKE_CASE_       ,   labels=SCREAMING_SNAKE_CASE_      )      )
                       guid_index += 1
        return examples
   def a  (						self       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      TextIO       ,   SCREAMING_SNAKE_CASE_       :      TextIO       ,   SCREAMING_SNAKE_CASE_       :      List      ) ->							List[Any]:
        __snake_case           =						0
        for sentence in parse_incr(SCREAMING_SNAKE_CASE_      ):
             __snake_case           =						preds_list[example_id]
             __snake_case           =						''
             for token in sentence:
                  out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0      )}) '
             out += "\n"
             writer.write(SCREAMING_SNAKE_CASE_      )
             example_id += 1





   def a  (						self       :      Union[str, Any]       ,   SCREAMING_SNAKE_CASE_       :      str      ) ->							List[str]:
        if path:
             with open(SCREAMING_SNAKE_CASE_       ,   'r'      ) as f:
                  return f.read().splitlines()
        else:
             return [
                 "ADJ",
                 "ADP",
                 "ADV",
                 "AUX",
                 "CCONJ",
                 "DET",
                 "INTJ",
                 "NOUN",
                 "NUM",
                 "PART",
                 "PRON",
                 "PROPN",
                 "PUNCT",
                 "SCONJ",
                 "SYM",
                 "VERB",
                 "X",
             ]












"""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)



