






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					:		str	  =						logging.get_logger(__name__)

__UpperCamelCase					:		str	  =						torch.device("""cpu""")
def      a_ (      )			->			Union[str, Any]:







				"""simple docstring"""
				snake_case__									=     'http://images.cocodataset.org/val2017/000000039769.jpg'
				snake_case__									=     Image.open(requests.get(_A      ,   stream=_A  ).raw  )
				return im
def      a_ (      _A  )			->			List[str]:







				"""simple docstring"""
				if swiftformer_name == "swiftformer_xs":
								return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]  )

				elif swiftformer_name == "swiftformer_s":
								return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]  )

				elif swiftformer_name == "swiftformer_l1":
								return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]  )

				elif swiftformer_name == "swiftformer_l3":
								return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]  )
def      a_ (      _A      ,   _A      ,   _A  )			->			Union[str, Any]:







				"""simple docstring"""
				snake_case__									=     dct.pop(_A  )
				snake_case__									=     val
def      a_ (      _A  )			->			List[Any]:







				"""simple docstring"""
				snake_case__									=     []
				for k in state_dict.keys():
								snake_case__									=     k
								if ".pwconv" in k:
												snake_case__									=     k_new.replace('.pwconv'      ,   '.point_wise_conv'  )
								if ".dwconv" in k:
												snake_case__									=     k_new.replace('.dwconv'      ,   '.depth_wise_conv'  )
								if ".Proj." in k:
												snake_case__									=     k_new.replace('.Proj.'      ,   '.proj.'  )
								if "patch_embed" in k_new:
												snake_case__									=     k_new.replace('patch_embed'      ,   'swiftformer.patch_embed.patch_embedding'  )
								if "network" in k_new:
												snake_case__									=     k_new.split('.'  )
												if ls[2].isdigit():
																snake_case__									=     'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]  )
												else:
																snake_case__									=     k_new.replace('network'      ,   'swiftformer.encoder.network'  )
								rename_keys.append((k, k_new)  )
				return rename_keys
@torch.no_grad()
def      a_ (      _A      ,   _A      ,   _A  )			->			List[str]:







				"""simple docstring"""
				snake_case__									=     SwiftFormerConfig()

				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				snake_case__									=     1000
				snake_case__									=     'huggingface/label-files'
				snake_case__									=     'imagenet-1k-id2label.json'
				snake_case__									=     json.load(open(hf_hub_download(_A      ,   _A      ,   repo_type='dataset'  )      ,   'r'  )  )
				snake_case__									=     {int(_A  ): v for k, v in idalabel.items()}
				snake_case__									=     idalabel
				snake_case__									=     {v: k for k, v in idalabel.items()}

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

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

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

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

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

				snake_case__									=     create_rename_keys(_A  )
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(_A      ,   _A      ,   _A  )

				# load HuggingFace model
				snake_case__									=     SwiftFormerForImageClassification(_A  ).eval()
				hf_model.load_state_dict(_A  )

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

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

				assert hf_logits.shape == torch.Size([1, 1000]  )
				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__":
							__UpperCamelCase					:		str	  =						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					:		Optional[Any]	  =						parser.parse_args()
							convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)












import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


__UpperCamelCase					:		Any	  =						datasets.utils.logging.get_logger(__name__)

__UpperCamelCase					:		Union[str, Any]	  =						["""names""", """prefix"""]
__UpperCamelCase					:		List[str]	  =						["""warn_bad_lines""", """error_bad_lines""", """mangle_dupe_cols"""]
__UpperCamelCase					:		Dict	  =						["""encoding_errors""", """on_bad_lines"""]
__UpperCamelCase					:		Any	  =						["""date_format"""]


@dataclass
class   __SCREAMING_SNAKE_CASE(	datasets.BuilderConfig	):
							_UpperCAmelCase     =       ","
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       "infer"
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       "."
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       '"'
							_UpperCAmelCase     =       0
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       0
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       1_0_0_0_0
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       "strict"
							_UpperCAmelCase     =       "error"
							_UpperCAmelCase     =       None
							def lowerCAmelCase_ (     self:      Tuple						)		->		int:
											if self.delimiter is not None:
															snake_case__									=     self.delimiter
											if self.column_names is not None:
															snake_case__									=     self.column_names


							@property
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		List[str]:
											snake_case__									=     {
											    'sep': self.sep,
											    'header': self.header,
											    'names': self.names,
											    'index_col': self.index_col,
											    'usecols': self.usecols,
											    'prefix': self.prefix,
											    'mangle_dupe_cols': self.mangle_dupe_cols,
											    'engine': self.engine,
											    'converters': self.converters,
											    'true_values': self.true_values,
											    'false_values': self.false_values,
											    'skipinitialspace': self.skipinitialspace,
											    'skiprows': self.skiprows,
											    'nrows': self.nrows,
											    'na_values': self.na_values,
											    'keep_default_na': self.keep_default_na,
											    'na_filter': self.na_filter,
											    'verbose': self.verbose,
											    'skip_blank_lines': self.skip_blank_lines,
											    'thousands': self.thousands,
											    'decimal': self.decimal,
											    'lineterminator': self.lineterminator,
											    'quotechar': self.quotechar,
											    'quoting': self.quoting,
											    'escapechar': self.escapechar,
											    'comment': self.comment,
											    'encoding': self.encoding,
											    'dialect': self.dialect,
											    'error_bad_lines': self.error_bad_lines,
											    'warn_bad_lines': self.warn_bad_lines,
											    'skipfooter': self.skipfooter,
											    'doublequote': self.doublequote,
											    'memory_map': self.memory_map,
											    'float_precision': self.float_precision,
											    'chunksize': self.chunksize,
											    'encoding_errors': self.encoding_errors,
											    'on_bad_lines': self.on_bad_lines,
											    'date_format': self.date_format,
											}

											# some kwargs must not be passed if they don't have a default value
											# some others are deprecated and we can also not pass them if they are the default value
											for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
															if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()       ,	UpperCamelCase						):
																			del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
											if not (datasets.config.PANDAS_VERSION.major >= 2):
															for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
																			del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
											if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
															for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
																			del pd_read_csv_kwargs[pd_read_csv_parameter]

											return pd_read_csv_kwargs


class   __SCREAMING_SNAKE_CASE(	datasets.ArrowBasedBuilder	):
							_UpperCAmelCase     =       CsvConfig
							def lowerCAmelCase_ (     self:      str						)		->		int:
											return datasets.DatasetInfo(features=self.config.features						)
							def lowerCAmelCase_ (     self:      Tuple       ,	UpperCamelCase:      Dict						)		->		int:
											if not self.config.data_files:
															raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''						)
											snake_case__									=     dl_manager.download_and_extract(self.config.data_files						)
											if isinstance(UpperCamelCase       ,	(str, list, tuple)						):
															snake_case__									=     data_files
															if isinstance(UpperCamelCase       ,	UpperCamelCase						):
																			snake_case__									=     [files]
															snake_case__									=     [dl_manager.iter_files(UpperCamelCase						) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,	gen_kwargs={'files': files}						)]
											snake_case__									=     []
											for split_name, files in data_files.items():
															if isinstance(UpperCamelCase       ,	UpperCamelCase						):
																			snake_case__									=     [files]
															snake_case__									=     [dl_manager.iter_files(UpperCamelCase						) for file in files]
															splits.append(datasets.SplitGenerator(name=UpperCamelCase       ,	gen_kwargs={'files': files}						)						)
											return splits
							def lowerCAmelCase_ (     self:      int       ,	UpperCamelCase:      pa.Table						)		->		pa.Table:
											if self.config.features is not None:
															snake_case__									=     self.config.features.arrow_schema
															if all(not require_storage_cast(UpperCamelCase						) for feature in self.config.features.values()						):
																			# cheaper cast
																			snake_case__									=     pa.Table.from_arrays([pa_table[field.name] for field in schema]       ,	schema=UpperCamelCase						)
															else:
																			# more expensive cast; allows str <-> int/float or str to Audio for example
																			snake_case__									=     table_cast(UpperCamelCase       ,	UpperCamelCase						)
											return pa_table


							def lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      Tuple						)		->		Tuple:
											snake_case__									=     self.config.features.arrow_schema if self.config.features else None
											# dtype allows reading an int column as str
											snake_case__									=     (
											    {
											        name: dtype.to_pandas_dtype() if not require_storage_cast(UpperCamelCase						) else object
											        for name, dtype, feature in zip(schema.names       ,	schema.types       ,	self.config.features.values()						)
											    }
											    if schema is not None
											    else None
											)
											for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase						)						):
															snake_case__									=     pd.read_csv(UpperCamelCase       ,	iterator=UpperCamelCase       ,	dtype=UpperCamelCase       ,	**self.config.pd_read_csv_kwargs						)
															try:
																			for batch_idx, df in enumerate(UpperCamelCase						):
																							snake_case__									=     pa.Table.from_pandas(UpperCamelCase						)
																							# Uncomment for debugging (will print the Arrow table size and elements)
																							# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																							# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																							yield (file_idx, batch_idx), self._cast_table(UpperCamelCase						)
															except ValueError as e:
																			logger.error(F'''Failed to read file \'{file}\' with error {type(UpperCamelCase						)}: {e}'''						)
																			raise




