




import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


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





def 				A__ (snake_case   :		List[str]		,	snake_case   :		Optional[int]		,	snake_case   :		List[Any]		,	snake_case   :		int				)     ->						str:
					__UpperCamelCase					:	Optional[Any]             =      original_name.split("""."""				)[0]
					__UpperCamelCase					:	Any             =      key.split("""."""				)
					__UpperCamelCase					:	int             =      int(key_list[key_list.index(snake_case				) - 2]				)
					__UpperCamelCase					:	int             =      int(key_list[key_list.index(snake_case				) - 1]				)
					__UpperCamelCase					:	Union[str, Any]             =      orig_block_num - offset

					__UpperCamelCase					:	Union[str, Any]             =      key.replace(F'''{orig_block_num}.{layer_num}.{original_name}'''		,	F'''block.{new_block_num}.{layer_num}.{new_name}'''				)
					return key





def 				A__ (snake_case   :		Any				)     ->						Tuple:
					__UpperCamelCase					:	int             =      OrderedDict()
					__UpperCamelCase      ,			__UpperCamelCase					:	str             =      0, 0
					for key, value in state_dict.items():
										if key.startswith("""network"""				):
															__UpperCamelCase					:	Any             =      key.replace("""network"""		,	"""poolformer.encoder"""				)
										if "proj" in key:
															# Works for the first embedding as well as the internal embedding layers
															if key.endswith("""bias"""				) and "patch_embed" not in key:
																				patch_emb_offset += 1
															__UpperCamelCase					:	Union[str, Any]             =      key[: key.find("""proj"""				)]
															__UpperCamelCase					:	List[str]             =      key.replace(snake_case		,	F'''patch_embeddings.{total_embed_found}.'''				)
															__UpperCamelCase					:	Any             =      key.replace("""proj"""		,	"""projection"""				)
															if key.endswith("""bias"""				):
																				total_embed_found += 1
										if "patch_embeddings" in key:
															__UpperCamelCase					:	Optional[Any]             =      """poolformer.encoder.""" + key
										if "mlp.fc1" in key:
															__UpperCamelCase					:	List[Any]             =      replace_key_with_offset(snake_case		,	snake_case		,	"""mlp.fc1"""		,	"""output.conv1"""				)
										if "mlp.fc2" in key:
															__UpperCamelCase					:	Any             =      replace_key_with_offset(snake_case		,	snake_case		,	"""mlp.fc2"""		,	"""output.conv2"""				)
										if "norm1" in key:
															__UpperCamelCase					:	Dict             =      replace_key_with_offset(snake_case		,	snake_case		,	"""norm1"""		,	"""before_norm"""				)
										if "norm2" in key:
															__UpperCamelCase					:	Union[str, Any]             =      replace_key_with_offset(snake_case		,	snake_case		,	"""norm2"""		,	"""after_norm"""				)
										if "layer_scale_1" in key:
															__UpperCamelCase					:	Optional[int]             =      replace_key_with_offset(snake_case		,	snake_case		,	"""layer_scale_1"""		,	"""layer_scale_1"""				)
										if "layer_scale_2" in key:
															__UpperCamelCase					:	Tuple             =      replace_key_with_offset(snake_case		,	snake_case		,	"""layer_scale_2"""		,	"""layer_scale_2"""				)
										if "head" in key:
															__UpperCamelCase					:	Optional[Any]             =      key.replace("""head"""		,	"""classifier"""				)
										__UpperCamelCase					:	int             =      value
					return new_state_dict





def 				A__ ()     ->						Optional[int]:
					__UpperCamelCase					:	str             =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
					__UpperCamelCase					:	Optional[Any]             =      Image.open(requests.get(snake_case		,	stream=snake_case				).raw				)

					return image





@torch.no_grad()
def 				A__ (snake_case   :		Any		,	snake_case   :		Tuple		,	snake_case   :		Any				)     ->						Optional[int]:
					__UpperCamelCase					:	Union[str, Any]             =      PoolFormerConfig()

					# set attributes based on model_name
					__UpperCamelCase					:	int             =      """huggingface/label-files"""
					__UpperCamelCase					:	List[str]             =      model_name[-3:]
					__UpperCamelCase					:	List[Any]             =      10_00
					__UpperCamelCase					:	Any             =      """imagenet-1k-id2label.json"""
					__UpperCamelCase					:	Union[str, Any]             =      (1, 10_00)

					# set config attributes
					__UpperCamelCase					:	int             =      json.load(open(hf_hub_download(snake_case		,	snake_case		,	repo_type="""dataset"""				)		,	"""r"""				)				)
					__UpperCamelCase					:	Optional[int]             =      {int(snake_case				): v for k, v in idalabel.items()}
					__UpperCamelCase					:	Any             =      idalabel
					__UpperCamelCase					:	List[str]             =      {v: k for k, v in idalabel.items()}
					if size == "s12":
										__UpperCamelCase					:	int             =      [2, 2, 6, 2]
										__UpperCamelCase					:	Tuple             =      [64, 1_28, 3_20, 5_12]
										__UpperCamelCase					:	Union[str, Any]             =      4.0
										__UpperCamelCase					:	str             =      0.9
					elif size == "s24":
										__UpperCamelCase					:	Dict             =      [4, 4, 12, 4]
										__UpperCamelCase					:	Tuple             =      [64, 1_28, 3_20, 5_12]
										__UpperCamelCase					:	List[Any]             =      4.0
										__UpperCamelCase					:	Dict             =      0.9
					elif size == "s36":
										__UpperCamelCase					:	Dict             =      [6, 6, 18, 6]
										__UpperCamelCase					:	Optional[Any]             =      [64, 1_28, 3_20, 5_12]
										__UpperCamelCase					:	Dict             =      4.0
										__UpperCamelCase					:	List[Any]             =      1e-6
										__UpperCamelCase					:	Optional[Any]             =      0.9
					elif size == "m36":
										__UpperCamelCase					:	str             =      [6, 6, 18, 6]
										__UpperCamelCase					:	List[str]             =      [96, 1_92, 3_84, 7_68]
										__UpperCamelCase					:	List[str]             =      4.0
										__UpperCamelCase					:	Tuple             =      1e-6
										__UpperCamelCase					:	Dict             =      0.95
					elif size == "m48":
										__UpperCamelCase					:	Any             =      [8, 8, 24, 8]
										__UpperCamelCase					:	Optional[int]             =      [96, 1_92, 3_84, 7_68]
										__UpperCamelCase					:	List[str]             =      4.0
										__UpperCamelCase					:	Union[str, Any]             =      1e-6
										__UpperCamelCase					:	List[str]             =      0.95
					else:
										raise ValueError(F'''Size {size} not supported'''				)

					# load image processor
					__UpperCamelCase					:	Tuple             =      PoolFormerImageProcessor(crop_pct=snake_case				)

					# Prepare image
					__UpperCamelCase					:	Any             =      prepare_img()
					__UpperCamelCase					:	Tuple             =      image_processor(images=snake_case		,	return_tensors="""pt"""				).pixel_values

					logger.info(F'''Converting model {model_name}...'''				)

					# load original state dict
					__UpperCamelCase					:	str             =      torch.load(snake_case		,	map_location=torch.device("""cpu"""				)				)

					# rename keys
					__UpperCamelCase					:	int             =      rename_keys(snake_case				)

					# create HuggingFace model and load state dict
					__UpperCamelCase					:	List[str]             =      PoolFormerForImageClassification(snake_case				)
					model.load_state_dict(snake_case				)
					model.eval()

					# Define image processor
					__UpperCamelCase					:	Optional[Any]             =      PoolFormerImageProcessor(crop_pct=snake_case				)
					__UpperCamelCase					:	str             =      image_processor(images=prepare_img()		,	return_tensors="""pt"""				).pixel_values

					# forward pass
					__UpperCamelCase					:	Optional[int]             =      model(snake_case				)
					__UpperCamelCase					:	Optional[Any]             =      outputs.logits

					# define expected logit slices for different models
					if size == "s12":
										__UpperCamelCase					:	Tuple             =      torch.tensor([-0.3045, -0.6758, -0.4869]				)
					elif size == "s24":
										__UpperCamelCase					:	int             =      torch.tensor([0.4402, -0.1374, -0.8045]				)
					elif size == "s36":
										__UpperCamelCase					:	Dict             =      torch.tensor([-0.6080, -0.5133, -0.5898]				)
					elif size == "m36":
										__UpperCamelCase					:	List[Any]             =      torch.tensor([0.3952, 0.2263, -1.2668]				)
					elif size == "m48":
										__UpperCamelCase					:	Optional[int]             =      torch.tensor([0.1167, -0.0656, -0.3423]				)
					else:
										raise ValueError(F'''Size {size} not supported'''				)

					# verify logits
					assert logits.shape == expected_shape
					assert torch.allclose(logits[0, :3]		,	snake_case		,	atol=1e-2				)

					# finally, save model and image processor
					logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''				)
					Path(snake_case				).mkdir(exist_ok=snake_case				)
					model.save_pretrained(snake_case				)
					print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
					image_processor.save_pretrained(snake_case				)


if __name__ == "__main__":
	a__		     =     argparse.ArgumentParser()

	parser.add_argument(
	    '''--model_name''',
	    default='''poolformer_s12''',
	    type=str,
	    help='''Name of the model you\'d like to convert.''',
	)
	parser.add_argument(
	    '''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
	)
	a__		     =     parser.parse_args()
	convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)












# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


a__		     =     logging.get_logger(__name__)

a__		     =     {}
a__		     =     {}
a__		     =     {}





def 				A__ (snake_case   :		type		,	snake_case   :		Optional[str]		,	snake_case   :		Optional[List[str]] = None		,	)     ->						str:
					__UpperCamelCase					:	int             =      aliases if aliases is not None else []
					if format_type in _FORMAT_TYPES:
										logger.warning(
										    F'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''				)
					__UpperCamelCase					:	Optional[Any]             =      formatter_cls
					for alias in set(aliases + [format_type]				):
										if alias in _FORMAT_TYPES_ALIASES:
															logger.warning(
															    F'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''				)
										__UpperCamelCase					:	Optional[int]             =      format_type





def 				A__ (snake_case   :		Exception		,	snake_case   :		Optional[str]		,	snake_case   :		Optional[List[str]] = None				)     ->						Tuple:
					__UpperCamelCase					:	List[Any]             =      aliases if aliases is not None else []
					for alias in set(aliases + [format_type]				):
										__UpperCamelCase					:	Optional[int]             =      unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')

if config.TORCH_AVAILABLE:
	from .torch_formatter import TorchFormatter

	_register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
	a__		     =     ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
	_register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])

if config.TF_AVAILABLE:
	from .tf_formatter import TFFormatter

	_register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
	a__		     =     ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
	_register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])

if config.JAX_AVAILABLE:
	from .jax_formatter import JaxFormatter

	_register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
	a__		     =     ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
	_register_unavailable_formatter(_jax_error, '''jax''', aliases=[])





def 				A__ (snake_case   :		Optional[str]				)     ->						Optional[str]:
					if format_type in _FORMAT_TYPES_ALIASES:
										return _FORMAT_TYPES_ALIASES[format_type]
					else:
										return format_type







def 				A__ (snake_case   :		Optional[str]		,	**snake_case   :		List[str]				)     ->						Formatter:
					__UpperCamelCase					:	int             =      get_format_type_from_alias(snake_case				)
					if format_type in _FORMAT_TYPES:
										return _FORMAT_TYPES[format_type](**snake_case				)
					if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
										raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
					else:
										raise ValueError(
										    F'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None				)}, but got \'{format_type}\''''				)






