





"""simple docstring"""




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()
_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				,	__a				):
	snake_case_						:     Tuple     =		original_name.split('.'				)[0]
	snake_case_						:     Optional[Any]     =		key.split('.'				)
	snake_case_						:     Union[str, Any]     =		int(key_list[key_list.index(lowercase__				) - 2]				)
	snake_case_						:     Optional[Any]     =		int(key_list[key_list.index(lowercase__				) - 1]				)
	snake_case_						:     List[str]     =		orig_block_num - offset

	snake_case_						:     str     =		key.replace(f"""{orig_block_num}.{layer_num}.{original_name}"""				,	f"""block.{new_block_num}.{layer_num}.{new_name}"""				)
	return key


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     Tuple     =		OrderedDict()
	snake_case_  ,snake_case_						:     Optional[Any]     =		0, 0
	for key, value in state_dict.items():
		if key.startswith('network'				):
			snake_case_						:     Optional[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
			snake_case_						:     Union[str, Any]     =		key[: key.find('proj'				)]
			snake_case_						:     List[Any]     =		key.replace(lowercase__				,	f"""patch_embeddings.{total_embed_found}."""				)
			snake_case_						:     List[str]     =		key.replace('proj'				,	'projection'				)
			if key.endswith('bias'				):
				total_embed_found += 1
		if "patch_embeddings" in key:
			snake_case_						:     Optional[int]     =		'poolformer.encoder.' + key
		if "mlp.fc1" in key:
			snake_case_						:     Any     =		replace_key_with_offset(lowercase__				,	lowercase__				,	'mlp.fc1'				,	'output.conv1'				)
		if "mlp.fc2" in key:
			snake_case_						:     Tuple     =		replace_key_with_offset(lowercase__				,	lowercase__				,	'mlp.fc2'				,	'output.conv2'				)
		if "norm1" in key:
			snake_case_						:     Union[str, Any]     =		replace_key_with_offset(lowercase__				,	lowercase__				,	'norm1'				,	'before_norm'				)
		if "norm2" in key:
			snake_case_						:     int     =		replace_key_with_offset(lowercase__				,	lowercase__				,	'norm2'				,	'after_norm'				)
		if "layer_scale_1" in key:
			snake_case_						:     str     =		replace_key_with_offset(lowercase__				,	lowercase__				,	'layer_scale_1'				,	'layer_scale_1'				)
		if "layer_scale_2" in key:
			snake_case_						:     List[str]     =		replace_key_with_offset(lowercase__				,	lowercase__				,	'layer_scale_2'				,	'layer_scale_2'				)
		if "head" in key:
			snake_case_						:     List[Any]     =		key.replace('head'				,	'classifier'				)
		snake_case_						:     Union[str, Any]     =		value
	return new_state_dict


def 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_						:     Tuple     =		'http://images.cocodataset.org/val2017/000000039769.jpg'
	snake_case_						:     Tuple     =		Image.open(requests.get(lowercase__				,	stream=lowercase__				).raw				)

	return image


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     int     =		PoolFormerConfig()

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

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

	# load image processor
	snake_case_						:     str     =		PoolFormerImageProcessor(crop_pct=lowercase__				)

	# Prepare image
	snake_case_						:     str     =		prepare_img()
	snake_case_						:     str     =		image_processor(images=lowercase__				,	return_tensors='pt'				).pixel_values

	logger.info(f"""Converting model {model_name}..."""				)

	# load original state dict
	snake_case_						:     Tuple     =		torch.load(lowercase__				,	map_location=torch.device('cpu'				)				)

	# rename keys
	snake_case_						:     Any     =		rename_keys(lowercase__				)

	# create HuggingFace model and load state dict
	snake_case_						:     Tuple     =		PoolFormerForImageClassification(lowercase__				)
	model.load_state_dict(lowercase__				)
	model.eval()

	# Define image processor
	snake_case_						:     List[Any]     =		PoolFormerImageProcessor(crop_pct=lowercase__				)
	snake_case_						:     List[str]     =		image_processor(images=prepare_img()				,	return_tensors='pt'				).pixel_values

	# forward pass
	snake_case_						:     int     =		model(lowercase__				)
	snake_case_						:     str     =		outputs.logits

	# define expected logit slices for different models
	if size == "s12":
		snake_case_						:     Union[str, Any]     =		torch.tensor([-0.3045, -0.6758, -0.4869]				)
	elif size == "s24":
		snake_case_						:     Union[str, Any]     =		torch.tensor([0.4402, -0.1374, -0.8045]				)
	elif size == "s36":
		snake_case_						:     List[Any]     =		torch.tensor([-0.6080, -0.5133, -0.5898]				)
	elif size == "m36":
		snake_case_						:     Union[str, Any]     =		torch.tensor([0.3952, 0.2263, -1.2668]				)
	elif size == "m48":
		snake_case_						:     Dict     =		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]				,	lowercase__				,	atol=1E-2				)

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


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						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."""
						)
						_SCREAMING_SNAKE_CASE 		=						parser.parse_args()
						convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)








from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE 		=						{
    """configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
    """feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
    """processing_mctct""": ["""MCTCTProcessor"""],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """MCTCTForCTC""",
						    """MCTCTModel""",
						    """MCTCTPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
						from .feature_extraction_mctct import MCTCTFeatureExtractor
						from .processing_mctct import MCTCTProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
						import sys

						_SCREAMING_SNAKE_CASE 		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

