




'''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()
_snake_case   :   str     							=	logging.get_logger(__name__)
def 	snake_case_					(UpperCamelCase :						Dict				,		UpperCamelCase :						Union[str, Any]				,		UpperCamelCase :						str				,		UpperCamelCase :						List[str]   ):





							'''simple docstring'''


							_a			  =       original_name.split('''.'''   )[0]
							_a			  =       key.split('''.'''   )
							_a			  =       int(key_list[key_list.index(UpperCamelCase   ) - 2]   )
							_a			  =       int(key_list[key_list.index(UpperCamelCase   ) - 1]   )
							_a			  =       orig_block_num - offset

							_a			  =       key.replace(f'{orig_block_num}.{layer_num}.{original_name}'				,		f'block.{new_block_num}.{layer_num}.{new_name}'   )
							return key
def 	snake_case_					(UpperCamelCase :						Optional[Any]   ):





							'''simple docstring'''


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





							'''simple docstring'''


							_a			  =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							_a			  =       Image.open(requests.get(UpperCamelCase				,		stream=UpperCamelCase   ).raw   )

							return image
@torch.no_grad()
def 	snake_case_					(UpperCamelCase :						int				,		UpperCamelCase :						int				,		UpperCamelCase :						int   ):





							'''simple docstring'''


							_a			  =       PoolFormerConfig()

							# set attributes based on model_name
							_a			  =       '''huggingface/label-files'''
							_a			  =       model_name[-3:]
							_a			  =       1000
							_a			  =       '''imagenet-1k-id2label.json'''
							_a			  =       (1, 1000)

							# set config attributes
							_a			  =       json.load(open(hf_hub_download(UpperCamelCase				,		UpperCamelCase				,		repo_type='''dataset'''   )				,		'''r'''   )   )
							_a			  =       {int(UpperCamelCase   ): v for k, v in idalabel.items()}
							_a			  =       idalabel
							_a			  =       {v: k for k, v in idalabel.items()}
							if size == "s12":
														_a			  =       [2, 2, 6, 2]
														_a			  =       [64, 128, 320, 512]
														_a			  =       4.0
														_a			  =       0.9
							elif size == "s24":
														_a			  =       [4, 4, 12, 4]
														_a			  =       [64, 128, 320, 512]
														_a			  =       4.0
														_a			  =       0.9
							elif size == "s36":
														_a			  =       [6, 6, 18, 6]
														_a			  =       [64, 128, 320, 512]
														_a			  =       4.0
														_a			  =       1e-6
														_a			  =       0.9
							elif size == "m36":
														_a			  =       [6, 6, 18, 6]
														_a			  =       [96, 192, 384, 768]
														_a			  =       4.0
														_a			  =       1e-6
														_a			  =       0.95
							elif size == "m48":
														_a			  =       [8, 8, 24, 8]
														_a			  =       [96, 192, 384, 768]
														_a			  =       4.0
														_a			  =       1e-6
														_a			  =       0.95
							else:
														raise ValueError(f'Size {size} not supported'   )

							# load image processor
							_a			  =       PoolFormerImageProcessor(crop_pct=UpperCamelCase   )

							# Prepare image
							_a			  =       prepare_img()
							_a			  =       image_processor(images=UpperCamelCase				,		return_tensors='''pt'''   ).pixel_values

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

							# load original state dict
							_a			  =       torch.load(UpperCamelCase				,		map_location=torch.device('''cpu'''   )   )

							# rename keys
							_a			  =       rename_keys(UpperCamelCase   )

							# create HuggingFace model and load state dict
							_a			  =       PoolFormerForImageClassification(UpperCamelCase   )
							model.load_state_dict(UpperCamelCase   )
							model.eval()

							# Define image processor
							_a			  =       PoolFormerImageProcessor(crop_pct=UpperCamelCase   )
							_a			  =       image_processor(images=prepare_img()				,		return_tensors='''pt'''   ).pixel_values

							# forward pass
							_a			  =       model(UpperCamelCase   )
							_a			  =       outputs.logits

							# define expected logit slices for different models
							if size == "s12":
														_a			  =       torch.tensor([-0.3045, -0.6758, -0.4869]   )
							elif size == "s24":
														_a			  =       torch.tensor([0.4402, -0.1374, -0.8045]   )
							elif size == "s36":
														_a			  =       torch.tensor([-0.6080, -0.5133, -0.5898]   )
							elif size == "m36":
														_a			  =       torch.tensor([0.3952, 0.2263, -1.2668]   )
							elif size == "m48":
														_a			  =       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]				,		UpperCamelCase				,		atol=1e-2   )

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


if __name__ == "__main__":
				_snake_case   :   Tuple     							=	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.'
				)
				_snake_case   :   Union[str, Any]     							=	parser.parse_args()
				convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)







'''simple docstring'''


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def 	snake_case_					(UpperCamelCase :						BertModel				,		UpperCamelCase :						str				,		UpperCamelCase :						str   ):





							'''simple docstring'''


							_a			  =       ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

							_a			  =       (
							    ('''layer.''', '''layer_'''),
							    ('''word_embeddings.weight''', '''word_embeddings'''),
							    ('''position_embeddings.weight''', '''position_embeddings'''),
							    ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
							    ('''.''', '''/'''),
							    ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
							    ('''LayerNorm/bias''', '''LayerNorm/beta'''),
							    ('''weight''', '''kernel'''),
							)

							if not os.path.isdir(UpperCamelCase   ):
														os.makedirs(UpperCamelCase   )

							_a			  =       model.state_dict()

							def to_tf_var_name(UpperCamelCase :						str   ):
														for patt, repl in iter(UpperCamelCase   ):
																					_a			  =       name.replace(UpperCamelCase				,		UpperCamelCase   )
														return f'bert/{name}'

							def create_tf_var(UpperCamelCase :						np.ndarray				,		UpperCamelCase :						str				,		UpperCamelCase :						tf.Session   ):
														_a			  =       tf.dtypes.as_dtype(tensor.dtype   )
														_a			  =       tf.get_variable(dtype=UpperCamelCase				,		shape=tensor.shape				,		name=UpperCamelCase				,		initializer=tf.zeros_initializer()   )
														session.run(tf.variables_initializer([tf_var]   )   )
														session.run(UpperCamelCase   )
														return tf_var

							tf.reset_default_graph()
							with tf.Session() as session:
														for var_name in state_dict:
																					_a			  =       to_tf_var_name(UpperCamelCase   )
																					_a			  =       state_dict[var_name].numpy()
																					if any(x in var_name for x in tensors_to_transpose   ):
																												_a			  =       torch_tensor.T
																					_a			  =       create_tf_var(tensor=UpperCamelCase				,		name=UpperCamelCase				,		session=UpperCamelCase   )
																					tf.keras.backend.set_value(UpperCamelCase				,		UpperCamelCase   )
																					_a			  =       session.run(UpperCamelCase   )
																					print(f'Successfully created {tf_name}: {np.allclose(UpperCamelCase				,		UpperCamelCase   )}'   )

														_a			  =       tf.train.Saver(tf.trainable_variables()   )
														saver.save(UpperCamelCase				,		os.path.join(UpperCamelCase				,		model_name.replace('''-'''				,		'''_'''   ) + '''.ckpt'''   )   )
def 	snake_case_					(UpperCamelCase :						Tuple=None   ):





							'''simple docstring'''


							_a			  =       argparse.ArgumentParser()
							parser.add_argument('''--model_name'''				,		type=UpperCamelCase				,		required=UpperCamelCase				,		help='''model name e.g. bert-base-uncased'''   )
							parser.add_argument(
							    '''--cache_dir'''				,		type=UpperCamelCase				,		default=UpperCamelCase				,		required=UpperCamelCase				,		help='''Directory containing pytorch model'''   )
							parser.add_argument('''--pytorch_model_path'''				,		type=UpperCamelCase				,		required=UpperCamelCase				,		help='''/path/to/<pytorch-model-name>.bin'''   )
							parser.add_argument('''--tf_cache_dir'''				,		type=UpperCamelCase				,		required=UpperCamelCase				,		help='''Directory in which to save tensorflow model'''   )
							_a			  =       parser.parse_args(UpperCamelCase   )

							_a			  =       BertModel.from_pretrained(
							    pretrained_model_name_or_path=args.model_name				,		state_dict=torch.load(args.pytorch_model_path   )				,		cache_dir=args.cache_dir				,		)

							convert_pytorch_checkpoint_to_tf(model=UpperCamelCase				,		ckpt_dir=args.tf_cache_dir				,		model_name=args.model_name   )


if __name__ == "__main__":
				main()

