



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_      :      Optional[Any]    =     logging.get_logger(__name__)






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 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     __a					(		__UpperCAmelCase       ):
 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     __a					(		):
 a__												= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 a__												= Image.open(requests.get(__UpperCAmelCase							,  stream=__UpperCAmelCase       ).raw       )

 return image






@torch.no_grad()
def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 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__":
     a_      :      Optional[Any]    =     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_      :      Optional[int]    =     parser.parse_args()
     convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)










import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a_      :      Optional[int]    =     '.'

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
a_      :      List[str]    =     [
    'Assert',
    'AssignVariableOp',
    'EmptyTensorList',
    'MergeV2Checkpoints',
    'ReadVariableOp',
    'ResourceGather',
    'RestoreV2',
    'SaveV2',
    'ShardedFilename',
    'StatefulPartitionedCall',
    'StaticRegexFullMatch',
    'VarHandleOp',
]






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= SavedModel()
 a__												= []

 with open(os.path.join(__UpperCAmelCase							,  '''utils'''							,  '''tf_ops'''							,  '''onnx.json'''       )       ) as f:
  a__												= json.load(__UpperCAmelCase       )['''opsets''']

 for i in range(1							,  opset + 1       ):
  onnx_ops.extend(onnx_opsets[str(__UpperCAmelCase       )]       )

 with open(__UpperCAmelCase							,  '''rb'''       ) as f:
  saved_model.ParseFromString(f.read()       )

 a__												= set()

 # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
 for meta_graph in saved_model.meta_graphs:
  # Add operations in the graph definition
  model_op_names.update(node.op for node in meta_graph.graph_def.node       )

  # Go through the functions in the graph definition
  for func in meta_graph.graph_def.library.function:
   # Add operations in each function
   model_op_names.update(node.op for node in func.node_def       )

    # Convert to list, sorted if you want
 a__												= sorted(__UpperCAmelCase       )
 a__												= []

 for op in model_op_names:
  if op not in onnx_ops and op not in INTERNAL_OPS:
   incompatible_ops.append(__UpperCAmelCase       )

 if strict and len(__UpperCAmelCase       ) > 0:
  raise Exception(f"Found the following incompatible ops for the opset {opset}:\n" + incompatible_ops       )
 elif len(__UpperCAmelCase       ) > 0:
  print(f"Found the following incompatible ops for the opset {opset}:"       )
  print(*__UpperCAmelCase							,  sep='''\n'''       )
 else:
  print(f"The saved model {saved_model_path} can properly be converted with ONNX."       )


if __name__ == "__main__":
     a_      :      Dict    =     argparse.ArgumentParser()
     parser.add_argument('--saved_model_path', help='Path of the saved model to check (the .pb file).')
     parser.add_argument(
         '--opset', default=12, type=int, help='The ONNX opset against which the model has to be tested.'
     )
     parser.add_argument(
         '--framework', choices=['onnx'], default='onnx', help='Frameworks against which to test the saved model.'
     )
     parser.add_argument(
         '--strict', action='store_true', help='Whether make the checking strict (raise errors) or not (raise warnings)'
     )
     a_      :      str    =     parser.parse_args()

     if args.framework == "onnx":
          onnx_compliancy(args.saved_model_path, args.strict, args.opset)





