
"""simple docstring"""

import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling






def 							A		(       snake_case   :str       ,      snake_case   :List[str]	)   ->			Any:
 __UpperCamelCase       						= old_name

 if "patch_embed" in old_name:
  __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= old_name.split('.'	)

  if layer == "0":
   __UpperCamelCase       						= old_name.replace('0'       ,      'convolution1'	)
  elif layer == "1":
   __UpperCamelCase       						= old_name.replace('1'       ,      'batchnorm_before'	)
  elif layer == "3":
   __UpperCamelCase       						= old_name.replace('3'       ,      'convolution2'	)
  else:
   __UpperCamelCase       						= old_name.replace('4'       ,      'batchnorm_after'	)

 if "network" in old_name and re.search(r'\d\.\d'       ,      snake_case	):
  __UpperCamelCase       						= r'\b\d{2}\b'
  if bool(re.search(snake_case       ,      snake_case	)	):
   __UpperCamelCase       						= re.search(r'\d\.\d\d.'       ,      snake_case	).group()
  else:
   __UpperCamelCase       						= re.search(r'\d\.\d.'       ,      snake_case	).group()
  if int(match[0]	) < 6:
   __UpperCamelCase       						= old_name.replace(snake_case       ,      ''	)
   __UpperCamelCase       						= trimmed_name.replace('network'       ,      match[0] + '.meta4D_layers.blocks.' + match[2:-1]	)
   __UpperCamelCase       						= 'intermediate_stages.' + trimmed_name
  else:
   __UpperCamelCase       						= old_name.replace(snake_case       ,      ''	)
   if int(match[2]	) < num_meta4D_last_stage:
    __UpperCamelCase       						= trimmed_name.replace('network'       ,      'meta4D_layers.blocks.' + match[2]	)
   else:
    __UpperCamelCase       						= str(int(match[2]	) - num_meta4D_last_stage	)
    __UpperCamelCase       						= trimmed_name.replace('network'       ,      'meta3D_layers.blocks.' + layer_index	)
    if "norm1" in old_name:
     __UpperCamelCase       						= trimmed_name.replace('norm1'       ,      'layernorm1'	)
    elif "norm2" in old_name:
     __UpperCamelCase       						= trimmed_name.replace('norm2'       ,      'layernorm2'	)
    elif "fc1" in old_name:
     __UpperCamelCase       						= trimmed_name.replace('fc1'       ,      'linear_in'	)
    elif "fc2" in old_name:
     __UpperCamelCase       						= trimmed_name.replace('fc2'       ,      'linear_out'	)

   __UpperCamelCase       						= 'last_stage.' + trimmed_name

 elif "network" in old_name and re.search(r'.\d.'       ,      snake_case	):
  __UpperCamelCase       						= old_name.replace('network'       ,      'intermediate_stages'	)

 if "fc" in new_name:
  __UpperCamelCase       						= new_name.replace('fc'       ,      'convolution'	)
 elif ("norm1" in new_name) and ("layernorm1" not in new_name):
  __UpperCamelCase       						= new_name.replace('norm1'       ,      'batchnorm_before'	)
 elif ("norm2" in new_name) and ("layernorm2" not in new_name):
  __UpperCamelCase       						= new_name.replace('norm2'       ,      'batchnorm_after'	)
 if "proj" in new_name:
  __UpperCamelCase       						= new_name.replace('proj'       ,      'projection'	)
 if "dist_head" in new_name:
  __UpperCamelCase       						= new_name.replace('dist_head'       ,      'distillation_classifier'	)
 elif "head" in new_name:
  __UpperCamelCase       						= new_name.replace('head'       ,      'classifier'	)
 elif "patch_embed" in new_name:
  __UpperCamelCase       						= 'efficientformer.' + new_name
 elif new_name == "norm.weight" or new_name == "norm.bias":
  __UpperCamelCase       						= new_name.replace('norm'       ,      'layernorm'	)
  __UpperCamelCase       						= 'efficientformer.' + new_name
 else:
  __UpperCamelCase       						= 'efficientformer.encoder.' + new_name

 return new_name






def 							A		(       snake_case   :Tuple       ,      snake_case   :Dict	)   ->			Union[str, Any]:
 for key in checkpoint.copy().keys():
  __UpperCamelCase       						= checkpoint.pop(snake_case	)
  __UpperCamelCase       						= val

 return checkpoint






def 							A		(       )   ->			Dict:
 __UpperCamelCase       						= 'http://images.cocodataset.org/val2017/000000039769.jpg'
 __UpperCamelCase       						= Image.open(requests.get(snake_case       ,      stream=snake_case	).raw	)

 return image






def 							A		(       snake_case   :Path       ,      snake_case   :Path       ,      snake_case   :Path       ,      snake_case   :bool	)   ->			Union[str, Any]:
 __UpperCamelCase       						= torch.load(snake_case       ,      map_location='cpu'	)['model']
 __UpperCamelCase       						= EfficientFormerConfig.from_json_file(snake_case	)
 __UpperCamelCase       						= EfficientFormerForImageClassificationWithTeacher(snake_case	)
 __UpperCamelCase       						= '_'.join(checkpoint_path.split('/'	)[-1].split('.'	)[0].split('_'	)[:-1]	)

 __UpperCamelCase       						= config.depths[-1] - config.num_metaad_blocks + 1
 __UpperCamelCase       						= convert_torch_checkpoint(snake_case       ,      snake_case	)

 model.load_state_dict(snake_case	)
 model.eval()

 __UpperCamelCase       						= {
     'bilinear': PILImageResampling.BILINEAR,
     'bicubic': PILImageResampling.BICUBIC,
     'nearest': PILImageResampling.NEAREST,
 }

 # prepare image
 __UpperCamelCase       						= prepare_img()
 __UpperCamelCase       						= 2_5_6
 __UpperCamelCase       						= 2_2_4
 __UpperCamelCase       						= EfficientFormerImageProcessor(
     size={'shortest_edge': image_size}       ,      crop_size={'height': crop_size, 'width': crop_size}       ,      resample=pillow_resamplings['bicubic']       ,      )
 __UpperCamelCase       						= processor(images=snake_case       ,      return_tensors='pt'	).pixel_values

 # original processing pipeline
 __UpperCamelCase       						= Compose(
     [
         Resize(snake_case       ,      interpolation=pillow_resamplings['bicubic']	),
         CenterCrop(snake_case	),
         ToTensor(),
         Normalize(snake_case       ,      snake_case	),
     ]	)
 __UpperCamelCase       						= image_transforms(snake_case	).unsqueeze(0	)

 assert torch.allclose(snake_case       ,      snake_case	)

 __UpperCamelCase       						= model(snake_case	)
 __UpperCamelCase       						= outputs.logits

 __UpperCamelCase       						= (1, 1_0_0_0)

 if "l1" in model_name:
  __UpperCamelCase       						= torch.Tensor(
      [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]	)
  assert torch.allclose(logits[0, :1_0]       ,      snake_case       ,      atol=1e-3	)
  assert logits.shape == expected_shape
 elif "l3" in model_name:
  __UpperCamelCase       						= torch.Tensor(
      [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]	)
  assert torch.allclose(logits[0, :1_0]       ,      snake_case       ,      atol=1e-3	)
  assert logits.shape == expected_shape
 elif "l7" in model_name:
  __UpperCamelCase       						= torch.Tensor(
      [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]	)
  assert logits.shape == expected_shape
 else:
  raise ValueError(
      f'Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'	)

 # Save Checkpoints
 Path(snake_case	).mkdir(exist_ok=snake_case	)
 model.save_pretrained(snake_case	)
 print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'	)
 processor.save_pretrained(snake_case	)
 print(f'Processor successfuly saved at {pytorch_dump_path}'	)

 if push_to_hub:
  print('Pushing model to the hub...'	)

  model.push_to_hub(
      repo_id=f'Bearnardd/{pytorch_dump_path}'       ,      commit_message='Add model'       ,      use_temp_dir=snake_case       ,      )
  processor.push_to_hub(
      repo_id=f'Bearnardd/{pytorch_dump_path}'       ,      commit_message='Add image processor'       ,      use_temp_dir=snake_case       ,      )


if __name__ == "__main__":
      UpperCamelCase			:       Any									=				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--pytorch_model_path",
          default=None,
          type=str,
          required=True,
          help="Path to EfficientFormer pytorch checkpoint.",
      )
      parser.add_argument(
          "--config_file",
          default=None,
          type=str,
          required=True,
          help="The json file for EfficientFormer model config.",
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )

      parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
      parser.add_argument(
          "--no-push_to_hub",
          dest="push_to_hub",
          action="store_false",
          help="Do not push model and image processor to the hub",
      )
      parser.set_defaults(push_to_hub=True)

      UpperCamelCase			:       Any									=				parser.parse_args()
      convert_efficientformer_checkpoint(
          checkpoint_path=args.pytorch_model_path,
          efficientformer_config_file=args.config_file,
          pytorch_dump_path=args.pytorch_dump_path,
          push_to_hub=args.push_to_hub,
      )





"""simple docstring"""

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
UpperCamelCase			:       Tuple									=				"."

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






def 							A		(       snake_case   :List[str]       ,      snake_case   :Optional[int]       ,      snake_case   :Optional[Any]	)   ->			Any:
 __UpperCamelCase       						= SavedModel()
 __UpperCamelCase       						= []

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

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

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

 __UpperCamelCase       						= 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
 __UpperCamelCase       						= sorted(snake_case	)
 __UpperCamelCase       						= []

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

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


if __name__ == "__main__":
      UpperCamelCase			:       List[str]									=				argparse.ArgumentParser()
      parser.add_argument("--saved_model_path", help="Path of the saved model to check (the .pb file).")
      parser.add_argument(
          "--opset", default=1_2, 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)"
      )
      UpperCamelCase			:       str									=				parser.parse_args()

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



