


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        snake_case_					(  snake_case      ,							snake_case					)  ->    Dict:
       lowercase__:       Dict        =       old_name

       if "patch_embed" in old_name:
              lowercase__:       Union[str, Any]        =       old_name.split('.'					)

              if layer == "0":
                     lowercase__:       str        =       old_name.replace('0'      ,							'convolution1'					)
              elif layer == "1":
                     lowercase__:       int        =       old_name.replace('1'      ,							'batchnorm_before'					)
              elif layer == "3":
                     lowercase__:       List[str]        =       old_name.replace('3'      ,							'convolution2'					)
              else:
                     lowercase__:       Union[str, Any]        =       old_name.replace('4'      ,							'batchnorm_after'					)

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

                     lowercase__:       Optional[Any]        =       '''last_stage.''' + trimmed_name

       elif "network" in old_name and re.search(R'.\d.'      ,							lowerCAmelCase__					):
              lowercase__:       str        =       old_name.replace('network'      ,							'intermediate_stages'					)

       if "fc" in new_name:
              lowercase__:       str        =       new_name.replace('fc'      ,							'convolution'					)
       elif ("norm1" in new_name) and ("layernorm1" not in new_name):
              lowercase__:       Any        =       new_name.replace('norm1'      ,							'batchnorm_before'					)
       elif ("norm2" in new_name) and ("layernorm2" not in new_name):
              lowercase__:       List[Any]        =       new_name.replace('norm2'      ,							'batchnorm_after'					)
       if "proj" in new_name:
              lowercase__:       int        =       new_name.replace('proj'      ,							'projection'					)
       if "dist_head" in new_name:
              lowercase__:       List[str]        =       new_name.replace('dist_head'      ,							'distillation_classifier'					)
       elif "head" in new_name:
              lowercase__:       Tuple        =       new_name.replace('head'      ,							'classifier'					)
       elif "patch_embed" in new_name:
              lowercase__:       Any        =       '''efficientformer.''' + new_name
       elif new_name == "norm.weight" or new_name == "norm.bias":
              lowercase__:       Optional[Any]        =       new_name.replace('norm'      ,							'layernorm'					)
              lowercase__:       Any        =       '''efficientformer.''' + new_name
       else:
              lowercase__:       List[Any]        =       '''efficientformer.encoder.''' + new_name

       return new_name




def        snake_case_					(  snake_case      ,							snake_case					)  ->    int:
       for key in checkpoint.copy().keys():
              lowercase__:       Any        =       checkpoint.pop(lowerCAmelCase__					)
              lowercase__:       str        =       val

       return checkpoint




def        snake_case_					(  )  ->    Union[str, Any]:
       lowercase__:       int        =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
       lowercase__:       str        =       Image.open(requests.get(lowerCAmelCase__      ,							stream=lowerCAmelCase__					).raw					)

       return image




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case					)  ->    Tuple:
       lowercase__:       Tuple        =       torch.load(lowerCAmelCase__      ,							map_location='cpu'					)['''model''']
       lowercase__:       Any        =       EfficientFormerConfig.from_json_file(lowerCAmelCase__					)
       lowercase__:       int        =       EfficientFormerForImageClassificationWithTeacher(lowerCAmelCase__					)
       lowercase__:       Optional[int]        =       '''_'''.join(checkpoint_path.split('/'					)[-1].split('.'					)[0].split('_'					)[:-1]					)

       lowercase__:       Optional[Any]        =       config.depths[-1] - config.num_metaad_blocks + 1
       lowercase__:       Optional[int]        =       convert_torch_checkpoint(lowerCAmelCase__      ,							lowerCAmelCase__					)

       model.load_state_dict(lowerCAmelCase__					)
       model.eval()

       lowercase__:       List[Any]        =       {
           '''bilinear''': PILImageResampling.BILINEAR,
           '''bicubic''': PILImageResampling.BICUBIC,
           '''nearest''': PILImageResampling.NEAREST,
       }

       # prepare image
       lowercase__:       Optional[Any]        =       prepare_img()
       lowercase__:       Optional[int]        =       2_56
       lowercase__:       Any        =       2_24
       lowercase__:       Tuple        =       EfficientFormerImageProcessor(
           size={'shortest_edge': image_size}      ,							crop_size={'height': crop_size, 'width': crop_size}      ,							resample=pillow_resamplings['bicubic']      ,							)
       lowercase__:       Any        =       processor(images=lowerCAmelCase__      ,							return_tensors='pt'					).pixel_values

       # original processing pipeline
       lowercase__:       Any        =       Compose(
           [
               Resize(lowerCAmelCase__      ,							interpolation=pillow_resamplings['bicubic']					),
               CenterCrop(lowerCAmelCase__					),
               ToTensor(),
               Normalize(lowerCAmelCase__      ,							lowerCAmelCase__					),
           ]					)
       lowercase__:       Union[str, Any]        =       image_transforms(lowerCAmelCase__					).unsqueeze(0					)

       assert torch.allclose(lowerCAmelCase__      ,							lowerCAmelCase__					)

       lowercase__:       Optional[Any]        =       model(lowerCAmelCase__					)
       lowercase__:       str        =       outputs.logits

       lowercase__:       Optional[int]        =       (1, 10_00)

       if "l1" in model_name:
              lowercase__:       Any        =       torch.Tensor(
                  [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]					)
              assert torch.allclose(logits[0, :10]      ,							lowerCAmelCase__      ,							atol=1e-3					)
              assert logits.shape == expected_shape
       elif "l3" in model_name:
              lowercase__:       List[Any]        =       torch.Tensor(
                  [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]					)
              assert torch.allclose(logits[0, :10]      ,							lowerCAmelCase__      ,							atol=1e-3					)
              assert logits.shape == expected_shape
       elif "l7" in model_name:
              lowercase__:       str        =       torch.Tensor(
                  [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]					)
              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(lowerCAmelCase__					).mkdir(exist_ok=lowerCAmelCase__					)
       model.save_pretrained(lowerCAmelCase__					)
       print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'					)
       processor.save_pretrained(lowerCAmelCase__					)
       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=lowerCAmelCase__      ,							)
              processor.push_to_hub(
                  repo_id=f'Bearnardd/{pytorch_dump_path}'      ,							commit_message='Add image processor'      ,							use_temp_dir=lowerCAmelCase__      ,							)


if __name__ == "__main__":
 __lowerCAmelCase			=							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)

 __lowerCAmelCase			=							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,
 )






import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


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




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    Any:
       lowercase__:       Dict        =       os.path.abspath(snake_case					)
       logger.info(f'Converting TensorFlow checkpoint from {tf_path}'					)
       # Load weights from TF model
       lowercase__:       Optional[Any]        =       tf.train.list_variables(snake_case					)
       lowercase__:       List[Any]        =       []
       lowercase__:       Tuple        =       []
       lowercase__:       int        =       []
       for full_name, shape in init_vars:
              # logger.info(f"Loading TF weight {name} with shape {shape}")
              lowercase__:       Union[str, Any]        =       full_name.split('/'					)
              if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
                     logger.info(f'Skipping non-model layer {full_name}'					)
                     continue
              if "optimizer" in full_name:
                     logger.info(f'Skipping optimization layer {full_name}'					)
                     continue
              if name[0] == "model":
                     # ignore initial 'model'
                     lowercase__:       str        =       name[1:]
              # figure out how many levels deep the name is
              lowercase__:       Optional[Any]        =       0
              for _name in name:
                     if _name.startswith('layer_with_weights'					):
                            depth += 1
                     else:
                            break
              layer_depth.append(snake_case					)
              # read data
              lowercase__:       Optional[Any]        =       tf.train.load_variable(snake_case      ,							snake_case					)
              names.append('/'.join(snake_case					)					)
              arrays.append(snake_case					)
       logger.info(f'Read a total of {len(snake_case					):,} layers'					)

       # Sanity check
       if len(set(snake_case					)					) != 1:
              raise ValueError(f'Found layer names with different depths (layer depth {list(set(snake_case					)					)})'					)
       lowercase__:       Any        =       list(set(snake_case					)					)[0]
       if layer_depth != 1:
              raise ValueError(
                  'The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'
                  ' heads.'					)

       # convert layers
       logger.info('Converting weights...'					)
       for full_name, array in zip(snake_case      ,							snake_case					):
              lowercase__:       Optional[int]        =       full_name.split('/'					)
              lowercase__:       List[Any]        =       model
              lowercase__:       Any        =       []
              for i, m_name in enumerate(snake_case					):
                     if m_name == ".ATTRIBUTES":
                            # variable names end with .ATTRIBUTES/VARIABLE_VALUE
                            break
                     if m_name.startswith('layer_with_weights'					):
                            lowercase__:       Any        =       int(m_name.split('-'					)[-1]					)
                            if layer_num <= 2:
                                   # embedding layers
                                   # layer_num 0: word_embeddings
                                   # layer_num 1: position_embeddings
                                   # layer_num 2: token_type_embeddings
                                   continue
                            elif layer_num == 3:
                                   # embedding LayerNorm
                                   trace.extend(['embeddings', 'LayerNorm']					)
                                   lowercase__:       str        =       getattr(snake_case      ,							'embeddings'					)
                                   lowercase__:       int        =       getattr(snake_case      ,							'LayerNorm'					)
                            elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
                                   # encoder layers
                                   trace.extend(['encoder', 'layer', str(layer_num - 4					)]					)
                                   lowercase__:       int        =       getattr(snake_case      ,							'encoder'					)
                                   lowercase__:       List[str]        =       getattr(snake_case      ,							'layer'					)
                                   lowercase__:       Union[str, Any]        =       pointer[layer_num - 4]
                            elif layer_num == config.num_hidden_layers + 4:
                                   # pooler layer
                                   trace.extend(['pooler', 'dense']					)
                                   lowercase__:       Tuple        =       getattr(snake_case      ,							'pooler'					)
                                   lowercase__:       Tuple        =       getattr(snake_case      ,							'dense'					)
                     elif m_name == "embeddings":
                            trace.append('embeddings'					)
                            lowercase__:       Union[str, Any]        =       getattr(snake_case      ,							'embeddings'					)
                            if layer_num == 0:
                                   trace.append('word_embeddings'					)
                                   lowercase__:       Union[str, Any]        =       getattr(snake_case      ,							'word_embeddings'					)
                            elif layer_num == 1:
                                   trace.append('position_embeddings'					)
                                   lowercase__:       Dict        =       getattr(snake_case      ,							'position_embeddings'					)
                            elif layer_num == 2:
                                   trace.append('token_type_embeddings'					)
                                   lowercase__:       Optional[Any]        =       getattr(snake_case      ,							'token_type_embeddings'					)
                            else:
                                   raise ValueError(f'Unknown embedding layer with name {full_name}'					)
                            trace.append('weight'					)
                            lowercase__:       List[str]        =       getattr(snake_case      ,							'weight'					)
                     elif m_name == "_attention_layer":
                            # self-attention layer
                            trace.extend(['attention', 'self']					)
                            lowercase__:       int        =       getattr(snake_case      ,							'attention'					)
                            lowercase__:       Union[str, Any]        =       getattr(snake_case      ,							'self'					)
                     elif m_name == "_attention_layer_norm":
                            # output attention norm
                            trace.extend(['attention', 'output', 'LayerNorm']					)
                            lowercase__:       str        =       getattr(snake_case      ,							'attention'					)
                            lowercase__:       Optional[Any]        =       getattr(snake_case      ,							'output'					)
                            lowercase__:       List[Any]        =       getattr(snake_case      ,							'LayerNorm'					)
                     elif m_name == "_attention_output_dense":
                            # output attention dense
                            trace.extend(['attention', 'output', 'dense']					)
                            lowercase__:       Optional[Any]        =       getattr(snake_case      ,							'attention'					)
                            lowercase__:       Optional[int]        =       getattr(snake_case      ,							'output'					)
                            lowercase__:       Optional[Any]        =       getattr(snake_case      ,							'dense'					)
                     elif m_name == "_output_dense":
                            # output dense
                            trace.extend(['output', 'dense']					)
                            lowercase__:       Union[str, Any]        =       getattr(snake_case      ,							'output'					)
                            lowercase__:       List[Any]        =       getattr(snake_case      ,							'dense'					)
                     elif m_name == "_output_layer_norm":
                            # output dense
                            trace.extend(['output', 'LayerNorm']					)
                            lowercase__:       Any        =       getattr(snake_case      ,							'output'					)
                            lowercase__:       str        =       getattr(snake_case      ,							'LayerNorm'					)
                     elif m_name == "_key_dense":
                            # attention key
                            trace.append('key'					)
                            lowercase__:       Tuple        =       getattr(snake_case      ,							'key'					)
                     elif m_name == "_query_dense":
                            # attention query
                            trace.append('query'					)
                            lowercase__:       List[str]        =       getattr(snake_case      ,							'query'					)
                     elif m_name == "_value_dense":
                            # attention value
                            trace.append('value'					)
                            lowercase__:       Optional[int]        =       getattr(snake_case      ,							'value'					)
                     elif m_name == "_intermediate_dense":
                            # attention intermediate dense
                            trace.extend(['intermediate', 'dense']					)
                            lowercase__:       Any        =       getattr(snake_case      ,							'intermediate'					)
                            lowercase__:       str        =       getattr(snake_case      ,							'dense'					)
                     elif m_name == "_output_layer_norm":
                            # output layer norm
                            trace.append('output'					)
                            lowercase__:       Union[str, Any]        =       getattr(snake_case      ,							'output'					)
                     # weights & biases
                     elif m_name in ["bias", "beta"]:
                            trace.append('bias'					)
                            lowercase__:       str        =       getattr(snake_case      ,							'bias'					)
                     elif m_name in ["kernel", "gamma"]:
                            trace.append('weight'					)
                            lowercase__:       Tuple        =       getattr(snake_case      ,							'weight'					)
                     else:
                            logger.warning(f'Ignored {m_name}'					)
        # for certain layers reshape is necessary
              lowercase__:       Any        =       '.'.join(snake_case					)
              if re.match(R'(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'      ,							snake_case					) or re.match(
                  R'(\S+)\.attention\.output\.dense\.weight'      ,							snake_case					):
                     lowercase__:       Union[str, Any]        =       array.reshape(pointer.data.shape					)
              if "kernel" in full_name:
                     lowercase__:       str        =       array.transpose()
              if pointer.shape == array.shape:
                     lowercase__:       Union[str, Any]        =       torch.from_numpy(snake_case					)
              else:
                     raise ValueError(
                         f'Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'
                         f' {array.shape}'					)
              logger.info(f'Successfully set variable {full_name} to PyTorch layer {trace}'					)
       return model




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    Any:
       # Instantiate model
       logger.info(f'Loading model based on config from {config_path}...'					)
       lowercase__:       int        =       BertConfig.from_json_file(snake_case					)
       lowercase__:       Tuple        =       BertModel(snake_case					)

       # Load weights from checkpoint
       logger.info(f'Loading weights from checkpoint {tf_checkpoint_path}...'					)
       load_tfa_weights_in_bert(snake_case      ,							snake_case      ,							snake_case					)

       # Save pytorch-model
       logger.info(f'Saving PyTorch model to {pytorch_dump_path}...'					)
       torch.save(model.state_dict()      ,							snake_case					)


if __name__ == "__main__":
 __lowerCAmelCase			=							argparse.ArgumentParser()
 parser.add_argument(
     '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow 2.x checkpoint path.'''
 )
 parser.add_argument(
     '''--bert_config_file''',
     type=str,
     required=True,
     help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''',
     type=str,
     required=True,
     help='''Path to the output PyTorch model (must include filename).''',
 )
 __lowerCAmelCase			=							parser.parse_args()
 convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)


