
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        __UpperCamelCase					(    lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    List[str]  ):
 __a			:  Dict		    =  old_name

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

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

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

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

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

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

 return new_name




def        __UpperCamelCase					(    lowerCAmelCase__							:    Any  ,       lowerCAmelCase__							:    int  ):
 for key in checkpoint.copy().keys():
  __a			:  Any		    =  checkpoint.pop(lowerCAmelCase__  )
  __a			:  str		    =  val

 return checkpoint




def        __UpperCamelCase					(    ):
 __a			:  int		    =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 __a			:  str		    =  Image.open(requests.get(lowerCAmelCase__  ,       stream=lowerCAmelCase__  ).raw  )

 return image




def        __UpperCamelCase					(    lowerCAmelCase__							:    Path  ,       lowerCAmelCase__							:    Path  ,       lowerCAmelCase__							:    Path  ,       lowerCAmelCase__							:    bool  ):
 __a			:  Tuple		    =  torch.load(lowerCAmelCase__  ,       map_location='''cpu'''  )['''model''']
 __a			:  Any		    =  EfficientFormerConfig.from_json_file(lowerCAmelCase__  )
 __a			:  int		    =  EfficientFormerForImageClassificationWithTeacher(lowerCAmelCase__  )
 __a			:  Optional[int]		    =  '''_'''.join(checkpoint_path.split('''/'''  )[-1].split('''.'''  )[0].split('''_'''  )[:-1]  )

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

 model.load_state_dict(lowerCAmelCase__  )
 model.eval()

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

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

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

 assert torch.allclose(lowerCAmelCase__  ,       lowerCAmelCase__  )

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

 __a			:  Optional[int]		    =  (1, 1_0_0_0)

 if "l1" in model_name:
  __a			:  Any		    =  torch.Tensor(
      [-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28]  )
  assert torch.allclose(logits[0, :1_0]  ,       lowerCAmelCase__  ,       atol=1e-3  )
  assert logits.shape == expected_shape
 elif "l3" in model_name:
  __a			:  List[Any]		    =  torch.Tensor(
      [-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27]  )
  assert torch.allclose(logits[0, :1_0]  ,       lowerCAmelCase__  ,       atol=1e-3  )
  assert logits.shape == expected_shape
 elif "l7" in model_name:
  __a			:  str		    =  torch.Tensor(
      [-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78]  )
  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__":
      lowercase__          =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)

      lowercase__          =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 os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


lowercase__          ='bert-base-cased'
lowercase__          ='google/pegasus-xsum'
lowercase__          =[' Sam ate lunch today.', 'Sams lunch ingredients.']
lowercase__          =['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
lowercase__          ='patrickvonplaten/t5-tiny-random'
lowercase__          ='sshleifer/bart-tiny-random'
lowercase__          ='sshleifer/tiny-mbart'
lowercase__          ='sshleifer/tiny-marian-en-de'




def        __UpperCamelCase					(    lowerCAmelCase__							:    Path  ,       lowerCAmelCase__							:    list  ):
 __a			:  List[Any]		    =  '''\n'''.join(lowerCAmelCase__  )
 Path(lowerCAmelCase__  ).open('''w'''  ).writelines(lowerCAmelCase__  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ):
 for split in ["train", "val", "test"]:
  _dump_articles(os.path.join(lowerCAmelCase__  ,       f"{split}.source"  )  ,       lowerCAmelCase__  )
  _dump_articles(os.path.join(lowerCAmelCase__  ,       f"{split}.target"  )  ,       lowerCAmelCase__  )
 return tmp_dir



class 		UpperCamelCase__  (	__lowercase   ):
   @parameterized.expand(
       [
           MBART_TINY,
           MARIAN_TINY,
           T5_TINY,
           BART_TINY,
           PEGASUS_XSUM,
       ] ,    )
   @slow
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: int       ):
    __a			:  Optional[Any]		    =  AutoTokenizer.from_pretrained(snake_case_       )
    __a			:  Optional[Any]		    =  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
    __a			:  Union[str, Any]		    =  max(len(tokenizer.encode(snake_case_       )       ) for a in ARTICLES       )
    __a			:  str		    =  max(len(tokenizer.encode(snake_case_       )       ) for a in SUMMARIES       )
    __a			:  str		    =  4
    __a			:  Dict		    =  8
    assert max_len_target > max_src_len  # Will be truncated
    assert max_len_source > max_src_len  # Will be truncated
    __a    ,	__a			:  Any		    =  '''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
    __a			:  List[Any]		    =  SeqaSeqDataset(
        snake_case_ ,    data_dir=snake_case_ ,    type_path='''train''' ,    max_source_length=snake_case_ ,    max_target_length=snake_case_ ,    src_lang=snake_case_ ,    tgt_lang=snake_case_ ,    )
    __a			:  Dict		    =  DataLoader(snake_case_ ,    batch_size=2 ,    collate_fn=train_dataset.collate_fn       )
    for batch in dataloader:
     assert isinstance(snake_case_ ,    snake_case_       )
     assert batch["attention_mask"].shape == batch["input_ids"].shape
     # show that articles were trimmed.
     assert batch["input_ids"].shape[1] == max_src_len
     # show that targets are the same len
     assert batch["labels"].shape[1] == max_tgt_len
     if tok_name != MBART_TINY:
      continue
     # check language codes in correct place
     __a			:  Dict		    =  shift_tokens_right(batch['''labels'''] ,    tokenizer.pad_token_id       )
     assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
     assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
     assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
     assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

     break  # No need to test every batch
   @parameterized.expand([BART_TINY, BERT_BASE_CASED]       )
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: str       ):
    __a			:  Union[str, Any]		    =  AutoTokenizer.from_pretrained(snake_case_       )
    __a			:  str		    =  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
    __a			:  Any		    =  max(len(tokenizer.encode(snake_case_       )       ) for a in ARTICLES       )
    __a			:  Any		    =  max(len(tokenizer.encode(snake_case_       )       ) for a in SUMMARIES       )
    __a			:  Dict		    =  4
    __a			:  Optional[int]		    =  LegacySeqaSeqDataset(
        snake_case_ ,    data_dir=snake_case_ ,    type_path='''train''' ,    max_source_length=2_0 ,    max_target_length=snake_case_ ,    )
    __a			:  Optional[Any]		    =  DataLoader(snake_case_ ,    batch_size=2 ,    collate_fn=train_dataset.collate_fn       )
    for batch in dataloader:
     assert batch["attention_mask"].shape == batch["input_ids"].shape
     # show that articles were trimmed.
     assert batch["input_ids"].shape[1] == max_len_source
     assert 2_0 >= batch["input_ids"].shape[1]  # trimmed significantly
     # show that targets were truncated
     assert batch["labels"].shape[1] == trunc_target  # Truncated
     assert max_len_target > trunc_target  # Truncated
     break  # No need to test every batch
   def 				lowerCAmelCase      (self			: List[str]       ):
    __a			:  int		    =  AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''       )

    __a			:  Any		    =  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
    __a			:  Optional[int]		    =  tmp_dir.joinpath('''train.source'''       ).open().readlines()
    __a			:  List[Any]		    =  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
    pack_data_dir(snake_case_ ,    snake_case_ ,    1_2_8 ,    snake_case_       )
    __a			:  Optional[Any]		    =  {x.name for x in tmp_dir.iterdir()}
    __a			:  Union[str, Any]		    =  {x.name for x in save_dir.iterdir()}
    __a			:  str		    =  save_dir.joinpath('''train.source'''       ).open().readlines()
    # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
    # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
    assert len(snake_case_       ) < len(snake_case_       )
    assert len(snake_case_       ) == 1
    assert len(packed_examples[0]       ) == sum(len(snake_case_       ) for x in orig_examples       )
    assert orig_paths == new_paths
   @pytest.mark.skipif(not FAIRSEQ_AVAILABLE ,    reason='''This test requires fairseq'''       )
   def 				lowerCAmelCase      (self			: Any       ):
    if not FAIRSEQ_AVAILABLE:
     return
    __a    ,	__a    ,	__a			:  Any		    =  self._get_dataset(max_len=6_4       )
    __a			:  int		    =  6_4
    __a			:  List[str]		    =  ds.make_dynamic_sampler(snake_case_ ,    required_batch_size_multiple=snake_case_       )
    __a			:  List[str]		    =  [len(snake_case_       ) for x in batch_sampler]
    assert len(set(snake_case_       )       ) > 1  # it's not dynamic batch size if every batch is the same length
    assert sum(snake_case_       ) == len(snake_case_       )  # no dropped or added examples
    __a			:  Union[str, Any]		    =  DataLoader(snake_case_ ,    batch_sampler=snake_case_ ,    collate_fn=ds.collate_fn ,    num_workers=2       )
    __a			:  Tuple		    =  []
    __a			:  Union[str, Any]		    =  []
    for batch in data_loader:
     __a			:  Any		    =  batch['''input_ids'''].shape
     __a			:  str		    =  src_shape[0]
     assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
     __a			:  Optional[Any]		    =  np.product(batch['''input_ids'''].shape       )
     num_src_per_batch.append(snake_case_       )
     if num_src_tokens > (max_tokens * 1.1):
      failures.append(snake_case_       )
    assert num_src_per_batch[0] == max(snake_case_       )
    if failures:
     raise AssertionError(f"too many tokens in {len(snake_case_       )} batches"       )
   def 				lowerCAmelCase      (self			: int       ):
    __a    ,	__a    ,	__a			:  Optional[int]		    =  self._get_dataset(max_len=5_1_2       )
    __a			:  Union[str, Any]		    =  2
    __a			:  str		    =  ds.make_sortish_sampler(snake_case_ ,    shuffle=snake_case_       )

    __a			:  Tuple		    =  DataLoader(snake_case_ ,    batch_size=snake_case_ ,    collate_fn=ds.collate_fn ,    num_workers=2       )
    __a			:  Tuple		    =  DataLoader(snake_case_ ,    batch_size=snake_case_ ,    collate_fn=ds.collate_fn ,    num_workers=2 ,    sampler=snake_case_       )

    __a			:  Optional[int]		    =  tokenizer.pad_token_id

    def count_pad_tokens(snake_case_			: Union[str, Any] ,    snake_case_			: List[str]="input_ids"       ):
     return [batch[k].eq(snake_case_       ).sum().item() for batch in data_loader]

    assert sum(count_pad_tokens(snake_case_ ,    k='''labels'''       )       ) < sum(count_pad_tokens(snake_case_ ,    k='''labels'''       )       )
    assert sum(count_pad_tokens(snake_case_       )       ) < sum(count_pad_tokens(snake_case_       )       )
    assert len(snake_case_       ) == len(snake_case_       )
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: int=1_0_0_0 ,    snake_case_			: Optional[Any]=1_2_8       ):
    if os.getenv('''USE_REAL_DATA''' ,    snake_case_       ):
     __a			:  Optional[int]		    =  '''examples/seq2seq/wmt_en_ro'''
     __a			:  List[Any]		    =  max_len * 2 * 6_4
     if not Path(snake_case_       ).joinpath('''train.len'''       ).exists():
      save_len_file(snake_case_ ,    snake_case_       )
    else:
     __a			:  int		    =  '''examples/seq2seq/test_data/wmt_en_ro'''
     __a			:  List[str]		    =  max_len * 4
     save_len_file(snake_case_ ,    snake_case_       )

    __a			:  str		    =  AutoTokenizer.from_pretrained(snake_case_       )
    __a			:  Optional[int]		    =  SeqaSeqDataset(
        snake_case_ ,    data_dir=snake_case_ ,    type_path='''train''' ,    max_source_length=snake_case_ ,    max_target_length=snake_case_ ,    n_obs=snake_case_ ,    )
    return ds, max_tokens, tokenizer
   def 				lowerCAmelCase      (self			: List[str]       ):
    __a    ,	__a    ,	__a			:  str		    =  self._get_dataset()
    __a			:  Optional[Any]		    =  set(DistributedSortishSampler(snake_case_ ,    2_5_6 ,    num_replicas=2 ,    rank=0 ,    add_extra_examples=snake_case_       )       )
    __a			:  Tuple		    =  set(DistributedSortishSampler(snake_case_ ,    2_5_6 ,    num_replicas=2 ,    rank=1 ,    add_extra_examples=snake_case_       )       )
    assert idsa.intersection(snake_case_       ) == set()







   @parameterized.expand(
       [
           MBART_TINY,
           MARIAN_TINY,
           T5_TINY,
           BART_TINY,
           PEGASUS_XSUM,
       ] ,    )
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: Union[str, Any]       ):
    __a			:  Union[str, Any]		    =  AutoTokenizer.from_pretrained(snake_case_ ,    use_fast=snake_case_       )
    if tok_name == MBART_TINY:
     __a			:  Any		    =  SeqaSeqDataset(
         snake_case_ ,    data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       ) ,    type_path='''train''' ,    max_source_length=4 ,    max_target_length=8 ,    src_lang='''EN''' ,    tgt_lang='''FR''' ,    )
     __a			:  Tuple		    =  train_dataset.dataset_kwargs
     assert "src_lang" in kwargs and "tgt_lang" in kwargs
    else:
     __a			:  Optional[Any]		    =  SeqaSeqDataset(
         snake_case_ ,    data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       ) ,    type_path='''train''' ,    max_source_length=4 ,    max_target_length=8 ,    )
     __a			:  List[Any]		    =  train_dataset.dataset_kwargs
     assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
     assert len(snake_case_       ) == 1 if tok_name == BART_TINY else len(snake_case_       ) == 0
