

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 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	List[Any]						)		->				Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      old_name

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

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

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

   SCREAMING_SNAKE_CASE		    =      'last_stage.' + trimmed_name

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

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

 return new_name


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Union[str, Any]						)		->				str:
 '''simple docstring'''


 for key in checkpoint.copy().keys():
  SCREAMING_SNAKE_CASE		    =      checkpoint.pop(_UpperCamelCase						)
  SCREAMING_SNAKE_CASE		    =      val

 return checkpoint


def 							__lowerCAmelCase     (							)		->				Optional[Any]:
 '''simple docstring'''


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

 return image


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Path		,				_UpperCamelCase		:	Path		,				_UpperCamelCase		:	Path		,				_UpperCamelCase		:	bool						)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      torch.load(_UpperCamelCase		,				map_location='cpu'						)['model']
 SCREAMING_SNAKE_CASE		    =      EfficientFormerConfig.from_json_file(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      EfficientFormerForImageClassificationWithTeacher(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      '_'.join(checkpoint_path.split('/'						)[-1].split('.'						)[0].split('_'						)[:-1]						)

 SCREAMING_SNAKE_CASE		    =      config.depths[-1] - config.num_metaad_blocks + 1
 SCREAMING_SNAKE_CASE		    =      convert_torch_checkpoint(_UpperCamelCase		,				_UpperCamelCase						)

 model.load_state_dict(_UpperCamelCase						)
 model.eval()

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

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

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

 assert torch.allclose(_UpperCamelCase		,				_UpperCamelCase						)

 SCREAMING_SNAKE_CASE		    =      model(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      outputs.logits

 SCREAMING_SNAKE_CASE		    =      (1, 10_00)

 if "l1" in model_name:
  SCREAMING_SNAKE_CASE		    =      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, :10]		,				_UpperCamelCase		,				atol=1e-3						)
  assert logits.shape == expected_shape
 elif "l3" in model_name:
  SCREAMING_SNAKE_CASE		    =      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, :10]		,				_UpperCamelCase		,				atol=1e-3						)
  assert logits.shape == expected_shape
 elif "l7" in model_name:
  SCREAMING_SNAKE_CASE		    =      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(_UpperCamelCase						).mkdir(exist_ok=_UpperCamelCase						)
 model.save_pretrained(_UpperCamelCase						)
 print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""						)
 processor.save_pretrained(_UpperCamelCase						)
 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=_UpperCamelCase		,				)
  processor.push_to_hub(
      repo_id=f"""Bearnardd/{pytorch_dump_path}"""		,				commit_message='Add image processor'		,				use_temp_dir=_UpperCamelCase		,				)


if __name__ == "__main__":
      a_     :    int     		=   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)

      a_     :    Tuple     		=   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 json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
      from transformers.models.rag.configuration_rag import RagConfig
      from transformers.models.rag.tokenization_rag import RagTokenizer




@require_faiss
@require_torch
class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):

    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      tempfile.mkdtemp()
     SCREAMING_SNAKE_CASE		    =      8

     # DPR tok
     SCREAMING_SNAKE_CASE		    =      [
         '[UNK]',
         '[CLS]',
         '[SEP]',
         '[PAD]',
         '[MASK]',
         'want',
         '##want',
         '##ed',
         'wa',
         'un',
         'runn',
         '##ing',
         ',',
         'low',
         'lowest',
     ]
     SCREAMING_SNAKE_CASE		    =      os.path.join(self.tmpdirname				,     'dpr_tokenizer'     )
     os.makedirs(snake_case__				,     exist_ok=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      os.path.join(snake_case__				,     DPR_VOCAB_FILES_NAMES['vocab_file']     )
     with open(self.vocab_file				,     'w'				,     encoding='utf-8'     ) as vocab_writer:
      vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]     )     )

     # BART tok
     SCREAMING_SNAKE_CASE		    =      [
         'l',
         'o',
         'w',
         'e',
         'r',
         's',
         't',
         'i',
         'd',
         'n',
         '\u0120',
         '\u0120l',
         '\u0120n',
         '\u0120lo',
         '\u0120low',
         'er',
         '\u0120lowest',
         '\u0120newer',
         '\u0120wider',
         '<unk>',
     ]
     SCREAMING_SNAKE_CASE		    =      dict(zip(snake_case__				,     range(len(snake_case__     )     )     )     )
     SCREAMING_SNAKE_CASE		    =      ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
     SCREAMING_SNAKE_CASE		    =      {'unk_token': '<unk>'}

     SCREAMING_SNAKE_CASE		    =      os.path.join(self.tmpdirname				,     'bart_tokenizer'     )
     os.makedirs(snake_case__				,     exist_ok=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      os.path.join(snake_case__				,     BART_VOCAB_FILES_NAMES['vocab_file']     )
     SCREAMING_SNAKE_CASE		    =      os.path.join(snake_case__				,     BART_VOCAB_FILES_NAMES['merges_file']     )
     with open(self.vocab_file				,     'w'				,     encoding='utf-8'     ) as fp:
      fp.write(json.dumps(snake_case__     ) + '\n'     )
     with open(self.merges_file				,     'w'				,     encoding='utf-8'     ) as fp:
      fp.write('\n'.join(snake_case__     )     )

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname				,     'dpr_tokenizer'     )     )

    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname				,     'bart_tokenizer'     )     )

    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     shutil.rmtree(self.tmpdirname     )

    @require_tokenizers
    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      os.path.join(self.tmpdirname				,     'rag_tokenizer'     )
     SCREAMING_SNAKE_CASE		    =      RagConfig(question_encoder=DPRConfig().to_dict()				,     generator=BartConfig().to_dict()     )
     SCREAMING_SNAKE_CASE		    =      RagTokenizer(question_encoder=self.get_dpr_tokenizer()				,     generator=self.get_bart_tokenizer()     )
     rag_config.save_pretrained(snake_case__     )
     rag_tokenizer.save_pretrained(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      RagTokenizer.from_pretrained(snake_case__				,     config=snake_case__     )
     self.assertIsInstance(new_rag_tokenizer.question_encoder				,     snake_case__     )
     self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()				,     rag_tokenizer.question_encoder.get_vocab()     )
     self.assertIsInstance(new_rag_tokenizer.generator				,     snake_case__     )
     self.assertEqual(new_rag_tokenizer.generator.get_vocab()				,     rag_tokenizer.generator.get_vocab()     )

    @slow
    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      RagTokenizer.from_pretrained('facebook/rag-token-nq'     )
     SCREAMING_SNAKE_CASE		    =      [
         'who got the first nobel prize in physics',
         'when is the next deadpool movie being released',
         'which mode is used for short wave broadcast service',
         'who is the owner of reading football club',
         'when is the next scandal episode coming out',
         'when is the last time the philadelphia won the superbowl',
         'what is the most current adobe flash player version',
         'how many episodes are there in dragon ball z',
         'what is the first step in the evolution of the eye',
         'where is gall bladder situated in human body',
         'what is the main mineral in lithium batteries',
         'who is the president of usa right now',
         'where do the greasers live in the outsiders',
         'panda is a national animal of which country',
         'what is the name of manchester united stadium',
     ]
     SCREAMING_SNAKE_CASE		    =      tokenizer(snake_case__     )
     self.assertIsNotNone(snake_case__     )




    @slow
    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      RagTokenizer.from_pretrained('facebook/rag-sequence-nq'     )
     SCREAMING_SNAKE_CASE		    =      [
         'who got the first nobel prize in physics',
         'when is the next deadpool movie being released',
         'which mode is used for short wave broadcast service',
         'who is the owner of reading football club',
         'when is the next scandal episode coming out',
         'when is the last time the philadelphia won the superbowl',
         'what is the most current adobe flash player version',
         'how many episodes are there in dragon ball z',
         'what is the first step in the evolution of the eye',
         'where is gall bladder situated in human body',
         'what is the main mineral in lithium batteries',
         'who is the president of usa right now',
         'where do the greasers live in the outsiders',
         'panda is a national animal of which country',
         'what is the name of manchester united stadium',
     ]
     SCREAMING_SNAKE_CASE		    =      tokenizer(snake_case__     )
     self.assertIsNotNone(snake_case__     )

