'''simple docstring'''

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 	UpperCamelCase_		(   snake_case_			:			Union[str, Any]		,						snake_case_			:			str		,						snake_case_			:			str		,						snake_case_			:			str )   ->	Tuple:






 '''simple docstring'''

 __lowerCAmelCase											=		original_name.split(""".""" )[0]
 __lowerCAmelCase											=		key.split(""".""" )
 __lowerCAmelCase											=		int(key_list[key_list.index(_UpperCamelCase ) - 2] )
 __lowerCAmelCase											=		int(key_list[key_list.index(_UpperCamelCase ) - 1] )
 __lowerCAmelCase											=		orig_block_num - offset

 __lowerCAmelCase											=		key.replace(f"""{orig_block_num}.{layer_num}.{original_name}"""		,						f"""block.{new_block_num}.{layer_num}.{new_name}""" )
 return key







def 	UpperCamelCase_		(   snake_case_			:			Optional[int] )   ->	int:






 '''simple docstring'''

 __lowerCAmelCase											=		OrderedDict()
 __lowerCAmelCase				,			__lowerCAmelCase											=		0, 0
 for key, value in state_dict.items():
  if key.startswith("""network""" ):
   __lowerCAmelCase											=		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
   __lowerCAmelCase											=		key[: key.find("""proj""" )]
   __lowerCAmelCase											=		key.replace(_UpperCamelCase		,						f"""patch_embeddings.{total_embed_found}.""" )
   __lowerCAmelCase											=		key.replace("""proj"""		,						"""projection""" )
   if key.endswith("""bias""" ):
    total_embed_found += 1
  if "patch_embeddings" in key:
   __lowerCAmelCase											=		"""poolformer.encoder.""" + key
  if "mlp.fc1" in key:
   __lowerCAmelCase											=		replace_key_with_offset(_UpperCamelCase		,						_UpperCamelCase		,						"""mlp.fc1"""		,						"""output.conv1""" )
  if "mlp.fc2" in key:
   __lowerCAmelCase											=		replace_key_with_offset(_UpperCamelCase		,						_UpperCamelCase		,						"""mlp.fc2"""		,						"""output.conv2""" )
  if "norm1" in key:
   __lowerCAmelCase											=		replace_key_with_offset(_UpperCamelCase		,						_UpperCamelCase		,						"""norm1"""		,						"""before_norm""" )
  if "norm2" in key:
   __lowerCAmelCase											=		replace_key_with_offset(_UpperCamelCase		,						_UpperCamelCase		,						"""norm2"""		,						"""after_norm""" )
  if "layer_scale_1" in key:
   __lowerCAmelCase											=		replace_key_with_offset(_UpperCamelCase		,						_UpperCamelCase		,						"""layer_scale_1"""		,						"""layer_scale_1""" )
  if "layer_scale_2" in key:
   __lowerCAmelCase											=		replace_key_with_offset(_UpperCamelCase		,						_UpperCamelCase		,						"""layer_scale_2"""		,						"""layer_scale_2""" )
  if "head" in key:
   __lowerCAmelCase											=		key.replace("""head"""		,						"""classifier""" )
  __lowerCAmelCase											=		value
 return new_state_dict







def 	UpperCamelCase_		(   )   ->	List[Any]:






 '''simple docstring'''

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

 return image







@torch.no_grad()
def 	UpperCamelCase_		(   snake_case_			:			Union[str, Any]		,						snake_case_			:			List[Any]		,						snake_case_			:			int )   ->	int:






 '''simple docstring'''

 __lowerCAmelCase											=		PoolFormerConfig()

 # set attributes based on model_name
 __lowerCAmelCase											=		"""huggingface/label-files"""
 __lowerCAmelCase											=		model_name[-3:]
 __lowerCAmelCase											=		10_00
 __lowerCAmelCase											=		"""imagenet-1k-id2label.json"""
 __lowerCAmelCase											=		(1, 10_00)

 # set config attributes
 __lowerCAmelCase											=		json.load(open(hf_hub_download(_UpperCamelCase		,						_UpperCamelCase		,						repo_type="""dataset""" )		,						"""r""" ) )
 __lowerCAmelCase											=		{int(_UpperCamelCase ): v for k, v in idalabel.items()}
 __lowerCAmelCase											=		idalabel
 __lowerCAmelCase											=		{v: k for k, v in idalabel.items()}
 if size == "s12":
  __lowerCAmelCase											=		[2, 2, 6, 2]
  __lowerCAmelCase											=		[64, 1_28, 3_20, 5_12]
  __lowerCAmelCase											=		4.0
  __lowerCAmelCase											=		0.9
 elif size == "s24":
  __lowerCAmelCase											=		[4, 4, 12, 4]
  __lowerCAmelCase											=		[64, 1_28, 3_20, 5_12]
  __lowerCAmelCase											=		4.0
  __lowerCAmelCase											=		0.9
 elif size == "s36":
  __lowerCAmelCase											=		[6, 6, 18, 6]
  __lowerCAmelCase											=		[64, 1_28, 3_20, 5_12]
  __lowerCAmelCase											=		4.0
  __lowerCAmelCase											=		1E-6
  __lowerCAmelCase											=		0.9
 elif size == "m36":
  __lowerCAmelCase											=		[6, 6, 18, 6]
  __lowerCAmelCase											=		[96, 1_92, 3_84, 7_68]
  __lowerCAmelCase											=		4.0
  __lowerCAmelCase											=		1E-6
  __lowerCAmelCase											=		0.9_5
 elif size == "m48":
  __lowerCAmelCase											=		[8, 8, 24, 8]
  __lowerCAmelCase											=		[96, 1_92, 3_84, 7_68]
  __lowerCAmelCase											=		4.0
  __lowerCAmelCase											=		1E-6
  __lowerCAmelCase											=		0.9_5
 else:
  raise ValueError(f"""Size {size} not supported""" )

 # load image processor
 __lowerCAmelCase											=		PoolFormerImageProcessor(crop_pct=_UpperCamelCase )

 # Prepare image
 __lowerCAmelCase											=		prepare_img()
 __lowerCAmelCase											=		image_processor(images=_UpperCamelCase		,						return_tensors="""pt""" ).pixel_values

 logger.info(f"""Converting model {model_name}...""" )

 # load original state dict
 __lowerCAmelCase											=		torch.load(_UpperCamelCase		,						map_location=torch.device("""cpu""" ) )

 # rename keys
 __lowerCAmelCase											=		rename_keys(_UpperCamelCase )

 # create HuggingFace model and load state dict
 __lowerCAmelCase											=		PoolFormerForImageClassification(_UpperCamelCase )
 model.load_state_dict(_UpperCamelCase )
 model.eval()

 # Define image processor
 __lowerCAmelCase											=		PoolFormerImageProcessor(crop_pct=_UpperCamelCase )
 __lowerCAmelCase											=		image_processor(images=prepare_img()		,						return_tensors="""pt""" ).pixel_values

 # forward pass
 __lowerCAmelCase											=		model(_UpperCamelCase )
 __lowerCAmelCase											=		outputs.logits

 # define expected logit slices for different models
 if size == "s12":
  __lowerCAmelCase											=		torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9] )
 elif size == "s24":
  __lowerCAmelCase											=		torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5] )
 elif size == "s36":
  __lowerCAmelCase											=		torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8] )
 elif size == "m36":
  __lowerCAmelCase											=		torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8] )
 elif size == "m48":
  __lowerCAmelCase											=		torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3] )
 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     : Tuple    =							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     : str    =							parser.parse_args()
 convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)









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__     )

