




'''simple docstring'''



import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint




def   lowercase__(			__UpperCamelCase:					Any    ):

      """simple docstring"""






      return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items()    )




def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					List[Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Dict    					=						{}

      for key, value in state_dict.items():
            if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
                  continue

            SCREAMING_SNAKE_CASE   :      List[Any]    					=						key.replace('heads.cmd.mim_head.cls.predictions'      ,'mmm_image_head'    )
            SCREAMING_SNAKE_CASE   :      int    					=						key.replace('heads.cmd.mlm_head.cls.predictions'      ,'mmm_text_head'    )
            SCREAMING_SNAKE_CASE   :      str    					=						key.replace('heads.cmd.itm_head.cls'      ,'itm_head'    )
            SCREAMING_SNAKE_CASE   :      Dict    					=						key.replace('heads.cmd.itm_head.pooler'      ,'itm_head.pooler'    )
            SCREAMING_SNAKE_CASE   :      Dict    					=						key.replace('heads.cmd.clip_head.logit_scale'      ,'flava.logit_scale'    )
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						key.replace('heads.fairseq_mlm.cls.predictions'      ,'mlm_head'    )
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						key.replace('heads.imagenet.mim_head.cls.predictions'      ,'mim_head'    )
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						key.replace('mm_text_projection'      ,'flava.text_to_mm_projection'    )
            SCREAMING_SNAKE_CASE   :      List[str]    					=						key.replace('mm_image_projection'      ,'flava.image_to_mm_projection'    )
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						key.replace('image_encoder.module'      ,'flava.image_model'    )
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						key.replace('text_encoder.module'      ,'flava.text_model'    )
            SCREAMING_SNAKE_CASE   :      Dict    					=						key.replace('mm_encoder.module.encoder.cls_token'      ,'flava.multimodal_model.cls_token'    )
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						key.replace('mm_encoder.module'      ,'flava.multimodal_model'    )
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						key.replace('text_projection'      ,'flava.text_projection'    )
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						key.replace('image_projection'      ,'flava.image_projection'    )

            SCREAMING_SNAKE_CASE   :      Dict    					=						value.float()

      for key, value in codebook_state_dict.items():
            SCREAMING_SNAKE_CASE   :      int    					=						value

      return upgrade




@torch.no_grad()
def   lowercase__(			__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					str      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					Optional[int]=None    ):

      """simple docstring"""






      if config_path is not None:
            SCREAMING_SNAKE_CASE   :      int    					=						FlavaConfig.from_pretrained(__UpperCamelCase    )
      else:
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						FlavaConfig()

      SCREAMING_SNAKE_CASE   :      str    					=						FlavaForPreTraining(__UpperCamelCase    ).eval()

      SCREAMING_SNAKE_CASE   :      int    					=						convert_dalle_checkpoint(__UpperCamelCase      ,__UpperCamelCase      ,save_checkpoint=__UpperCamelCase    )

      if os.path.exists(__UpperCamelCase    ):
            SCREAMING_SNAKE_CASE   :      int    					=						torch.load(__UpperCamelCase      ,map_location='cpu'    )
      else:
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.hub.load_state_dict_from_url(__UpperCamelCase      ,map_location='cpu'    )

      SCREAMING_SNAKE_CASE   :      int    					=						upgrade_state_dict(__UpperCamelCase      ,__UpperCamelCase    )
      hf_model.load_state_dict(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						hf_model.state_dict()
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						count_parameters(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      int    					=						count_parameters(__UpperCamelCase    ) + count_parameters(__UpperCamelCase    )

      assert torch.allclose(__UpperCamelCase      ,__UpperCamelCase      ,atol=1e-3    )

      hf_model.save_pretrained(__UpperCamelCase    )


if __name__ == "__main__":
      UpperCamelCase_    	=				argparse.ArgumentParser()
      parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
      parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
      parser.add_argument("--codebook_path", default=None, type=str, help="Path to flava codebook checkpoint")
      parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
      UpperCamelCase_    	=				parser.parse_args()

      convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)







import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 			__lowerCAmelCase  (						lowerCAmelCase__	,						unittest.TestCase ):
 lowerCAmelCase__							       =	XLMTokenizer
 lowerCAmelCase__							       =	False




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    __lowerCamelCase    				=     [
        '''l''',
        '''o''',
        '''w''',
        '''e''',
        '''r''',
        '''s''',
        '''t''',
        '''i''',
        '''d''',
        '''n''',
        '''w</w>''',
        '''r</w>''',
        '''t</w>''',
        '''lo''',
        '''low''',
        '''er</w>''',
        '''low</w>''',
        '''lowest</w>''',
        '''newer</w>''',
        '''wider</w>''',
        '''<unk>''',
    ]
    __lowerCamelCase    				=     dict(zip(__UpperCAmelCase						,					range(len(__UpperCAmelCase					)					)					)					)
    __lowerCamelCase    				=     ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

    __lowerCamelCase    				=     os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES['''vocab_file''']					)
    __lowerCamelCase    				=     os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES['''merges_file''']					)
    with open(self.vocab_file						,					'''w'''					) as fp:
       fp.write(json.dumps(__UpperCAmelCase					)					)
    with open(self.merges_file						,					'''w'''					) as fp:
       fp.write('''\n'''.join(__UpperCAmelCase					)					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     '''lower newer'''
    __lowerCamelCase    				=     '''lower newer'''
    return input_text, output_text




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     XLMTokenizer(self.vocab_file						,					self.merges_file					)

    __lowerCamelCase    				=     '''lower'''
    __lowerCamelCase    				=     ['''low''', '''er</w>''']
    __lowerCamelCase    				=     tokenizer.tokenize(__UpperCAmelCase					)
    self.assertListEqual(__UpperCAmelCase						,					__UpperCAmelCase					)

    __lowerCamelCase    				=     tokens + ['''<unk>''']
    __lowerCamelCase    				=     [14, 15, 20]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase					)						,					__UpperCAmelCase					)




 @slow
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''					)

    __lowerCamelCase    				=     tokenizer.encode('''sequence builders'''						,					add_special_tokens=__UpperCAmelCase					)
    __lowerCamelCase    				=     tokenizer.encode('''multi-sequence build'''						,					add_special_tokens=__UpperCAmelCase					)

    __lowerCamelCase    				=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase					)
    __lowerCamelCase    				=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase						,					__UpperCAmelCase					)

    assert encoded_sentence == [0] + text + [1]
    assert encoded_pair == [0] + text + [1] + text_a + [1]






