

'''simple docstring'''




import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 							lowercase_						(		a			,							unittest.TestCase			):

  '''simple docstring'''





  __lowerCAmelCase       :			int     	= DebertaTokenizer
  __lowerCAmelCase       :			Optional[Any]     	= True
  __lowerCAmelCase       :			Union[str, Any]     	= DebertaTokenizerFast
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    UpperCAmelCase    				=						[
        'l',
        'o',
        'w',
        'e',
        'r',
        's',
        't',
        'i',
        'd',
        'n',
        '\u0120',
        '\u0120l',
        '\u0120n',
        '\u0120lo',
        '\u0120low',
        'er',
        '\u0120lowest',
        '\u0120newer',
        '\u0120wider',
        '[UNK]',
    ]
    UpperCAmelCase    				=						dict(zip(a_ ,			range(len(a_  )  )  )  )
    UpperCAmelCase    				=						['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
    UpperCAmelCase    				=						{'unk_token': '[UNK]'}

    UpperCAmelCase    				=						os.path.join(self.tmpdirname ,			VOCAB_FILES_NAMES['vocab_file']  )
    UpperCAmelCase    				=						os.path.join(self.tmpdirname ,			VOCAB_FILES_NAMES['merges_file']  )
    with open(self.vocab_file ,			'w' ,			encoding='utf-8'  ) as fp:
      fp.write(json.dumps(a_  ) + '\n'  )
    with open(self.merges_file ,			'w' ,			encoding='utf-8'  ) as fp:
      fp.write('\n'.join(a_  )  )
  def     snake_case_      (  self ,			**a_  )				->					str:


    """simple docstring"""
    kwargs.update(self.special_tokens_map  )
    return self.tokenizer_class.from_pretrained(self.tmpdirname ,			**a_  )
  def     snake_case_      (  self ,			a_  )				->					Optional[Any]:


    """simple docstring"""
    UpperCAmelCase    				=						'lower newer'
    UpperCAmelCase    				=						'lower newer'
    return input_text, output_text
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_tokenizer()
    UpperCAmelCase    				=						'lower newer'
    UpperCAmelCase    				=						['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
    UpperCAmelCase    				=						tokenizer.tokenize(a_  )
    self.assertListEqual(a_ ,			a_  )

    UpperCAmelCase    				=						tokens + [tokenizer.unk_token]
    UpperCAmelCase    				=						[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(a_  ) ,			a_  )
  def     snake_case_      (  self  )				->					List[Any]:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_tokenizer()
    UpperCAmelCase    				=						tokenizer('Hello' ,			'World'  )
    UpperCAmelCase    				=						[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
    self.assertListEqual(tokd['token_type_ids'] ,			a_  )
  @slow
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						self.tokenizer_class.from_pretrained('microsoft/deberta-base'  )

    UpperCAmelCase    				=						tokenizer.encode('sequence builders' ,			add_special_tokens=a_  )
    UpperCAmelCase    				=						tokenizer.encode('multi-sequence build' ,			add_special_tokens=a_  )

    UpperCAmelCase    				=						tokenizer.encode(
        'sequence builders' ,			add_special_tokens=a_ ,			add_prefix_space=a_  )
    UpperCAmelCase    				=						tokenizer.encode(
        'sequence builders' ,			'multi-sequence build' ,			add_special_tokens=a_ ,			add_prefix_space=a_  )

    UpperCAmelCase    				=						tokenizer.build_inputs_with_special_tokens(a_  )
    UpperCAmelCase    				=						tokenizer.build_inputs_with_special_tokens(a_ ,			a_  )

    assert encoded_sentence == encoded_text_from_decode
    assert encoded_pair == encoded_pair_from_decode





  @slow
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						[self.tokenizer_class]
    if self.test_rust_tokenizer:
      tokenizer_classes.append(self.rust_tokenizer_class  )

    for tokenizer_class in tokenizer_classes:
      UpperCAmelCase    				=						tokenizer_class.from_pretrained('microsoft/deberta-base'  )

      UpperCAmelCase    				=						[
          'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
          'ALBERT incorporates two parameter reduction techniques',
          'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
          ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
          ' vocabulary embedding.',
      ]

      UpperCAmelCase    				=						tokenizer(a_ ,			padding=a_  )
      UpperCAmelCase    				=						[tokenizer.decode(a_ ,			skip_special_tokens=a_  ) for seq in encoding['input_ids']]

      # fmt: off
      UpperCAmelCase    				=						{
          'input_ids': [
              [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 3_5, 8_3, 2_5_1_9_1, 1_6_3, 1_8_8_5_4, 1_3, 1_2_1_5_6, 1_2, 1_6_1_0_1, 2_5_3_7_6, 1_3_8_0_7, 9, 2_2_2_0_5, 2_7_8_9_3, 1_6_3_5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
              [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 2_4_5_3_6, 8_0, 4_3_7_9_7, 4_8_7_8, 7_3_7_3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
              [1, 1_3_3, 7_8, 6_5, 1_6, 1_0, 3_7_2_4, 1_5_3_8, 3_3_1_8_3, 1_1_3_0_3, 4_3_7_9_7, 1_9_3_8, 4, 8_7_0, 2_4_1_6_5, 2_9_1_0_5, 5, 7_3_9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 3_6_1_7_3, 8_8, 8_0, 6_5_0, 7_8_2_1, 4_5_9_4_0, 6, 5_2, 2_5_5_9, 5, 1_8_3_6, 9, 5, 7_3_9_7, 1_3_1_7_1, 3_1, 5, 1_8_3_6, 9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 4, 2]
          ],
          'token_type_ids': [
              [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
              [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
              [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
          ],
          'attention_mask': [
              [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
              [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
              [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
          ]
      }
      # fmt: on

      UpperCAmelCase    				=						[
          'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
          'ALBERT incorporates two parameter reduction techniques',
          'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
          ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
          ' vocabulary embedding.',
      ]

      self.assertDictEqual(encoding.data ,			a_  )

      for expected, decoded in zip(a_ ,			a_  ):
        self.assertEqual(a_ ,			a_  )





'''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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str    ):
  # encoder.embeddings are double copied in original FLAVA
  return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items()    )


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int							, SCREAMING_SNAKE_CASE   : Optional[int]    ):
  UpperCAmelCase    				=						{}

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

    UpperCAmelCase    				=						key.replace('heads.cmd.mim_head.cls.predictions'							, 'mmm_image_head'    )
    UpperCAmelCase    				=						key.replace('heads.cmd.mlm_head.cls.predictions'							, 'mmm_text_head'    )
    UpperCAmelCase    				=						key.replace('heads.cmd.itm_head.cls'							, 'itm_head'    )
    UpperCAmelCase    				=						key.replace('heads.cmd.itm_head.pooler'							, 'itm_head.pooler'    )
    UpperCAmelCase    				=						key.replace('heads.cmd.clip_head.logit_scale'							, 'flava.logit_scale'    )
    UpperCAmelCase    				=						key.replace('heads.fairseq_mlm.cls.predictions'							, 'mlm_head'    )
    UpperCAmelCase    				=						key.replace('heads.imagenet.mim_head.cls.predictions'							, 'mim_head'    )
    UpperCAmelCase    				=						key.replace('mm_text_projection'							, 'flava.text_to_mm_projection'    )
    UpperCAmelCase    				=						key.replace('mm_image_projection'							, 'flava.image_to_mm_projection'    )
    UpperCAmelCase    				=						key.replace('image_encoder.module'							, 'flava.image_model'    )
    UpperCAmelCase    				=						key.replace('text_encoder.module'							, 'flava.text_model'    )
    UpperCAmelCase    				=						key.replace('mm_encoder.module.encoder.cls_token'							, 'flava.multimodal_model.cls_token'    )
    UpperCAmelCase    				=						key.replace('mm_encoder.module'							, 'flava.multimodal_model'    )
    UpperCAmelCase    				=						key.replace('text_projection'							, 'flava.text_projection'    )
    UpperCAmelCase    				=						key.replace('image_projection'							, 'flava.image_projection'    )

    UpperCAmelCase    				=						value.float()

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

  return upgrade


@torch.no_grad()
def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[str]							, SCREAMING_SNAKE_CASE   : Optional[Any]							, SCREAMING_SNAKE_CASE   : Optional[int]							, SCREAMING_SNAKE_CASE   : Any=None    ):
  if config_path is not None:
    UpperCAmelCase    				=						FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE    )
  else:
    UpperCAmelCase    				=						FlavaConfig()

  UpperCAmelCase    				=						FlavaForPreTraining(SCREAMING_SNAKE_CASE    ).eval()

  UpperCAmelCase    				=						convert_dalle_checkpoint(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, save_checkpoint=SCREAMING_SNAKE_CASE    )

  if os.path.exists(SCREAMING_SNAKE_CASE    ):
    UpperCAmelCase    				=						torch.load(SCREAMING_SNAKE_CASE							, map_location='cpu'    )
  else:
    UpperCAmelCase    				=						torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE							, map_location='cpu'    )

  UpperCAmelCase    				=						upgrade_state_dict(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  hf_model.load_state_dict(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						hf_model.state_dict()
  UpperCAmelCase    				=						count_parameters(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						count_parameters(SCREAMING_SNAKE_CASE    ) + count_parameters(SCREAMING_SNAKE_CASE    )

  assert torch.allclose(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, atol=1E-3    )

  hf_model.save_pretrained(SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
  _a					:			str							=    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')
  _a					:			str							=    parser.parse_args()

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


