"""simple docstring"""






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import (
           AutoProcessor,
           BertTokenizerFast,
           BlipImageProcessor,
           GPTaTokenizer,
           InstructBlipProcessor,
           PreTrainedTokenizerFast,
       )





@require_vision
class 				_A		( unittest.TestCase			):

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			tempfile.mkdtemp()

            lowercase														=			BlipImageProcessor()
            lowercase														=			GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""		)
            lowercase														=			BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""		)

            lowercase														=			InstructBlipProcessor(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)

            processor.save_pretrained(self.tmpdirname		)

      def        A__ (		self	, **__lowerCAmelCase		):







            """simple docstring"""



            return AutoProcessor.from_pretrained(self.tmpdirname	, **__lowerCAmelCase		).tokenizer

      def        A__ (		self	, **__lowerCAmelCase		):







            """simple docstring"""



            return AutoProcessor.from_pretrained(self.tmpdirname	, **__lowerCAmelCase		).image_processor

      def        A__ (		self	, **__lowerCAmelCase		):







            """simple docstring"""



            return AutoProcessor.from_pretrained(self.tmpdirname	, **__lowerCAmelCase		).qformer_tokenizer

      def        A__ (		self		):







            """simple docstring"""



            shutil.rmtree(self.tmpdirname		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			[np.random.randint(255	, size=(3, 30, 400)	, dtype=np.uinta		)]

            lowercase														=			[Image.fromarray(np.moveaxis(__lowerCAmelCase	, 0	, -1		)		) for x in image_inputs]

            return image_inputs

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			InstructBlipProcessor(
                tokenizer=self.get_tokenizer()	, image_processor=self.get_image_processor()	, qformer_tokenizer=self.get_qformer_tokenizer()	, )
            processor.save_pretrained(self.tmpdirname		)

            lowercase														=			self.get_tokenizer(bos_token="""(BOS)"""	, eos_token="""(EOS)"""		)
            lowercase														=			self.get_image_processor(do_normalize=__lowerCAmelCase	, padding_value=1.0		)

            lowercase														=			InstructBlipProcessor.from_pretrained(
                self.tmpdirname	, bos_token="""(BOS)"""	, eos_token="""(EOS)"""	, do_normalize=__lowerCAmelCase	, padding_value=1.0		)

            self.assertEqual(processor.tokenizer.get_vocab()	, tokenizer_add_kwargs.get_vocab()		)
            self.assertIsInstance(processor.tokenizer	, __lowerCAmelCase		)

            self.assertEqual(processor.image_processor.to_json_string()	, image_processor_add_kwargs.to_json_string()		)
            self.assertIsInstance(processor.image_processor	, __lowerCAmelCase		)
            self.assertIsInstance(processor.qformer_tokenizer	, __lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.get_image_processor()
            lowercase														=			self.get_tokenizer()
            lowercase														=			self.get_qformer_tokenizer()

            lowercase														=			InstructBlipProcessor(
                tokenizer=__lowerCAmelCase	, image_processor=__lowerCAmelCase	, qformer_tokenizer=__lowerCAmelCase		)

            lowercase														=			self.prepare_image_inputs()

            lowercase														=			image_processor(__lowerCAmelCase	, return_tensors="""np"""		)
            lowercase														=			processor(images=__lowerCAmelCase	, return_tensors="""np"""		)

            for key in input_feat_extract.keys():
                  self.assertAlmostEqual(input_feat_extract[key].sum()	, input_processor[key].sum()	, delta=1E-2		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.get_image_processor()
            lowercase														=			self.get_tokenizer()
            lowercase														=			self.get_qformer_tokenizer()

            lowercase														=			InstructBlipProcessor(
                tokenizer=__lowerCAmelCase	, image_processor=__lowerCAmelCase	, qformer_tokenizer=__lowerCAmelCase		)

            lowercase														=			"""lower newer"""

            lowercase														=			processor(text=__lowerCAmelCase		)

            lowercase														=			tokenizer(__lowerCAmelCase	, return_token_type_ids=__lowerCAmelCase		)
            lowercase														=			qformer_tokenizer(__lowerCAmelCase	, return_token_type_ids=__lowerCAmelCase		)

            for key in encoded_tokens.keys():
                  self.assertListEqual(encoded_tokens[key]	, encoded_processor[key]		)

            for key in encoded_tokens_qformer.keys():
                  self.assertListEqual(encoded_tokens_qformer[key]	, encoded_processor["""qformer_""" + key]		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.get_image_processor()
            lowercase														=			self.get_tokenizer()
            lowercase														=			self.get_qformer_tokenizer()

            lowercase														=			InstructBlipProcessor(
                tokenizer=__lowerCAmelCase	, image_processor=__lowerCAmelCase	, qformer_tokenizer=__lowerCAmelCase		)

            lowercase														=			"""lower newer"""
            lowercase														=			self.prepare_image_inputs()

            lowercase														=			processor(text=__lowerCAmelCase	, images=__lowerCAmelCase		)

            self.assertListEqual(
                list(inputs.keys()		)	, ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""]	, )

            # test if it raises when no input is passed
            with pytest.raises(__lowerCAmelCase		):
                  processor()

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.get_image_processor()
            lowercase														=			self.get_tokenizer()
            lowercase														=			self.get_qformer_tokenizer()

            lowercase														=			InstructBlipProcessor(
                tokenizer=__lowerCAmelCase	, image_processor=__lowerCAmelCase	, qformer_tokenizer=__lowerCAmelCase		)

            lowercase														=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            lowercase														=			processor.batch_decode(__lowerCAmelCase		)
            lowercase														=			tokenizer.batch_decode(__lowerCAmelCase		)

            self.assertListEqual(__lowerCAmelCase	, __lowerCAmelCase		)







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.get_image_processor()
            lowercase														=			self.get_tokenizer()
            lowercase														=			self.get_qformer_tokenizer()

            lowercase														=			InstructBlipProcessor(
                tokenizer=__lowerCAmelCase	, image_processor=__lowerCAmelCase	, qformer_tokenizer=__lowerCAmelCase		)

            lowercase														=			"""lower newer"""
            lowercase														=			self.prepare_image_inputs()

            lowercase														=			processor(text=__lowerCAmelCase	, images=__lowerCAmelCase		)

            self.assertListEqual(
                list(inputs.keys()		)	, ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""]	, )




"""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     UpperCAmelCase__ (       lowerCAmelCase__  :str	)				->					Optional[int]:
      '''simple docstring'''


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


def     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :Union[str, Any]	)				->					str:
      '''simple docstring'''


      lowercase														=			{}

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

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

            lowercase														=			value.float()

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

      return upgrade


@torch.no_grad()
def     UpperCAmelCase__ (       lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :Optional[int]      ,	lowerCAmelCase__  :str=None	)				->					int:
      '''simple docstring'''


      if config_path is not None:
            lowercase														=			FlavaConfig.from_pretrained(lowerCAmelCase__	)
      else:
            lowercase														=			FlavaConfig()

      lowercase														=			FlavaForPreTraining(lowerCAmelCase__	).eval()

      lowercase														=			convert_dalle_checkpoint(lowerCAmelCase__      ,	lowerCAmelCase__      ,	save_checkpoint=lowerCAmelCase__	)

      if os.path.exists(lowerCAmelCase__	):
            lowercase														=			torch.load(lowerCAmelCase__      ,	map_location="""cpu"""	)
      else:
            lowercase														=			torch.hub.load_state_dict_from_url(lowerCAmelCase__      ,	map_location="""cpu"""	)

      lowercase														=			upgrade_state_dict(lowerCAmelCase__      ,	lowerCAmelCase__	)
      hf_model.load_state_dict(lowerCAmelCase__	)
      lowercase														=			hf_model.state_dict()
      lowercase														=			count_parameters(lowerCAmelCase__	)
      lowercase														=			count_parameters(lowerCAmelCase__	) + count_parameters(lowerCAmelCase__	)

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

      hf_model.save_pretrained(lowerCAmelCase__	)


if __name__ == "__main__":
       __lowerCAmelCase					: Tuple        =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""")
       __lowerCAmelCase					: List[str]        =parser.parse_args()

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



