


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     a   (    __a     )						->			Any:



					'''simple docstring'''






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


def     a   (    __a				,      __a     )						->			Optional[Any]:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				{}

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

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

										UpperCamelCase__     :Optional[int]							      =				value.float()

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

					return upgrade


@torch.no_grad()
def     a   (    __a				,      __a				,      __a				,      __a=None     )						->			List[Any]:



					'''simple docstring'''






					if config_path is not None:
										UpperCamelCase__     :str							      =				FlavaConfig.from_pretrained(__lowerCAmelCase     )
					else:
										UpperCamelCase__     :str							      =				FlavaConfig()

					UpperCamelCase__     :Union[str, Any]							      =				FlavaForPreTraining(__lowerCAmelCase     ).eval()

					UpperCamelCase__     :List[Any]							      =				convert_dalle_checkpoint(__lowerCAmelCase				,      __lowerCAmelCase				,      save_checkpoint=__lowerCAmelCase     )

					if os.path.exists(__lowerCAmelCase     ):
										UpperCamelCase__     :List[str]							      =				torch.load(__lowerCAmelCase				,      map_location='''cpu'''     )
					else:
										UpperCamelCase__     :List[Any]							      =				torch.hub.load_state_dict_from_url(__lowerCAmelCase				,      map_location='''cpu'''     )

					UpperCamelCase__     :Tuple							      =				upgrade_state_dict(__lowerCAmelCase				,      __lowerCAmelCase     )
					hf_model.load_state_dict(__lowerCAmelCase     )
					UpperCamelCase__     :Any							      =				hf_model.state_dict()
					UpperCamelCase__     :str							      =				count_parameters(__lowerCAmelCase     )
					UpperCamelCase__     :Any							      =				count_parameters(__lowerCAmelCase     ) + count_parameters(__lowerCAmelCase     )

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

					hf_model.save_pretrained(__lowerCAmelCase     )


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

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



'''simple docstring'''
import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
		import torch
@require_torch
@require_optimum
@slow
class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				'''hf-internal-testing/tiny-random-t5'''
							UpperCamelCase__     :int							      =				AutoTokenizer.from_pretrained(UpperCamelCase_							)
							UpperCamelCase__     :int							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

							UpperCamelCase__     :int							      =				tokenizer('''This is me'''					,     return_tensors='''pt'''							)

							UpperCamelCase__     :Optional[int]							      =				model.to_bettertransformer()

							self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

							UpperCamelCase__     :int							      =				model.generate(**UpperCamelCase_							)

							UpperCamelCase__     :Optional[Any]							      =				model.reverse_bettertransformer()

							self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

							with tempfile.TemporaryDirectory() as tmpdirname:
												model.save_pretrained(UpperCamelCase_							)

												UpperCamelCase__     :Tuple							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

												self.assertFalse(
												    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()							)							)

												UpperCamelCase__     :Tuple							      =				model_reloaded.generate(**UpperCamelCase_							)
												self.assertTrue(torch.allclose(UpperCamelCase_					,     UpperCamelCase_							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				'''hf-internal-testing/tiny-random-t5'''
							UpperCamelCase__     :Dict							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

							UpperCamelCase__     :Union[str, Any]							      =				model.to_bettertransformer()

							with tempfile.TemporaryDirectory() as tmpdirname:
												with self.assertRaises(UpperCamelCase_							):
																	model.save_pretrained(UpperCamelCase_							)

												UpperCamelCase__     :Tuple							      =				model.reverse_bettertransformer()
												model.save_pretrained(UpperCamelCase_							)