



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      (			lowerCamelCase__   ):
			'''simple docstring'''





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



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Any     =					{}

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

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

						A_     :						int     =					value.float()

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

			return upgrade



@torch.no_grad()
def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__=None   ):
			'''simple docstring'''





			if config_path is not None:
						A_     :						Union[str, Any]     =					FlavaConfig.from_pretrained(lowercase__   )
			else:
						A_     :						Union[str, Any]     =					FlavaConfig()

			A_     :						str     =					FlavaForPreTraining(lowercase__   ).eval()

			A_     :						Union[str, Any]     =					convert_dalle_checkpoint(lowercase__						,				lowercase__						,				save_checkpoint=lowercase__   )

			if os.path.exists(lowercase__   ):
						A_     :						str     =					torch.load(lowercase__						,				map_location="""cpu"""   )
			else:
						A_     :						List[str]     =					torch.hub.load_state_dict_from_url(lowercase__						,				map_location="""cpu"""   )

			A_     :						Dict     =					upgrade_state_dict(lowercase__						,				lowercase__   )
			hf_model.load_state_dict(lowercase__   )
			A_     :						Union[str, Any]     =					hf_model.state_dict()
			A_     :						Any     =					count_parameters(lowercase__   )
			A_     :						str     =					count_parameters(lowercase__   ) + count_parameters(lowercase__   )

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

			hf_model.save_pretrained(lowercase__   )


if __name__ == "__main__":
						lowerCamelCase							:Optional[Any]       = 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							:Optional[int]       = parser.parse_args()

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




'''simple docstring'''




import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad







class      _lowerCAmelCase							(      unittest.TestCase			):


	def    _a     (self							):
				A_     :						Dict     =					10


	def    _a     (self							):
				A_     :						str     =					[1, 2, 3, 4]
				A_     :						List[str]     =					[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
				self.assertEqual(truncate_or_pad(lowercase	,				self.block_size	,				0							)	,				lowercase							)


	def    _a     (self							):
				A_     :						str     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				A_     :						Tuple     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				self.assertEqual(truncate_or_pad(lowercase	,				self.block_size	,				0							)	,				lowercase							)


	def    _a     (self							):
				A_     :						List[Any]     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
				A_     :						str     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				self.assertEqual(truncate_or_pad(lowercase	,				self.block_size	,				0							)	,				lowercase							)


	def    _a     (self							):
				A_     :						Any     =					"""It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this."""
				A_,   A_     :						Optional[int]     =					process_story(lowercase							)
				self.assertEqual(lowercase	,				[]							)


	def    _a     (self							):
				A_     :						Union[str, Any]     =					""""""
				A_,   A_     :						Union[str, Any]     =					process_story(lowercase							)
				self.assertEqual(lowercase	,				[]							)
				self.assertEqual(lowercase	,				[]							)


	def    _a     (self							):
				A_     :						List[Any]     =					(
				    """It was the year of Our Lord one thousand seven hundred and """
				    """seventy-five\n\nSpiritual revelations were conceded to England """
				    """at that favoured period, as at this.\n@highlight\n\nIt was the best of times"""
				)
				A_,   A_     :						int     =					process_story(lowercase							)

				A_     :						List[Any]     =					[
				    """It was the year of Our Lord one thousand seven hundred and seventy-five.""",
				    """Spiritual revelations were conceded to England at that favoured period, as at this.""",
				]
				self.assertEqual(lowercase	,				lowercase							)

				A_     :						Tuple     =					["""It was the best of times."""]
				self.assertEqual(lowercase	,				lowercase							)


	def    _a     (self							):
				A_     :						Dict     =					torch.tensor([1, 2, 3, 4]							)
				A_     :						Union[str, Any]     =					torch.tensor([1, 1, 1, 1]							)
				np.testing.assert_array_equal(build_mask(lowercase	,				0							).numpy()	,				expected.numpy()							)


	def    _a     (self							):
				A_     :						Union[str, Any]     =					torch.tensor([1, 2, 3, 4, 23, 23, 23]							)
				A_     :						List[str]     =					torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
				np.testing.assert_array_equal(build_mask(lowercase	,				23							).numpy()	,				expected.numpy()							)


	def    _a     (self							):
				A_     :						Optional[Any]     =					torch.tensor([8, 2, 3, 4, 1, 1, 1]							)
				A_     :						Tuple     =					torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
				np.testing.assert_array_equal(build_mask(lowercase	,				1							).numpy()	,				expected.numpy()							)


	def    _a     (self							):
				A_     :						List[str]     =					101
				A_     :						str     =					torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]							)
				A_     :						int     =					torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]							)

				A_     :						Optional[Any]     =					compute_token_type_ids(lowercase	,				lowercase							)
				np.testing.assert_array_equal(lowercase	,				lowercase							)