



"""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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple			):



					"""simple docstring"""





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







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Any				,    SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					{}

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

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

										snake_case_					:		Tuple 					=					value.float()

					for key, value in codebook_state_dict.items():
										snake_case_					:		Union[str, Any] 					=					value

					return upgrade







@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Any				,    SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  Tuple=None			):



					"""simple docstring"""





					if config_path is not None:
										snake_case_					:		Dict 					=					FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE__			)
					else:
										snake_case_					:		Union[str, Any] 					=					FlavaConfig()

					snake_case_					:		Optional[Any] 					=					FlavaForPreTraining(SCREAMING_SNAKE_CASE__			).eval()

					snake_case_					:		str 					=					convert_dalle_checkpoint(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    save_checkpoint=SCREAMING_SNAKE_CASE__			)

					if os.path.exists(SCREAMING_SNAKE_CASE__			):
										snake_case_					:		str 					=					torch.load(SCREAMING_SNAKE_CASE__				,    map_location="""cpu"""			)
					else:
										snake_case_					:		str 					=					torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__				,    map_location="""cpu"""			)

					snake_case_					:		Tuple 					=					upgrade_state_dict(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					hf_model.load_state_dict(SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Any 					=					hf_model.state_dict()
					snake_case_					:		Any 					=					count_parameters(SCREAMING_SNAKE_CASE__			)
					snake_case_					:		int 					=					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_			   =					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_			   =					parser.parse_args()

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









"""simple docstring"""

from random import randint
from tempfile import TemporaryFile

import numpy as np







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Optional[int]			):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					0
					if start < end:
										snake_case_					:		Union[str, Any] 					=					randint(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										snake_case_					:		List[Any] 					=					a[end]
										snake_case_					:		Dict 					=					a[pivot]
										snake_case_					:		Any 					=					temp

										snake_case_					,			snake_case_					:		Dict 					=					_in_place_partition(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    p - 1			)
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE__				,    p + 1				,    SCREAMING_SNAKE_CASE__			)
					return count







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					0
					snake_case_					:		List[Any] 					=					randint(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Dict 					=					a[end]
					snake_case_					:		List[Any] 					=					a[pivot]
					snake_case_					:		Optional[Any] 					=					temp
					snake_case_					:		List[str] 					=					start - 1
					for index in range(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			):
										count += 1
										if a[index] < a[end]:  # check if current val is less than pivot value
															snake_case_					:		Any 					=					new_pivot_index + 1
															snake_case_					:		Tuple 					=					a[new_pivot_index]
															snake_case_					:		Optional[int] 					=					a[index]
															snake_case_					:		Tuple 					=					temp

					snake_case_					:		Union[str, Any] 					=					a[new_pivot_index + 1]
					snake_case_					:		Union[str, Any] 					=					a[end]
					snake_case_					:		Union[str, Any] 					=					temp
					return new_pivot_index + 1, count


a_			   =					TemporaryFile()
a_			   =					100  # 1000 elements are to be sorted


a_   ,    a_			   =					0, 1  # mean and standard deviation
a_			   =					np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a_			   =					np.load(outfile)
a_			   =					len(M) - 1
a_			   =					_in_place_quick_sort(M, 0, r)

print(
    '''No of Comparisons for 100 elements selected from a standard normal distribution'''
    '''is :'''
)


print(z)




