


'''simple docstring'''

from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
	import tensorflow as tf


_UpperCAmelCase :       Optional[Any]										= logging.get_logger(__name__)


@dataclass
class 					a__ (       __SCREAMING_SNAKE_CASE					):







				"""simple docstring"""



				__UpperCamelCase			:						Dict		=		[
				    'no_inference',
				    'no_cuda',
				    'no_tpu',
				    'no_speed',
				    'no_memory',
				    'no_env_print',
				    'no_multi_process',
				]




				def __init__(self				,	**__lowercase						):
										for deprecated_arg in self.deprecated_args:
																if deprecated_arg in kwargs:
																						__lowerCAmelCase											= deprecated_arg[3:]
																						__lowerCAmelCase											= not kwargs.pop(__lowercase						)
																						logger.warning(
																						    F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
																						    F""" {positive_arg}={kwargs[positive_arg]}"""						)
										__lowerCAmelCase											= kwargs.pop('''tpu_name'''				,	self.tpu_name						)
										__lowerCAmelCase											= kwargs.pop('''device_idx'''				,	self.device_idx						)
										__lowerCAmelCase											= kwargs.pop('''eager_mode'''				,	self.eager_mode						)
										__lowerCAmelCase											= kwargs.pop('''use_xla'''				,	self.use_xla						)
										super().__init__(**__lowercase						)

				__UpperCamelCase			:						Union[str, Any]		=		field(
				    default=__SCREAMING_SNAKE_CASE							,  metadata={'help': 'Name of TPU'}							,  )
				__UpperCamelCase			:						List[str]		=		field(
				    default=0							,  metadata={'help': 'CPU / GPU device index. Defaults to 0.'}							,  )
				__UpperCamelCase			:						Union[str, Any]		=		field(default=__SCREAMING_SNAKE_CASE							,  metadata={'help': 'Benchmark models in eager model.'}					)
				__UpperCamelCase			:						List[str]		=		field(
				    default=__SCREAMING_SNAKE_CASE							,  metadata={
				        'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
				    }							,  )




				@cached_property
				def     _snake_case     (self						):
										requires_backends(self				,	['''tf''']						)
										__lowerCAmelCase											= None
										if self.tpu:
																try:
																						if self.tpu_name:
																												__lowerCAmelCase											= tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name						)
																						else:
																												__lowerCAmelCase											= tf.distribute.cluster_resolver.TPUClusterResolver()
																except ValueError:
																						__lowerCAmelCase											= None
										return tpu




				@cached_property
				def     _snake_case     (self						):
										requires_backends(self				,	['''tf''']						)
										if self.is_tpu:
																tf.config.experimental_connect_to_cluster(self._setup_tpu						)
																tf.tpu.experimental.initialize_tpu_system(self._setup_tpu						)

																__lowerCAmelCase											= tf.distribute.TPUStrategy(self._setup_tpu						)
										else:
																# currently no multi gpu is allowed
																if self.is_gpu:
																						# TODO: Currently only single GPU is supported
																						tf.config.set_visible_devices(self.gpu_list[self.device_idx]				,	'''GPU'''						)
																						__lowerCAmelCase											= tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}"""						)
																else:
																						tf.config.set_visible_devices([]				,	'''GPU'''						)  # disable GPU
																						__lowerCAmelCase											= tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}"""						)

										return strategy




				@property
				def     _snake_case     (self						):
										requires_backends(self				,	['''tf''']						)
										return self._setup_tpu is not None




				@property
				def     _snake_case     (self						):
										requires_backends(self				,	['''tf''']						)
										return self._setup_strategy




				@property
				def     _snake_case     (self						):
										requires_backends(self				,	['''tf''']						)
										return tf.config.list_physical_devices('''GPU'''						)




				@property
				def     _snake_case     (self						):
										requires_backends(self				,	['''tf''']						)
										if self.cuda:
																return len(self.gpu_list						)
										return 0




				@property
				def     _snake_case     (self						):
										return self.n_gpu > 0



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			(	a_						)		->	List[str]:







				"""simple docstring"""



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


def 						UpperCAmelCase			(	a_		,   a_						)		->	Tuple:







				"""simple docstring"""



				__A   			= {}

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

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

								__A   			= value.float()

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

				return upgrade


@torch.no_grad()
def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_=None						)		->	Tuple:







				"""simple docstring"""



				if config_path is not None:
								__A   			= FlavaConfig.from_pretrained(a_						)
				else:
								__A   			= FlavaConfig()

				__A   			= FlavaForPreTraining(a_						).eval()

				__A   			= convert_dalle_checkpoint(a_		,   a_		,   save_checkpoint=a_						)

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

				__A   			= upgrade_state_dict(a_		,   a_						)
				hf_model.load_state_dict(a_						)
				__A   			= hf_model.state_dict()
				__A   			= count_parameters(a_						)
				__A   			= count_parameters(a_						) + count_parameters(a_						)

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

				hf_model.save_pretrained(a_						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							: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')
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				parser.parse_args()

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