


'''simple docstring'''

import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


_UpperCAmelCase :       int										= logging.get_logger(__name__)


class 					a__ (       __SCREAMING_SNAKE_CASE					):







				"""simple docstring"""



				__UpperCamelCase			:						List[str]		=		'AutoTokenizer'
				__UpperCamelCase			:						int		=		['tokenizer']

				__UpperCamelCase			:						Tuple		=		{
				    'semantic_prompt': 1,
				    'coarse_prompt': 2,
				    'fine_prompt': 2,
				}




				def __init__(self				,	__lowercase				,	__lowercase=None						):
										super().__init__(__lowercase						)

										__lowerCAmelCase											= speaker_embeddings




				@classmethod
				def     _snake_case     (cls				,	__lowercase				,	__lowercase="speaker_embeddings_path.json"				,	**__lowercase						):

										if speaker_embeddings_dict_path is not None:
																__lowerCAmelCase											= get_file_from_repo(
																    __lowercase				,	__lowercase				,	subfolder=kwargs.pop('''subfolder'''				,	__lowercase						)				,	cache_dir=kwargs.pop('''cache_dir'''				,	__lowercase						)				,	force_download=kwargs.pop('''force_download'''				,	__lowercase						)				,	proxies=kwargs.pop('''proxies'''				,	__lowercase						)				,	resume_download=kwargs.pop('''resume_download'''				,	__lowercase						)				,	local_files_only=kwargs.pop('''local_files_only'''				,	__lowercase						)				,	use_auth_token=kwargs.pop('''use_auth_token'''				,	__lowercase						)				,	revision=kwargs.pop('''revision'''				,	__lowercase						)				,	)
																if speaker_embeddings_path is None:
																						logger.warning(
																						    F"""`{os.path.join(__lowercase				,	__lowercase						)}` does not exists
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."""						)
																						__lowerCAmelCase											= None
																else:
																						with open(__lowercase						) as speaker_embeddings_json:
																												__lowerCAmelCase											= json.load(__lowercase						)
										else:
																__lowerCAmelCase											= None

										__lowerCAmelCase											= AutoTokenizer.from_pretrained(__lowercase				,	**__lowercase						)

										return cls(tokenizer=__lowercase				,	speaker_embeddings=__lowercase						)




				def     _snake_case     (self				,	__lowercase				,	__lowercase="speaker_embeddings_path.json"				,	__lowercase="speaker_embeddings"				,	__lowercase = False				,	**__lowercase				,	):
										if self.speaker_embeddings is not None:
																os.makedirs(os.path.join(__lowercase				,	__lowercase				,	'''v2'''						)				,	exist_ok=__lowercase						)

																__lowerCAmelCase											= {}

																__lowerCAmelCase											= save_directory

																for prompt_key in self.speaker_embeddings:
																						if prompt_key != "repo_or_path":
																												__lowerCAmelCase											= self._load_voice_preset(__lowercase						)

																												__lowerCAmelCase											= {}
																												for key in self.speaker_embeddings[prompt_key]:
																																		np.save(
																																		    os.path.join(
																																		        embeddings_dict['''repo_or_path''']				,	__lowercase				,	F"""{prompt_key}_{key}"""						)				,	voice_preset[key]				,	allow_pickle=__lowercase				,	)
																																		__lowerCAmelCase											= os.path.join(__lowercase				,	F"""{prompt_key}_{key}.npy"""						)

																												__lowerCAmelCase											= tmp_dict

																with open(os.path.join(__lowercase				,	__lowercase						)				,	'''w'''						) as fp:
																						json.dump(__lowercase				,	__lowercase						)

										super().save_pretrained(__lowercase				,	__lowercase				,	**__lowercase						)




				def     _snake_case     (self				,	__lowercase = None				,	**__lowercase						):
										__lowerCAmelCase											= self.speaker_embeddings[voice_preset]

										__lowerCAmelCase											= {}
										for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
																if key not in voice_preset_paths:
																						raise ValueError(
																						    F"""Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]."""						)

																__lowerCAmelCase											= get_file_from_repo(
																    self.speaker_embeddings.get('''repo_or_path'''				,	'''/'''						)				,	voice_preset_paths[key]				,	subfolder=kwargs.pop('''subfolder'''				,	__lowercase						)				,	cache_dir=kwargs.pop('''cache_dir'''				,	__lowercase						)				,	force_download=kwargs.pop('''force_download'''				,	__lowercase						)				,	proxies=kwargs.pop('''proxies'''				,	__lowercase						)				,	resume_download=kwargs.pop('''resume_download'''				,	__lowercase						)				,	local_files_only=kwargs.pop('''local_files_only'''				,	__lowercase						)				,	use_auth_token=kwargs.pop('''use_auth_token'''				,	__lowercase						)				,	revision=kwargs.pop('''revision'''				,	__lowercase						)				,	)
																if path is None:
																						raise ValueError(
																						    F"""`{os.path.join(self.speaker_embeddings.get("repo_or_path"				,	"/"						)				,	voice_preset_paths[key]						)}` does not exists
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings."""						)

																__lowerCAmelCase											= np.load(__lowercase						)

										return voice_preset_dict




				def     _snake_case     (self				,	__lowercase = None						):
										for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
																if key not in voice_preset:
																						raise ValueError(F"""Voice preset unrecognized, missing {key} as a key."""						)

																if not isinstance(voice_preset[key]				,	np.ndarray						):
																						raise ValueError(F"""{key} voice preset must be a {str(self.preset_shape[key]						)}D ndarray."""						)

																if len(voice_preset[key].shape						) != self.preset_shape[key]:
																						raise ValueError(F"""{key} voice preset must be a {str(self.preset_shape[key]						)}D ndarray."""						)




				def __call__(self				,	__lowercase=None				,	__lowercase=None				,	__lowercase="pt"				,	__lowercase=2_56				,	__lowercase=False				,	__lowercase=True				,	__lowercase=False				,	**__lowercase				,	):
										if voice_preset is not None and not isinstance(__lowercase				,	__lowercase						):
																if (
																    isinstance(__lowercase				,	__lowercase						)
																    and self.speaker_embeddings is not None
																    and voice_preset in self.speaker_embeddings
																):
																						__lowerCAmelCase											= self._load_voice_preset(__lowercase						)

																else:
																						if isinstance(__lowercase				,	__lowercase						) and not voice_preset.endswith('''.npz'''						):
																												__lowerCAmelCase											= voice_preset + '''.npz'''

																						__lowerCAmelCase											= np.load(__lowercase						)

										if voice_preset is not None:
																self._validate_voice_preset_dict(__lowercase				,	**__lowercase						)
																__lowerCAmelCase											= BatchFeature(data=__lowercase				,	tensor_type=__lowercase						)

										__lowerCAmelCase											= self.tokenizer(
										    __lowercase				,	return_tensors=__lowercase				,	padding='''max_length'''				,	max_length=__lowercase				,	return_attention_mask=__lowercase				,	return_token_type_ids=__lowercase				,	add_special_tokens=__lowercase				,	**__lowercase				,	)

										if voice_preset is not None:
																__lowerCAmelCase											= voice_preset

										return encoded_text



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)
