




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     (				lowercase      :			str	):


      '''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     (				lowercase      :			List[Any]      ,				lowercase      :			str	):


      '''simple docstring'''



      lowerCamelCase_							=       {}

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

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

            lowerCamelCase_							=       value.float()

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

      return upgrade

@torch.no_grad()
def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Union[str, Any]      ,				lowercase      :			Optional[Any]      ,				lowercase      :			Optional[int]      ,				lowercase      :			Optional[int]=None	):


      '''simple docstring'''



      if config_path is not None:
            lowerCamelCase_							=       FlavaConfig.from_pretrained(lowercase	)
      else:
            lowerCamelCase_							=       FlavaConfig()

      lowerCamelCase_							=       FlavaForPreTraining(lowercase	).eval()

      lowerCamelCase_							=       convert_dalle_checkpoint(lowercase      ,				lowercase      ,				save_checkpoint=lowercase	)

      if os.path.exists(lowercase	):
            lowerCamelCase_							=       torch.load(lowercase      ,				map_location='cpu'	)
      else:
            lowerCamelCase_							=       torch.hub.load_state_dict_from_url(lowercase      ,				map_location='cpu'	)

      lowerCamelCase_							=       upgrade_state_dict(lowercase      ,				lowercase	)
      hf_model.load_state_dict(lowercase	)
      lowerCamelCase_							=       hf_model.state_dict()
      lowerCamelCase_							=       count_parameters(lowercase	)
      lowerCamelCase_							=       count_parameters(lowercase	) + count_parameters(lowercase	)

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

      hf_model.save_pretrained(lowercase	)


if __name__ == "__main__":
 lowerCamelCase							:						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"""
from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__    						=			logging.get_logger(__name__)

lowerCAmelCase__    						=			{
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}


class _lowerCAmelCase						(    __UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					Any 					=				'time_series_transformer'
						SCREAMING_SNAKE_CASE_:					List[str] 					=				{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						    'num_hidden_layers': 'encoder_layers',
						}







						def __init__(    self   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = "student_t"   ,				lowerCAmelCase_ = "nll"   ,				lowerCAmelCase_ = 1   ,				lowerCAmelCase_ = [1, 2, 3, 4, 5, 6, 7]   ,				lowerCAmelCase_ = "mean"   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = 3_2   ,				lowerCAmelCase_ = 3_2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = True   ,				lowerCAmelCase_ = "gelu"   ,				lowerCAmelCase_ = 6_4   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 1_0_0   ,				lowerCAmelCase_ = 0.02   ,				lowerCAmelCase_=True   ,				**lowerCAmelCase_   ,				)		->							str:
										# time series specific configuration
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    prediction_length
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    context_length or prediction_length
										_SCREAMING_SNAKE_CASE   :		Any       		=    distribution_output
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    loss
										_SCREAMING_SNAKE_CASE   :		Any       		=    input_size
										_SCREAMING_SNAKE_CASE   :		Any       		=    num_time_features
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    lags_sequence
										_SCREAMING_SNAKE_CASE   :		Dict       		=    scaling
										_SCREAMING_SNAKE_CASE   :		Any       		=    num_dynamic_real_features
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    num_static_real_features
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    num_static_categorical_features
										if cardinality and num_static_categorical_features > 0:
														if len(lowerCAmelCase_							) != num_static_categorical_features:
																		raise ValueError(
																		    'The cardinality should be a list of the same length as `num_static_categorical_features`'							)
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    cardinality
										else:
														_SCREAMING_SNAKE_CASE   :		Dict       		=    [0]
										if embedding_dimension and num_static_categorical_features > 0:
														if len(lowerCAmelCase_							) != num_static_categorical_features:
																		raise ValueError(
																		    'The embedding dimension should be a list of the same length as `num_static_categorical_features`'							)
														_SCREAMING_SNAKE_CASE   :		int       		=    embedding_dimension
										else:
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    [min(5_0   ,				(cat + 1) // 2							) for cat in self.cardinality]
										_SCREAMING_SNAKE_CASE   :		str       		=    num_parallel_samples

										# Transformer architecture configuration
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    input_size * len(lowerCAmelCase_							) + self._number_of_features
										_SCREAMING_SNAKE_CASE   :		int       		=    d_model
										_SCREAMING_SNAKE_CASE   :		str       		=    encoder_attention_heads
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    decoder_attention_heads
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    encoder_ffn_dim
										_SCREAMING_SNAKE_CASE   :		Dict       		=    decoder_ffn_dim
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    encoder_layers
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    decoder_layers

										_SCREAMING_SNAKE_CASE   :		Dict       		=    dropout
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    attention_dropout
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    activation_dropout
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    encoder_layerdrop
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    decoder_layerdrop

										_SCREAMING_SNAKE_CASE   :		Dict       		=    activation_function
										_SCREAMING_SNAKE_CASE   :		str       		=    init_std

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    use_cache

										super().__init__(is_encoder_decoder=lowerCAmelCase_   ,				**lowerCAmelCase_							)







						@property
						def 			A      (    self							)		->							int:
										return (
										    sum(self.embedding_dimension							)
										    + self.num_dynamic_real_features
										    + self.num_time_features
										    + self.num_static_real_features
										    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
										)

