
"""simple docstring"""

import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()






def 							A						(    snake_case				:int    , snake_case				:Optional[Any]    , snake_case				:Optional[Any]    , snake_case				:Optional[Any]    , snake_case				:Tuple		)				->     Optional[Any]:
 # Initialise PyTorch model.
 # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
 # TapasConfig to False.

 # initialize configuration from json file
 __UpperCamelCase      =	TapasConfig.from_json_file(lowerCAmelCase__		)
 # set absolute/relative position embeddings parameter
 __UpperCamelCase      =	reset_position_index_per_cell

 # set remaining parameters of TapasConfig as well as the model based on the task
 if task == "SQA":
  __UpperCamelCase      =	TapasForQuestionAnswering(config=lowerCAmelCase__		)
 elif task == "WTQ":
  # run_task_main.py hparams
  __UpperCamelCase      =	4
  __UpperCamelCase      =	True
  # hparam_utils.py hparams
  __UpperCamelCase      =	0.664_694
  __UpperCamelCase      =	0.207_951
  __UpperCamelCase      =	0.121_194
  __UpperCamelCase      =	True
  __UpperCamelCase      =	True
  __UpperCamelCase      =	False
  __UpperCamelCase      =	0.0_352_513

  __UpperCamelCase      =	TapasForQuestionAnswering(config=lowerCAmelCase__		)
 elif task == "WIKISQL_SUPERVISED":
  # run_task_main.py hparams
  __UpperCamelCase      =	4
  __UpperCamelCase      =	False
  # hparam_utils.py hparams
  __UpperCamelCase      =	36.4_519
  __UpperCamelCase      =	0.903_421
  __UpperCamelCase      =	2_2_2.0_8_8
  __UpperCamelCase      =	True
  __UpperCamelCase      =	True
  __UpperCamelCase      =	True
  __UpperCamelCase      =	0.763_141

  __UpperCamelCase      =	TapasForQuestionAnswering(config=lowerCAmelCase__		)
 elif task == "TABFACT":
  __UpperCamelCase      =	TapasForSequenceClassification(config=lowerCAmelCase__		)
 elif task == "MLM":
  __UpperCamelCase      =	TapasForMaskedLM(config=lowerCAmelCase__		)
 elif task == "INTERMEDIATE_PRETRAINING":
  __UpperCamelCase      =	TapasModel(config=lowerCAmelCase__		)
 else:
  raise ValueError(f'Task {task} not supported.'		)

 print(f'Building PyTorch model from configuration: {config}'		)
 # Load weights from tf checkpoint
 load_tf_weights_in_tapas(lowerCAmelCase__    , lowerCAmelCase__    , lowerCAmelCase__		)

 # Save pytorch-model (weights and configuration)
 print(f'Save PyTorch model to {pytorch_dump_path}'		)
 model.save_pretrained(lowerCAmelCase__		)

 # Save tokenizer files
 print(f'Save tokenizer files to {pytorch_dump_path}'		)
 __UpperCamelCase      =	TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + 'vocab.txt'    , model_max_length=5_1_2		)
 tokenizer.save_pretrained(lowerCAmelCase__		)

 print('Used relative position embeddings:'    , model.config.reset_position_index_per_cell		)


if __name__ == "__main__":
      UpperCamelCase     :    int       =			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
      )
      parser.add_argument(
          "--reset_position_index_per_cell",
          default=False,
          action="store_true",
          help="Whether to use relative position embeddings or not. Defaults to True.",
      )
      parser.add_argument(
          "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--tapas_config_file",
          default=None,
          type=str,
          required=True,
          help=(
              "The config json file corresponding to the pre-trained TAPAS model. \n"
              "This specifies the model architecture."
          ),
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      UpperCamelCase     :    Tuple       =			parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.task,
          args.reset_position_index_per_cell,
          args.tf_checkpoint_path,
          args.tapas_config_file,
          args.pytorch_dump_path,
      )





"""simple docstring"""

import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel






def 							A						(    snake_case				:Dict    , snake_case				:Optional[Any]    , snake_case				:List[Any]		)				->     Optional[Any]:
 __UpperCamelCase      =	OmegaConf.load(snake_case		)
 __UpperCamelCase      =	torch.load(snake_case    , map_location='cpu'		)['model']
 __UpperCamelCase      =	list(state_dict.keys()		)

 # extract state_dict for VQVAE
 __UpperCamelCase      =	{}
 __UpperCamelCase      =	'first_stage_model.'
 for key in keys:
  if key.startswith(snake_case		):
   __UpperCamelCase      =	state_dict[key]

    # extract state_dict for UNetLDM
 __UpperCamelCase      =	{}
 __UpperCamelCase      =	'model.diffusion_model.'
 for key in keys:
  if key.startswith(snake_case		):
   __UpperCamelCase      =	state_dict[key]

 __UpperCamelCase      =	config.model.params.first_stage_config.params
 __UpperCamelCase      =	config.model.params.unet_config.params

 __UpperCamelCase      =	VQModel(**snake_case		).eval()
 vqvae.load_state_dict(snake_case		)

 __UpperCamelCase      =	UNetLDMModel(**snake_case		).eval()
 unet.load_state_dict(snake_case		)

 __UpperCamelCase      =	DDIMScheduler(
     timesteps=config.model.params.timesteps    , beta_schedule='scaled_linear'    , beta_start=config.model.params.linear_start    , beta_end=config.model.params.linear_end    , clip_sample=snake_case    , )

 __UpperCamelCase      =	LDMPipeline(snake_case    , snake_case    , snake_case		)
 pipeline.save_pretrained(snake_case		)


if __name__ == "__main__":
      UpperCamelCase     :    Dict       =			argparse.ArgumentParser()
      parser.add_argument("--checkpoint_path", type=str, required=True)
      parser.add_argument("--config_path", type=str, required=True)
      parser.add_argument("--output_path", type=str, required=True)
      UpperCamelCase     :    Optional[int]       =			parser.parse_args()

      convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)



