


import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
 import onnxruntime as ort


UpperCAmelCase_ :     Dict								=      logging.get_logger(__name__)

UpperCAmelCase_ :     Optional[Any]								=      {
    'tensor(bool)': np.bool_,
    'tensor(int8)': np.inta,
    'tensor(uint8)': np.uinta,
    'tensor(int16)': np.intaa,
    'tensor(uint16)': np.uintaa,
    'tensor(int32)': np.intaa,
    'tensor(uint32)': np.uintaa,
    'tensor(int64)': np.intaa,
    'tensor(uint64)': np.uintaa,
    'tensor(float16)': np.floataa,
    'tensor(float)': np.floataa,
    'tensor(double)': np.floataa,
}

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase=None      ,      **__lowercase						):

  """simple docstring"""







  logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'						)
  __A  :		str       =			model
  __A  :		Optional[int]       =			kwargs.get('model_save_dir'      ,      __lowercase						)
  __A  :		List[str]       =			kwargs.get('latest_model_name'      ,      __lowercase						)


 def __call__(      self      ,      **__lowercase						):

  """simple docstring"""







  __A  :		Optional[Any]       =			{k: np.array(__lowercase						) for k, v in kwargs.items()}
  return self.model.run(__lowercase      ,      __lowercase						)


 @staticmethod
 def  snake_case__   (      __lowercase      ,      __lowercase=None      ,      __lowercase=None						):

  """simple docstring"""







  if provider is None:
   logger.info('No onnxruntime provider specified, using CPUExecutionProvider'						)
   __A  :		Dict       =			'CPUExecutionProvider'

  return ort.InferenceSession(__lowercase      ,      providers=[provider]      ,      sess_options=__lowercase						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None      ,      **__lowercase						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			file_name if file_name is not None else ONNX_WEIGHTS_NAME

  __A  :		int       =			self.model_save_dir.joinpath(self.latest_model_name						)
  __A  :		str       =			Path(__lowercase						).joinpath(__lowercase						)
  try:
   shutil.copyfile(__lowercase      ,      __lowercase						)
  except shutil.SameFileError:
   pass

  # copy external weights (for models >2GB)
  __A  :		Optional[Any]       =			self.model_save_dir.joinpath(__lowercase						)
  if src_path.exists():
   __A  :		List[Any]       =			Path(__lowercase						).joinpath(__lowercase						)
   try:
    shutil.copyfile(__lowercase      ,      __lowercase						)
   except shutil.SameFileError:
    pass


 def  snake_case__   (      self      ,      __lowercase      ,      **__lowercase      ,      ):

  """simple docstring"""







  if os.path.isfile(__lowercase						):
   logger.error(F"""Provided path ({save_directory}) should be a directory, not a file"""						)
   return

  os.makedirs(__lowercase      ,      exist_ok=__lowercase						)

  # saving model weights/files
  self._save_pretrained(__lowercase      ,      **__lowercase						)


 @classmethod
 def  snake_case__   (      cls      ,      __lowercase      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = False      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		List[str]       =			file_name if file_name is not None else ONNX_WEIGHTS_NAME
  # load model from local directory
  if os.path.isdir(__lowercase						):
   __A  :		Tuple       =			OnnxRuntimeModel.load_model(
       os.path.join(__lowercase      ,      __lowercase						)      ,      provider=__lowercase      ,      sess_options=__lowercase						)
   __A  :		List[str]       =			Path(__lowercase						)
  # load model from hub
  else:
   # download model
   __A  :		int       =			hf_hub_download(
       repo_id=__lowercase      ,      filename=__lowercase      ,      use_auth_token=__lowercase      ,      revision=__lowercase      ,      cache_dir=__lowercase      ,      force_download=__lowercase      ,      )
   __A  :		int       =			Path(__lowercase						).parent
   __A  :		List[Any]       =			Path(__lowercase						).name
   __A  :		int       =			OnnxRuntimeModel.load_model(__lowercase      ,      provider=__lowercase      ,      sess_options=__lowercase						)
  return cls(model=__lowercase      ,      **__lowercase						)







 @classmethod
 def  snake_case__   (      cls      ,      __lowercase      ,      __lowercase = True      ,      __lowercase = None      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		List[Any]       =			None
  if len(str(__lowercase						).split('@'						)						) == 2:
   __A  :		Tuple       =			model_id.split('@'						)

  return cls._from_pretrained(
      model_id=__lowercase      ,      revision=__lowercase      ,      cache_dir=__lowercase      ,      force_download=__lowercase      ,      use_auth_token=__lowercase      ,      **__lowercase      ,      )










'''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 			_lowercase       (				UpperCamelCase__       :					Optional[int],				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					Optional[int]			):
 # 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
 __A  :		Optional[int]       =			TapasConfig.from_json_file(UpperCamelCase__			)
 # set absolute/relative position embeddings parameter
 __A  :		List[str]       =			reset_position_index_per_cell

 # set remaining parameters of TapasConfig as well as the model based on the task
 if task == "SQA":
  __A  :		Optional[int]       =			TapasForQuestionAnswering(config=UpperCamelCase__			)
 elif task == "WTQ":
  # run_task_main.py hparams
  __A  :		List[str]       =			4
  __A  :		Any       =			True
  # hparam_utils.py hparams
  __A  :		Any       =			0.664694
  __A  :		Dict       =			0.207951
  __A  :		Optional[Any]       =			0.121194
  __A  :		Any       =			True
  __A  :		Any       =			True
  __A  :		int       =			False
  __A  :		Optional[int]       =			0.0352513

  __A  :		List[Any]       =			TapasForQuestionAnswering(config=UpperCamelCase__			)
 elif task == "WIKISQL_SUPERVISED":
  # run_task_main.py hparams
  __A  :		Tuple       =			4
  __A  :		Optional[int]       =			False
  # hparam_utils.py hparams
  __A  :		List[str]       =			36.4519
  __A  :		Union[str, Any]       =			0.903421
  __A  :		List[str]       =			222.088
  __A  :		Optional[Any]       =			True
  __A  :		Optional[int]       =			True
  __A  :		Union[str, Any]       =			True
  __A  :		str       =			0.763141

  __A  :		Union[str, Any]       =			TapasForQuestionAnswering(config=UpperCamelCase__			)
 elif task == "TABFACT":
  __A  :		Union[str, Any]       =			TapasForSequenceClassification(config=UpperCamelCase__			)
 elif task == "MLM":
  __A  :		Any       =			TapasForMaskedLM(config=UpperCamelCase__			)
 elif task == "INTERMEDIATE_PRETRAINING":
  __A  :		List[Any]       =			TapasModel(config=UpperCamelCase__			)
 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(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

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

 # Save tokenizer files
 print(f"""Save tokenizer files to {pytorch_dump_path}"""			)
 __A  :		int       =			TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt',				model_max_length=512			)
 tokenizer.save_pretrained(UpperCamelCase__			)

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


if __name__ == "__main__":
 UpperCAmelCase_ :     Dict								=      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_ :     Any								=      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,
 )






