


"""simple docstring"""







from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np






class 						_A  (  _a  ):







				"""simple docstring"""




				def      __snake_case							( self    :   int    ,			__UpperCAmelCase    :   float):
						return 0.0






def 					lowercase      (    A_  ,       A_	)->				tuple[int | float, int | float]:
		'''simple docstring'''







		a						:		Tuple											=      min([-20, np.min(fft_results[1 : samplerate // 2 - 1]	)]	)
		a						:		List[str]											=      max([20, np.max(fft_results[1 : samplerate // 2 - 1]	)]	)
		return lowest, highest


def 					lowercase      (    A_  ,       A_	)->				None:
		'''simple docstring'''







		a						:		List[str]											=      512
		a						:		Optional[Any]											=      [1] + [0] * (size - 1)
		a						:		Tuple											=      [filter_type.process(A_	) for item in inputs]

		a						:		Optional[int]											=      [0] * (samplerate - size)  # zero-padding
		outputs += filler
		a						:		int											=      np.abs(np.fft.fft(A_	)	)
		a						:		Dict											=      20 * np.logaa(A_	)

		# Frequencies on log scale from 24 to nyquist frequency
		plt.xlim(24  ,       samplerate / 2 - 1	)
		plt.xlabel("Frequency (Hz)"	)
		plt.xscale("log"	)

		# Display within reasonable bounds
		a						:		Union[str, Any]											=      get_bounds(A_  ,       A_	)
		plt.ylim(max([-80, bounds[0]]	)  ,       min([80, bounds[1]]	)	)
		plt.ylabel("Gain (dB)"	)

		plt.plot(A_	)
		plt.show()







def 					lowercase      (    A_  ,       A_	)->				None:
		'''simple docstring'''







		a						:		Dict											=      512
		a						:		str											=      [1] + [0] * (size - 1)
		a						:		Any											=      [filter_type.process(A_	) for item in inputs]

		a						:		Tuple											=      [0] * (samplerate - size)  # zero-padding
		outputs += filler
		a						:		Optional[Any]											=      np.angle(np.fft.fft(A_	)	)

		# Frequencies on log scale from 24 to nyquist frequency
		plt.xlim(24  ,       samplerate / 2 - 1	)
		plt.xlabel("Frequency (Hz)"	)
		plt.xscale("log"	)

		plt.ylim(-2 * pi  ,       2 * pi	)
		plt.ylabel("Phase shift (Radians)"	)
		plt.plot(np.unwrap(A_  ,       -2 * pi	)	)
		plt.show()










"""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      (    A_  ,       A_  ,       A_  ,       A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		Optional[int]											=      TapasConfig.from_json_file(A_	)
  # set absolute/relative position embeddings parameter
  a						:		str											=      reset_position_index_per_cell

  # set remaining parameters of TapasConfig as well as the model based on the task
  if task == "SQA":
    a						:		Dict											=      TapasForQuestionAnswering(config=A_	)
  elif task == "WTQ":
    # run_task_main.py hparams
    a						:		Any											=      4
    a						:		Dict											=      True
    # hparam_utils.py hparams
    a						:		str											=      0.6_6_4_6_9_4
    a						:		Optional[int]											=      0.2_0_7_9_5_1
    a						:		Optional[Any]											=      0.1_2_1_1_9_4
    a						:		Union[str, Any]											=      True
    a						:		int											=      True
    a						:		Tuple											=      False
    a						:		Dict											=      0.0_3_5_2_5_1_3

    a						:		List[str]											=      TapasForQuestionAnswering(config=A_	)
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    a						:		Union[str, Any]											=      4
    a						:		List[Any]											=      False
    # hparam_utils.py hparams
    a						:		Dict											=      3_6.4_5_1_9
    a						:		List[str]											=      0.9_0_3_4_2_1
    a						:		Optional[Any]											=      2_2_2.0_8_8
    a						:		Dict											=      True
    a						:		Union[str, Any]											=      True
    a						:		List[str]											=      True
    a						:		List[str]											=      0.7_6_3_1_4_1

    a						:		Any											=      TapasForQuestionAnswering(config=A_	)
  elif task == "TABFACT":
    a						:		int											=      TapasForSequenceClassification(config=A_	)
  elif task == "MLM":
    a						:		int											=      TapasForMaskedLM(config=A_	)
  elif task == "INTERMEDIATE_PRETRAINING":
    a						:		List[Any]											=      TapasModel(config=A_	)
  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(A_  ,       A_  ,       A_	)

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

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

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


if __name__ == "__main__":
 __lowercase    =				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."""
 )
 __lowercase    =				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,
 )






