


"""simple docstring"""






import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
					import tensorflow as tf


class 			SCREAMING_SNAKE_CASE_  (   TensorFormatter[Mapping, '''tf.Tensor''', Mapping]):




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__=None	,					**lowerCamelCase__)   ->	int:







								'''simple docstring'''




								super().__init__(features=__lowerCamelCase)
								snake_case__  :						List[Any]		=      tf_tensor_kwargs
								import tensorflow as tf  # noqa: import tf at initialization




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	List[Any]:







								'''simple docstring'''




								import tensorflow as tf

								if isinstance(__lowerCamelCase	,					__lowerCamelCase) and column:
									if all(
									    isinstance(__lowerCamelCase	,					tf.Tensor) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column):
										return tf.stack(__lowerCamelCase)
									elif all(
									    isinstance(__lowerCamelCase	,					(tf.Tensor, tf.RaggedTensor)) and x.ndim == 1 and x.dtype == column[0].dtype
									    for x in column):
										# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
										return tf.ragged.stack(__lowerCamelCase)

								return column




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	Any:







								'''simple docstring'''




								import tensorflow as tf

								if value is None:
									return value

								snake_case__  :						List[str]		=      {}

								if isinstance(__lowerCamelCase	,					(np.number, np.ndarray)) and np.issubdtype(value.dtype	,					np.integer):
									snake_case__  :						Dict		=      {"dtype": tf.intaa}
								elif isinstance(__lowerCamelCase	,					(np.number, np.ndarray)) and np.issubdtype(value.dtype	,					np.floating):
									snake_case__  :						List[Any]		=      {"dtype": tf.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
									import PIL.Image

									if isinstance(__lowerCamelCase	,					PIL.Image.Image):
										snake_case__  :						str		=      np.asarray(__lowerCamelCase)

								return tf.convert_to_tensor(__lowerCamelCase	,					**{**default_dtype, **self.tf_tensor_kwargs})




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								import tensorflow as tf

								# support for torch, tf, jax etc.
								if config.TORCH_AVAILABLE and "torch" in sys.modules:
									import torch

									if isinstance(__lowerCamelCase	,					torch.Tensor):
										return self._tensorize(data_struct.detach().cpu().numpy()[()])
								if hasattr(__lowerCamelCase	,					"__array__") and not isinstance(__lowerCamelCase	,					tf.Tensor):
									snake_case__  :						Optional[int]		=      data_struct.__array__()
								# support for nested types like struct of list of struct
								if isinstance(__lowerCamelCase	,					np.ndarray):
									if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
										return self._consolidate([self.recursive_tensorize(__lowerCamelCase) for substruct in data_struct])
								elif isinstance(__lowerCamelCase	,					(list, tuple)):
									return self._consolidate([self.recursive_tensorize(__lowerCamelCase) for substruct in data_struct])
								return self._tensorize(__lowerCamelCase)




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								return map_nested(self._recursive_tensorize	,					__lowerCamelCase	,					map_list=__lowerCamelCase)




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	Mapping:







								'''simple docstring'''




								snake_case__  :						Any		=      self.numpy_arrow_extractor().extract_row(__lowerCamelCase)
								snake_case__  :						Union[str, Any]		=      self.python_features_decoder.decode_row(__lowerCamelCase)
								return self.recursive_tensorize(__lowerCamelCase)




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	"tf.Tensor":







								'''simple docstring'''




								snake_case__  :						int		=      self.numpy_arrow_extractor().extract_column(__lowerCamelCase)
								snake_case__  :						Tuple		=      self.python_features_decoder.decode_column(__lowerCamelCase	,					pa_table.column_names[0])
								snake_case__  :						Optional[Any]		=      self.recursive_tensorize(__lowerCamelCase)
								snake_case__  :						Dict		=      self._consolidate(__lowerCamelCase)
								return column







							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	Mapping:







								'''simple docstring'''




								snake_case__  :						Tuple		=      self.numpy_arrow_extractor().extract_batch(__lowerCamelCase)
								snake_case__  :						Optional[int]		=      self.python_features_decoder.decode_batch(__lowerCamelCase)
								snake_case__  :						Tuple		=      self.recursive_tensorize(__lowerCamelCase)
								for column_name in batch:
									snake_case__  :						int		=      self._consolidate(batch[column_name])
								return batch













"""simple docstring"""

import math






def _lowerCAmelCase						(	__lowerCamelCase:int ):

						'''simple docstring'''






						if number == 0:
												__magic_name__      =  "Zero"
												return words
						else:
												__magic_name__      =  math.logaa(__lowerCamelCase )
												__magic_name__      =  digits + 1
												__magic_name__      =  {}
												__magic_name__      =  ""
												__magic_name__      =  "One"
												__magic_name__      =  "Two"
												__magic_name__      =  "Three"
												__magic_name__      =  "Four"
												__magic_name__      =  "Five"
												__magic_name__      =  "Six"
												__magic_name__      =  "Seven"
												__magic_name__      =  "Eight"
												__magic_name__      =  "Nine"

												__magic_name__      =  {}
												__magic_name__      =  ""
												__magic_name__      =  "Twenty"
												__magic_name__      =  "Thirty"
												__magic_name__      =  "Forty"
												__magic_name__      =  "Fifty"
												__magic_name__      =  "Sixty"
												__magic_name__      =  "Seventy"
												__magic_name__      =  "Eighty"
												__magic_name__      =  "Ninety"

												__magic_name__      =  {}
												__magic_name__      =  "Ten"
												__magic_name__      =  "Eleven"
												__magic_name__      =  "Twelve"
												__magic_name__      =  "Thirteen"
												__magic_name__      =  "Fourteen"
												__magic_name__      =  "Fifteen"
												__magic_name__      =  "Sixteen"
												__magic_name__      =  "Seventeen"
												__magic_name__      =  "Eighteen"
												__magic_name__      =  "Nineteen"

												__magic_name__      =  {}
												__magic_name__      =  "Hundred,"
												__magic_name__      =  "Thousand,"
												__magic_name__      =  "Lakh,"
												__magic_name__      =  "Crore,"

												__magic_name__      =  number
												__magic_name__      =  ""
												__magic_name__      =  0
												__magic_name__      =  int(__lowerCamelCase )
												while counter < digits:
																		__magic_name__      =  temp_num % 1_0
																		if counter % 2 == 0:
																								__magic_name__      =  ""
																								if counter in placevalue and current != 0:
																														__magic_name__      =  placevalue[counter]
																								if counter == 2:
																														__magic_name__      =  singles[current] + addition + words
																								elif counter == 0:
																														if ((temp_num % 1_0_0) // 1_0) == 1:
																																				__magic_name__      =  teens[current] + addition + words
																																				__magic_name__      =  temp_num // 1_0
																																				counter += 1
																														else:
																																				__magic_name__      =  singles[current] + addition + words

																								else:
																														__magic_name__      =  doubles[current] + addition + words

																		else:
																								if counter == 1:
																														if current == 1:
																																				__magic_name__      =  teens[number % 1_0] + words
																														else:
																																				__magic_name__      =  ""
																																				if counter in placevalue:
																																										__magic_name__      =  placevalue[counter]
																																				__magic_name__      =  doubles[current] + addition + words
																								else:
																														__magic_name__      =  ""
																														if counter in placevalue:
																																				if current == 0 and ((temp_num % 1_0_0) // 1_0) == 0:
																																										__magic_name__      =  ""
																																				else:
																																										__magic_name__      =  placevalue[counter]
																														if ((temp_num % 1_0_0) // 1_0) == 1:
																																				__magic_name__      =  teens[current] + addition + words
																																				__magic_name__      =  temp_num // 1_0
																																				counter += 1
																														else:
																																				__magic_name__      =  singles[current] + addition + words
																		counter += 1
																		__magic_name__      =  temp_num // 1_0
						return words


if __name__ == "__main__":
			import doctest

			doctest.testmod()



