






import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


_a   			=			logging.get_logger(__name__)

if is_vision_available():
		import PIL.Image
		from PIL import Image
		from PIL.Image import Image as ImageType
else:
		_a   			=			object

if is_torch_available():
		import torch

if is_soundfile_availble():
		import soundfile as sf





class __lowerCamelCase				:





	"""simple docstring"""






	def __init__(							self ,					UpperCAmelCase					):
							"""simple docstring"""







							_UpperCAmelCase             =    value

	def __str__(							self					):
							"""simple docstring"""







							return self.to_string()

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							logger.error(
							    'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'					)
							return self._value

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							logger.error(
							    'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'					)
							return str(self._value					)





class __lowerCamelCase				(   _a  , _a):





	"""simple docstring"""






	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							return self._value

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							return self._value





class __lowerCamelCase				(   _a  , _a):





	"""simple docstring"""






	def __init__(							self ,					UpperCAmelCase					):
							"""simple docstring"""







							super().__init__(UpperCAmelCase					)

							if not is_vision_available():
													raise ImportError('PIL must be installed in order to handle images.'					)

							_UpperCAmelCase             =    None
							_UpperCAmelCase             =    None
							_UpperCAmelCase             =    None

							if isinstance(UpperCAmelCase ,					UpperCAmelCase					):
													_UpperCAmelCase             =    value
							elif isinstance(UpperCAmelCase ,					(str, pathlib.Path)					):
													_UpperCAmelCase             =    value
							elif isinstance(UpperCAmelCase ,					torch.Tensor					):
													_UpperCAmelCase             =    value
							else:
													raise ValueError(F"""Unsupported type for {self.__class__.__name__}: {type(UpperCAmelCase					)}"""					)

	def  UpperCamelCase					(							self ,					UpperCAmelCase=None ,					UpperCAmelCase=None					):
							"""simple docstring"""







							from IPython.display import Image, display

							display(Image(self.to_string()					)					)

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							if self._raw is not None:
													return self._raw

							if self._path is not None:
													_UpperCAmelCase             =    Image.open(self._path					)
													return self._raw

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							if self._path is not None:
													return self._path

							if self._raw is not None:
													_UpperCAmelCase             =    tempfile.mkdtemp()
													_UpperCAmelCase             =    os.path.join(UpperCAmelCase ,					str(uuid.uuida()					) + '.png'					)
													self._raw.save(self._path					)

													return self._path

							if self._tensor is not None:
													_UpperCAmelCase             =    self._tensor.cpu().detach().numpy()

													# There is likely simpler than load into image into save
													_UpperCAmelCase             =    Image.fromarray((array * 255).astype(np.uinta					)					)

													_UpperCAmelCase             =    tempfile.mkdtemp()
													_UpperCAmelCase             =    os.path.join(UpperCAmelCase ,					str(uuid.uuida()					) + '.png'					)

													img.save(self._path					)

													return self._path





class __lowerCamelCase				(   _a):





	"""simple docstring"""






	def __init__(							self ,					UpperCAmelCase ,					UpperCAmelCase=1_6000					):
							"""simple docstring"""







							super().__init__(UpperCAmelCase					)

							if not is_soundfile_availble():
													raise ImportError('soundfile must be installed in order to handle audio.'					)

							_UpperCAmelCase             =    None
							_UpperCAmelCase             =    None

							_UpperCAmelCase             =    samplerate

							if isinstance(UpperCAmelCase ,					(str, pathlib.Path)					):
													_UpperCAmelCase             =    value
							elif isinstance(UpperCAmelCase ,					torch.Tensor					):
													_UpperCAmelCase             =    value
							else:
													raise ValueError(F"""Unsupported audio type: {type(UpperCAmelCase					)}"""					)

	def  UpperCamelCase					(							self ,					UpperCAmelCase=None ,					UpperCAmelCase=None					):
							"""simple docstring"""







							from IPython.display import Audio, display

							display(Audio(self.to_string() ,					rate=self.samplerate					)					)

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							if self._tensor is not None:
													return self._tensor

							if self._path is not None:
													_UpperCAmelCase      ,				_UpperCAmelCase             =    sf.read(self._path					)
													_UpperCAmelCase             =    torch.tensor(UpperCAmelCase					)
													return self._tensor

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							if self._path is not None:
													return self._path

							if self._tensor is not None:
													_UpperCAmelCase             =    tempfile.mkdtemp()
													_UpperCAmelCase             =    os.path.join(UpperCAmelCase ,					str(uuid.uuida()					) + '.wav'					)
													sf.write(self._path ,					self._tensor ,					samplerate=self.samplerate					)
													return self._path



_a   			=			{'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
_a   			=			{str: AgentText}

if is_vision_available():
		_a   			=			AgentImage


def __A					(   *__lowerCAmelCase				,	**__lowerCAmelCase			)->						List[str]:





						"""simple docstring"""



						_UpperCAmelCase             =    [(arg.to_raw() if isinstance(lowerCamelCase__				,	lowerCamelCase__			) else arg) for arg in args]
						_UpperCAmelCase             =    {k: (v.to_raw() if isinstance(lowerCamelCase__				,	lowerCamelCase__			) else v) for k, v in kwargs.items()}
						return args, kwargs


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase=None			)->						Union[str, Any]:





						"""simple docstring"""



						if isinstance(lowerCamelCase__				,	lowerCamelCase__			):
												_UpperCAmelCase             =    {}
												for i, (k, v) in enumerate(outputs.items()			):
																		if output_types is not None:
																								# If the class has defined outputs, we can map directly according to the class definition
																								if output_types[i] in AGENT_TYPE_MAPPING:
																														_UpperCAmelCase             =    AGENT_TYPE_MAPPING[output_types[i]](lowerCamelCase__			)
																								else:
																														_UpperCAmelCase             =    AgentType(lowerCamelCase__			)

																		else:
																								# If the class does not have defined output, then we map according to the type
																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
																														if isinstance(lowerCamelCase__				,	_k			):
																																				_UpperCAmelCase             =    _v(lowerCamelCase__			)
																								if k not in decoded_outputs:
																														_UpperCAmelCase             =    AgentType[v]

						elif isinstance(lowerCamelCase__				,	(list, tuple)			):
												_UpperCAmelCase             =    type(lowerCamelCase__			)()
												for i, v in enumerate(lowerCamelCase__			):
																		if output_types is not None:
																								# If the class has defined outputs, we can map directly according to the class definition
																								if output_types[i] in AGENT_TYPE_MAPPING:
																														decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](lowerCamelCase__			)			)
																								else:
																														decoded_outputs.append(AgentType(lowerCamelCase__			)			)
																		else:
																								# If the class does not have defined output, then we map according to the type
																								_UpperCAmelCase             =    False
																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
																														if isinstance(lowerCamelCase__				,	_k			):
																																				decoded_outputs.append(_v(lowerCamelCase__			)			)
																																				_UpperCAmelCase             =    True

																								if not found:
																														decoded_outputs.append(AgentType(lowerCamelCase__			)			)

						else:
												if output_types[0] in AGENT_TYPE_MAPPING:
																		# If the class has defined outputs, we can map directly according to the class definition
																		_UpperCAmelCase             =    AGENT_TYPE_MAPPING[output_types[0]](lowerCamelCase__			)

												else:
																		# If the class does not have defined output, then we map according to the type
																		for _k, _v in INSTANCE_TYPE_MAPPING.items():
																								if isinstance(lowerCamelCase__				,	_k			):
																														return _v(lowerCamelCase__			)
																		return AgentType(lowerCamelCase__			)

						return decoded_outputs








import dataclasses
import enum
import functools
import math
import re

# The following script is adapted from the script of TaPas.
# Original: https://github.com/google-research/tapas/master/wikisql_utils.py
from typing import Any, List, Text


_a   			=						'''none'''
_a   			=						'''none'''


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase			)->						Any:





						"""simple docstring"""



						_UpperCAmelCase             =    value.split(__lowerCAmelCase			)
						return len(__lowerCAmelCase			) > 1 and any((len(__lowerCAmelCase			) == 3 for x in split)			)


def __A					(   __lowerCAmelCase			)->						List[str]:





						"""simple docstring"""



						if isinstance(__lowerCAmelCase				,	__lowerCAmelCase			):
												return value
						if isinstance(__lowerCAmelCase				,	__lowerCAmelCase			):
												return float(__lowerCAmelCase			)
						if not isinstance(__lowerCAmelCase				,	__lowerCAmelCase			):
												raise ValueError('Argument value is not a string. Can\'t parse it as float'			)
						_UpperCAmelCase             =    value

						try:
												# Example: 1,000.7
												if "." in sanitized and "," in sanitized:
																		return float(sanitized.replace(','				,	''			)			)
												# 1,000
												if "," in sanitized and _split_thousands(','				,	__lowerCAmelCase			):
																		return float(sanitized.replace(','				,	''			)			)
												# 5,5556
												if "," in sanitized and sanitized.count(','			) == 1 and not _split_thousands(','				,	__lowerCAmelCase			):
																		return float(sanitized.replace(','				,	'.'			)			)
												# 0.0.0.1
												if sanitized.count('.'			) > 1:
																		return float(sanitized.replace('.'				,	''			)			)
												# 0,0,0,1
												if sanitized.count(','			) > 1:
																		return float(sanitized.replace(','				,	''			)			)
												return float(__lowerCAmelCase			)
						except ValueError:
												# Avoid adding the sanitized value in the error message.
												raise ValueError('Unable to convert value to float'			)


def __A					(   __lowerCAmelCase			)->						Any:





						"""simple docstring"""



						if answer is None:
												return None
						try:
												_UpperCAmelCase             =    convert_to_float(__lowerCAmelCase			)
												if isinstance(__lowerCAmelCase				,	__lowerCAmelCase			) and math.isnan(__lowerCAmelCase			):
																		return None
												return value
						except ValueError:
												return answer.lower()


_a   			=						{
    '''text''': lambda x: x,
    '''real''': convert_to_float,
}





class __lowerCamelCase				(   enum.Enum):





	"""simple docstring"""






	UpperCamelCase__			=   0
	UpperCamelCase__			=   1
	UpperCamelCase__			=   2
	UpperCamelCase__			=   3
	UpperCamelCase__			=   4
	UpperCamelCase__			=   5





class __lowerCamelCase				(   enum.Enum):





	"""simple docstring"""






	UpperCamelCase__			=   0
	UpperCamelCase__			=   1
	UpperCamelCase__			=   2





@dataclasses.dataclass
class __lowerCamelCase				:





	"""simple docstring"""






	UpperCamelCase__			=   42
	UpperCamelCase__			=   42
	UpperCamelCase__			=   42



_a   			=						re.compile(r'''\w+|[^\w\s]+''', re.UNICODE | re.MULTILINE | re.DOTALL)


def __A					(   __lowerCAmelCase			)->						Optional[Any]:





						"""simple docstring"""



						return list(_TOKENIZER.findall(x.lower()			)			)


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)->						Any:





						"""simple docstring"""



						if operator == _Operator.EQUALS:
												return src == tgt
						elif operator == _Operator.GREATER:
												return src > tgt
						elif operator == _Operator.LESSER:
												return src < tgt
						raise ValueError(F"""Unknown operator: {operator}"""			)


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)->						str:





						"""simple docstring"""



						_UpperCAmelCase             =    table['types']
						return _TYPE_CONVERTER[types[column]](__lowerCAmelCase			)


def __A					(   __lowerCAmelCase			)->						str:





						"""simple docstring"""



						return isinstance(__lowerCAmelCase				,	__lowerCAmelCase			)


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)->						str:





						"""simple docstring"""



						for cond in conditions:
												_UpperCAmelCase             =    row[cond.column]

												_UpperCAmelCase             =    _parse_value(__lowerCAmelCase				,	cond.column				,	cond.cmp_value			)

												if _is_string(__lowerCAmelCase			) and _is_string(__lowerCAmelCase			):
																		_UpperCAmelCase             =    _normalize_for_match(__lowerCAmelCase			)
																		_UpperCAmelCase             =    _normalize_for_match(__lowerCAmelCase			)

												if not isinstance(__lowerCAmelCase				,	type(__lowerCAmelCase			)			):
																		raise ValueError('Type difference {} != {}'.format(type(__lowerCAmelCase			)				,	type(__lowerCAmelCase			)			)			)

												if not _compare(cond.operator				,	__lowerCAmelCase				,	__lowerCAmelCase			):
																		return False
						return True


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)->						str:





						"""simple docstring"""



						if not answer_coordinates:
												if aggregation_op == _Aggregation.COUNT:
																		return 0.0
												else:
																		return EMPTY_ANSWER_AGG

    # Count can support non numeric answers.
						if aggregation_op == _Aggregation.COUNT:
												return float(len(__lowerCAmelCase			)			)

						# If we have just one answer, if float returns it or try a conversion.
						_UpperCAmelCase             =    [table['rows'][i][j] for (i, j) in answer_coordinates]
						if len(__lowerCAmelCase			) == 1:
												try:
																		return convert_to_float(values[0]			)
												except ValueError as e:
																		if aggregation_op != _Aggregation.NONE:
																								raise e

						if aggregation_op == _Aggregation.NONE:
												return None

						# Other aggregation only support numeric values. Bail out if we have strings.
						if not all((isinstance(__lowerCAmelCase				,	(int, float)			) for v in values)			):
												return None

						if aggregation_op == _Aggregation.SUM:
												return float(sum(__lowerCAmelCase			)			)
						elif aggregation_op == _Aggregation.AVERAGE:
												return sum(__lowerCAmelCase			) / len(__lowerCAmelCase			)
						else:
												raise ValueError(F"""Unknown aggregation: {aggregation_op}"""			)


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase			)->						Any:





						"""simple docstring"""



						_UpperCAmelCase             =    sql_query['agg']
						if aggregation_op_index >= 3:
												_UpperCAmelCase             =    _Aggregation(__lowerCAmelCase			)
						else:
												_UpperCAmelCase             =    _Aggregation.NONE

						_UpperCAmelCase             =    sql_query['sel']
						_UpperCAmelCase             =    [
						    _Condition(__lowerCAmelCase				,	_Operator(__lowerCAmelCase			)				,	__lowerCAmelCase			)
						    for column, operator, cmp_value in zip(
						        sql_query['conds']['column_index']				,	sql_query['conds']['operator_index']				,	sql_query['conds']['condition']			)
						]

						_UpperCAmelCase             =    []
						for row in range(len(table['rows']			)			):
												if _respect_conditions(__lowerCAmelCase				,	table['rows'][row]				,	__lowerCAmelCase			):
																		indices.append((row, target_column)			)

						if not indices:
												return [], aggregation_op

						if len(__lowerCAmelCase			) == 1:
												return indices, aggregation_op

						# Parsing of MIN/MAX.
						if aggregation_op_index in (1, 2):
												_UpperCAmelCase             =    {2: min, 1: max}
												_UpperCAmelCase             =    [(table['rows'][i][j], index) for index, (i, j) in enumerate(__lowerCAmelCase			)]
												_UpperCAmelCase             =    functools.reduce(operators[sql_query['agg']]				,	__lowerCAmelCase			)

												_UpperCAmelCase             =    [indices[reduced[1]]]
												return ret, _Aggregation.NONE

						return indices, aggregation_op


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)->						Dict:





						"""simple docstring"""



						if float_answer is not None:
												return [str(__lowerCAmelCase			)]
						return [str(table['real_rows'][r][c]			) for r, c in answer_coordinates]


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase			)->						List:





						"""simple docstring"""



						_UpperCAmelCase      ,				_UpperCAmelCase             =    _get_answer_coordinates(__lowerCAmelCase				,	__lowerCAmelCase			)
						_UpperCAmelCase             =    _get_float_answer(__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)
						_UpperCAmelCase             =    _get_answer_text(__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)
						# keep the original data the same with TaPas
						if len(__lowerCAmelCase			) == 0:
												_UpperCAmelCase             =    [EMPTY_ANSWER]
						return answer_text
