




"""simple docstring"""

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


_lowerCamelCase					:     str				       =    logging.get_logger(__name__)

if is_vision_available():
		import PIL.Image
		from PIL import Image
		from PIL.Image import Image as ImageType
else:
		_lowerCamelCase					:     List[Any]				       =    object

if is_torch_available():
		import torch

if is_soundfile_availble():
		import soundfile as sf

class     lowercase :







				def __init__(							self				:   Optional[Any] ,       _lowerCamelCase				:   Any    ):



					"""simple docstring"""

					A_					:			Optional[Any]						=						value







				def __str__(							self				:   Union[str, Any]    ):



					"""simple docstring"""

					return self.to_string()







				def    a_					(							self				:   Tuple    ):



					"""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    a_					(							self				:   Optional[int]    ):



					"""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     lowercase (      __UpperCAmelCase       ,		__UpperCAmelCase):







				def    a_					(							self				:   int    ):



					"""simple docstring"""

					return self._value







				def    a_					(							self				:   Dict    ):



					"""simple docstring"""

					return self._value

class     lowercase (      __UpperCAmelCase       ,		__UpperCAmelCase):







				def __init__(							self				:   Tuple ,       _lowerCamelCase				:   Any    ):



					"""simple docstring"""

					super().__init__(_lowerCamelCase    )

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

					A_					:			Union[str, Any]						=						None
					A_					:			Tuple						=						None
					A_					:			int						=						None

					if isinstance(_lowerCamelCase ,       _lowerCamelCase    ):
						A_					:			Tuple						=						value
					elif isinstance(_lowerCamelCase ,       (str, pathlib.Path)    ):
						A_					:			Union[str, Any]						=						value
					elif isinstance(_lowerCamelCase ,       torch.Tensor    ):
						A_					:			Dict						=						value
					else:
						raise ValueError(F"""Unsupported type for {self.__class__.__name__}: {type(_lowerCamelCase    )}"""    )







				def    a_					(							self				:   str ,       _lowerCamelCase				:   Dict=None ,       _lowerCamelCase				:   Optional[int]=None    ):



					"""simple docstring"""

					from IPython.display import Image, display

					display(Image(self.to_string()    )    )







				def    a_					(							self				:   List[Any]    ):



					"""simple docstring"""

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

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







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

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

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

						return self._path

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

						# There is likely simpler than load into image into save
						A_					:			Union[str, Any]						=						Image.fromarray((array * 2_55).astype(np.uinta    )    )

						A_					:			Optional[Any]						=						tempfile.mkdtemp()
						A_					:			List[Any]						=						os.path.join(_lowerCamelCase ,       str(uuid.uuida()    ) + '''.png'''    )

						img.save(self._path    )

						return self._path

class     lowercase (      __UpperCAmelCase):







				def __init__(							self				:   Dict ,       _lowerCamelCase				:   List[str] ,       _lowerCamelCase				:   str=1_60_00    ):



					"""simple docstring"""

					super().__init__(_lowerCamelCase    )

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

					A_					:			Optional[Any]						=						None
					A_					:			str						=						None

					A_					:			Any						=						samplerate

					if isinstance(_lowerCamelCase ,       (str, pathlib.Path)    ):
						A_					:			Optional[int]						=						value
					elif isinstance(_lowerCamelCase ,       torch.Tensor    ):
						A_					:			Optional[Any]						=						value
					else:
						raise ValueError(F"""Unsupported audio type: {type(_lowerCamelCase    )}"""    )







				def    a_					(							self				:   str ,       _lowerCamelCase				:   Optional[Any]=None ,       _lowerCamelCase				:   Union[str, Any]=None    ):



					"""simple docstring"""

					from IPython.display import Audio, display

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







				def    a_					(							self				:   List[str]    ):



					"""simple docstring"""

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

					if self._path is not None:
						A_				,	A_					:			Dict						=						sf.read(self._path    )
						A_					:			Union[str, Any]						=						torch.tensor(_lowerCamelCase    )
						return self._tensor







				def    a_					(							self				:   str    ):



					"""simple docstring"""

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

					if self._tensor is not None:
						A_					:			Any						=						tempfile.mkdtemp()
						A_					:			Tuple						=						os.path.join(_lowerCamelCase ,       str(uuid.uuida()    ) + '''.wav'''    )
						sf.write(self._path ,       self._tensor ,       samplerate=self.samplerate    )
						return self._path
_lowerCamelCase					:     Union[str, Any]				       =    {'text': AgentText, 'image': AgentImage, 'audio': AgentAudio}
_lowerCamelCase					:     str				       =    {str: AgentText}

if is_vision_available():
		_lowerCamelCase					:     List[str]				       =    AgentImage

def 						lowercase_ (						*_UpperCAmelCase					, **_UpperCAmelCase		):




	"""simple docstring"""






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

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase=None		):




	"""simple docstring"""






	if isinstance(_UpperCAmelCase					, _UpperCAmelCase		):
		A_					:			Tuple						=						{}
		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:
					A_					:			str						=						AGENT_TYPE_MAPPING[output_types[i]](_UpperCAmelCase		)
				else:
					A_					:			Optional[int]						=						AgentType(_UpperCAmelCase		)

			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(_UpperCAmelCase					, _k		):
						A_					:			List[str]						=						_v(_UpperCAmelCase		)
				if k not in decoded_outputs:
					A_					:			Union[str, Any]						=						AgentType[v]

	elif isinstance(_UpperCAmelCase					, (list, tuple)		):
		A_					:			Optional[int]						=						type(_UpperCAmelCase		)()
		for i, v in enumerate(_UpperCAmelCase		):
			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]](_UpperCAmelCase		)		)
				else:
					decoded_outputs.append(AgentType(_UpperCAmelCase		)		)
			else:
				# If the class does not have defined output, then we map according to the type
				A_					:			List[Any]						=						False
				for _k, _v in INSTANCE_TYPE_MAPPING.items():
					if isinstance(_UpperCAmelCase					, _k		):
						decoded_outputs.append(_v(_UpperCAmelCase		)		)
						A_					:			str						=						True

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

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

		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(_UpperCAmelCase					, _k		):
					return _v(_UpperCAmelCase		)
			return AgentType(_UpperCAmelCase		)

	return decoded_outputs











"""simple docstring"""

import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	A_					:			Optional[int]						=						copy.deepcopy(_UpperCAmelCase		)
	A_					:			int						=						sorted(_UpperCAmelCase		)
	A_					:			Tuple						=						[bisect.bisect_right(_UpperCAmelCase					, _UpperCAmelCase		) for y in x]
	return quantized

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase=0		):




	"""simple docstring"""






	A_					:			List[Any]						=						np.arange(start=3					, stop=_UpperCAmelCase					, step=4		).tolist() if k > 0 else [10]
	A_					:			List[Any]						=						_quantize(_UpperCAmelCase					, _UpperCAmelCase		)
	# count number of elements per group
	A_					:			Tuple						=						np.unique(_UpperCAmelCase					, return_counts=_UpperCAmelCase		)[1]
	A_					:			Optional[int]						=						[0] + bins + [np.inf]
	logger.info('''Using {} as bins for aspect lengths quantization'''.format(_UpperCAmelCase		)		)
	logger.info('''Count of instances per bin: {}'''.format(_UpperCAmelCase		)		)
	return groups

class     lowercase (      __UpperCAmelCase):







				def __init__(							self				:   Union[str, Any] ,       _lowerCamelCase				:   List[Any] ,       _lowerCamelCase				:   Dict ,       _lowerCamelCase				:   Dict    ):



					"""simple docstring"""

					if not isinstance(_lowerCamelCase ,       _lowerCamelCase    ):
						raise ValueError(
						    '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(_lowerCamelCase    )    )
					A_					:			List[str]						=						sampler
					A_					:			str						=						group_ids
					A_					:			Any						=						batch_size







				def __iter__(							self				:   List[str]    ):



					"""simple docstring"""

					A_					:			List[str]						=						defaultdict(_lowerCamelCase    )
					A_					:			Dict						=						defaultdict(_lowerCamelCase    )

					A_					:			List[str]						=						0
					for idx in self.sampler:
						A_					:			Union[str, Any]						=						self.group_ids[idx]
						buffer_per_group[group_id].append(_lowerCamelCase    )
						samples_per_group[group_id].append(_lowerCamelCase    )
						if len(buffer_per_group[group_id]    ) == self.batch_size:
							yield buffer_per_group[group_id]  # TODO
							num_batches += 1
							del buffer_per_group[group_id]
						assert len(buffer_per_group[group_id]    ) < self.batch_size

					# now we have run out of elements that satisfy
					# the group criteria, let's return the remaining
					# elements so that the size of the sampler is
					# deterministic
					A_					:			int						=						len(self    )
					A_					:			Dict						=						expected_num_batches - num_batches
					if num_remaining > 0:
						# for the remaining batches, group the batches by similar lengths
						A_					:			Optional[Any]						=						[]
						for group_id, idxs in sorted(buffer_per_group.items() ,       key=lambda _lowerCamelCase    : x[0]    ):
							batch_idx.extend(_lowerCamelCase    )
							if len(_lowerCamelCase    ) >= self.batch_size:
								yield batch_idx[: self.batch_size]
								A_					:			str						=						batch_idx[self.batch_size :]
								num_remaining -= 1
						if len(_lowerCamelCase    ) > 0:
							yield batch_idx
							num_remaining -= 1
					assert num_remaining == 0







				def __len__(							self				:   List[Any]    ):



					"""simple docstring"""

					return (len(self.sampler    ) + self.batch_size - 1) // self.batch_size





