

import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


lowercase_									=    [nn.LayerNorm]

lowercase_									=    logging.get_logger(__name__)

lowercase_									=    version.parse(version.parse(torch.__version__).base_version)

lowercase_									=    parsed_torch_version_base >= version.parse("2.0")
lowercase_									=    parsed_torch_version_base >= version.parse("1.12")
lowercase_									=    parsed_torch_version_base >= version.parse("1.11")
lowercase_									=    parsed_torch_version_base < version.parse("1.11")
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Dict       ,     SCREAMING_SNAKE_CASE__     :      List[str]       ,     SCREAMING_SNAKE_CASE__     :      str       ,     SCREAMING_SNAKE_CASE__     :      Any       ,     self     :      Optional[int] )				-> Union[str, Any]:





    '''simple docstring'''






    from torch import _softmax_backward_data

    if is_torch_less_than_1_11:
        return _softmax_backward_data(UpperCamelCase__       ,     UpperCamelCase__       ,     parent.dim       ,     self )
    else:
        return _softmax_backward_data(UpperCamelCase__       ,     UpperCamelCase__       ,     parent.dim       ,     self.dtype )
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      nn.Linear       ,     SCREAMING_SNAKE_CASE__     :      torch.LongTensor       ,     SCREAMING_SNAKE_CASE__     :      int = 0 )				-> nn.Linear:





    '''simple docstring'''






    A__        =  index.to(layer.weight.device )
    A__        =  layer.weight.index_select(UpperCamelCase__       ,     UpperCamelCase__ ).clone().detach()
    if layer.bias is not None:
        if dim == 1:
            A__        =  layer.bias.clone().detach()
        else:
            A__        =  layer.bias[index].clone().detach()
    A__        =  list(layer.weight.size() )
    A__        =  len(UpperCamelCase__ )
    A__        =  nn.Linear(new_size[1]       ,     new_size[0]       ,     bias=layer.bias is not None ).to(layer.weight.device )
    A__        =  False
    new_layer.weight.copy_(W.contiguous() )
    A__        =  True
    if layer.bias is not None:
        A__        =  False
        new_layer.bias.copy_(b.contiguous() )
        A__        =  True
    return new_layer


class 					A		(				nn.Module					):







    """simple docstring"""





    def __init__( self					:			int,lowercase_					:			Tuple,lowercase_					:			Optional[Any]						)->       Union[str, Any]:





        '''simple docstring'''







        super().__init__()
        A__        =  nf
        A__        =  nn.Parameter(torch.empty(__UpperCamelCase,__UpperCamelCase						)						)
        A__        =  nn.Parameter(torch.zeros(__UpperCamelCase						)						)
        nn.init.normal_(self.weight,std=0.02						)


    def 		snake_case__	( self					:			Dict,lowercase_					:			Optional[int]						)->       Dict:





        '''simple docstring'''







        A__        =  x.size()[:-1] + (self.nf,)
        A__        =  torch.addmm(self.bias,x.view(-1,x.size(-1						)						),self.weight						)
        A__        =  x.view(__UpperCamelCase						)
        return x




def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      ConvaD       ,     SCREAMING_SNAKE_CASE__     :      torch.LongTensor       ,     SCREAMING_SNAKE_CASE__     :      int = 1 )				-> ConvaD:





    '''simple docstring'''






    A__        =  index.to(layer.weight.device )
    A__        =  layer.weight.index_select(UpperCamelCase__       ,     UpperCamelCase__ ).clone().detach()
    if dim == 0:
        A__        =  layer.bias.clone().detach()
    else:
        A__        =  layer.bias[index].clone().detach()
    A__        =  list(layer.weight.size() )
    A__        =  len(UpperCamelCase__ )
    A__        =  ConvaD(new_size[1]       ,     new_size[0] ).to(layer.weight.device )
    A__        =  False
    new_layer.weight.copy_(W.contiguous() )
    A__        =  True
    A__        =  False
    new_layer.bias.copy_(b.contiguous() )
    A__        =  True
    return new_layer
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Union[nn.Linear, ConvaD]       ,     SCREAMING_SNAKE_CASE__     :      torch.LongTensor       ,     SCREAMING_SNAKE_CASE__     :      Optional[int] = None )				-> Union[nn.Linear, ConvaD]:





    '''simple docstring'''






    if isinstance(UpperCamelCase__       ,     nn.Linear ):
        return prune_linear_layer(UpperCamelCase__       ,     UpperCamelCase__       ,     dim=0 if dim is None else dim )
    elif isinstance(UpperCamelCase__       ,     UpperCamelCase__ ):
        return prune_convad_layer(UpperCamelCase__       ,     UpperCamelCase__       ,     dim=1 if dim is None else dim )
    else:
        raise ValueError(f'Can\'t prune layer of class {layer.__class__}' )
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Callable[..., torch.Tensor]       ,     SCREAMING_SNAKE_CASE__     :      int       ,     SCREAMING_SNAKE_CASE__     :      int       ,     *SCREAMING_SNAKE_CASE__     :      List[str] )				-> torch.Tensor:





    '''simple docstring'''






    assert len(UpperCamelCase__ ) > 0, f'{input_tensors} has to be a tuple/list of tensors'

    # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
    A__        =  len(inspect.signature(UpperCamelCase__ ).parameters )
    if num_args_in_forward_chunk_fn != len(UpperCamelCase__ ):
        raise ValueError(
            f'forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(UpperCamelCase__ )} input '
            'tensors are given' )

    if chunk_size > 0:
        A__        =  input_tensors[0].shape[chunk_dim]
        for input_tensor in input_tensors:
            if input_tensor.shape[chunk_dim] != tensor_shape:
                raise ValueError(
                    f'All input tenors have to be of the same shape: {tensor_shape}, '
                    f'found shape {input_tensor.shape[chunk_dim]}' )

        if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
            raise ValueError(
                f'The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '
                f'size {chunk_size}' )

        A__        =  input_tensors[0].shape[chunk_dim] // chunk_size

        # chunk input tensor into tuples
        A__        =  tuple(input_tensor.chunk(UpperCamelCase__       ,     dim=UpperCamelCase__ ) for input_tensor in input_tensors )
        # apply forward fn to every tuple
        A__        =  tuple(forward_fn(*UpperCamelCase__ ) for input_tensors_chunk in zip(*UpperCamelCase__ ) )
        # concatenate output at same dimension
        return torch.cat(UpperCamelCase__       ,     dim=UpperCamelCase__ )

    return forward_fn(*UpperCamelCase__ )
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[int]       ,     SCREAMING_SNAKE_CASE__     :      int       ,     SCREAMING_SNAKE_CASE__     :      int       ,     SCREAMING_SNAKE_CASE__     :      Set[int] )				-> Tuple[Set[int], torch.LongTensor]:





    '''simple docstring'''






    A__        =  torch.ones(UpperCamelCase__       ,     UpperCamelCase__ )
    A__        =  set(UpperCamelCase__ ) - already_pruned_heads  # Convert to set and remove already pruned heads
    for head in heads:
        # Compute how many pruned heads are before the head and move the index accordingly
        A__        =  head - sum(1 if h < head else 0 for h in already_pruned_heads )
        A__        =  0
    A__        =  mask.view(-1 ).contiguous().eq(1 )
    A__        =  torch.arange(len(UpperCamelCase__ ) )[mask].long()
    return heads, index
def 		_snake_case(       *SCREAMING_SNAKE_CASE__     :      Union[torch.Tensor, List[torch.Tensor]]       ,     SCREAMING_SNAKE_CASE__     :      Optional[str] = None )				-> Tuple[torch.Tensor, ...]:





    '''simple docstring'''






    return torch.meshgrid(*UpperCamelCase__       ,     indexing=UpperCamelCase__ )

def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      torch.Tensor )				-> Tuple[torch.device, int, int]:





    '''simple docstring'''






    return tensor.device, storage_ptr(UpperCamelCase__ ), storage_size(UpperCamelCase__ )






'''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


_lowercase       :						Dict						=logging.get_logger(__name__)

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

if is_torch_available():
				import torch

if is_soundfile_availble():
				import soundfile as sf






class 			_SCREAMING_SNAKE_CASE				:



			def __init__(     self		:						List[str]    ,			__UpperCamelCase		:						Optional[int]			)      ->      List[str]:
							"""simple docstring"""





							snake_case__      :   int      =    value



			def __str__(     self		:						int			)      ->      str:
							"""simple docstring"""





							return self.to_string()



			def 				lowerCAmelCase (     self		:						List[Any]			)      ->      List[str]:
							"""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 				lowerCAmelCase (     self		:						Optional[Any]			)      ->      str:
							"""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 			_SCREAMING_SNAKE_CASE				(lowercase__,	lowercase__      ):



			def 				lowerCAmelCase (     self		:						str			)      ->      Tuple:
							"""simple docstring"""





							return self._value







			def 				lowerCAmelCase (     self		:						Optional[int]			)      ->      Union[str, Any]:
							"""simple docstring"""





							return self._value






class 			_SCREAMING_SNAKE_CASE				(lowercase__,	lowercase__      ):



			def __init__(     self		:						str    ,			__UpperCamelCase		:						List[Any]			)      ->      Dict:
							"""simple docstring"""





							super().__init__(__UpperCamelCase			)

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

							snake_case__      :   str      =    None
							snake_case__      :   List[Any]      =    None
							snake_case__      :   Tuple      =    None

							if isinstance(__UpperCamelCase    ,			__UpperCamelCase			):
											snake_case__      :   List[Any]      =    value
							elif isinstance(__UpperCamelCase    ,			(str, pathlib.Path)			):
											snake_case__      :   int      =    value
							elif isinstance(__UpperCamelCase    ,			torch.Tensor			):
											snake_case__      :   Union[str, Any]      =    value
							else:
											raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(__UpperCamelCase			)}'''			)



			def 				lowerCAmelCase (     self		:						str    ,			__UpperCamelCase		:						Dict=None    ,			__UpperCamelCase		:						Any=None			)      ->      List[Any]:
							"""simple docstring"""





							from IPython.display import Image, display

							display(Image(self.to_string()			)			)



			def 				lowerCAmelCase (     self		:						List[str]			)      ->      str:
							"""simple docstring"""





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

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







			def 				lowerCAmelCase (     self		:						Dict			)      ->      Optional[Any]:
							"""simple docstring"""





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

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

											return self._path

							if self._tensor is not None:
											snake_case__      :   List[str]      =    self._tensor.cpu().detach().numpy()

											# There is likely simpler than load into image into save
											snake_case__      :   List[Any]      =    Image.fromarray((array * 255).astype(np.uinta			)			)

											snake_case__      :   str      =    tempfile.mkdtemp()
											snake_case__      :   Dict      =    os.path.join(__UpperCamelCase    ,			str(uuid.uuida()			) + '''.png'''			)

											img.save(self._path			)

											return self._path






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):



			def __init__(     self		:						Optional[Any]    ,			__UpperCamelCase		:						List[Any]    ,			__UpperCamelCase		:						str=16000			)      ->      Union[str, Any]:
							"""simple docstring"""





							super().__init__(__UpperCamelCase			)

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

							snake_case__      :   List[str]      =    None
							snake_case__      :   Optional[int]      =    None

							snake_case__      :   Any      =    samplerate

							if isinstance(__UpperCamelCase    ,			(str, pathlib.Path)			):
											snake_case__      :   int      =    value
							elif isinstance(__UpperCamelCase    ,			torch.Tensor			):
											snake_case__      :   int      =    value
							else:
											raise ValueError(F'''Unsupported audio type: {type(__UpperCamelCase			)}'''			)



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						List[Any]=None    ,			__UpperCamelCase		:						Optional[Any]=None			)      ->      Any:
							"""simple docstring"""





							from IPython.display import Audio, display

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



			def 				lowerCAmelCase (     self		:						Union[str, Any]			)      ->      Union[str, Any]:
							"""simple docstring"""





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

							if self._path is not None:
											snake_case__  ,      snake_case__      :   Tuple      =    sf.read(self._path			)
											snake_case__      :   Tuple      =    torch.tensor(__UpperCamelCase			)
											return self._tensor







			def 				lowerCAmelCase (     self		:						Any			)      ->      Dict:
							"""simple docstring"""





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

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



_lowercase       :						Union[str, Any]						={"text": AgentText, "image": AgentImage, "audio": AgentAudio}
_lowercase       :						List[Any]						={str: AgentText}

if is_vision_available():
				_lowercase       :						Optional[Any]						=AgentImage


def 							__UpperCAmelCase				(				*UpperCamelCase__				:Optional[int]   ,				**UpperCamelCase__				:List[Any]			)		->						Optional[Any]:
				snake_case__      :   List[str]      =    [(arg.to_raw() if isinstance(UpperCamelCase__   ,				UpperCamelCase__			) else arg) for arg in args]
				snake_case__      :   Any      =    {k: (v.to_raw() if isinstance(UpperCamelCase__   ,				UpperCamelCase__			) else v) for k, v in kwargs.items()}
				return args, kwargs



def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[Any]   ,				UpperCamelCase__				:Tuple=None			)		->						Dict:
				if isinstance(UpperCamelCase__   ,				UpperCamelCase__			):
								snake_case__      :   Any      =    {}
								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:
																				snake_case__      :   List[Any]      =    AGENT_TYPE_MAPPING[output_types[i]](UpperCamelCase__			)
																else:
																				snake_case__      :   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			):
																								snake_case__      :   str      =    _v(UpperCamelCase__			)
																if k not in decoded_outputs:
																				snake_case__      :   Any      =    AgentType[v]

				elif isinstance(UpperCamelCase__   ,				(list, tuple)			):
								snake_case__      :   List[str]      =    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
																snake_case__      :   Any      =    False
																for _k, _v in INSTANCE_TYPE_MAPPING.items():
																				if isinstance(UpperCamelCase__   ,				_k			):
																								decoded_outputs.append(_v(UpperCamelCase__			)			)
																								snake_case__      :   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
												snake_case__      :   Tuple      =    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




