




import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

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







class 		__UpperCAmelCase(		TensorFormatter[Mapping, np.ndarray, Mapping]			):





     """simple docstring"""



     def __init__(   self			:							List[Any]    ,					A			:							Any=None    ,					**A			:							Dict     )      ->      Dict:






         """simple docstring"""
         super().__init__(features=__lowercase     )
         a__  			=							np_array_kwargs

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]    ,					A			:							List[Any]     )      ->      Optional[Any]:






         """simple docstring"""
         if isinstance(__lowercase    ,					__lowercase     ):
             if column and all(
                 isinstance(__lowercase    ,					np.ndarray     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
                 return np.stack(__lowercase     )
             else:
                 # don't use np.array(column, dtype=object)
                 # since it fails in certain cases
                 # see https://stackoverflow.com/q/51005699
                 a__  			=							np.empty(len(__lowercase     )    ,					dtype=__lowercase     )
                 a__  			=							column
                 return out
         return column

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]    ,					A			:							Any     )      ->      int:






         """simple docstring"""
         if isinstance(__lowercase    ,					(str, bytes, type(__lowercase     ))     ):
             return value
         elif isinstance(__lowercase    ,					(np.character, np.ndarray)     ) and np.issubdtype(value.dtype    ,					np.character     ):
             return value
         elif isinstance(__lowercase    ,					np.number     ):
             return value

         a__  			=							{}

         if isinstance(__lowercase    ,					np.ndarray     ) and np.issubdtype(value.dtype    ,					np.integer     ):
             a__  			=							{"""dtype""": np.intaa}
         elif isinstance(__lowercase    ,					np.ndarray     ) and np.issubdtype(value.dtype    ,					np.floating     ):
             a__  			=							{"""dtype""": np.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
             import PIL.Image

             if isinstance(__lowercase    ,					PIL.Image.Image     ):
                 return np.asarray(__lowercase    ,					**self.np_array_kwargs     )

         return np.asarray(__lowercase    ,					**{**default_dtype, **self.np_array_kwargs}     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict    ,					A			:							Any     )      ->      int:






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

             if isinstance(__lowercase    ,					torch.Tensor     ):
                 return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
         if hasattr(__lowercase    ,					"""__array__"""     ) and not isinstance(__lowercase    ,					(np.ndarray, np.character, np.number)     ):
             a__  			=							data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(__lowercase    ,					np.ndarray     ):
             if data_struct.dtype == object:
                 return self._consolidate([self.recursive_tensorize(__lowercase     ) for substruct in data_struct]     )
         if isinstance(__lowercase    ,					(list, tuple)     ):
             return self._consolidate([self.recursive_tensorize(__lowercase     ) for substruct in data_struct]     )
         return self._tensorize(__lowercase     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]    ,					A			:							Optional[int]     )      ->      Dict:






         """simple docstring"""
         return map_nested(self._recursive_tensorize    ,					__lowercase    ,					map_list=__lowercase     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any    ,					A			:							str     )      ->      Dict:






         """simple docstring"""
         a__  			=							self.numpy_arrow_extractor().extract_row(__lowercase     )
         a__  			=							self.python_features_decoder.decode_row(__lowercase     )
         return self.recursive_tensorize(__lowercase     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							str    ,					A			:							List[str]     )      ->      Tuple:






         """simple docstring"""
         a__  			=							self.numpy_arrow_extractor().extract_column(__lowercase     )
         a__  			=							self.python_features_decoder.decode_column(__lowercase    ,					pa_table.column_names[0]     )
         a__  			=							self.recursive_tensorize(__lowercase     )
         a__  			=							self._consolidate(__lowercase     )
         return column






     def 					__SCREAMING_SNAKE_CASE		(   self			:							str    ,					A			:							Optional[Any]     )      ->      Tuple:






         """simple docstring"""
         a__  			=							self.numpy_arrow_extractor().extract_batch(__lowercase     )
         a__  			=							self.python_features_decoder.decode_batch(__lowercase     )
         a__  			=							self.recursive_tensorize(__lowercase     )
         for column_name in batch:
             a__  			=							self._consolidate(batch[column_name]     )
         return batch










'''simple docstring'''

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


_UpperCAmelCase					:			Optional[Any]				     =					[nn.LayerNorm]

_UpperCAmelCase					:			Dict				     =					logging.get_logger(__name__)

_UpperCAmelCase					:			Tuple				     =					version.parse(version.parse(torch.__version__).base_version)

_UpperCAmelCase					:			Optional[int]				     =					parsed_torch_version_base >= version.parse("""2.0""")
_UpperCAmelCase					:			Optional[int]				     =					parsed_torch_version_base >= version.parse("""1.12""")
_UpperCAmelCase					:			List[str]				     =					parsed_torch_version_base >= version.parse("""1.11""")
_UpperCAmelCase					:			Optional[int]				     =					parsed_torch_version_base < version.parse("""1.11""")

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					self):
						from torch import _softmax_backward_data

						if is_torch_less_than_1_11:
												return _softmax_backward_data(lowerCamelCase,					lowerCamelCase,					parent.dim,					self)
						else:
												return _softmax_backward_data(lowerCamelCase,					lowerCamelCase,					parent.dim,					self.dtype)

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


class    a__					(			nn.Module   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase							,      __lowercase		):
										super().__init__()
										__lowerCAmelCase											=					nf
										__lowerCAmelCase											=					nn.Parameter(torch.empty(__lowercase							,      __lowercase		)		)
										__lowerCAmelCase											=					nn.Parameter(torch.zeros(__lowercase		)		)
										nn.init.normal_(self.weight							,      std=0.0_2		)




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					x.size()[:-1] + (self.nf,)
										__lowerCAmelCase											=					torch.addmm(self.bias							,      x.view(-1							,      x.size(-1		)		)							,      self.weight		)
										__lowerCAmelCase											=					x.view(__lowercase		)
										return x

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase = 1):
						__lowerCAmelCase											=					index.to(layer.weight.device)
						__lowerCAmelCase											=					layer.weight.index_select(lowerCamelCase,					lowerCamelCase).clone().detach()
						if dim == 0:
												__lowerCAmelCase											=					layer.bias.clone().detach()
						else:
												__lowerCAmelCase											=					layer.bias[index].clone().detach()
						__lowerCAmelCase											=					list(layer.weight.size())
						__lowerCAmelCase											=					len(lowerCamelCase)
						__lowerCAmelCase											=					ConvaD(new_size[1],					new_size[0]).to(layer.weight.device)
						__lowerCAmelCase											=					False
						new_layer.weight.copy_(W.contiguous())
						__lowerCAmelCase											=					True
						__lowerCAmelCase											=					False
						new_layer.bias.copy_(b.contiguous())
						__lowerCAmelCase											=					True
						return new_layer

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase = None):
						if isinstance(lowerCamelCase,					nn.Linear):
												return prune_linear_layer(lowerCamelCase,					lowerCamelCase,					dim=0 if dim is None else dim)
						elif isinstance(lowerCamelCase,					lowerCamelCase):
												return prune_convad_layer(lowerCamelCase,					lowerCamelCase,					dim=1 if dim is None else dim)
						else:
												raise ValueError(F"""Can't prune layer of class {layer.__class__}""")

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					*lowerCamelCase):
						assert len(lowerCamelCase) > 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
						__lowerCAmelCase											=					len(inspect.signature(lowerCamelCase).parameters)
						if num_args_in_forward_chunk_fn != len(lowerCamelCase):
												raise ValueError(
												    F"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(lowerCamelCase)} input """
												    '''tensors are given''')

						if chunk_size > 0:
												__lowerCAmelCase											=					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}""")

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

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

						return forward_fn(*lowerCamelCase)

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase):
						__lowerCAmelCase											=					torch.ones(lowerCamelCase,					lowerCamelCase)
						__lowerCAmelCase											=					set(lowerCamelCase) - 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
												__lowerCAmelCase											=					head - sum(1 if h < head else 0 for h in already_pruned_heads)
												__lowerCAmelCase											=					0
						__lowerCAmelCase											=					mask.view(-1).contiguous().eq(1)
						__lowerCAmelCase											=					torch.arange(len(lowerCamelCase))[mask].long()
						return heads, index

def 				__magic_name__(    *lowerCamelCase,					lowerCamelCase = None):
						return torch.meshgrid(*lowerCamelCase,					indexing=lowerCamelCase)




def 				__magic_name__(    lowerCamelCase):
						return tensor.device, storage_ptr(lowerCamelCase), storage_size(lowerCamelCase)

