






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   :						int							=  [nn.LayerNorm]

__UpperCamelCase   :						List[str]							=  logging.get_logger(__name__)

__UpperCamelCase   :						str							=  version.parse(version.parse(torch.__version__).base_version)

__UpperCamelCase   :						List[str]							=  parsed_torch_version_base >= version.parse("""2.0""")
__UpperCamelCase   :						Dict							=  parsed_torch_version_base >= version.parse("""1.12""")
__UpperCamelCase   :						Tuple							=  parsed_torch_version_base >= version.parse("""1.11""")
__UpperCamelCase   :						Union[str, Any]							=  parsed_torch_version_base < version.parse("""1.11""")
def        a_	( _A ,				_A ,				_A ,				_A ,				self					)     ->     Any:







				"""simple docstring"""
				from torch import _softmax_backward_data

				if is_torch_less_than_1_11:
								return _softmax_backward_data(_A ,				_A ,				parent.dim ,				self					)
				else:
								return _softmax_backward_data(_A ,				_A ,				parent.dim ,				self.dtype					)
def        a_	( _A ,				_A ,				_A = 0					)     ->     nn.Linear:







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


class 					__SCREAMING_SNAKE_CASE(		nn.Module		):
							def __init__(       self: Union[str, Any]    ,		UpperCamelCase: Dict    ,		UpperCamelCase: List[Any]  )						->						Any:
											super().__init__()
											snake_case__													=       nf
											snake_case__													=       nn.Parameter(torch.empty(UpperCamelCase    ,		UpperCamelCase  )  )
											snake_case__													=       nn.Parameter(torch.zeros(UpperCamelCase  )  )
											nn.init.normal_(self.weight    ,		std=0.02  )


							def lowerCAmelCase_	(       self: Optional[Any]    ,		UpperCamelCase: int  )						->						List[Any]:
											snake_case__													=       x.size()[:-1] + (self.nf,)
											snake_case__													=       torch.addmm(self.bias    ,		x.view(-1    ,		x.size(-1  )  )    ,		self.weight  )
											snake_case__													=       x.view(UpperCamelCase  )
											return x


def        a_	( _A ,				_A ,				_A = 1					)     ->     ConvaD:







				"""simple docstring"""
				snake_case__													=       index.to(layer.weight.device					)
				snake_case__													=       layer.weight.index_select(_A ,				_A					).clone().detach()
				if dim == 0:
								snake_case__													=       layer.bias.clone().detach()
				else:
								snake_case__													=       layer.bias[index].clone().detach()
				snake_case__													=       list(layer.weight.size()					)
				snake_case__													=       len(_A					)
				snake_case__													=       ConvaD(new_size[1] ,				new_size[0]					).to(layer.weight.device					)
				snake_case__													=       False
				new_layer.weight.copy_(W.contiguous()					)
				snake_case__													=       True
				snake_case__													=       False
				new_layer.bias.copy_(b.contiguous()					)
				snake_case__													=       True
				return new_layer
def        a_	( _A ,				_A ,				_A = None					)     ->     Union[nn.Linear, ConvaD]:







				"""simple docstring"""
				if isinstance(_A ,				nn.Linear					):
								return prune_linear_layer(_A ,				_A ,				dim=0 if dim is None else dim					)
				elif isinstance(_A ,				_A					):
								return prune_convad_layer(_A ,				_A ,				dim=1 if dim is None else dim					)
				else:
								raise ValueError(f'''Can\'t prune layer of class {layer.__class__}'''					)
def        a_	( _A ,				_A ,				_A ,				*_A					)     ->     torch.Tensor:







				"""simple docstring"""
				assert len(_A					) > 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
				snake_case__													=       len(inspect.signature(_A					).parameters					)
				if num_args_in_forward_chunk_fn != len(_A					):
								raise ValueError(
								    f'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(_A					)} input '''
								    'tensors are given'					)

				if chunk_size > 0:
								snake_case__													=       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}'''					)

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

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

				return forward_fn(*_A					)
def        a_	( _A ,				_A ,				_A ,				_A					)     ->     Tuple[Set[int], torch.LongTensor]:







				"""simple docstring"""
				snake_case__													=       torch.ones(_A ,				_A					)
				snake_case__													=       set(_A					) - 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
								snake_case__													=       head - sum(1 if h < head else 0 for h in already_pruned_heads					)
								snake_case__													=       0
				snake_case__													=       mask.view(-1					).contiguous().eq(1					)
				snake_case__													=       torch.arange(len(_A					)					)[mask].long()
				return heads, index
def        a_	( *_A ,				_A = None					)     ->     Tuple[torch.Tensor, ...]:







				"""simple docstring"""
				return torch.meshgrid(*_A ,				indexing=_A					)


def        a_	( _A					)     ->     Tuple[torch.device, int, int]:







				"""simple docstring"""
				return tensor.device, storage_ptr(_A					), storage_size(_A					)












import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__UpperCamelCase   :						List[Any]							=  logging.getLogger(__name__)
def        a_	( )     ->     int:







				"""simple docstring"""
				snake_case__													=       argparse.ArgumentParser(description='PyTorch Transformer Language Model'					)
				parser.add_argument('--model_name' ,				type=_A ,				default='transfo-xl-wt103' ,				help='pretrained model name'					)
				parser.add_argument(
				    '--split' ,				type=_A ,				default='test' ,				choices=['all', 'valid', 'test'] ,				help='which split to evaluate'					)
				parser.add_argument('--batch_size' ,				type=_A ,				default=10 ,				help='batch size'					)
				parser.add_argument('--tgt_len' ,				type=_A ,				default=128 ,				help='number of tokens to predict'					)
				parser.add_argument('--ext_len' ,				type=_A ,				default=0 ,				help='length of the extended context'					)
				parser.add_argument('--mem_len' ,				type=_A ,				default=1600 ,				help='length of the retained previous heads'					)
				parser.add_argument('--clamp_len' ,				type=_A ,				default=1000 ,				help='max positional embedding index'					)
				parser.add_argument('--no_cuda' ,				action='store_true' ,				help='Do not use CUDA even though CUA is available'					)
				parser.add_argument('--work_dir' ,				type=_A ,				required=_A ,				help='path to the work_dir'					)
				parser.add_argument('--no_log' ,				action='store_true' ,				help='do not log the eval result'					)
				parser.add_argument('--same_length' ,				action='store_true' ,				help='set same length attention with masking'					)
				parser.add_argument('--server_ip' ,				type=_A ,				default='' ,				help='Can be used for distant debugging.'					)
				parser.add_argument('--server_port' ,				type=_A ,				default='' ,				help='Can be used for distant debugging.'					)
				snake_case__													=       parser.parse_args()
				assert args.ext_len >= 0, "extended context length must be non-negative"

				if args.server_ip and args.server_port:
								# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
								import ptvsd

								print('Waiting for debugger attach'					)
								ptvsd.enable_attach(address=(args.server_ip, args.server_port) ,				redirect_output=_A					)
								ptvsd.wait_for_attach()

				snake_case__													=       torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'					)
				logger.info('device: {}'.format(_A					)					)

				# Load a pre-processed dataset
				# You can also build the corpus yourself using TransfoXLCorpus methods
				# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
				# and tokenizing the dataset
				# The pre-processed corpus is a convertion (using the conversion script )
				snake_case__													=       TransfoXLCorpus.from_pretrained(args.model_name					)

				snake_case__													=       corpus.get_iterator('valid' ,				args.batch_size ,				args.tgt_len ,				device=_A ,				ext_len=args.ext_len					)
				snake_case__													=       corpus.get_iterator('test' ,				args.batch_size ,				args.tgt_len ,				device=_A ,				ext_len=args.ext_len					)

				# Load a pre-trained model
				snake_case__													=       TransfoXLLMHeadModel.from_pretrained(args.model_name					)
				model.to(_A					)

				logger.info(
				    'Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
				        args.batch_size ,				args.tgt_len ,				args.ext_len ,				args.mem_len ,				args.clamp_len					)					)

				model.reset_memory_length(args.mem_len					)
				if args.clamp_len > 0:
								snake_case__													=       args.clamp_len
				if args.same_length:
								snake_case__													=       True

				###############################################################################
				# Evaluation code
				###############################################################################
				def evaluate(_A					):
								# Turn on evaluation mode which disables dropout.
								model.eval()
								snake_case__							,  snake_case__													=       0, 0.0
								snake_case__													=       time.time()
								with torch.no_grad():
												snake_case__													=       None
												for idx, (data, target, seq_len) in enumerate(_A					):
																snake_case__													=       model(_A ,				lm_labels=_A ,				mems=_A					)
																snake_case__							,  snake_case__							,  snake_case__													=       ret
																snake_case__													=       loss.mean()
																total_loss += seq_len * loss.item()
																total_len += seq_len
												snake_case__													=       time.time() - start_time
								logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(_A ,				1000 * total_time / (idx + 1)					)					)
								return total_loss / total_len

				# Run on test data.
				if args.split == "all":
								snake_case__													=       evaluate(_A					)
								snake_case__													=       evaluate(_A					)
				elif args.split == "valid":
								snake_case__													=       evaluate(_A					)
								snake_case__													=       None
				elif args.split == "test":
								snake_case__													=       evaluate(_A					)
								snake_case__													=       None

				def format_log(_A ,				_A					):
								snake_case__													=       '| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(_A ,				_A ,				math.exp(_A					)					)
								return log_str

				snake_case__													=       ''
				if valid_loss is not None:
								log_str += format_log(_A ,				'valid'					)
				if test_loss is not None:
								log_str += format_log(_A ,				'test'					)

				logger.info('=' * 100					)
				logger.info(_A					)
				logger.info('=' * 100					)


if __name__ == "__main__":
							main()




