






"""simple docstring"""






import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


SCREAMING_SNAKE_CASE    :    Optional[Any]        =							trt.Logger(trt.Logger.WARNING)
SCREAMING_SNAKE_CASE    :    List[Any]        =							absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

SCREAMING_SNAKE_CASE    :    Dict        =							logging.getLogger(__name__)

SCREAMING_SNAKE_CASE    :    Tuple        =							argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    '''--onnx_model_path''',
    default=None,
    type=str,
    required=True,
    help='''Path to ONNX model: ''',
)

parser.add_argument(
    '''--output_dir''',
    default=None,
    type=str,
    required=True,
    help='''The output directory where the model checkpoints and predictions will be written.''',
)

# Other parameters

parser.add_argument(
    '''--tokenizer_name''',
    default='''''',
    type=str,
    required=True,
    help='''Pretrained tokenizer name or path if not the same as model_name''',
)

parser.add_argument(
    '''--version_2_with_negative''',
    action='''store_true''',
    help='''If true, the SQuAD examples contain some that do not have an answer.''',
)
parser.add_argument(
    '''--null_score_diff_threshold''',
    type=float,
    default=0.0,
    help='''If null_score - best_non_null is greater than the threshold predict null.''',
)

parser.add_argument(
    '''--max_seq_length''',
    default=384,
    type=int,
    help=(
        '''The maximum total input sequence length after WordPiece tokenization. Sequences '''
        '''longer than this will be truncated, and sequences shorter than this will be padded.'''
    ),
)
parser.add_argument(
    '''--doc_stride''',
    default=128,
    type=int,
    help='''When splitting up a long document into chunks, how much stride to take between chunks.''',
)

parser.add_argument('''--per_device_eval_batch_size''', default=8, type=int, help='''Batch size per GPU/CPU for evaluation.''')

parser.add_argument(
    '''--n_best_size''',
    default=20,
    type=int,
    help='''The total number of n-best predictions to generate in the nbest_predictions.json output file.''',
)
parser.add_argument(
    '''--max_answer_length''',
    default=30,
    type=int,
    help=(
        '''The maximum length of an answer that can be generated. This is needed because the start '''
        '''and end predictions are not conditioned on one another.'''
    ),
)

parser.add_argument('''--seed''', type=int, default=42, help='''random seed for initialization''')

parser.add_argument(
    '''--dataset_name''',
    type=str,
    default=None,
    required=True,
    help='''The name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
    '''--dataset_config_name''',
    type=str,
    default=None,
    help='''The configuration name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
    '''--preprocessing_num_workers''', type=int, default=4, help='''A csv or a json file containing the training data.'''
)
parser.add_argument('''--overwrite_cache''', action='''store_true''', help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
    '''--fp16''',
    action='''store_true''',
    help='''Whether to use 16-bit (mixed) precision instead of 32-bit''',
)
parser.add_argument(
    '''--int8''',
    action='''store_true''',
    help='''Whether to use INT8''',
)

SCREAMING_SNAKE_CASE    :    int        =							parser.parse_args()

if args.tokenizer_name:
					SCREAMING_SNAKE_CASE    :    str        =							AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
					raise ValueError(
					    '''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
					    '''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''
					)

logger.info('''Training/evaluation parameters %s''', args)

SCREAMING_SNAKE_CASE    :    Union[str, Any]        =							args.per_device_eval_batch_size

SCREAMING_SNAKE_CASE    :    Union[str, Any]        =							(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
SCREAMING_SNAKE_CASE    :    int        =							True

SCREAMING_SNAKE_CASE    :    Any        =							'''temp_engine/bert-fp32.engine'''
if args.fpaa:
					SCREAMING_SNAKE_CASE    :    Optional[int]        =							'''temp_engine/bert-fp16.engine'''
if args.inta:
					SCREAMING_SNAKE_CASE    :    Tuple        =							'''temp_engine/bert-int8.engine'''

# import ONNX file
if not os.path.exists('''temp_engine'''):
					os.makedirs('''temp_engine''')

SCREAMING_SNAKE_CASE    :    List[Any]        =							1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
					with open(args.onnx_model_path, '''rb''') as model:
										if not parser.parse(model.read()):
															for error in range(parser.num_errors):
																				print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
					SCREAMING_SNAKE_CASE    :    Optional[int]        =							[network.get_input(i) for i in range(network.num_inputs)]
					SCREAMING_SNAKE_CASE    :    List[Any]        =							[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

					with builder.create_builder_config() as config:
										SCREAMING_SNAKE_CASE    :    Optional[Any]        =							1 << 50
										if STRICT_TYPES:
															config.set_flag(trt.BuilderFlag.STRICT_TYPES)
										if args.fpaa:
															config.set_flag(trt.BuilderFlag.FPaa)
										if args.inta:
															config.set_flag(trt.BuilderFlag.INTa)
										SCREAMING_SNAKE_CASE    :    int        =							builder.create_optimization_profile()
										config.add_optimization_profile(profile)
										for i in range(len(input_names)):
															profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
										SCREAMING_SNAKE_CASE    :    List[Any]        =							builder.build_engine(network, config)

										# serialize_engine and store in file (can be directly loaded and deserialized):
										with open(engine_name, '''wb''') as f:
															f.write(engine.serialize())



def 			__lowerCamelCase       (       lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__					):
				A__							=							np.asarray(inputs['input_ids']							,dtype=np.intaa					)
				A__							=							np.asarray(inputs['attention_mask']							,dtype=np.intaa					)
				A__							=							np.asarray(inputs['token_type_ids']							,dtype=np.intaa					)

				# Copy inputs
				cuda.memcpy_htod_async(d_inputs[0]							,input_ids.ravel()							,UpperCamelCase_					)
				cuda.memcpy_htod_async(d_inputs[1]							,attention_mask.ravel()							,UpperCamelCase_					)
				cuda.memcpy_htod_async(d_inputs[2]							,token_type_ids.ravel()							,UpperCamelCase_					)
				# start time
				A__							=							time.time()
				# Run inference
				context.execute_async(
				    bindings=[int(UpperCamelCase_					) for d_inp in d_inputs] + [int(UpperCamelCase_					), int(UpperCamelCase_					)]							,stream_handle=stream.handle					)
				# Transfer predictions back from GPU
				cuda.memcpy_dtoh_async(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_					)
				cuda.memcpy_dtoh_async(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_					)
				# Synchronize the stream and take time
				stream.synchronize()
				# end time
				A__							=							time.time()
				A__							=							end_time - start_time
				A__							=							(h_outputa, h_outputa)
				# print(outputs)
				return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
SCREAMING_SNAKE_CASE    :    List[Any]        =							Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
					datasets.utils.logging.set_verbosity_warning()
					transformers.utils.logging.set_verbosity_info()
else:
					datasets.utils.logging.set_verbosity_error()
					transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
					set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
					# Downloading and loading a dataset from the hub.
					SCREAMING_SNAKE_CASE    :    Tuple        =							load_dataset(args.dataset_name, args.dataset_config_name)
else:
					raise ValueError('''Evaluation requires a dataset name''')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

SCREAMING_SNAKE_CASE    :    str        =							raw_datasets['''validation'''].column_names

SCREAMING_SNAKE_CASE    :    Optional[int]        =							'''question''' if '''question''' in column_names else column_names[0]
SCREAMING_SNAKE_CASE    :    List[str]        =							'''context''' if '''context''' in column_names else column_names[1]
SCREAMING_SNAKE_CASE    :    str        =							'''answers''' if '''answers''' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
SCREAMING_SNAKE_CASE    :    str        =							tokenizer.padding_side == '''right'''

if args.max_seq_length > tokenizer.model_max_length:
					logger.warning(
					    f'''The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'''
					    f'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'''
					)

SCREAMING_SNAKE_CASE    :    Optional[Any]        =							min(args.max_seq_length, tokenizer.model_max_length)



def 			__lowerCamelCase       (       lowerCAmelCase__					):
				# Some of the questions have lots of whitespace on the left, which is not useful and will make the
				# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
				# left whitespace
				A__							=							[q.lstrip() for q in examples[question_column_name]]

				# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
				# in one example possible giving several features when a context is long, each of those features having a
				# context that overlaps a bit the context of the previous feature.
				A__							=							tokenizer(
				    examples[question_column_name if pad_on_right else context_column_name]							,examples[context_column_name if pad_on_right else question_column_name]							,truncation='only_second' if pad_on_right else 'only_first'							,max_length=UpperCamelCase_							,stride=args.doc_stride							,return_overflowing_tokens=UpperCamelCase_							,return_offsets_mapping=UpperCamelCase_							,padding='max_length'							,)

				# Since one example might give us several features if it has a long context, we need a map from a feature to
				# its corresponding example. This key gives us just that.
				A__							=							tokenized_examples.pop('overflow_to_sample_mapping'					)

				# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
				# corresponding example_id and we will store the offset mappings.
				A__							=							[]

				for i in range(len(tokenized_examples['input_ids']					)					):
								# Grab the sequence corresponding to that example (to know what is the context and what is the question).
								A__							=							tokenized_examples.sequence_ids(UpperCamelCase_					)
								A__							=							1 if pad_on_right else 0

								# One example can give several spans, this is the index of the example containing this span of text.
								A__							=							sample_mapping[i]
								tokenized_examples["example_id"].append(examples['id'][sample_index]					)

								# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
								# position is part of the context or not.
								A__							=							[
								    (o if sequence_ids[k] == context_index else None)
								    for k, o in enumerate(tokenized_examples['offset_mapping'][i]					)
								]

				return tokenized_examples


SCREAMING_SNAKE_CASE    :    List[Any]        =							raw_datasets['''validation''']
# Validation Feature Creation
SCREAMING_SNAKE_CASE    :    Optional[int]        =							eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc='''Running tokenizer on validation dataset''',
)

SCREAMING_SNAKE_CASE    :    Optional[Any]        =							default_data_collator

SCREAMING_SNAKE_CASE    :    Optional[Any]        =							eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
SCREAMING_SNAKE_CASE    :    Optional[Any]        =							DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)



def 			__lowerCamelCase       (       lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__="eval"					):
				# Post-processing: we match the start logits and end logits to answers in the original context.
				A__							=							postprocess_qa_predictions(
				    examples=UpperCamelCase_							,features=UpperCamelCase_							,predictions=UpperCamelCase_							,version_2_with_negative=args.version_2_with_negative							,n_best_size=args.n_best_size							,max_answer_length=args.max_answer_length							,null_score_diff_threshold=args.null_score_diff_threshold							,output_dir=args.output_dir							,prefix=UpperCamelCase_							,)
				# Format the result to the format the metric expects.
				if args.version_2_with_negative:
								A__							=							[
								    {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
								]
				else:
								A__							=							[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

				A__							=							[{'id': ex['id'], 'answers': ex[answer_column_name]} for ex in examples]
				return EvalPrediction(predictions=UpperCamelCase_							,label_ids=UpperCamelCase_					)


SCREAMING_SNAKE_CASE    :    int        =							load_metric('''squad_v2''' if args.version_2_with_negative else '''squad''')

# Evaluation!
logger.info('''Loading ONNX model %s for evaluation''', args.onnx_model_path)
with open(engine_name, '''rb''') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
					# setup for TRT inferrence
					for i in range(len(input_names)):
										context.set_binding_shape(i, INPUT_SHAPE)
					assert context.all_binding_shapes_specified



					def 			__lowerCamelCase       (       lowerCAmelCase__					):
									return trt.volume(engine.get_binding_shape(UpperCamelCase_					)					) * engine.get_binding_dtype(UpperCamelCase_					).itemsize

					# Allocate device memory for inputs and outputs.
					SCREAMING_SNAKE_CASE    :    Dict        =							[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

					# Allocate output buffer
					SCREAMING_SNAKE_CASE    :    Dict        =							cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
					SCREAMING_SNAKE_CASE    :    Any        =							cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
					SCREAMING_SNAKE_CASE    :    Dict        =							cuda.mem_alloc(h_outputa.nbytes)
					SCREAMING_SNAKE_CASE    :    List[str]        =							cuda.mem_alloc(h_outputa.nbytes)

					# Create a stream in which to copy inputs/outputs and run inference.
					SCREAMING_SNAKE_CASE    :    List[Any]        =							cuda.Stream()

					# Evaluation
					logger.info('''***** Running Evaluation *****''')
					logger.info(f'''  Num examples = {len(eval_dataset)}''')
					logger.info(f'''  Batch size = {args.per_device_eval_batch_size}''')

					SCREAMING_SNAKE_CASE    :    Tuple        =							0.0
					SCREAMING_SNAKE_CASE    :    List[Any]        =							0
					SCREAMING_SNAKE_CASE    :    int        =							timeit.default_timer()

					SCREAMING_SNAKE_CASE    :    int        =							None
					for step, batch in enumerate(eval_dataloader):
										SCREAMING_SNAKE_CASE ,						SCREAMING_SNAKE_CASE    :    Tuple        =							model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
										total_time += infer_time
										niter += 1

										SCREAMING_SNAKE_CASE ,						SCREAMING_SNAKE_CASE    :    List[str]        =							outputs
										SCREAMING_SNAKE_CASE    :    List[Any]        =							torch.tensor(start_logits)
										SCREAMING_SNAKE_CASE    :    List[Any]        =							torch.tensor(end_logits)

										# necessary to pad predictions and labels for being gathered
										SCREAMING_SNAKE_CASE    :    Optional[Any]        =							accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
										SCREAMING_SNAKE_CASE    :    List[str]        =							accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

										SCREAMING_SNAKE_CASE    :    List[Any]        =							(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
										SCREAMING_SNAKE_CASE    :    List[str]        =							logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

					if all_preds is not None:
										SCREAMING_SNAKE_CASE    :    Optional[int]        =							nested_truncate(all_preds, len(eval_dataset))

					SCREAMING_SNAKE_CASE    :    List[str]        =							timeit.default_timer() - start_time
					logger.info('''  Evaluation done in total %f secs (%f sec per example)''', evalTime, evalTime / len(eval_dataset))
					# Inference time from TRT
					logger.info('''Average Inference Time = {:.3f} ms'''.format(total_time * 1_000 / niter))
					logger.info('''Total Inference Time =  {:.3f} ms'''.format(total_time * 1_000))







					logger.info('''Total Number of Inference =  %d''', niter)

SCREAMING_SNAKE_CASE    :    Any        =							post_processing_function(eval_examples, eval_dataset, all_preds)
SCREAMING_SNAKE_CASE    :    Any        =							metric.compute(predictions=prediction.predictions, references=prediction.label_ids)







logger.info(f'''Evaluation metrics: {eval_metric}''')







from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class      _UpperCAmelCase      (							A__      ,   A__      ,   A__  ):
     UpperCamelCase__		     =					[r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']




     @register_to_config
     def __init__(					self			,    a__			,    a__			,    a__ = None			,    a__ = 5_0_2_5_7			,    a__ = 1_0_2_4			,    a__ = 7_6_8			,    a__ = 1_2			,    a__ = 1_2			,    a__ = None			,    a__ = "gelu_new"			,    a__ = 0.1			,    a__ = 0.1			,    a__ = 0.1			,    a__ = 1e-5			,    a__ = 0.0_2			,    a__ = True			,    a__ = True			,    a__ = False			,    a__ = False			,    ):
         super().__init__()

         A__					=       prefix_length

         if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
             raise ValueError(
                 F"`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"
                 F" `n_embd`: {n_embd} are not equal.")

         A__					=       prefix_inner_dim
         A__					=       prefix_hidden_dim

         A__					=       (
             nn.Linear(self.prefix_inner_dim			,    self.prefix_hidden_dim)
             if self.prefix_hidden_dim is not None
             else nn.Identity()
         )
         A__					=       (
             nn.Linear(self.prefix_hidden_dim			,    a__) if self.prefix_hidden_dim is not None else nn.Identity()
         )

         A__					=       GPTaConfig(
             vocab_size=a__			,    n_positions=a__			,    n_embd=a__			,    n_layer=a__			,    n_head=a__			,    n_inner=a__			,    activation_function=a__			,    resid_pdrop=a__			,    embd_pdrop=a__			,    attn_pdrop=a__			,    layer_norm_epsilon=a__			,    initializer_range=a__			,    scale_attn_weights=a__			,    use_cache=a__			,    scale_attn_by_inverse_layer_idx=a__			,    reorder_and_upcast_attn=a__			,    )
         A__					=       GPTaLMHeadModel(a__)




     def 		snake_case_		(					self			,    a__			,    a__			,    a__ = None			,    a__ = None			,    ):
         A__					=       self.transformer.transformer.wte(a__)
         A__					=       self.encode_prefix(a__)
         A__					=       self.decode_prefix(a__)
         A__					=       torch.cat((prefix_embeds, embedding_text)			,    dim=1)

         if labels is not None:
             A__					=       self.get_dummy_token(input_ids.shape[0]			,    input_ids.device)
             A__					=       torch.cat((dummy_token, input_ids)			,    dim=1)
         A__					=       self.transformer(inputs_embeds=a__			,    labels=a__			,    attention_mask=a__)
         if self.prefix_hidden_dim is not None:
             return out, hidden
         else:
             return out




     def 		snake_case_		(					self			,    a__			,    a__):
         return torch.zeros(a__			,    self.prefix_length			,    dtype=torch.intaa			,    device=a__)




     def 		snake_case_		(					self			,    a__):
         return self.encode_prefix(a__)




     @torch.no_grad()
     def 		snake_case_		(					self			,    a__			,    a__			,    a__):
         A__					=       torch.split(a__			,    1			,    dim=0)
         A__					=       []
         A__					=       []
         for feature in features:
             A__					=       self.decode_prefix(feature.to(a__))  # back to the clip feature
             # Only support beam search for now
             A__			,						A__					=       self.generate_beam(
                 input_embeds=a__			,    device=a__			,    eos_token_id=a__)
             generated_tokens.append(output_tokens[0])
             generated_seq_lengths.append(seq_lengths[0])
         A__					=       torch.stack(a__)
         A__					=       torch.stack(a__)
         return generated_tokens, generated_seq_lengths




     @torch.no_grad()
     def 		snake_case_		(					self			,    a__=None			,    a__=None			,    a__=None			,    a__ = 5			,    a__ = 6_7			,    a__ = 1.0			,    a__ = None			,    ):
         A__					=       eos_token_id
         A__					=       None
         A__					=       None
         A__					=       torch.ones(a__			,    device=a__			,    dtype=torch.int)
         A__					=       torch.zeros(a__			,    device=a__			,    dtype=torch.bool)

         if input_embeds is not None:
             A__					=       input_embeds
         else:
             A__					=       self.transformer.transformer.wte(a__)

         for i in range(a__):
             A__					=       self.transformer(inputs_embeds=a__)
             A__					=       outputs.logits
             A__					=       logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
             A__					=       logits.softmax(-1).log()

             if scores is None:
                 A__			,						A__					=       logits.topk(a__			,    -1)
                 A__					=       generated.expand(a__			,    *generated.shape[1:])
                 A__			,						A__					=       next_tokens.permute(1			,    0), scores.squeeze(0)
                 if tokens is None:
                     A__					=       next_tokens
                 else:
                     A__					=       tokens.expand(a__			,    *tokens.shape[1:])
                     A__					=       torch.cat((tokens, next_tokens)			,    dim=1)
             else:
                 A__					=       -float(np.inf)
                 A__					=       0
                 A__					=       scores[:, None] + logits
                 seq_lengths[~is_stopped] += 1
                 A__					=       scores_sum / seq_lengths[:, None]
                 A__			,						A__					=       scores_sum_average.view(-1).topk(a__			,    -1)
                 A__					=       next_tokens // scores_sum.shape[1]
                 A__					=       seq_lengths[next_tokens_source]
                 A__					=       next_tokens % scores_sum.shape[1]
                 A__					=       next_tokens.unsqueeze(1)
                 A__					=       tokens[next_tokens_source]
                 A__					=       torch.cat((tokens, next_tokens)			,    dim=1)
                 A__					=       generated[next_tokens_source]
                 A__					=       scores_sum_average * seq_lengths
                 A__					=       is_stopped[next_tokens_source]

             A__					=       self.transformer.transformer.wte(next_tokens.squeeze()).view(generated.shape[0]			,    1			,    -1)
             A__					=       torch.cat((generated, next_token_embed)			,    dim=1)
             A__					=       is_stopped + next_tokens.eq(a__).squeeze()
             if is_stopped.all():
                 break

         A__					=       scores / seq_lengths
         A__					=       scores.argsort(descending=a__)
         # tokens tensors are already padded to max_seq_length
         A__					=       [tokens[i] for i in order]
         A__					=       torch.stack(a__			,    dim=0)
         A__					=       torch.tensor([seq_lengths[i] for i in order]			,    dtype=seq_lengths.dtype)
         return output_texts, seq_lengths
