

import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed





class A__    ( _UpperCAmelCase ):



 """simple docstring"""







 def __init__(		self :	List[str]     ,						A_ :	List[str]     ,						A_ :	str     ,						A_ :	List[str]=1_0_2_4     ,						A_ :	int=1_0_2_4     ,						A_ :	str=3.6       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    tokenizer
      _lowerCAmelCase   :					Optional[Any]        =    tokenizer.bos_token_id
      _lowerCAmelCase   :					List[Any]        =    dataset
      _lowerCAmelCase   :					int        =    seq_length
      _lowerCAmelCase   :					Any        =    seq_length * chars_per_token * num_of_sequences


 def __iter__(		self :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    iter(self.dataset       )
      _lowerCAmelCase   :					Optional[int]        =    True
      while more_examples:
           _lowerCAmelCase   :					Any        =    [], 0
           while True:
                if buffer_len >= self.input_characters:
                     break
                try:
                     buffer.append(next(lowercase__       )["content"]       )
                     buffer_len += len(buffer[-1]       )
                except StopIteration:
                     _lowerCAmelCase   :					int        =    False
                     break
           _lowerCAmelCase   :					Optional[Any]        =    tokenizer(lowercase__     ,						truncation=lowercase__       )["""input_ids"""]
           _lowerCAmelCase   :					List[Any]        =    []
           for tokenized_input in tokenized_inputs:
                all_token_ids.extend(tokenized_input + [self.concat_token_id]       )
           for i in range(0     ,						len(lowercase__       )     ,						self.seq_length       ):
                _lowerCAmelCase   :					Union[str, Any]        =    all_token_ids[i : i + self.seq_length]
                if len(lowercase__       ) == self.seq_length:
                     yield torch.tensor(lowercase__       )







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      str:

     """simple docstring"""







     _lowerCAmelCase   :					List[Any]        =    {"""streaming""": True}
     _lowerCAmelCase   :					Any        =    load_dataset(args.dataset_name		,							split="train"		,							**SCREAMING_SNAKE_CASE__				)
     _lowerCAmelCase   :					List[str]        =    ConstantLengthDataset(SCREAMING_SNAKE_CASE__		,							SCREAMING_SNAKE_CASE__		,							seq_length=args.seq_length				)
     _lowerCAmelCase   :					Optional[Any]        =    DataLoader(SCREAMING_SNAKE_CASE__		,							batch_size=args.batch_size				)
     return eval_dataloader







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      int:

     """simple docstring"""







     model.eval()
     _lowerCAmelCase   :					List[Any]        =    []
     for step, batch in enumerate(SCREAMING_SNAKE_CASE__				):
          with torch.no_grad():
               _lowerCAmelCase   :					List[str]        =    model(SCREAMING_SNAKE_CASE__		,							labels=SCREAMING_SNAKE_CASE__				)
          _lowerCAmelCase   :					Any        =    outputs.loss.repeat(args.batch_size				)
          losses.append(accelerator.gather(SCREAMING_SNAKE_CASE__				)				)

          if args.max_eval_steps > 0 and step >= args.max_eval_steps:
               break
     _lowerCAmelCase   :					Any        =    torch.mean(torch.cat(SCREAMING_SNAKE_CASE__				)				)
     try:
          _lowerCAmelCase   :					List[str]        =    torch.exp(SCREAMING_SNAKE_CASE__				)
     except OverflowError:
          _lowerCAmelCase   :					int        =    float("inf"				)
     return loss.item(), perplexity.item()


# Setup Accelerator
__UpperCAmelCase    				=     Accelerator()

# Parse configuration
__UpperCAmelCase    				=     HfArgumentParser(EvaluationArguments)
__UpperCAmelCase    				=     parser.parse_args()
set_seed(args.seed)

# Logging
__UpperCAmelCase    				=     logging.getLogger(__name__)
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)

# Load model and tokenizer
__UpperCAmelCase    				=     AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__UpperCAmelCase    				=     AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
__UpperCAmelCase    				=     create_dataloader(args)

# Prepare everything with our `accelerator`.
__UpperCAmelCase				,__UpperCAmelCase    				=     accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
__UpperCAmelCase				,__UpperCAmelCase    				=     evaluate(args)



logger.info(f'''loss/eval: {eval_loss}, perplexity: {perplexity}''')









# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

__UpperCAmelCase    				=     TypeVar('T')





class A__    ( Generic[T] ):



 """simple docstring"""







 def __init__(		self :	Optional[int]     ,						A_ :	bool = True       ):






      '''simple docstring'''




      _lowerCAmelCase   :					dict[T, list[T]]        =    {}  # dictionary of lists
      _lowerCAmelCase   :					List[Any]        =    directed

 def    __magic_name__       (		self :	Any     ,						A_ :	T     ,						A_ :	T       ):






      '''simple docstring'''




      if not self.directed:  # For undirected graphs
           # if both source vertex and destination vertex are both present in the
           # adjacency list, add destination vertex to source vertex list of adjacent
           # vertices and add source vertex to destination vertex list of adjacent
           # vertices.
           if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                self.adj_list[source_vertex].append(A_       )
                self.adj_list[destination_vertex].append(A_       )
           # if only source vertex is present in adjacency list, add destination vertex
           # to source vertex list of adjacent vertices, then create a new vertex with
           # destination vertex as key and assign a list containing the source vertex
           # as it's first adjacent vertex.
           elif source_vertex in self.adj_list:
                self.adj_list[source_vertex].append(A_       )
                _lowerCAmelCase   :					List[Any]        =    [source_vertex]
           # if only destination vertex is present in adjacency list, add source vertex
           # to destination vertex list of adjacent vertices, then create a new vertex
           # with source vertex as key and assign a list containing the source vertex
           # as it's first adjacent vertex.
           elif destination_vertex in self.adj_list:
                self.adj_list[destination_vertex].append(A_       )
                _lowerCAmelCase   :					List[str]        =    [destination_vertex]
           # if both source vertex and destination vertex are not present in adjacency
           # list, create a new vertex with source vertex as key and assign a list
           # containing the destination vertex as it's first adjacent vertex also
           # create a new vertex with destination vertex as key and assign a list
           # containing the source vertex as it's first adjacent vertex.
           else:
                _lowerCAmelCase   :					List[str]        =    [destination_vertex]
                _lowerCAmelCase   :					Dict        =    [source_vertex]
      else:  # For directed graphs
           # if both source vertex and destination vertex are present in adjacency
           # list, add destination vertex to source vertex list of adjacent vertices.
           if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                self.adj_list[source_vertex].append(A_       )
           # if only source vertex is present in adjacency list, add destination
           # vertex to source vertex list of adjacent vertices and create a new vertex
           # with destination vertex as key, which has no adjacent vertex
           elif source_vertex in self.adj_list:
                self.adj_list[source_vertex].append(A_       )
                _lowerCAmelCase   :					Tuple        =    []
           # if only destination vertex is present in adjacency list, create a new
           # vertex with source vertex as key and assign a list containing destination
           # vertex as first adjacent vertex
           elif destination_vertex in self.adj_list:
                _lowerCAmelCase   :					Dict        =    [destination_vertex]
           # if both source vertex and destination vertex are not present in adjacency
           # list, create a new vertex with source vertex as key and a list containing
           # destination vertex as it's first adjacent vertex. Then create a new vertex
           # with destination vertex as key, which has no adjacent vertex
           else:
                _lowerCAmelCase   :					Any        =    [destination_vertex]
                _lowerCAmelCase   :					Union[str, Any]        =    []

      return self


 def __repr__(		self :	List[str]       ):






      '''simple docstring'''




      return pformat(self.adj_list       )






