






'''simple docstring'''
import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs


class 							_UpperCAmelCase    (		snake_case      ):




    def __init__(      self	:							Optional[int]	,  a	:							Dict	,  a	:							List[str]=None	,  a	:							Any=True	,  a	:							Optional[Any]=None	,  **a	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     Tuple							     =  parent
       lowercase_						:     List[Any]							     =  config_class
       lowercase_						:     Union[str, Any]							     =  has_text_modality
       lowercase_						:     Tuple							     =  kwargs
       lowercase_						:     List[str]							     =  common_properties




    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  self.config_class(**self.inputs_dict      )
       lowercase_						:     Optional[Any]							     =  (
           ["hidden_size", "num_attention_heads", "num_hidden_layers"]
           if self.common_properties is None
           else self.common_properties
       )

       # Add common fields for text models
       if self.has_text_modality:
          common_properties.extend(["vocab_size"]      )

       # Test that config has the common properties as getters
       for prop in common_properties:
          self.parent.assertTrue(hasattr(a	,  a      )	,  msg=f"""`{prop}` does not exist"""      )

       # Test that config has the common properties as setter
       for idx, name in enumerate(a      ):
          try:
             setattr(a	,  a	,  a      )
             self.parent.assertEqual(
                 getattr(a	,  a      )	,  a	,  msg=f"""`{name} value {idx} expected, but was {getattr(a	,  a      )}"""      )
          except NotImplementedError:
             # Some models might not be able to implement setters for common_properties
             # In that case, a NotImplementedError is raised
             pass

        # Test if config class can be called with Config(prop_name=..)
       for idx, name in enumerate(a      ):
          try:
             lowercase_						:     Union[str, Any]							     =  self.config_class(**{name: idx}      )
             self.parent.assertEqual(
                 getattr(a	,  a      )	,  a	,  msg=f"""`{name} value {idx} expected, but was {getattr(a	,  a      )}"""      )
          except NotImplementedError:
             # Some models might not be able to implement setters for common_properties
             # In that case, a NotImplementedError is raised
             pass




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       lowercase_						:     Tuple							     =  self.config_class(**self.inputs_dict      )
       lowercase_						:     Dict							     =  json.loads(config.to_json_string()      )
       for key, value in self.inputs_dict.items():
          self.parent.assertEqual(obj[key]	,  a      )




    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  self.config_class(**self.inputs_dict      )

       with tempfile.TemporaryDirectory() as tmpdirname:
          lowercase_						:     int							     =  os.path.join(a	,  "config.json"      )
          config_first.to_json_file(a      )
          lowercase_						:     int							     =  self.config_class.from_json_file(a      )

       self.parent.assertEqual(config_second.to_dict()	,  config_first.to_dict()      )




    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  self.config_class(**self.inputs_dict      )

       with tempfile.TemporaryDirectory() as tmpdirname:
          config_first.save_pretrained(a      )
          lowercase_						:     Any							     =  self.config_class.from_pretrained(a      )

       self.parent.assertEqual(config_second.to_dict()	,  config_first.to_dict()      )




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  self.config_class(**self.inputs_dict      )

       lowercase_						:     Optional[Any]							     =  "test"
       with tempfile.TemporaryDirectory() as tmpdirname:
          lowercase_						:     Any							     =  os.path.join(a	,  a      )
          config_first.save_pretrained(a      )
          lowercase_						:     Optional[Any]							     =  self.config_class.from_pretrained(a	,  subfolder=a      )

       self.parent.assertEqual(config_second.to_dict()	,  config_first.to_dict()      )




    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       lowercase_						:     int							     =  self.config_class(**self.inputs_dict	,  num_labels=5      )
       self.parent.assertEqual(len(config.idalabel      )	,  5      )
       self.parent.assertEqual(len(config.labelaid      )	,  5      )

       lowercase_						:     Optional[Any]							     =  3
       self.parent.assertEqual(len(config.idalabel      )	,  3      )
       self.parent.assertEqual(len(config.labelaid      )	,  3      )




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       if self.config_class.is_composition:
          return
       lowercase_						:     Optional[int]							     =  self.config_class()
       self.parent.assertIsNotNone(a      )




    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       lowercase_						:     int							     =  copy.deepcopy(a      )
       lowercase_						:     Tuple							     =  self.config_class(**a      )
       lowercase_						:     Tuple							     =  []
       for key, value in config_common_kwargs.items():
          if key == "torch_dtype":
             if not is_torch_available():
                continue
             else:
                import torch

                if config.torch_dtype != torch.floataa:
                   wrong_values.append(("torch_dtype", config.torch_dtype, torch.floataa)      )
          elif getattr(a	,  a      ) != value:
             wrong_values.append((key, getattr(a	,  a      ), value)      )

       if len(a      ) > 0:
          lowercase_						:     Tuple							     =  "\n".join([f"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values]      )
          raise ValueError(f"""The following keys were not properly set in the config:\n{errors}"""      )





    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       self.create_and_test_config_common_properties()
       self.create_and_test_config_to_json_string()
       self.create_and_test_config_to_json_file()
       self.create_and_test_config_from_and_save_pretrained()
       self.create_and_test_config_from_and_save_pretrained_subfolder()
       self.create_and_test_config_with_num_labels()
       self.check_config_can_be_init_without_params()
       self.check_config_arguments_init()












'''simple docstring'''
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 							_UpperCAmelCase    (		snake_case      ):




    def __init__(      self	:							Optional[Any]	,  a	:							Dict	,  a	:							int	,  a	:							Any=1_0_2_4	,  a	:							Tuple=1_0_2_4	,  a	:							Optional[int]=3.6      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  tokenizer
       lowercase_						:     Union[str, Any]							     =  tokenizer.bos_token_id
       lowercase_						:     Union[str, Any]							     =  dataset
       lowercase_						:     Optional[Any]							     =  seq_length
       lowercase_						:     Any							     =  seq_length * chars_per_token * num_of_sequences





    def __iter__(      self	:							int      ):






       '''simple docstring'''






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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Union[str, Any]							     =  {"streaming": True}
   lowercase_						:     List[Any]							     =  load_dataset(args.dataset_name    ,		split="train"    ,		**_UpperCamelCase				)
   lowercase_						:     int							     =  ConstantLengthDataset(_UpperCamelCase    ,		_UpperCamelCase    ,		seq_length=args.seq_length				)
   lowercase_						:     Dict							     =  DataLoader(_UpperCamelCase    ,		batch_size=args.batch_size				)
   return eval_dataloader




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




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

      if args.max_eval_steps > 0 and step >= args.max_eval_steps:
         break
   lowercase_						:     Optional[int]							     =  torch.mean(torch.cat(_UpperCamelCase				)				)
   try:
      lowercase_						:     Dict							     =  torch.exp(_UpperCamelCase				)
   except OverflowError:
      lowercase_						:     Dict							     =  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}""")




