





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      lowercase__    (							__A  ):







  def __init__(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase=1_024    ,						_lowercase=1_024    ,						_lowercase=3.6						):
       lowerCAmelCase_					:       Union[str, Any]    =		tokenizer
       lowerCAmelCase_					:       Optional[Any]    =		tokenizer.bos_token_id
       lowerCAmelCase_					:       int    =		dataset
       lowerCAmelCase_					:       Optional[int]    =		seq_length
       lowerCAmelCase_					:       Optional[Any]    =		seq_length * chars_per_token * num_of_sequences







  def __iter__(	self						):
       lowerCAmelCase_					:       List[str]    =		iter(self.dataset						)
       lowerCAmelCase_					:       int    =		True
       while more_examples:
            lowerCAmelCase_					:       str    =		[], 0
            while True:
                 if buffer_len >= self.input_characters:
                      break
                 try:
                      buffer.append(next(_lowercase						)["""content"""]						)
                      buffer_len += len(buffer[-1]						)
                 except StopIteration:
                      lowerCAmelCase_					:       List[str]    =		False
                      break
            lowerCAmelCase_					:       Tuple    =		tokenizer(_lowercase    ,						truncation=_lowercase						)["""input_ids"""]
            lowerCAmelCase_					:       Dict    =		[]
            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_					:       Tuple    =		all_token_ids[i : i + self.seq_length]
                 if len(_lowercase						) == self.seq_length:
                      yield torch.tensor(_lowercase						)






def 							_lowerCAmelCase (  _a   :					Dict			)	->			Optional[Any]:
     lowerCAmelCase_					:       Any    =		{"""streaming""": True}
     lowerCAmelCase_					:       Optional[int]    =		load_dataset(args.dataset_name					,   split="""train"""					,   **_a			)
     lowerCAmelCase_					:       List[str]    =		ConstantLengthDataset(_a					,   _a					,   seq_length=args.seq_length			)
     lowerCAmelCase_					:       Dict    =		DataLoader(_a					,   batch_size=args.batch_size			)
     return eval_dataloader






def 							_lowerCAmelCase (  _a   :					Any			)	->			Tuple:
     model.eval()
     lowerCAmelCase_					:       Any    =		[]
     for step, batch in enumerate(_a			):
          with torch.no_grad():
               lowerCAmelCase_					:       Union[str, Any]    =		model(_a					,   labels=_a			)
          lowerCAmelCase_					:       Optional[Any]    =		outputs.loss.repeat(args.batch_size			)
          losses.append(accelerator.gather(_a			)			)

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


# Setup Accelerator
UpperCAmelCase_					:   Any 							=						Accelerator()

# Parse configuration
UpperCAmelCase_					:   int 							=						HfArgumentParser(EvaluationArguments)
UpperCAmelCase_					:   List[Any] 							=						parser.parse_args()
set_seed(args.seed)

# Logging
UpperCAmelCase_					:   Any 							=						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_					:   Dict 							=						AutoModelForCausalLM.from_pretrained(args.model_ckpt)
UpperCAmelCase_					:   List[str] 							=						AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
UpperCAmelCase_					:   Any 							=						create_dataloader(args)

# Prepare everything with our `accelerator`.
UpperCAmelCase_					:   Optional[int] 							=						accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
UpperCAmelCase_					:   Optional[Any] 							=						evaluate(args)







logger.info(F"""loss/eval: {eval_loss}, perplexity: {perplexity}""")







import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from requests.exceptions import HTTPError

from transformers.utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    TF2_WEIGHTS_NAME,
    TRANSFORMERS_CACHE,
    WEIGHTS_NAME,
    cached_file,
    get_file_from_repo,
    has_file,
)


UpperCAmelCase_					:   List[str] 							=						"""hf-internal-testing/tiny-random-bert"""
UpperCAmelCase_					:   List[Any] 							=						os.path.join(TRANSFORMERS_CACHE, """models--hf-internal-testing--tiny-random-bert""")
UpperCAmelCase_					:   Tuple 							=						"""9b8c223d42b2188cb49d29af482996f9d0f3e5a6"""





class      lowercase__    (							unittest.TestCase  ):







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Union[str, Any]    =		cached_file(_lowercase    ,						_lowercase						)
       # Should have downloaded the file in here
       self.assertTrue(os.path.isdir(_lowercase						)						)
       # Cache should contain at least those three subfolders:
       for subfolder in ["blobs", "refs", "snapshots"]:
            self.assertTrue(os.path.isdir(os.path.join(_lowercase    ,						_lowercase						)						)						)
       with open(os.path.join(_lowercase    ,						"""refs"""    ,						"""main"""						)						) as f:
            lowerCAmelCase_					:       Any    =		f.read()
       self.assertEqual(_lowercase    ,						os.path.join(_lowercase    ,						"""snapshots"""    ,						_lowercase    ,						_lowercase						)						)
       self.assertTrue(os.path.isfile(_lowercase						)						)

       # File is cached at the same place the second time.
       lowerCAmelCase_					:       Dict    =		cached_file(_lowercase    ,						_lowercase						)
       self.assertEqual(_lowercase    ,						_lowercase						)

       # Using a specific revision to test the full commit hash.
       lowerCAmelCase_					:       Optional[int]    =		cached_file(_lowercase    ,						_lowercase    ,						revision="""9b8c223"""						)
       self.assertEqual(_lowercase    ,						os.path.join(_lowercase    ,						"""snapshots"""    ,						_lowercase    ,						_lowercase						)						)







  def 	UpperCAmelCase__		(	self						):
       with self.assertRaisesRegex(_lowercase    ,						"""is not a valid model identifier"""						):
            lowerCAmelCase_					:       Dict    =		cached_file("""tiny-random-bert"""    ,						_lowercase						)

       with self.assertRaisesRegex(_lowercase    ,						"""is not a valid git identifier"""						):
            lowerCAmelCase_					:       Tuple    =		cached_file(_lowercase    ,						_lowercase    ,						revision="""aaaa"""						)

       with self.assertRaisesRegex(_lowercase    ,						"""does not appear to have a file named"""						):
            lowerCAmelCase_					:       List[Any]    =		cached_file(_lowercase    ,						"""conf"""						)







  def 	UpperCAmelCase__		(	self						):
       with self.assertRaisesRegex(_lowercase    ,						"""does not appear to have a file named"""						):
            lowerCAmelCase_					:       Any    =		cached_file(_lowercase    ,						"""conf"""						)

       with open(os.path.join(_lowercase    ,						"""refs"""    ,						"""main"""						)						) as f:
            lowerCAmelCase_					:       str    =		f.read()
       self.assertTrue(os.path.isfile(os.path.join(_lowercase    ,						""".no_exist"""    ,						_lowercase    ,						"""conf"""						)						)						)

       lowerCAmelCase_					:       Union[str, Any]    =		cached_file(_lowercase    ,						"""conf"""    ,						_raise_exceptions_for_missing_entries=_lowercase						)
       self.assertIsNone(_lowercase						)

       lowerCAmelCase_					:       Any    =		cached_file(_lowercase    ,						"""conf"""    ,						local_files_only=_lowercase    ,						_raise_exceptions_for_missing_entries=_lowercase						)
       self.assertIsNone(_lowercase						)

       lowerCAmelCase_					:       Any    =		mock.Mock()
       lowerCAmelCase_					:       Optional[Any]    =		500
       lowerCAmelCase_					:       Any    =		{}
       lowerCAmelCase_					:       List[str]    =		HTTPError
       lowerCAmelCase_					:       List[str]    =		{}

       # Under the mock environment we get a 500 error when trying to reach the tokenizer.
       with mock.patch("""requests.Session.request"""    ,						return_value=_lowercase						) as mock_head:
            lowerCAmelCase_					:       Union[str, Any]    =		cached_file(_lowercase    ,						"""conf"""    ,						_raise_exceptions_for_connection_errors=_lowercase						)
            self.assertIsNone(_lowercase						)
            # This check we did call the fake head request
            mock_head.assert_called()







  def 	UpperCAmelCase__		(	self						):
       self.assertTrue(has_file("""hf-internal-testing/tiny-bert-pt-only"""    ,						_lowercase						)						)
       self.assertFalse(has_file("""hf-internal-testing/tiny-bert-pt-only"""    ,						_lowercase						)						)
       self.assertFalse(has_file("""hf-internal-testing/tiny-bert-pt-only"""    ,						_lowercase						)						)







  def 	UpperCAmelCase__		(	self						):
       # `get_file_from_repo` returns None if the file does not exist
       self.assertIsNone(get_file_from_repo("""bert-base-cased"""    ,						"""ahah.txt"""						)						)

       # The function raises if the repository does not exist.
       with self.assertRaisesRegex(_lowercase    ,						"""is not a valid model identifier"""						):
            get_file_from_repo("""bert-base-case"""    ,						_lowercase						)

       # The function raises if the revision does not exist.
       with self.assertRaisesRegex(_lowercase    ,						"""is not a valid git identifier"""						):
            get_file_from_repo("""bert-base-cased"""    ,						_lowercase    ,						revision="""ahaha"""						)

       lowerCAmelCase_					:       Union[str, Any]    =		get_file_from_repo("""bert-base-cased"""    ,						_lowercase						)
       # The name is the cached name which is not very easy to test, so instead we load the content.
       lowerCAmelCase_					:       Any    =		json.loads(open(_lowercase    ,						"""r"""						).read()						)
       self.assertEqual(config["""hidden_size"""]    ,						768						)







  def 	UpperCAmelCase__		(	self						):
       with tempfile.TemporaryDirectory() as tmp_dir:
            lowerCAmelCase_					:       Optional[Any]    =		Path(_lowercase						) / """a.txt"""
            filename.touch()
            self.assertEqual(get_file_from_repo(_lowercase    ,						"""a.txt"""						)    ,						str(_lowercase						)						)

            self.assertIsNone(get_file_from_repo(_lowercase    ,						"""b.txt"""						)						)
