




'''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       A   (		_a   ):







							def __init__(  self	:  Optional[int]							,      lowerCAmelCase_	:  Optional[Any]							,      lowerCAmelCase_	:  Union[str, Any]							,      lowerCAmelCase_	:  List[str]=10_24							,      lowerCAmelCase_	:  Optional[Any]=10_24							,      lowerCAmelCase_	:  Tuple=3.6 )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    tokenizer
														_a     					=    tokenizer.bos_token_id
														_a     					=    dataset
														_a     					=    seq_length
														_a     					=    seq_length * chars_per_token * num_of_sequences







							def __iter__(  self	:  Any )      ->							int:


														"""simple docstring"""


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



def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


							_a     					=    {'''streaming''': True}
							_a     					=    load_dataset(args.dataset_name			,						split='''train'''			,						**UpperCamelCase				)
							_a     					=    ConstantLengthDataset(UpperCamelCase			,						UpperCamelCase			,						seq_length=args.seq_length				)
							_a     					=    DataLoader(UpperCamelCase			,						batch_size=args.batch_size				)
							return eval_dataloader
def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


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

														if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																					break
							_a     					=    torch.mean(torch.cat(UpperCamelCase				)				)
							try:
														_a     					=    torch.exp(UpperCamelCase				)
							except OverflowError:
														_a     					=    float('''inf'''				)
							return loss.item(), perplexity.item()


# Setup Accelerator
_snake_case				:				List[str]       							=		Accelerator()

# Parse configuration
_snake_case				:				List[str]       							=		HfArgumentParser(EvaluationArguments)
_snake_case				:				Optional[int]       							=		parser.parse_args()
set_seed(args.seed)

# Logging
_snake_case				:				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
_snake_case				:				Dict       							=		AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_snake_case				:				Optional[Any]       							=		AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_snake_case				:				List[str]       							=		create_dataloader(args)

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

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

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







'''simple docstring'''


import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


_snake_case				:				int       							=		get_tests_dir('fixtures')
_snake_case				:				Tuple       							=		get_tests_dir('fixtures/dummy_feature_extractor_config.json')
_snake_case				:				Optional[int]       							=		get_tests_dir('fixtures/dummy-config.json')

class       A   (		unittest.TestCase   ):







							def        __lowerCAmelCase      (  self	:  int )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    0







							def        __lowerCAmelCase      (  self	:  List[str] )      ->							int:


														"""simple docstring"""


														_a     					=    AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h''' )
														self.assertIsInstance(lowerCAmelCase_							,      lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  str )      ->							Tuple:


														"""simple docstring"""


														_a     					=    AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ )
														self.assertIsInstance(lowerCAmelCase_							,      lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  List[str] )      ->							Any:


														"""simple docstring"""


														with tempfile.TemporaryDirectory() as tmpdirname:
																					_a     					=    WavaVecaConfig()

																					# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
																					_a     					=    AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ ).to_dict()

																					config_dict.pop('''feature_extractor_type''' )
																					_a     					=    WavaVecaFeatureExtractor(**lowerCAmelCase_ )

																					# save in new folder
																					model_config.save_pretrained(lowerCAmelCase_ )
																					config.save_pretrained(lowerCAmelCase_ )

																					_a     					=    AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ )

																					# make sure private variable is not incorrectly saved
																					_a     					=    json.loads(config.to_json_string() )
																					self.assertTrue('''_processor_class''' not in dict_as_saved )

														self.assertIsInstance(lowerCAmelCase_							,      lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							Optional[Any]:


														"""simple docstring"""


														_a     					=    AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ )
														self.assertIsInstance(lowerCAmelCase_							,      lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							str:


														"""simple docstring"""


														with self.assertRaisesRegex(
														    lowerCAmelCase_							,      '''bert-base is not a local folder and is not a valid model identifier''' ):
																					_a     					=    AutoFeatureExtractor.from_pretrained('''bert-base''' )







							def        __lowerCAmelCase      (  self	:  Tuple )      ->							Union[str, Any]:


														"""simple docstring"""


														with self.assertRaisesRegex(
														    lowerCAmelCase_							,      R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
																					_a     					=    AutoFeatureExtractor.from_pretrained(lowerCAmelCase_							,      revision='''aaaaaa''' )







							def        __lowerCAmelCase      (  self	:  Any )      ->							Dict:


														"""simple docstring"""


														with self.assertRaisesRegex(
														    lowerCAmelCase_							,      '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''							,      ):
																					_a     					=    AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model''' )







							def        __lowerCAmelCase      (  self	:  List[Any] )      ->							Any:


														"""simple docstring"""


														with self.assertRaises(lowerCAmelCase_ ):
																					_a     					=    AutoFeatureExtractor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_feature_extractor''' )
														# If remote code is disabled, we can't load this config.
														with self.assertRaises(lowerCAmelCase_ ):
																					_a     					=    AutoFeatureExtractor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_feature_extractor'''							,      trust_remote_code=lowerCAmelCase_ )

														_a     					=    AutoFeatureExtractor.from_pretrained(
														    '''hf-internal-testing/test_dynamic_feature_extractor'''							,      trust_remote_code=lowerCAmelCase_ )
														self.assertEqual(feature_extractor.__class__.__name__							,      '''NewFeatureExtractor''' )

														# Test feature extractor can be reloaded.
														with tempfile.TemporaryDirectory() as tmp_dir:
																					feature_extractor.save_pretrained(lowerCAmelCase_ )
																					_a     					=    AutoFeatureExtractor.from_pretrained(lowerCAmelCase_							,      trust_remote_code=lowerCAmelCase_ )
														self.assertEqual(reloaded_feature_extractor.__class__.__name__							,      '''NewFeatureExtractor''' )







							def        __lowerCAmelCase      (  self	:  int )      ->							Optional[Any]:


														"""simple docstring"""


														try:
																					AutoConfig.register('''custom'''							,      lowerCAmelCase_ )
																					AutoFeatureExtractor.register(lowerCAmelCase_							,      lowerCAmelCase_ )
																					# Trying to register something existing in the Transformers library will raise an error
																					with self.assertRaises(lowerCAmelCase_ ):
																												AutoFeatureExtractor.register(lowerCAmelCase_							,      lowerCAmelCase_ )

																					# Now that the config is registered, it can be used as any other config with the auto-API
																					_a     					=    CustomFeatureExtractor.from_pretrained(lowerCAmelCase_ )
																					with tempfile.TemporaryDirectory() as tmp_dir:
																												feature_extractor.save_pretrained(lowerCAmelCase_ )
																												_a     					=    AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ )
																												self.assertIsInstance(lowerCAmelCase_							,      lowerCAmelCase_ )

														finally:
																					if "custom" in CONFIG_MAPPING._extra_content:
																												del CONFIG_MAPPING._extra_content["custom"]
																					if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																												del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							Any:


														"""simple docstring"""


														class       A   (		_a   ):
																					lowercase_          =    True

														try:
																					AutoConfig.register('''custom'''							,      lowerCAmelCase_ )
																					AutoFeatureExtractor.register(lowerCAmelCase_							,      lowerCAmelCase_ )
																					# If remote code is not set, the default is to use local
																					_a     					=    AutoFeatureExtractor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_feature_extractor''' )
																					self.assertEqual(feature_extractor.__class__.__name__							,      '''NewFeatureExtractor''' )
																					self.assertTrue(feature_extractor.is_local )

																					# If remote code is disabled, we load the local one.
																					_a     					=    AutoFeatureExtractor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_feature_extractor'''							,      trust_remote_code=lowerCAmelCase_ )
																					self.assertEqual(feature_extractor.__class__.__name__							,      '''NewFeatureExtractor''' )
																					self.assertTrue(feature_extractor.is_local )

																					# If remote is enabled, we load from the Hub
																					_a     					=    AutoFeatureExtractor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_feature_extractor'''							,      trust_remote_code=lowerCAmelCase_ )
																					self.assertEqual(feature_extractor.__class__.__name__							,      '''NewFeatureExtractor''' )
																					self.assertTrue(not hasattr(lowerCAmelCase_							,      '''is_local''' ) )

														finally:
																					if "custom" in CONFIG_MAPPING._extra_content:
																												del CONFIG_MAPPING._extra_content["custom"]
																					if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																												del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]

