
import os
import sys
import unittest


_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
_SCREAMING_SNAKE_CASE    :      Dict          =			os.path.join(git_repo_path, 'src', 'transformers')

_SCREAMING_SNAKE_CASE    :      Dict          =			'\n{0} = None\n'

_SCREAMING_SNAKE_CASE    :      Dict          =			'\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


_SCREAMING_SNAKE_CASE    :      int          =			'\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'







class  UpperCamelCase__		(    unittest.TestCase   ):





	def   __snake_case				(			self		):
					A__  :	List[str]         =							find_backend('''    _import_structure["models.albert"].append("AlbertTokenizerFast")'''		)
					self.assertIsNone(UpperCamelCase__		)

					A__  :	Tuple         =							find_backend('''    if not is_tokenizers_available():'''		)
					self.assertEqual(UpperCamelCase__				,       '''tokenizers'''		)

					A__  :	Tuple         =							find_backend('''    if not is_tensorflow_text_available():'''		)
					self.assertEqual(UpperCamelCase__				,       '''tensorflow_text'''		)

					A__  :	Any         =							find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):'''		)
					self.assertEqual(UpperCamelCase__				,       '''sentencepiece_and_tokenizers'''		)

					A__  :	int         =							find_backend(
					    '''    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'''		)
					self.assertEqual(UpperCamelCase__				,       '''sentencepiece_and_tensorflow_text'''		)

					A__  :	List[Any]         =							find_backend(
					    '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'''		)
					self.assertEqual(UpperCamelCase__				,       '''sentencepiece_and_tokenizers_and_vision'''		)





	def   __snake_case				(			self		):
					A__  :	Dict         =							read_init()
					# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
					self.assertIn('''torch'''				,       UpperCamelCase__		)
					self.assertIn('''tensorflow_text'''				,       UpperCamelCase__		)
					self.assertIn('''sentencepiece_and_tokenizers'''				,       UpperCamelCase__		)

					# Likewise, we can't assert on the exact content of a key
					self.assertIn('''BertModel'''				,       objects['''torch''']		)
					self.assertIn('''TFBertModel'''				,       objects['''tf''']		)
					self.assertIn('''FlaxBertModel'''				,       objects['''flax''']		)
					self.assertIn('''BertModel'''				,       objects['''torch''']		)
					self.assertIn('''TFBertTokenizer'''				,       objects['''tensorflow_text''']		)
					self.assertIn('''convert_slow_tokenizer'''				,       objects['''sentencepiece_and_tokenizers''']		)





	def   __snake_case				(			self		):
					A__  :	str         =							create_dummy_object('''CONSTANT'''				,       '''\'torch\''''		)
					self.assertEqual(UpperCamelCase__				,       '''\nCONSTANT = None\n'''		)

					A__  :	int         =							create_dummy_object('''function'''				,       '''\'torch\''''		)
					self.assertEqual(
					    UpperCamelCase__				,       '''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''		)

					A__  :	Optional[Any]         =							'''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')
'''
					A__  :	Union[str, Any]         =							create_dummy_object('''FakeClass'''				,       '''\'torch\''''		)
					self.assertEqual(UpperCamelCase__				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Tuple         =							'''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
'''
					A__  :	Any         =							create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}		)
					self.assertEqual(dummy_files['''torch''']				,       UpperCamelCase__		)

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__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__=1024				,       UpperCamelCase__=1024				,       UpperCamelCase__=3.6		):
					A__  :	str         =							tokenizer
					A__  :	int         =							tokenizer.bos_token_id
					A__  :	List[Any]         =							dataset
					A__  :	Tuple         =							seq_length
					A__  :	Any         =							seq_length * chars_per_token * num_of_sequences





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




def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[Any]       ) ->       Any:

				"""simple docstring"""







				A__  :	Any         =							{'''streaming''': True}
				A__  :	List[str]         =							load_dataset(args.dataset_name							,						split='''train'''							,						**__UpperCamelCase       )
				A__  :	List[str]         =							ConstantLengthDataset(__UpperCamelCase							,						__UpperCamelCase							,						seq_length=args.seq_length       )
				A__  :	int         =							DataLoader(__UpperCamelCase							,						batch_size=args.batch_size       )
				return eval_dataloader

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]       ) ->       Dict:

				"""simple docstring"""







				model.eval()
				A__  :	Dict         =							[]
				for step, batch in enumerate(__UpperCamelCase       ):
								with torch.no_grad():
												A__  :	Any         =							model(__UpperCamelCase							,						labels=__UpperCamelCase       )
								A__  :	Tuple         =							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__  :	Tuple         =							torch.mean(torch.cat(__UpperCamelCase       )       )
				try:
								A__  :	Optional[Any]         =							torch.exp(__UpperCamelCase       )
				except OverflowError:
								A__  :	Union[str, Any]         =							float('''inf'''       )
				return loss.item(), perplexity.item()


# Setup Accelerator
_SCREAMING_SNAKE_CASE    :      List[Any]          =			Accelerator()

# Parse configuration
_SCREAMING_SNAKE_CASE    :      Optional[int]          =			HfArgumentParser(EvaluationArguments)
_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			parser.parse_args()
set_seed(args.seed)

# Logging
_SCREAMING_SNAKE_CASE    :      Dict          =			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
_SCREAMING_SNAKE_CASE    :      Optional[int]          =			AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_SCREAMING_SNAKE_CASE    :      List[str]          =			AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			create_dataloader(args)

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

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



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