





'''simple docstring'''




import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


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



def 						__A						(       ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							List[str]						=	argparse.ArgumentParser(description="""PyTorch Transformer Language Model"""					)
		parser.add_argument("""--model_name"""	,	type=lowerCamelCase_	,	default="""transfo-xl-wt103"""	,	help="""pretrained model name"""					)
		parser.add_argument(
		    """--split"""	,	type=lowerCamelCase_	,	default="""test"""	,	choices=["""all""", """valid""", """test"""]	,	help="""which split to evaluate"""					)
		parser.add_argument("""--batch_size"""	,	type=lowerCamelCase_	,	default=10	,	help="""batch size"""					)
		parser.add_argument("""--tgt_len"""	,	type=lowerCamelCase_	,	default=1_28	,	help="""number of tokens to predict"""					)
		parser.add_argument("""--ext_len"""	,	type=lowerCamelCase_	,	default=0	,	help="""length of the extended context"""					)
		parser.add_argument("""--mem_len"""	,	type=lowerCamelCase_	,	default=16_00	,	help="""length of the retained previous heads"""					)
		parser.add_argument("""--clamp_len"""	,	type=lowerCamelCase_	,	default=10_00	,	help="""max positional embedding index"""					)
		parser.add_argument("""--no_cuda"""	,	action="""store_true"""	,	help="""Do not use CUDA even though CUA is available"""					)
		parser.add_argument("""--work_dir"""	,	type=lowerCamelCase_	,	required=lowerCamelCase_	,	help="""path to the work_dir"""					)
		parser.add_argument("""--no_log"""	,	action="""store_true"""	,	help="""do not log the eval result"""					)
		parser.add_argument("""--same_length"""	,	action="""store_true"""	,	help="""set same length attention with masking"""					)
		parser.add_argument("""--server_ip"""	,	type=lowerCamelCase_	,	default=""""""	,	help="""Can be used for distant debugging."""					)
		parser.add_argument("""--server_port"""	,	type=lowerCamelCase_	,	default=""""""	,	help="""Can be used for distant debugging."""					)
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	parser.parse_args()
		assert args.ext_len >= 0, "extended context length must be non-negative"

		if args.server_ip and args.server_port:
				# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
				import ptvsd

				print("""Waiting for debugger attach"""					)
				ptvsd.enable_attach(address=(args.server_ip, args.server_port)	,	redirect_output=lowerCamelCase_					)
				ptvsd.wait_for_attach()

		SCREAMING_SNAKE_CASE				:							Dict						=	torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""					)
		logger.info("""device: {}""".format(lowerCamelCase_					)					)

		# Load a pre-processed dataset
		# You can also build the corpus yourself using TransfoXLCorpus methods
		# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
		# and tokenizing the dataset
		# The pre-processed corpus is a convertion (using the conversion script )
		SCREAMING_SNAKE_CASE				:							int						=	TransfoXLCorpus.from_pretrained(args.model_name					)

		SCREAMING_SNAKE_CASE				:							Optional[int]						=	corpus.get_iterator("""valid"""	,	args.batch_size	,	args.tgt_len	,	device=lowerCamelCase_	,	ext_len=args.ext_len					)
		SCREAMING_SNAKE_CASE				:							List[str]						=	corpus.get_iterator("""test"""	,	args.batch_size	,	args.tgt_len	,	device=lowerCamelCase_	,	ext_len=args.ext_len					)

		# Load a pre-trained model
		SCREAMING_SNAKE_CASE				:							Tuple						=	TransfoXLLMHeadModel.from_pretrained(args.model_name					)
		model.to(lowerCamelCase_					)

		logger.info(
		    """Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}""".format(
		        args.batch_size	,	args.tgt_len	,	args.ext_len	,	args.mem_len	,	args.clamp_len					)					)

		model.reset_memory_length(args.mem_len					)
		if args.clamp_len > 0:
				SCREAMING_SNAKE_CASE				:							List[str]						=	args.clamp_len
		if args.same_length:
				SCREAMING_SNAKE_CASE				:							Optional[Any]						=	True

		###############################################################################
		# Evaluation code
		###############################################################################
		def evaluate(lowerCamelCase_					):
				# Turn on evaluation mode which disables dropout.
				model.eval()
				SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							str						=	0, 0.0
				SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	time.time()
				with torch.no_grad():
						SCREAMING_SNAKE_CASE				:							Optional[int]						=	None
						for idx, (data, target, seq_len) in enumerate(lowerCamelCase_					):
								SCREAMING_SNAKE_CASE				:							List[str]						=	model(lowerCamelCase_	,	lm_labels=lowerCamelCase_	,	mems=lowerCamelCase_					)
								SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Tuple						=	ret
								SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	loss.mean()
								total_loss += seq_len * loss.item()
								total_len += seq_len
						SCREAMING_SNAKE_CASE				:							Any						=	time.time() - start_time
				logger.info("""Time : {:.2f}s, {:.2f}ms/segment""".format(lowerCamelCase_	,	10_00 * total_time / (idx + 1)					)					)
				return total_loss / total_len

		# Run on test data.
		if args.split == "all":
				SCREAMING_SNAKE_CASE				:							int						=	evaluate(lowerCamelCase_					)
				SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	evaluate(lowerCamelCase_					)
		elif args.split == "valid":
				SCREAMING_SNAKE_CASE				:							Optional[int]						=	evaluate(lowerCamelCase_					)
				SCREAMING_SNAKE_CASE				:							Any						=	None
		elif args.split == "test":
				SCREAMING_SNAKE_CASE				:							Any						=	evaluate(lowerCamelCase_					)
				SCREAMING_SNAKE_CASE				:							List[str]						=	None

		def format_log(lowerCamelCase_	,	lowerCamelCase_					):
				SCREAMING_SNAKE_CASE				:							Any						=	"""| {0} loss {1:5.2f} | {0} ppl {2:9.3f} """.format(lowerCamelCase_	,	lowerCamelCase_	,	math.exp(lowerCamelCase_					)					)
				return log_str

		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	""""""
		if valid_loss is not None:
				log_str += format_log(lowerCamelCase_	,	"""valid"""					)
		if test_loss is not None:
				log_str += format_log(lowerCamelCase_	,	"""test"""					)

		logger.info("""=""" * 1_00					)
		logger.info(lowerCamelCase_					)
		logger.info("""=""" * 1_00					)


if __name__ == "__main__":
							main()










'''simple docstring'''




import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
							from PIL import Image




class       UpperCamelCase__			(			unittest.TestCase		,			lowercase_	):




					"""simple docstring"""


					def lowerCamelCase_	(		self							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							int						=	load_tool("""image-captioning"""		)
							self.tool.setup()
							SCREAMING_SNAKE_CASE				:							str						=	load_tool("""image-captioning"""     ,		remote=lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	self.tool(lowerCamelCase_		)
							self.assertEqual(lowerCamelCase_     ,		"""two cats sleeping on a couch"""		)


					def lowerCamelCase_	(		self							:	str		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	self.remote_tool(lowerCamelCase_		)
							self.assertEqual(lowerCamelCase_     ,		"""two cats sleeping on a couch"""		)


					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
							SCREAMING_SNAKE_CASE				:							str						=	self.tool(image=lowerCamelCase_		)
							self.assertEqual(lowerCamelCase_     ,		"""two cats sleeping on a couch"""		)



					def lowerCamelCase_	(		self							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	self.remote_tool(image=lowerCamelCase_		)
							self.assertEqual(lowerCamelCase_     ,		"""two cats sleeping on a couch"""		)



