



'''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 			_lowerCAmelCase(      ) ->							List[Any]:
							lowerCAmelCase__  =   argparse.ArgumentParser(description="""PyTorch Transformer Language Model"""						)
							parser.add_argument("""--model_name"""					, type=UpperCAmelCase_					, default="""transfo-xl-wt103"""					, help="""pretrained model name"""						)
							parser.add_argument(
							    """--split"""					, type=UpperCAmelCase_					, default="""test"""					, choices=["""all""", """valid""", """test"""]					, help="""which split to evaluate"""						)
							parser.add_argument("""--batch_size"""					, type=UpperCAmelCase_					, default=10					, help="""batch size"""						)
							parser.add_argument("""--tgt_len"""					, type=UpperCAmelCase_					, default=128					, help="""number of tokens to predict"""						)
							parser.add_argument("""--ext_len"""					, type=UpperCAmelCase_					, default=0					, help="""length of the extended context"""						)
							parser.add_argument("""--mem_len"""					, type=UpperCAmelCase_					, default=1600					, help="""length of the retained previous heads"""						)
							parser.add_argument("""--clamp_len"""					, type=UpperCAmelCase_					, default=1000					, 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=UpperCAmelCase_					, required=UpperCAmelCase_					, 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=UpperCAmelCase_					, default=""""""					, help="""Can be used for distant debugging."""						)
							parser.add_argument("""--server_port"""					, type=UpperCAmelCase_					, default=""""""					, help="""Can be used for distant debugging."""						)
							lowerCAmelCase__  =   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=UpperCAmelCase_						)
														ptvsd.wait_for_attach()

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

							# 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 )
							lowerCAmelCase__  =   TransfoXLCorpus.from_pretrained(args.model_name						)

							lowerCAmelCase__  =   corpus.get_iterator("""valid"""					, args.batch_size					, args.tgt_len					, device=UpperCAmelCase_					, ext_len=args.ext_len						)
							lowerCAmelCase__  =   corpus.get_iterator("""test"""					, args.batch_size					, args.tgt_len					, device=UpperCAmelCase_					, ext_len=args.ext_len						)

							# Load a pre-trained model
							lowerCAmelCase__  =   TransfoXLLMHeadModel.from_pretrained(args.model_name						)
							model.to(UpperCAmelCase_						)

							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:
														lowerCAmelCase__  =   args.clamp_len
							if args.same_length:
														lowerCAmelCase__  =   True

							###############################################################################
							# Evaluation code
							###############################################################################
							def evaluate(UpperCAmelCase_      :	Optional[Any]						):
														# Turn on evaluation mode which disables dropout.
														model.eval()
														lowerCAmelCase__  =   0, 0.0
														lowerCAmelCase__  =   time.time()
														with torch.no_grad():
																					lowerCAmelCase__  =   None
																					for idx, (data, target, seq_len) in enumerate(UpperCAmelCase_						):
																												lowerCAmelCase__  =   model(UpperCAmelCase_					, lm_labels=UpperCAmelCase_					, mems=UpperCAmelCase_						)
																												lowerCAmelCase__  =   ret
																												lowerCAmelCase__  =   loss.mean()
																												total_loss += seq_len * loss.item()
																												total_len += seq_len
																					lowerCAmelCase__  =   time.time() - start_time
														logger.info("""Time : {:.2f}s, {:.2f}ms/segment""".format(UpperCAmelCase_					, 1000 * total_time / (idx + 1)						)						)
														return total_loss / total_len

							# Run on test data.
							if args.split == "all":
														lowerCAmelCase__  =   evaluate(UpperCAmelCase_						)
														lowerCAmelCase__  =   evaluate(UpperCAmelCase_						)
							elif args.split == "valid":
														lowerCAmelCase__  =   evaluate(UpperCAmelCase_						)
														lowerCAmelCase__  =   None
							elif args.split == "test":
														lowerCAmelCase__  =   evaluate(UpperCAmelCase_						)
														lowerCAmelCase__  =   None

							def format_log(UpperCAmelCase_      :	Dict					, UpperCAmelCase_      :	Optional[int]						):
														lowerCAmelCase__  =   "| {0} loss {1:5.2f} | {0} ppl {2:9.3f} ".format(UpperCAmelCase_					, UpperCAmelCase_					, math.exp(UpperCAmelCase_						)						)
														return log_str

							lowerCAmelCase__  =   ""
							if valid_loss is not None:
														log_str += format_log(UpperCAmelCase_					, """valid"""						)
							if test_loss is not None:
														log_str += format_log(UpperCAmelCase_					, """test"""						)

							logger.info("""=""" * 100						)
							logger.info(UpperCAmelCase_						)
							logger.info("""=""" * 100						)


if __name__ == "__main__":
		main()











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  __lowerCAmelCase							(				unittest.TestCase   ,			lowerCAmelCase):






       def        SCREAMING_SNAKE_CASE     ( self:       Dict					):
         lowercase     :Any   						=    load_tool("image-segmentation"					)
         self.tool.setup()
         lowercase     :int   						=    load_tool("image-segmentation"							,       remote=_lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       str					):
         lowercase     :Any   						=    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
         lowercase     :Dict   						=    self.tool(_lowerCAmelCase							,       "cat"					)
         self.assertTrue(isinstance(_lowerCAmelCase							,       Image.Image					)					)






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]					):
         lowercase     :Dict   						=    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
         lowercase     :List[Any]   						=    self.remote_tool(_lowerCAmelCase							,       "cat"					)
         self.assertTrue(isinstance(_lowerCAmelCase							,       Image.Image					)					)






       def        SCREAMING_SNAKE_CASE     ( self:       List[Any]					):
         lowercase     :Optional[Any]   						=    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
         lowercase     :Dict   						=    self.tool(image=_lowerCAmelCase							,       label="cat"					)
         self.assertTrue(isinstance(_lowerCAmelCase							,       Image.Image					)					)






       def        SCREAMING_SNAKE_CASE     ( self:       Tuple					):
         lowercase     :int   						=    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
         lowercase     :List[str]   						=    self.remote_tool(image=_lowerCAmelCase							,       label="cat"					)
         self.assertTrue(isinstance(_lowerCAmelCase							,       Image.Image					)					)



