


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
)
_A		:					List[Any]          =							logging.getLogger(__name__)



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

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

   # 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:					str	 =						TransfoXLCorpus.from_pretrained(args.model_name			)

   __lowerCamelCase:					List[Any]	 =						corpus.get_iterator("""valid"""    ,   args.batch_size    ,   args.tgt_len    ,   device=snake_case    ,   ext_len=args.ext_len			)
   __lowerCamelCase:					Optional[Any]	 =						corpus.get_iterator("""test"""    ,   args.batch_size    ,   args.tgt_len    ,   device=snake_case    ,   ext_len=args.ext_len			)

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

   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:					int	 =						args.clamp_len
   if args.same_length:
      __lowerCamelCase:					Optional[int]	 =						True

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

   # Run on test data.
   if args.split == "all":
      __lowerCamelCase:					Optional[Any]	 =						evaluate(snake_case			)
      __lowerCamelCase:					str	 =						evaluate(snake_case			)
   elif args.split == "valid":
      __lowerCamelCase:					Any	 =						evaluate(snake_case			)
      __lowerCamelCase:					Dict	 =						None
   elif args.split == "test":
      __lowerCamelCase:					int	 =						evaluate(snake_case			)
      __lowerCamelCase:					List[Any]	 =						None

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

   __lowerCamelCase:					Dict	 =						""""""
   if valid_loss is not None:
      log_str += format_log(snake_case    ,   """valid"""			)
   if test_loss is not None:
      log_str += format_log(snake_case    ,   """test"""			)

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


if __name__ == "__main__":
   main()






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import MobileNetVaImageProcessor



class 							a       (      unittest.TestCase ):





     def __init__(     self       :					Optional[int]     ,						SCREAMING_SNAKE_CASE_       :					str     ,						SCREAMING_SNAKE_CASE_       :					List[str]=7     ,						SCREAMING_SNAKE_CASE_       :					Union[str, Any]=3     ,						SCREAMING_SNAKE_CASE_       :					List[Any]=18     ,						SCREAMING_SNAKE_CASE_       :					List[str]=30     ,						SCREAMING_SNAKE_CASE_       :					List[Any]=400     ,						SCREAMING_SNAKE_CASE_       :					Dict=True     ,						SCREAMING_SNAKE_CASE_       :					Any=None     ,						SCREAMING_SNAKE_CASE_       :					List[str]=True     ,						SCREAMING_SNAKE_CASE_       :					Any=None     ,						):
        __lowerCamelCase:					List[Any]	 =						size if size is not None else {"""shortest_edge""": 20}
        __lowerCamelCase:					Tuple	 =						crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
        __lowerCamelCase:					List[Any]	 =						parent
        __lowerCamelCase:					Optional[int]	 =						batch_size
        __lowerCamelCase:					str	 =						num_channels
        __lowerCamelCase:					Dict	 =						image_size
        __lowerCamelCase:					Tuple	 =						min_resolution
        __lowerCamelCase:					Union[str, Any]	 =						max_resolution
        __lowerCamelCase:					Tuple	 =						do_resize
        __lowerCamelCase:					List[str]	 =						size
        __lowerCamelCase:					int	 =						do_center_crop
        __lowerCamelCase:					Tuple	 =						crop_size







     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[Any]				):
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }



@require_torch
@require_vision
class 							a       (      _UpperCAmelCase  ,unittest.TestCase ):
     UpperCAmelCase__			:	int       =						MobileNetVaImageProcessor if is_vision_available() else None





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]				):
        __lowerCamelCase:					Tuple	 =						MobileNetVaImageProcessingTester(self				)





     @property
     def 			SCREAMING_SNAKE_CASE__							(     self       :					str				):
        return self.image_processor_tester.prepare_image_processor_dict()





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]				):
        __lowerCamelCase:					List[Any]	 =						self.image_processing_class(**self.image_processor_dict				)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""do_resize"""				)				)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""size"""				)				)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""do_center_crop"""				)				)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""center_crop"""				)				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Any				):
        __lowerCamelCase:					str	 =						self.image_processing_class.from_dict(self.image_processor_dict				)
        self.assertEqual(image_processor.size     ,						{"""shortest_edge""": 20}				)
        self.assertEqual(image_processor.crop_size     ,						{"""height""": 18, """width""": 18}				)

        __lowerCamelCase:					Optional[int]	 =						self.image_processing_class.from_dict(self.image_processor_dict     ,						size=42     ,						crop_size=84				)
        self.assertEqual(image_processor.size     ,						{"""shortest_edge""": 42}				)
        self.assertEqual(image_processor.crop_size     ,						{"""height""": 84, """width""": 84}				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[Any]				):
        pass





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        # Initialize image_processing
        __lowerCamelCase:					List[str]	 =						self.image_processing_class(**self.image_processor_dict				)
        # create random PIL images
        __lowerCamelCase:					str	 =						prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=SCREAMING_SNAKE_CASE_				)
        for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,						Image.Image				)

        # Test not batched input
        __lowerCamelCase:					Optional[Any]	 =						image_processing(image_inputs[0]     ,						return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,						)

        # Test batched
        __lowerCamelCase:					Tuple	 =						image_processing(SCREAMING_SNAKE_CASE_     ,						return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,						)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        # Initialize image_processing
        __lowerCamelCase:					Union[str, Any]	 =						self.image_processing_class(**self.image_processor_dict				)
        # create random numpy tensors
        __lowerCamelCase:					Tuple	 =						prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=SCREAMING_SNAKE_CASE_     ,						numpify=SCREAMING_SNAKE_CASE_				)
        for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,						np.ndarray				)

        # Test not batched input
        __lowerCamelCase:					List[Any]	 =						image_processing(image_inputs[0]     ,						return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,						)

        # Test batched
        __lowerCamelCase:					Optional[Any]	 =						image_processing(SCREAMING_SNAKE_CASE_     ,						return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,						)







     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        # Initialize image_processing
        __lowerCamelCase:					str	 =						self.image_processing_class(**self.image_processor_dict				)
        # create random PyTorch tensors
        __lowerCamelCase:					List[str]	 =						prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=SCREAMING_SNAKE_CASE_     ,						torchify=SCREAMING_SNAKE_CASE_				)
        for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,						torch.Tensor				)

        # Test not batched input
        __lowerCamelCase:					Union[str, Any]	 =						image_processing(image_inputs[0]     ,						return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,						)

        # Test batched
        __lowerCamelCase:					Optional[int]	 =						image_processing(SCREAMING_SNAKE_CASE_     ,						return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape     ,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )     ,						)


