



'''simple docstring'''





import unittest

import numpy as np
from datasets import load_dataset

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 SegformerImageProcessor






class __A      (  unittest.TestCase						):



 '''simple docstring'''







 def __init__(self			,       A			,       A=7			,       A=3			,       A=30			,       A=400			,       A=True			,       A=None			,       A=True			,       A=[0.5, 0.5, 0.5]			,       A=[0.5, 0.5, 0.5]			,       A=False			,       )   ->					Optional[Any]:


      """simple docstring"""



      _a     =  size if size is not None else {'''height''': 30, '''width''': 30}
      _a     =  parent
      _a     =  batch_size
      _a     =  num_channels
      _a     =  min_resolution
      _a     =  max_resolution
      _a     =  do_resize
      _a     =  size
      _a     =  do_normalize
      _a     =  image_mean
      _a     =  image_std
      _a     =  do_reduce_labels



 def  a__  (self	)   ->					Any:


      """simple docstring"""



      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_reduce_labels": self.do_reduce_labels,
      }





def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  load_dataset('''hf-internal-testing/fixtures_ade20k'''  ,     split='''test''')

     _a     =  Image.open(dataset[0]['''file'''])
     _a     =  Image.open(dataset[1]['''file'''])

     return image, map




def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  load_dataset('''hf-internal-testing/fixtures_ade20k'''  ,     split='''test''')

     _a     =  Image.open(dataset[0]['''file'''])
     _a     =  Image.open(dataset[1]['''file'''])
     _a     =  Image.open(dataset[2]['''file'''])
     _a     =  Image.open(dataset[3]['''file'''])

     return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class __A      (  A       ,       unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase   :	Optional[int]										=	SegformerImageProcessor if is_vision_available() else None



 def  a__  (self	)   ->					str:


      """simple docstring"""



      _a     =  SegformerImageProcessingTester(self	)



 @property
 def  a__  (self	)   ->					List[str]:


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



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


      """simple docstring"""



      _a     =  self.image_processing_class(**self.image_processor_dict	)
      self.assertTrue(hasattr(A			,       '''do_resize'''	)	)
      self.assertTrue(hasattr(A			,       '''size'''	)	)
      self.assertTrue(hasattr(A			,       '''do_normalize'''	)	)
      self.assertTrue(hasattr(A			,       '''image_mean'''	)	)
      self.assertTrue(hasattr(A			,       '''image_std'''	)	)
      self.assertTrue(hasattr(A			,       '''do_reduce_labels'''	)	)



 def  a__  (self	)   ->					Optional[int]:


      """simple docstring"""



      _a     =  self.image_processing_class.from_dict(self.image_processor_dict	)
      self.assertEqual(image_processor.size			,       {'''height''': 30, '''width''': 30}	)
      self.assertEqual(image_processor.do_reduce_labels			,       A	)

      _a     =  self.image_processing_class.from_dict(self.image_processor_dict			,       size=42			,       reduce_labels=A	)
      self.assertEqual(image_processor.size			,       {'''height''': 42, '''width''': 42}	)
      self.assertEqual(image_processor.do_reduce_labels			,       A	)



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


      """simple docstring"""



      pass



 def  a__  (self	)   ->					Dict:


      """simple docstring"""



      _a     =  self.image_processing_class(**self.image_processor_dict	)
      # create random PIL images
      _a     =  prepare_image_inputs(self.image_processor_tester			,       equal_resolution=A	)
      for image in image_inputs:
           self.assertIsInstance(A			,       Image.Image	)

      # Test not batched input
      _a     =  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.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )

      # Test batched
      _a     =  image_processing(A			,       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.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )



 def  a__  (self	)   ->					str:


      """simple docstring"""



      _a     =  self.image_processing_class(**self.image_processor_dict	)
      # create random numpy tensors
      _a     =  prepare_image_inputs(self.image_processor_tester			,       equal_resolution=A			,       numpify=A	)
      for image in image_inputs:
           self.assertIsInstance(A			,       np.ndarray	)

      # Test not batched input
      _a     =  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.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )

      # Test batched
      _a     =  image_processing(A			,       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.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )



 def  a__  (self	)   ->					Dict:


      """simple docstring"""



      _a     =  self.image_processing_class(**self.image_processor_dict	)
      # create random PyTorch tensors
      _a     =  prepare_image_inputs(self.image_processor_tester			,       equal_resolution=A			,       torchify=A	)
      for image in image_inputs:
           self.assertIsInstance(A			,       torch.Tensor	)

      # Test not batched input
      _a     =  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.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )

      # Test batched
      _a     =  image_processing(A			,       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.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )



 def  a__  (self	)   ->					Tuple:


      """simple docstring"""



      _a     =  self.image_processing_class(**self.image_processor_dict	)
      # create random PyTorch tensors
      _a     =  prepare_image_inputs(self.image_processor_tester			,       equal_resolution=A			,       torchify=A	)
      _a     =  []
      for image in image_inputs:
           self.assertIsInstance(A			,       torch.Tensor	)
           maps.append(torch.zeros(image.shape[-2:]	).long()	)

      # Test not batched input
      _a     =  image_processing(image_inputs[0]			,       maps[0]			,       return_tensors='''pt'''	)
      self.assertEqual(
          encoding['''pixel_values'''].shape			,       (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )
      self.assertEqual(
          encoding['''labels'''].shape			,       (
              1,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )
      self.assertEqual(encoding['''labels'''].dtype			,       torch.long	)
      self.assertTrue(encoding['''labels'''].min().item() >= 0	)
      self.assertTrue(encoding['''labels'''].max().item() <= 255	)

      # Test batched
      _a     =  image_processing(A			,       A			,       return_tensors='''pt'''	)
      self.assertEqual(
          encoding['''pixel_values'''].shape			,       (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )
      self.assertEqual(
          encoding['''labels'''].shape			,       (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )
      self.assertEqual(encoding['''labels'''].dtype			,       torch.long	)
      self.assertTrue(encoding['''labels'''].min().item() >= 0	)
      self.assertTrue(encoding['''labels'''].max().item() <= 255	)

      # Test not batched input (PIL images)
      _a  ,   _a     =  prepare_semantic_single_inputs()

      _a     =  image_processing(A			,       A			,       return_tensors='''pt'''	)
      self.assertEqual(
          encoding['''pixel_values'''].shape			,       (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )
      self.assertEqual(
          encoding['''labels'''].shape			,       (
              1,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )
      self.assertEqual(encoding['''labels'''].dtype			,       torch.long	)
      self.assertTrue(encoding['''labels'''].min().item() >= 0	)
      self.assertTrue(encoding['''labels'''].max().item() <= 255	)

      # Test batched input (PIL images)
      _a  ,   _a     =  prepare_semantic_batch_inputs()

      _a     =  image_processing(A			,       A			,       return_tensors='''pt'''	)
      self.assertEqual(
          encoding['''pixel_values'''].shape			,       (
              2,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )
      self.assertEqual(
          encoding['''labels'''].shape			,       (
              2,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )			,       )
      self.assertEqual(encoding['''labels'''].dtype			,       torch.long	)
      self.assertTrue(encoding['''labels'''].min().item() >= 0	)
      self.assertTrue(encoding['''labels'''].max().item() <= 255	)



 def  a__  (self	)   ->					Tuple:


      """simple docstring"""



      _a     =  self.image_processing_class(**self.image_processor_dict	)

      # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
      _a  ,   _a     =  prepare_semantic_single_inputs()
      _a     =  image_processing(A			,       A			,       return_tensors='''pt'''	)
      self.assertTrue(encoding['''labels'''].min().item() >= 0	)
      self.assertTrue(encoding['''labels'''].max().item() <= 150	)

      _a     =  True
      _a     =  image_processing(A			,       A			,       return_tensors='''pt'''	)
      self.assertTrue(encoding['''labels'''].min().item() >= 0	)
      self.assertTrue(encoding['''labels'''].max().item() <= 255	)








'''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
)
lowercase_			=  logging.getLogger(__name__)




def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  argparse.ArgumentParser(description='''PyTorch Transformer Language Model''')
     parser.add_argument('''--model_name'''  ,     type=__A  ,     default='''transfo-xl-wt103'''  ,     help='''pretrained model name''')
     parser.add_argument(
         '''--split'''  ,     type=__A  ,     default='''test'''  ,     choices=['''all''', '''valid''', '''test''']  ,     help='''which split to evaluate''')
     parser.add_argument('''--batch_size'''  ,     type=__A  ,     default=10  ,     help='''batch size''')
     parser.add_argument('''--tgt_len'''  ,     type=__A  ,     default=128  ,     help='''number of tokens to predict''')
     parser.add_argument('''--ext_len'''  ,     type=__A  ,     default=0  ,     help='''length of the extended context''')
     parser.add_argument('''--mem_len'''  ,     type=__A  ,     default=1_600  ,     help='''length of the retained previous heads''')
     parser.add_argument('''--clamp_len'''  ,     type=__A  ,     default=1_000  ,     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=__A  ,     required=__A  ,     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=__A  ,     default=''''''  ,     help='''Can be used for distant debugging.''')
     parser.add_argument('''--server_port'''  ,     type=__A  ,     default=''''''  ,     help='''Can be used for distant debugging.''')
     _a     =  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=__A)
          ptvsd.wait_for_attach()

     _a     =  torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''')
     logger.info('''device: {}'''.format(__A))

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

     _a     =  corpus.get_iterator('''valid'''  ,     args.batch_size  ,     args.tgt_len  ,     device=__A  ,     ext_len=args.ext_len)
     _a     =  corpus.get_iterator('''test'''  ,     args.batch_size  ,     args.tgt_len  ,     device=__A  ,     ext_len=args.ext_len)

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

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

     ###############################################################################
     # Evaluation code
     ###############################################################################
     def evaluate(__A):
          # Turn on evaluation mode which disables dropout.
          model.eval()
          _a  ,   _a     =  0, 0.0
          _a     =  time.time()
          with torch.no_grad():
               _a     =  None
               for idx, (data, target, seq_len) in enumerate(__A):
                    _a     =  model(__A  ,     lm_labels=__A  ,     mems=__A)
                    _a  ,   _a  ,   _a     =  ret
                    _a     =  loss.mean()
                    total_loss += seq_len * loss.item()
                    total_len += seq_len
               _a     =  time.time() - start_time
          logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(__A  ,     1_000 * total_time / (idx + 1)))
          return total_loss / total_len

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

     def format_log(__A  ,     __A):
          _a     =  '''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(__A  ,     __A  ,     math.exp(__A))
          return log_str

     _a     =  ''''''
     if valid_loss is not None:
          log_str += format_log(__A  ,     '''valid''')
     if test_loss is not None:
          log_str += format_log(__A  ,     '''test''')

     logger.info('''=''' * 100)
     logger.info(__A)
     logger.info('''=''' * 100)


if __name__ == "__main__":
  main()



