
"""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
)
SCREAMING_SNAKE_CASE_     				=   logging.getLogger(__name__)
def 			__UpperCAmelCase		(		)     ->       str:
   _UpperCAmelCase   	=					argparse.ArgumentParser(description="PyTorch Transformer Language Model"				)
   parser.add_argument("--model_name"    ,  type=lowercase__    ,  default="transfo-xl-wt103"    ,  help="pretrained model name"				)
   parser.add_argument(
       "--split"    ,  type=lowercase__    ,  default="test"    ,  choices=["all", "valid", "test"]    ,  help="which split to evaluate"				)
   parser.add_argument("--batch_size"    ,  type=lowercase__    ,  default=10    ,  help="batch size"				)
   parser.add_argument("--tgt_len"    ,  type=lowercase__    ,  default=1_28    ,  help="number of tokens to predict"				)
   parser.add_argument("--ext_len"    ,  type=lowercase__    ,  default=0    ,  help="length of the extended context"				)
   parser.add_argument("--mem_len"    ,  type=lowercase__    ,  default=16_00    ,  help="length of the retained previous heads"				)
   parser.add_argument("--clamp_len"    ,  type=lowercase__    ,  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=lowercase__    ,  required=lowercase__    ,  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=lowercase__    ,  default=""    ,  help="Can be used for distant debugging."				)
   parser.add_argument("--server_port"    ,  type=lowercase__    ,  default=""    ,  help="Can be used for distant debugging."				)
   _UpperCAmelCase   	=					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=lowercase__				)
      ptvsd.wait_for_attach()

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

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

   _UpperCAmelCase   	=					corpus.get_iterator("valid"    ,  args.batch_size    ,  args.tgt_len    ,  device=lowercase__    ,  ext_len=args.ext_len				)
   _UpperCAmelCase   	=					corpus.get_iterator("test"    ,  args.batch_size    ,  args.tgt_len    ,  device=lowercase__    ,  ext_len=args.ext_len				)

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

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

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

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

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

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

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


if __name__ == "__main__":
  main()



"""simple docstring"""







import collections
import inspect
import unittest

from transformers import NatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import NatBackbone, NatForImageClassification, NatModel
  from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor






class     __a			:







       def __init__(					self							,lowerCamelCase_							,lowerCamelCase_=13							,lowerCamelCase_=64							,lowerCamelCase_=4							,lowerCamelCase_=3							,lowerCamelCase_=16							,lowerCamelCase_=[1, 2, 1]							,lowerCamelCase_=[2, 4, 8]							,lowerCamelCase_=3							,lowerCamelCase_=2.0							,lowerCamelCase_=True							,lowerCamelCase_=0.0							,lowerCamelCase_=0.0							,lowerCamelCase_=0.1							,lowerCamelCase_="gelu"							,lowerCamelCase_=True							,lowerCamelCase_=0.0_2							,lowerCamelCase_=1e-5							,lowerCamelCase_=True							,lowerCamelCase_=None							,lowerCamelCase_=True							,lowerCamelCase_=10							,lowerCamelCase_=["stage1", "stage2"]							,lowerCamelCase_=[1, 2]							,):



          '''simple docstring'''

          _UpperCAmelCase   	=					parent
          _UpperCAmelCase   	=					batch_size
          _UpperCAmelCase   	=					image_size
          _UpperCAmelCase   	=					patch_size
          _UpperCAmelCase   	=					num_channels
          _UpperCAmelCase   	=					embed_dim
          _UpperCAmelCase   	=					depths
          _UpperCAmelCase   	=					num_heads
          _UpperCAmelCase   	=					kernel_size
          _UpperCAmelCase   	=					mlp_ratio
          _UpperCAmelCase   	=					qkv_bias
          _UpperCAmelCase   	=					hidden_dropout_prob
          _UpperCAmelCase   	=					attention_probs_dropout_prob
          _UpperCAmelCase   	=					drop_path_rate
          _UpperCAmelCase   	=					hidden_act
          _UpperCAmelCase   	=					patch_norm
          _UpperCAmelCase   	=					layer_norm_eps
          _UpperCAmelCase   	=					initializer_range
          _UpperCAmelCase   	=					is_training
          _UpperCAmelCase   	=					scope
          _UpperCAmelCase   	=					use_labels
          _UpperCAmelCase   	=					num_labels
          _UpperCAmelCase   	=					out_features
          _UpperCAmelCase   	=					out_indices







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

          _UpperCAmelCase   	=					None
          if self.use_labels:
             _UpperCAmelCase   	=					ids_tensor([self.batch_size]							,self.num_labels		)

          _UpperCAmelCase   	=					self.get_config()

          return config, pixel_values, labels







       def    _snake_case			(					self		):



          '''simple docstring'''

          return NatConfig(
              num_labels=self.num_labels							,image_size=self.image_size							,patch_size=self.patch_size							,num_channels=self.num_channels							,embed_dim=self.embed_dim							,depths=self.depths							,num_heads=self.num_heads							,kernel_size=self.kernel_size							,mlp_ratio=self.mlp_ratio							,qkv_bias=self.qkv_bias							,hidden_dropout_prob=self.hidden_dropout_prob							,attention_probs_dropout_prob=self.attention_probs_dropout_prob							,drop_path_rate=self.drop_path_rate							,hidden_act=self.hidden_act							,patch_norm=self.patch_norm							,layer_norm_eps=self.layer_norm_eps							,initializer_range=self.initializer_range							,out_features=self.out_features							,out_indices=self.out_indices							,)







       def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase   	=					NatModel(config=lowerCamelCase_		)
          model.to(lowerCamelCase_		)
          model.eval()
          _UpperCAmelCase   	=					model(lowerCamelCase_		)

          _UpperCAmelCase   	=					_UpperCAmelCase   	=					(config.image_size // config.patch_size) // (2 ** (len(config.depths		) - 1))
          _UpperCAmelCase   	=					int(config.embed_dim * 2 ** (len(config.depths		) - 1)		)

          self.parent.assertEqual(
              result.last_hidden_state.shape							,(self.batch_size, expected_height, expected_width, expected_dim)		)







       def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase   	=					NatForImageClassification(lowerCamelCase_		)
          model.to(lowerCamelCase_		)
          model.eval()
          _UpperCAmelCase   	=					model(lowerCamelCase_							,labels=lowerCamelCase_		)
          self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_labels)		)

          # test greyscale images
          _UpperCAmelCase   	=					1
          _UpperCAmelCase   	=					NatForImageClassification(lowerCamelCase_		)
          model.to(lowerCamelCase_		)
          model.eval()

          _UpperCAmelCase   	=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
          _UpperCAmelCase   	=					model(lowerCamelCase_		)
          self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_labels)		)







       def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase   	=					NatBackbone(config=lowerCamelCase_		)
          model.to(lowerCamelCase_		)
          model.eval()
          _UpperCAmelCase   	=					model(lowerCamelCase_		)

          # verify hidden states
          self.parent.assertEqual(len(result.feature_maps		)							,len(config.out_features		)		)
          self.parent.assertListEqual(list(result.feature_maps[0].shape		)							,[self.batch_size, model.channels[0], 16, 16]		)

          # verify channels
          self.parent.assertEqual(len(model.channels		)							,len(config.out_features		)		)

          # verify backbone works with out_features=None
          _UpperCAmelCase   	=					None
          _UpperCAmelCase   	=					NatBackbone(config=lowerCamelCase_		)
          model.to(lowerCamelCase_		)
          model.eval()
          _UpperCAmelCase   	=					model(lowerCamelCase_		)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps		)							,1		)
          self.parent.assertListEqual(list(result.feature_maps[0].shape		)							,[self.batch_size, model.channels[-1], 4, 4]		)

          # verify channels
          self.parent.assertEqual(len(model.channels		)							,1		)







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					self.prepare_config_and_inputs()
          _UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase   	=					config_and_inputs
          _UpperCAmelCase   	=					{"pixel_values": pixel_values}
          return config, inputs_dict






@require_natten
@require_torch
class     __a			(					lowerCAmelCase_				,	lowerCAmelCase_				,	unittest.TestCase			):
       _lowerCamelCase							:							str       =			(
           (
               NatModel,
               NatForImageClassification,
               NatBackbone,
           )
           if is_torch_available()
           else ()
       )
       _lowerCamelCase							:							int       =			(
           {'feature-extraction': NatModel, 'image-classification': NatForImageClassification}
           if is_torch_available()
           else {}
       )
       _lowerCamelCase							:							Optional[Any]       =			False

       _lowerCamelCase							:							Optional[Any]       =			False
       _lowerCamelCase							:							Optional[int]       =			False
       _lowerCamelCase							:							List[str]       =			False
       _lowerCamelCase							:							str       =			False







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					NatModelTester(self		)
          _UpperCAmelCase   	=					ConfigTester(self							,config_class=lowerCamelCase_							,embed_dim=37		)







       def    _snake_case			(					self		):



          '''simple docstring'''

          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()







       def    _snake_case			(					self		):



          '''simple docstring'''

          return







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase_		)







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_		)







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*lowerCamelCase_		)







       @unittest.skip(reason="Nat does not use inputs_embeds"		)
       def    _snake_case			(					self		):



          '''simple docstring'''

          pass







       @unittest.skip(reason="Nat does not use feedforward chunking"		)
       def    _snake_case			(					self		):



          '''simple docstring'''

          pass







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase	,							_UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             _UpperCAmelCase   	=					model_class(lowerCamelCase_		)
             self.assertIsInstance(model.get_input_embeddings()							,(nn.Module)		)
             _UpperCAmelCase   	=					model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(lowerCamelCase_							,nn.Linear		)		)







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase	,							_UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             _UpperCAmelCase   	=					model_class(lowerCamelCase_		)
             _UpperCAmelCase   	=					inspect.signature(model.forward		)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             _UpperCAmelCase   	=					[*signature.parameters.keys()]

             _UpperCAmelCase   	=					["pixel_values"]
             self.assertListEqual(arg_names[:1]							,lowerCamelCase_		)







       def    _snake_case			(					self		):



          '''simple docstring'''

          self.skipTest("Nat's attention operation is handled entirely by NATTEN."		)







       def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase   	=					model_class(lowerCamelCase_		)
          model.to(lowerCamelCase_		)
          model.eval()

          with torch.no_grad():
             _UpperCAmelCase   	=					model(**self._prepare_for_class(lowerCamelCase_							,lowerCamelCase_		)		)

          _UpperCAmelCase   	=					outputs.hidden_states

          _UpperCAmelCase   	=					getattr(
              self.model_tester							,"expected_num_hidden_layers"							,len(self.model_tester.depths		) + 1		)
          self.assertEqual(len(lowerCamelCase_		)							,lowerCamelCase_		)

          # Nat has a different seq_length
          _UpperCAmelCase   	=					(
              config.patch_size
              if isinstance(config.patch_size							,collections.abc.Iterable		)
              else (config.patch_size, config.patch_size)
          )

          _UpperCAmelCase   	=					image_size[0] // patch_size[0]
          _UpperCAmelCase   	=					image_size[1] // patch_size[1]

          self.assertListEqual(
              list(hidden_states[0].shape[-3:]		)							,[height, width, self.model_tester.embed_dim]							,)

          if model_class.__name__ != "NatBackbone":
             _UpperCAmelCase   	=					outputs.reshaped_hidden_states
             self.assertEqual(len(lowerCamelCase_		)							,lowerCamelCase_		)

             _UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase   	=					reshaped_hidden_states[0].shape
             _UpperCAmelCase   	=					(
                 reshaped_hidden_states[0].view(lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		).permute(0							,2							,3							,1		)
             )
             self.assertListEqual(
                 list(reshaped_hidden_states.shape[-3:]		)							,[height, width, self.model_tester.embed_dim]							,)







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase	,							_UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs_for_common()

          _UpperCAmelCase   	=					(
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size							,collections.abc.Iterable		)
              else (self.model_tester.image_size, self.model_tester.image_size)
          )

          for model_class in self.all_model_classes:
             _UpperCAmelCase   	=					True
             self.check_hidden_states_output(lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             _UpperCAmelCase   	=					True

             self.check_hidden_states_output(lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		)







       @slow
       def    _snake_case			(					self		):



          '''simple docstring'''

          for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _UpperCAmelCase   	=					NatModel.from_pretrained(lowerCamelCase_		)
             self.assertIsNotNone(lowerCamelCase_		)







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase	,							_UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs_for_common()

          _UpperCAmelCase   	=					_config_zero_init(lowerCamelCase_		)
          for model_class in self.all_model_classes:
             _UpperCAmelCase   	=					model_class(config=lowerCamelCase_		)
             for name, param in model.named_parameters():
                if "embeddings" not in name and param.requires_grad:
                   self.assertIn(
                       ((param.data.mean() * 1e9).round() / 1e9).item()							,[0.0, 1.0]							,msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''							,)






@require_natten
@require_vision
@require_torch
class     __a			(					unittest.TestCase			):







       @cached_property
       def    _snake_case			(					self		):



          '''simple docstring'''

          return AutoImageProcessor.from_pretrained("shi-labs/nat-mini-in1k-224"		) if is_vision_available() else None







       @slow
       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					NatForImageClassification.from_pretrained("shi-labs/nat-mini-in1k-224"		).to(lowerCamelCase_		)
          _UpperCAmelCase   	=					self.default_image_processor

          _UpperCAmelCase   	=					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
          _UpperCAmelCase   	=					image_processor(images=lowerCamelCase_							,return_tensors="pt"		).to(lowerCamelCase_		)

          # forward pass
          with torch.no_grad():
             _UpperCAmelCase   	=					model(**lowerCamelCase_		)

          # verify the logits
          _UpperCAmelCase   	=					torch.Size((1, 1000)		)
          self.assertEqual(outputs.logits.shape							,lowerCamelCase_		)
          _UpperCAmelCase   	=					torch.tensor([0.3_8_0_5, -0.8_6_7_6, -0.3_9_1_2]		).to(lowerCamelCase_		)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]							,lowerCamelCase_							,atol=1e-4		)		)






@require_torch
@require_natten
class     __a			(					unittest.TestCase				,	lowerCAmelCase_			):
       _lowerCamelCase							:							Any       =			(NatBackbone,) if is_torch_available() else ()
       _lowerCamelCase							:							Dict       =			NatConfig







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					NatModelTester(self		)

