





"""simple docstring"""
import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
		import torch

		from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
		from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import GLPNImageProcessor






class       _SCREAMING_SNAKE_CASE(     lowerCAmelCase_						):




							def 					_UpperCamelCase  (		self							)    ->   List[Any]:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :Tuple	       =						self.config_class(**self.inputs_dict							)
													self.parent.assertTrue(hasattr(__a	,'''hidden_sizes'''							)							)
													self.parent.assertTrue(hasattr(__a	,'''num_attention_heads'''							)							)
													self.parent.assertTrue(hasattr(__a	,'''num_encoder_blocks'''							)							)






class       _SCREAMING_SNAKE_CASE:




							def __init__(		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=13	,SCREAMING_SNAKE_CASE__=64	,SCREAMING_SNAKE_CASE__=3	,SCREAMING_SNAKE_CASE__=4	,SCREAMING_SNAKE_CASE__=[2, 2, 2, 2]	,SCREAMING_SNAKE_CASE__=[8, 4, 2, 1]	,SCREAMING_SNAKE_CASE__=[16, 32, 64, 1_28]	,SCREAMING_SNAKE_CASE__=[1, 4, 8, 16]	,SCREAMING_SNAKE_CASE__=[1, 2, 4, 8]	,SCREAMING_SNAKE_CASE__=True	,SCREAMING_SNAKE_CASE__=True	,SCREAMING_SNAKE_CASE__="gelu"	,SCREAMING_SNAKE_CASE__=0.1	,SCREAMING_SNAKE_CASE__=0.1	,SCREAMING_SNAKE_CASE__=0.0_2	,SCREAMING_SNAKE_CASE__=16	,SCREAMING_SNAKE_CASE__=3	,SCREAMING_SNAKE_CASE__=None	,)    ->   List[str]:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						parent
													__SCREAMING_SNAKE_CASE     :List[Any]	       =						batch_size
													__SCREAMING_SNAKE_CASE     :List[str]	       =						image_size
													__SCREAMING_SNAKE_CASE     :Any	       =						num_channels
													__SCREAMING_SNAKE_CASE     :str	       =						num_encoder_blocks
													__SCREAMING_SNAKE_CASE     :Optional[int]	       =						sr_ratios
													__SCREAMING_SNAKE_CASE     :Any	       =						depths
													__SCREAMING_SNAKE_CASE     :Dict	       =						hidden_sizes
													__SCREAMING_SNAKE_CASE     :List[Any]	       =						downsampling_rates
													__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						num_attention_heads
													__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						is_training
													__SCREAMING_SNAKE_CASE     :List[str]	       =						use_labels
													__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						hidden_act
													__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						hidden_dropout_prob
													__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						attention_probs_dropout_prob
													__SCREAMING_SNAKE_CASE     :List[Any]	       =						initializer_range
													__SCREAMING_SNAKE_CASE     :Any	       =						decoder_hidden_size
													__SCREAMING_SNAKE_CASE     :Dict	       =						num_labels
													__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						scope




							def 					_UpperCamelCase  (		self							)    ->   Any:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

													__SCREAMING_SNAKE_CASE     :Tuple	       =						None
													if self.use_labels:
																			__SCREAMING_SNAKE_CASE     :Dict	       =						ids_tensor([self.batch_size, self.image_size, self.image_size]	,self.num_labels							)

													__SCREAMING_SNAKE_CASE     :int	       =						self.get_config()
													return config, pixel_values, labels




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





													"""simple docstring"""






													return GLPNConfig(
													    image_size=self.image_size	,num_channels=self.num_channels	,num_encoder_blocks=self.num_encoder_blocks	,depths=self.depths	,hidden_sizes=self.hidden_sizes	,num_attention_heads=self.num_attention_heads	,hidden_act=self.hidden_act	,hidden_dropout_prob=self.hidden_dropout_prob	,attention_probs_dropout_prob=self.attention_probs_dropout_prob	,initializer_range=self.initializer_range	,decoder_hidden_size=self.decoder_hidden_size	,)




							def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)    ->   Optional[Any]:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :Optional[int]	       =						GLPNModel(config=__a							)
													model.to(__a							)
													model.eval()
													__SCREAMING_SNAKE_CASE     :Tuple	       =						model(__a							)
													__SCREAMING_SNAKE_CASE     :Optional[int]	       =						self.image_size // (self.downsampling_rates[-1] * 2)
													self.parent.assertEqual(
													    result.last_hidden_state.shape	,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)							)




							def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)    ->   List[str]:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :Optional[int]	       =						self.num_labels
													__SCREAMING_SNAKE_CASE     :List[Any]	       =						GLPNForDepthEstimation(__a							)
													model.to(__a							)
													model.eval()
													__SCREAMING_SNAKE_CASE     :List[str]	       =						model(__a							)
													self.parent.assertEqual(result.predicted_depth.shape	,(self.batch_size, self.image_size, self.image_size)							)
													__SCREAMING_SNAKE_CASE     :List[Any]	       =						model(__a	,labels=__a							)
													self.parent.assertEqual(result.predicted_depth.shape	,(self.batch_size, self.image_size, self.image_size)							)




							def 					_UpperCamelCase  (		self							)    ->   Any:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						self.prepare_config_and_inputs()
													__SCREAMING_SNAKE_CASE     :Tuple	       =						config_and_inputs
													__SCREAMING_SNAKE_CASE     :List[str]	       =						{'pixel_values': pixel_values}
													return config, inputs_dict






@require_torch
class       _SCREAMING_SNAKE_CASE(     lowerCAmelCase_					,			lowerCAmelCase_					,			unittest.TestCase						):
							SCREAMING_SNAKE_CASE_     :   Dict					=						(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
							SCREAMING_SNAKE_CASE_     :   Dict					=						(
							    {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
							)

							SCREAMING_SNAKE_CASE_     :   Any					=						False
							SCREAMING_SNAKE_CASE_     :   List[Any]					=						False
							SCREAMING_SNAKE_CASE_     :   Optional[int]					=						False




							def 					_UpperCamelCase  (		self							)    ->   Any:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						GLPNModelTester(self							)
													__SCREAMING_SNAKE_CASE     :Tuple	       =						GLPNConfigTester(self	,config_class=__a							)




							def 					_UpperCamelCase  (		self							)    ->   Any:





													"""simple docstring"""






													self.config_tester.run_common_tests()




							def 					_UpperCamelCase  (		self							)    ->   Any:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :Optional[int]	       =						self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__a							)




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





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :Tuple	       =						self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_depth_estimation(*__a							)




							@unittest.skip('''GLPN does not use inputs_embeds'''							)
							def 					_UpperCamelCase  (		self							)    ->   int:





													"""simple docstring"""






													pass




							@unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods'''							)
							def 					_UpperCamelCase  (		self							)    ->   str:





													"""simple docstring"""






													pass




							def 					_UpperCamelCase  (		self							)    ->   Dict:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :str	       =						self.model_tester.prepare_config_and_inputs_for_common()

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

																			__SCREAMING_SNAKE_CASE     :Tuple	       =						['pixel_values']
																			self.assertListEqual(arg_names[:1]	,__a							)




							def 					_UpperCamelCase  (		self							)    ->   str:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :List[Any]	       =						self.model_tester.prepare_config_and_inputs_for_common()
													__SCREAMING_SNAKE_CASE     :Dict	       =						True

													for model_class in self.all_model_classes:
																			__SCREAMING_SNAKE_CASE     :Dict	       =						True
																			__SCREAMING_SNAKE_CASE     :Tuple	       =						False
																			__SCREAMING_SNAKE_CASE     :Tuple	       =						True
																			__SCREAMING_SNAKE_CASE     :Dict	       =						model_class(__a							)
																			model.to(__a							)
																			model.eval()
																			with torch.no_grad():
																									__SCREAMING_SNAKE_CASE     :str	       =						model(**self._prepare_for_class(__a	,__a							)							)
																			__SCREAMING_SNAKE_CASE     :Dict	       =						outputs.attentions

																			__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						sum(self.model_tester.depths							)
																			self.assertEqual(len(__a							)	,__a							)

																			# check that output_attentions also work using config
																			del inputs_dict["output_attentions"]
																			__SCREAMING_SNAKE_CASE     :List[Any]	       =						True
																			__SCREAMING_SNAKE_CASE     :str	       =						model_class(__a							)
																			model.to(__a							)
																			model.eval()
																			with torch.no_grad():
																									__SCREAMING_SNAKE_CASE     :Dict	       =						model(**self._prepare_for_class(__a	,__a							)							)
																			__SCREAMING_SNAKE_CASE     :Any	       =						outputs.attentions

																			self.assertEqual(len(__a							)	,__a							)

																			# verify the first attentions (first block, first layer)
																			__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						(self.model_tester.image_size // 4) ** 2
																			__SCREAMING_SNAKE_CASE     :int	       =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
																			self.assertListEqual(
																			    list(attentions[0].shape[-3:]							)	,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]	,)

																			# verify the last attentions (last block, last layer)
																			__SCREAMING_SNAKE_CASE     :int	       =						(self.model_tester.image_size // 32) ** 2
																			__SCREAMING_SNAKE_CASE     :List[str]	       =						(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
																			self.assertListEqual(
																			    list(attentions[-1].shape[-3:]							)	,[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]	,)
																			__SCREAMING_SNAKE_CASE     :Dict	       =						len(__a							)

																			# Check attention is always last and order is fine
																			__SCREAMING_SNAKE_CASE     :Tuple	       =						True
																			__SCREAMING_SNAKE_CASE     :Dict	       =						True
																			__SCREAMING_SNAKE_CASE     :int	       =						model_class(__a							)
																			model.to(__a							)
																			model.eval()
																			with torch.no_grad():
																									__SCREAMING_SNAKE_CASE     :int	       =						model(**self._prepare_for_class(__a	,__a							)							)

																			self.assertEqual(out_len + 1	,len(__a							)							)

																			__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						outputs.attentions

																			self.assertEqual(len(__a							)	,__a							)
																			# verify the first attentions (first block, first layer)
																			__SCREAMING_SNAKE_CASE     :int	       =						(self.model_tester.image_size // 4) ** 2
																			__SCREAMING_SNAKE_CASE     :Tuple	       =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
																			self.assertListEqual(
																			    list(self_attentions[0].shape[-3:]							)	,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]	,)




							def 					_UpperCamelCase  (		self							)    ->   List[str]:





													"""simple docstring"""






													def check_hidden_states_output(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							):
																			__SCREAMING_SNAKE_CASE     :Tuple	       =						model_class(__a							)
																			model.to(__a							)
																			model.eval()

																			with torch.no_grad():
																									__SCREAMING_SNAKE_CASE     :int	       =						model(**self._prepare_for_class(__a	,__a							)							)

																			__SCREAMING_SNAKE_CASE     :Dict	       =						outputs.hidden_states

																			__SCREAMING_SNAKE_CASE     :Any	       =						self.model_tester.num_encoder_blocks
																			self.assertEqual(len(__a							)	,__a							)

																			# verify the first hidden states (first block)
																			self.assertListEqual(
																			    list(hidden_states[0].shape[-3:]							)	,[
																			        self.model_tester.hidden_sizes[0],
																			        self.model_tester.image_size // 4,
																			        self.model_tester.image_size // 4,
																			    ]	,)

													__SCREAMING_SNAKE_CASE     :Dict	       =						self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__SCREAMING_SNAKE_CASE     :Optional[int]	       =						True
																			check_hidden_states_output(__a	,__a	,__a							)

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						True

																			check_hidden_states_output(__a	,__a	,__a							)




							def 					_UpperCamelCase  (		self							)    ->   Any:





													"""simple docstring"""






													if not self.model_tester.is_training:
																			return

													__SCREAMING_SNAKE_CASE     :Tuple	       =						self.model_tester.prepare_config_and_inputs_for_common()
													__SCREAMING_SNAKE_CASE     :str	       =						True

													for model_class in self.all_model_classes:
																			if model_class in get_values(__a							):
																									continue
																			# TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
																			# this can then be incorporated into _prepare_for_class in test_modeling_common.py
																			if model_class.__name__ == "GLPNForDepthEstimation":
																									__SCREAMING_SNAKE_CASE     :Any	       =						inputs_dict['pixel_values'].shape
																									__SCREAMING_SNAKE_CASE     :Dict	       =						torch.zeros(
																									    [self.model_tester.batch_size, height, width]	,device=__a							).long()
																			__SCREAMING_SNAKE_CASE     :int	       =						model_class(__a							)
																			model.to(__a							)
																			model.train()
																			__SCREAMING_SNAKE_CASE     :Any	       =						self._prepare_for_class(__a	,__a	,return_labels=__a							)
																			__SCREAMING_SNAKE_CASE     :str	       =						model(**__a							).loss
																			loss.backward()




							@slow
							def 					_UpperCamelCase  (		self							)    ->   Optional[Any]:





													"""simple docstring"""






													for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__SCREAMING_SNAKE_CASE     :str	       =						GLPNModel.from_pretrained(__a							)
																			self.assertIsNotNone(__a							)




def   __lowerCamelCase     (							)    ->   List[str]:
						__SCREAMING_SNAKE_CASE     :str	       =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
						return image







@require_torch
@require_vision
@slow
class       _SCREAMING_SNAKE_CASE(     unittest.TestCase						):




							@slow
							def 					_UpperCamelCase  (		self							)    ->   Tuple:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :List[str]	       =						GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
													__SCREAMING_SNAKE_CASE     :Optional[int]	       =						GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(__a							)

													__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						prepare_img()
													__SCREAMING_SNAKE_CASE     :List[str]	       =						image_processor(images=__a	,return_tensors='''pt'''							).to(__a							)

													# forward pass
													with torch.no_grad():
																			__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						model(**__a							)

													# verify the predicted depth
													__SCREAMING_SNAKE_CASE     :List[str]	       =						torch.Size([1, 4_80, 6_40]							)
													self.assertEqual(outputs.predicted_depth.shape	,__a							)

													__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						torch.tensor(
													    [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]							).to(__a							)

													self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]	,__a	,atol=1E-4							)							)






"""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
)
lowerCamelCase_          =	logging.getLogger(__name__)



def   __lowerCamelCase     (							)    ->   Optional[Any]:
      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						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=1_28				,     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=16_00				,     help='''length of the retained previous heads'''							)
      parser.add_argument('''--clamp_len'''				,     type=a_				,     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=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.'''							)
      __SCREAMING_SNAKE_CASE     :Optional[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=a_							)
            ptvsd.wait_for_attach()

      __SCREAMING_SNAKE_CASE     :Tuple	       =						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 )
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						TransfoXLCorpus.from_pretrained(args.model_name							)

      __SCREAMING_SNAKE_CASE     :Tuple	       =						corpus.get_iterator('''valid'''				,     args.batch_size				,     args.tgt_len				,     device=a_				,     ext_len=args.ext_len							)
      __SCREAMING_SNAKE_CASE     :Tuple	       =						corpus.get_iterator('''test'''				,     args.batch_size				,     args.tgt_len				,     device=a_				,     ext_len=args.ext_len							)

      # Load a pre-trained model
      __SCREAMING_SNAKE_CASE     :int	       =						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:
            __SCREAMING_SNAKE_CASE     :Dict	       =						args.clamp_len
      if args.same_length:
            __SCREAMING_SNAKE_CASE     :int	       =						True

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

      # Run on test data.
      if args.split == "all":
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						evaluate(a_							)
            __SCREAMING_SNAKE_CASE     :Any	       =						evaluate(a_							)
      elif args.split == "valid":
            __SCREAMING_SNAKE_CASE     :Tuple	       =						evaluate(a_							)
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						None
      elif args.split == "test":
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						evaluate(a_							)
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						None

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

      __SCREAMING_SNAKE_CASE     :Tuple	       =						''''''
      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('''=''' * 1_00							)
      logger.info(a_							)
      logger.info('''=''' * 1_00							)


if __name__ == "__main__":
  main()