"""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_:       str			     =       logging.getLogger(__name__)







def        __a (		):






     '''simple docstring'''



     lowercase__								=				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."   )
     lowercase__								=				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()

     lowercase__								=				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 )
     lowercase__								=				TransfoXLCorpus.from_pretrained(args.model_name   )

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

     # Load a pre-trained model
     lowercase__								=				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:
          lowercase__								=				args.clamp_len
     if args.same_length:
          lowercase__								=				True

     ###############################################################################
     # Evaluation code
     ###############################################################################
     def evaluate(A   ):
          # Turn on evaluation mode which disables dropout.
          model.eval()
          lowercase__			,    lowercase__								=				0, 0.0
          lowercase__								=				time.time()
          with torch.no_grad():
               lowercase__								=				None
               for idx, (data, target, seq_len) in enumerate(lowercase_   ):
                    lowercase__								=				model(lowercase_     ,					lm_labels=lowercase_     ,					mems=lowercase_   )
                    lowercase__			,    lowercase__			,    lowercase__								=				ret
                    lowercase__								=				loss.mean()
                    total_loss += seq_len * loss.item()
                    total_len += seq_len
               lowercase__								=				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":
          lowercase__								=				evaluate(lowercase_   )
          lowercase__								=				evaluate(lowercase_   )
     elif args.split == "valid":
          lowercase__								=				evaluate(lowercase_   )
          lowercase__								=				None
     elif args.split == "test":
          lowercase__								=				evaluate(lowercase_   )
          lowercase__								=				None

     def format_log(A     ,					A   ):
          lowercase__								=				"| {0} loss {1:5.2f} | {0} ppl {2:9.3f} ".format(lowercase_     ,					lowercase_     ,					math.exp(lowercase_   )   )
          return log_str

     lowercase__								=				""
     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"""







from __future__ import annotations

import unittest

from transformers import CTRLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers.models.ctrl.modeling_tf_ctrl import (
         TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFCTRLForSequenceClassification,
         TFCTRLLMHeadModel,
         TFCTRLModel,
     )






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					parent
      __lowerCamelCase    		=					13
      __lowerCamelCase    		=					7
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					99
      __lowerCamelCase    		=					32
      __lowerCamelCase    		=					2
      __lowerCamelCase    		=					4
      __lowerCamelCase    		=					37
      __lowerCamelCase    		=					'gelu'
      __lowerCamelCase    		=					0.1
      __lowerCamelCase    		=					0.1
      __lowerCamelCase    		=					512
      __lowerCamelCase    		=					16
      __lowerCamelCase    		=					2
      __lowerCamelCase    		=					0.02
      __lowerCamelCase    		=					3
      __lowerCamelCase    		=					4
      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					self.vocab_size - 1







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size	)

      __lowerCamelCase    		=					None
      if self.use_input_mask:
         __lowerCamelCase    		=					random_attention_mask([self.batch_size, self.seq_length]	)

      __lowerCamelCase    		=					None
      if self.use_token_type_ids:
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.type_vocab_size	)

      __lowerCamelCase    		=					None
      if self.use_mc_token_ids:
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.num_choices] ,				self.seq_length	)

      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					None
      if self.use_labels:
         __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.type_sequence_label_size	)
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels	)
         __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.num_choices	)

      __lowerCamelCase    		=					CTRLConfig(
          vocab_size=self.vocab_size ,				n_embd=self.hidden_size ,				n_layer=self.num_hidden_layers ,				n_head=self.num_attention_heads ,				n_positions=self.max_position_embeddings ,				pad_token_id=self.pad_token_id ,				)

      __lowerCamelCase    		=					ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,				2	)

      return (
          config,
          input_ids,
          input_mask,
          head_mask,
          token_type_ids,
          mc_token_ids,
          sequence_labels,
          token_labels,
          choice_labels,
      )







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				*_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFCTRLModel(config=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					[input_ids, None, input_mask]  # None is the input for 'past'
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)

      self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				*_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFCTRLLMHeadModel(config=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				*_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.num_labels
      __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.type_sequence_label_size	)
      __lowerCamelCase    		=					{
          'input_ids': input_ids,
          'token_type_ids': token_type_ids,
          'labels': sequence_labels,
      }
      __lowerCamelCase    		=					TFCTRLForSequenceClassification(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.prepare_config_and_inputs()

      (
          (
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,
      )    		=					config_and_inputs

      __lowerCamelCase    		=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
      return config, inputs_dict






@require_tf
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase    ,				UpperCAmelCase    ,				unittest.TestCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Union[str, Any]    =    (TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
   __UpperCAmelCase	:							List[str]    =    (TFCTRLLMHeadModel,) if is_tf_available() else ()
   __UpperCAmelCase	:							Dict    =    (
       {
           "feature-extraction": TFCTRLModel,
           "text-classification": TFCTRLForSequenceClassification,
           "text-generation": TFCTRLLMHeadModel,
           "zero-shot": TFCTRLForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   __UpperCAmelCase	:							str    =    False
   __UpperCAmelCase	:							Tuple    =    False







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
         # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
         # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
         # config could not be created.
         return True

      return False







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFCTRLModelTester(self	)
      __lowerCamelCase    		=					ConfigTester(self ,				config_class=_SCREAMING_SNAKE_CASE ,				n_embd=37	)







   def    _A				(	self	):


      '''simple docstring'''

      self.config_tester.run_common_tests()







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_model(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_lm_head(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_for_sequence_classification(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCamelCase    		=					[TFCTRLLMHeadModel]
      __lowerCamelCase    		=					[TFCTRLForSequenceClassification]

      for model_class in self.all_model_classes:
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.build()  # may be needed for the get_bias() call below
         assert isinstance(model.get_input_embeddings() ,				tf.keras.layers.Layer	)

         if model_class in list_lm_models:
            __lowerCamelCase    		=					model.get_output_embeddings()
            assert isinstance(_SCREAMING_SNAKE_CASE ,				tf.keras.layers.Layer	)
            __lowerCamelCase    		=					model.get_bias()
            assert isinstance(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
            for k, v in name.items():
               assert isinstance(_SCREAMING_SNAKE_CASE ,				tf.Variable	)
         elif model_class in list_other_models_with_output_ebd:
            __lowerCamelCase    		=					model.get_output_embeddings()
            assert isinstance(_SCREAMING_SNAKE_CASE ,				tf.keras.layers.Layer	)
            __lowerCamelCase    		=					model.get_bias()
            assert name is None
         else:
            __lowerCamelCase    		=					model.get_output_embeddings()
            assert x is None
            __lowerCamelCase    		=					model.get_bias()
            assert name is None







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __lowerCamelCase    		=					TFCTRLModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
         self.assertIsNotNone(_SCREAMING_SNAKE_CASE	)






@require_tf
class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFCTRLLMHeadModel.from_pretrained('ctrl'	)
      __lowerCamelCase    		=					tf.convert_to_tensor([[11859, 0, 1611, 8]] ,				dtype=tf.intaa	)  # Legal the president is
      __lowerCamelCase    		=					[
          11859,
          0,
          1611,
          8,
          5,
          150,
          26449,
          2,
          19,
          348,
          469,
          3,
          2595,
          48,
          20740,
          246533,
          246533,
          19,
          30,
          5,
      ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

      __lowerCamelCase    		=					model.generate(_SCREAMING_SNAKE_CASE ,				do_sample=_SCREAMING_SNAKE_CASE	)
      self.assertListEqual(output_ids[0].numpy().tolist() ,				_SCREAMING_SNAKE_CASE	)






