



'''simple docstring'''





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
  import torch






class __A      :



 '''simple docstring'''







 def __init__(self			,       A			,       A=14			,       A=7			,       A=True			,       A=True			,       A=False			,       A=True			,       A=99			,       A=32			,       A=4			,       A=4			,       A=[[["global", "local"], 2]]			,       A=37			,       A="gelu"			,       A=0.1			,       A=0.1			,       A=512			,       A=7			,       A=0.02			,       )   ->					Union[str, Any]:


      """simple docstring"""



      _a     =  parent
      _a     =  batch_size
      _a     =  seq_length
      _a     =  is_training
      _a     =  use_input_mask
      _a     =  use_token_type_ids
      _a     =  use_labels
      _a     =  vocab_size
      _a     =  hidden_size
      _a     =  num_hidden_layers
      _a     =  num_attention_heads
      _a     =  attention_types
      _a     =  intermediate_size
      _a     =  hidden_act
      _a     =  hidden_dropout_prob
      _a     =  attention_probs_dropout_prob
      _a     =  max_position_embeddings
      _a     =  window_size
      _a     =  initializer_range
      _a     =  None
      _a     =  vocab_size - 1
      _a     =  vocab_size - 1
      _a     =  vocab_size - 1



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


      """simple docstring"""



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

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

      _a     =  GPTNeoConfig(
          vocab_size=self.vocab_size			,       hidden_size=self.hidden_size			,       num_layers=self.num_hidden_layers			,       num_heads=self.num_attention_heads			,       max_position_embeddings=self.max_position_embeddings			,       use_cache=A			,       bos_token_id=self.bos_token_id			,       eos_token_id=self.eos_token_id			,       pad_token_id=self.pad_token_id			,       window_size=self.window_size			,       attention_types=self.attention_types			,       )

      return (config, input_ids, input_mask)



 def  a__  (self	)   ->					Dict:


      """simple docstring"""



      _a     =  self.prepare_config_and_inputs()
      _a  ,   _a  ,   _a     =  config_and_inputs
      _a     =  {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
      return config, inputs_dict



 def  a__  (self			,       A			,       A			,       A			,       A	)   ->					Any:


      """simple docstring"""



      _a     =  20
      _a     =  model_class_name(A	)

      _a     =  model.init_cache(input_ids.shape[0]			,       A	)
      _a     =  jnp.ones((input_ids.shape[0], max_decoder_length)			,       dtype='''i4'''	)

      _a     =  jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1	)[None, :]			,       (input_ids.shape[0], input_ids.shape[-1] - 1)	)
      _a     =  model(
          input_ids[:, :-1]			,       attention_mask=A			,       past_key_values=A			,       position_ids=A			,       )

      _a     =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,       dtype='''i4'''	)
      _a     =  model(
          input_ids[:, -1:]			,       attention_mask=A			,       past_key_values=outputs_cache.past_key_values			,       position_ids=A			,       )

      _a     =  model(A	)

      _a     =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
      self.parent.assertTrue(diff < 1E-3			,       msg=f'''Max diff is {diff}'''	)



 def  a__  (self			,       A			,       A			,       A			,       A	)   ->					str:


      """simple docstring"""



      _a     =  20
      _a     =  model_class_name(A	)

      _a     =  jnp.concatenate(
          [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])	)]			,       axis=-1			,       )

      _a     =  model.init_cache(input_ids.shape[0]			,       A	)
      _a     =  jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1	)[None, :]			,       (input_ids.shape[0], input_ids.shape[-1] - 1)	)

      _a     =  model(
          input_ids[:, :-1]			,       attention_mask=A			,       past_key_values=A			,       position_ids=A			,       )
      _a     =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,       dtype='''i4'''	)
      _a     =  model(
          input_ids[:, -1:]			,       past_key_values=outputs_cache.past_key_values			,       attention_mask=A			,       position_ids=A			,       )

      _a     =  model(A			,       attention_mask=A	)

      _a     =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
      self.parent.assertTrue(diff < 1E-3			,       msg=f'''Max diff is {diff}'''	)






@require_flax
class __A      (  A       ,       A       ,       unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase   :	Union[str, Any]										=	(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
 __lowerCamelCase   :	int										=	(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()



 def  a__  (self	)   ->					Tuple:


      """simple docstring"""



      _a     =  FlaxGPTNeoModelTester(self	)



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


      """simple docstring"""



      for model_class_name in self.all_model_classes:
           _a  ,   _a  ,   _a     =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward(A			,       A			,       A			,       A	)



 def  a__  (self	)   ->					Any:


      """simple docstring"""



      for model_class_name in self.all_model_classes:
           _a  ,   _a  ,   _a     =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward_with_attn_mask(
               A			,       A			,       A			,       A	)



 @slow
 def  a__  (self	)   ->					Dict:


      """simple docstring"""



      _a     =  GPTaTokenizer.from_pretrained('''gpt2'''			,       pad_token='''<|endoftext|>'''			,       padding_side='''left'''	)
      _a     =  tokenizer(['''Hello this is a long string''', '''Hey''']			,       return_tensors='''np'''			,       padding=A			,       truncation=A	)

      _a     =  FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M'''	)
      _a     =  False
      _a     =  model.config.eos_token_id

      _a     =  jax.jit(model.generate	)

      _a     =  jit_generate(
          inputs['''input_ids''']			,       attention_mask=inputs['''attention_mask''']			,       pad_token_id=tokenizer.pad_token_id	).sequences

      _a     =  tokenizer.batch_decode(A			,       skip_special_tokens=A	)

      _a     =  [
          '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
          '''Hey, I\'m a little late to the party. I\'m going to''',
      ]

      self.assertListEqual(A			,       A	)



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


      """simple docstring"""



      _a  ,   _a     =  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__	):
                # prepare inputs
                _a     =  self._prepare_for_class(A			,       A	)
                _a     =  {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

                # load corresponding PyTorch class
                _a     =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
                _a     =  getattr(A			,       A	)

                _a  ,   _a     =  pt_inputs['''input_ids'''].shape
                _a     =  np.random.randint(0			,       seq_length - 1			,       size=(batch_size,)	)
                for batch_idx, start_index in enumerate(A	):
                     _a     =  0
                     _a     =  1
                     _a     =  0
                     _a     =  1
                _a     =  pt_model_class(A	).eval()
                _a     =  model_class(A			,       dtype=jnp.floataa	)

                _a     =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,       A	)
                _a     =  fx_state

                with torch.no_grad():
                     _a     =  pt_model(**A	).to_tuple()

                _a     =  fx_model(**A	).to_tuple()
                self.assertEqual(len(A	)			,       len(A	)			,       '''Output lengths differ between Flax and PyTorch'''	)
                for fx_output, pt_output in zip(A			,       A	):
                     self.assert_almost_equals(fx_output[:, -1]			,       pt_output[:, -1].numpy()			,       4E-2	)

                with tempfile.TemporaryDirectory() as tmpdirname:
                     pt_model.save_pretrained(A	)
                     _a     =  model_class.from_pretrained(A			,       from_pt=A	)

                _a     =  fx_model_loaded(**A	).to_tuple()
                self.assertEqual(
                    len(A	)			,       len(A	)			,       '''Output lengths differ between Flax and PyTorch'''	)
                for fx_output_loaded, pt_output in zip(A			,       A	):
                     self.assert_almost_equals(fx_output_loaded[:, -1]			,       pt_output[:, -1].numpy()			,       4E-2	)



 @is_pt_flax_cross_test
 def  a__  (self	)   ->					Dict:


      """simple docstring"""



      _a  ,   _a     =  self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__	):
                # prepare inputs
                _a     =  self._prepare_for_class(A			,       A	)
                _a     =  {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

                # load corresponding PyTorch class
                _a     =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
                _a     =  getattr(A			,       A	)

                _a     =  pt_model_class(A	).eval()
                _a     =  model_class(A			,       dtype=jnp.floataa	)

                _a     =  load_flax_weights_in_pytorch_model(A			,       fx_model.params	)
                _a  ,   _a     =  pt_inputs['''input_ids'''].shape
                _a     =  np.random.randint(0			,       seq_length - 1			,       size=(batch_size,)	)
                for batch_idx, start_index in enumerate(A	):
                     _a     =  0
                     _a     =  1
                     _a     =  0
                     _a     =  1

                # make sure weights are tied in PyTorch
                pt_model.tie_weights()

                with torch.no_grad():
                     _a     =  pt_model(**A	).to_tuple()

                _a     =  fx_model(**A	).to_tuple()
                self.assertEqual(len(A	)			,       len(A	)			,       '''Output lengths differ between Flax and PyTorch'''	)
                for fx_output, pt_output in zip(A			,       A	):
                     self.assert_almost_equals(fx_output[:, -1]			,       pt_output[:, -1].numpy()			,       4E-2	)

                with tempfile.TemporaryDirectory() as tmpdirname:
                     fx_model.save_pretrained(A	)
                     _a     =  pt_model_class.from_pretrained(A			,       from_flax=A	)

                with torch.no_grad():
                     _a     =  pt_model_loaded(**A	).to_tuple()

                self.assertEqual(
                    len(A	)			,       len(A	)			,       '''Output lengths differ between Flax and PyTorch'''	)
                for fx_output, pt_output in zip(A			,       A	):
                     self.assert_almost_equals(fx_output[:, -1]			,       pt_output[:, -1].numpy()			,       4E-2	)



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


      """simple docstring"""



      for model_class_name in self.all_model_classes:
           _a     =  model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M'''	)
           _a     =  model(np.ones((1, 1)	)	)
           self.assertIsNotNone(A	)








'''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()



