





'''simple docstring'''


import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           MODEL_FOR_PRETRAINING_MAPPING,
           NezhaForMaskedLM,
           NezhaForMultipleChoice,
           NezhaForNextSentencePrediction,
           NezhaForPreTraining,
           NezhaForQuestionAnswering,
           NezhaForSequenceClassification,
           NezhaForTokenClassification,
           NezhaModel,
       )
       from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST

class  lowerCAmelCase_		:




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase=13      ,  _lowerCAmelCase=7      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=99      ,  _lowerCAmelCase=32      ,  _lowerCAmelCase=5      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=37      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=128      ,  _lowerCAmelCase=32      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=None      ,  )     ->				str:
     _lowerCAmelCase						      =   parent
     _lowerCAmelCase						      =   batch_size
     _lowerCAmelCase						      =   seq_length
     _lowerCAmelCase						      =   is_training
     _lowerCAmelCase						      =   use_input_mask
     _lowerCAmelCase						      =   use_token_type_ids
     _lowerCAmelCase						      =   use_labels
     _lowerCAmelCase						      =   vocab_size
     _lowerCAmelCase						      =   hidden_size
     _lowerCAmelCase						      =   num_hidden_layers
     _lowerCAmelCase						      =   num_attention_heads
     _lowerCAmelCase						      =   intermediate_size
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   max_position_embeddings
     _lowerCAmelCase						      =   type_vocab_size
     _lowerCAmelCase						      =   type_sequence_label_size
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   num_labels
     _lowerCAmelCase						      =   num_choices
     _lowerCAmelCase						      =   scope




 def 				_snake_case (						self		)     ->				Optional[int]:
     _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
     _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						      =   self.get_config()

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




 def 				_snake_case (						self		)     ->				Dict:
     return NezhaConfig(
         vocab_size=self.vocab_size      ,  hidden_size=self.hidden_size      ,  num_hidden_layers=self.num_hidden_layers      ,  num_attention_heads=self.num_attention_heads      ,  intermediate_size=self.intermediate_size      ,  hidden_act=self.hidden_act      ,  hidden_dropout_prob=self.hidden_dropout_prob      ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,  max_position_embeddings=self.max_position_embeddings      ,  type_vocab_size=self.type_vocab_size      ,  is_decoder=_lowerCAmelCase      ,  initializer_range=self.initializer_range      ,  )




 def 				_snake_case (						self		)     ->				str:
     (
         (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      
     )						      =   self.prepare_config_and_inputs()

     _lowerCAmelCase						      =   True
     _lowerCAmelCase						      =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
     _lowerCAmelCase						      =   ids_tensor([self.batch_size, self.seq_length]      ,  vocab_size=2		)

     return (
         config,
         input_ids,
         token_type_ids,
         input_mask,
         sequence_labels,
         token_labels,
         choice_labels,
         encoder_hidden_states,
         encoder_attention_mask,
     )




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Dict:
     _lowerCAmelCase						      =   NezhaModel(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase		)
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase		)
     _lowerCAmelCase						      =   model(_lowerCAmelCase		)
     self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length, self.hidden_size)		)
     self.parent.assertEqual(result.pooler_output.shape      ,  (self.batch_size, self.hidden_size)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  )     ->				Optional[int]:
     _lowerCAmelCase						      =   True
     _lowerCAmelCase						      =   NezhaModel(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(
         _lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase      ,  encoder_hidden_states=_lowerCAmelCase      ,  encoder_attention_mask=_lowerCAmelCase      ,  )
     _lowerCAmelCase						      =   model(
         _lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase      ,  encoder_hidden_states=_lowerCAmelCase      ,  )
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase		)
     self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length, self.hidden_size)		)
     self.parent.assertEqual(result.pooler_output.shape      ,  (self.batch_size, self.hidden_size)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Any:
     _lowerCAmelCase						      =   NezhaForMaskedLM(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase      ,  labels=_lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.seq_length, self.vocab_size)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				str:
     _lowerCAmelCase						      =   NezhaForNextSentencePrediction(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(
         _lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase      ,  labels=_lowerCAmelCase      ,  )
     self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, 2)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				str:
     _lowerCAmelCase						      =   NezhaForPreTraining(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(
         _lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase      ,  labels=_lowerCAmelCase      ,  next_sentence_label=_lowerCAmelCase      ,  )
     self.parent.assertEqual(result.prediction_logits.shape      ,  (self.batch_size, self.seq_length, self.vocab_size)		)
     self.parent.assertEqual(result.seq_relationship_logits.shape      ,  (self.batch_size, 2)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				str:
     _lowerCAmelCase						      =   NezhaForQuestionAnswering(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(
         _lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase      ,  start_positions=_lowerCAmelCase      ,  end_positions=_lowerCAmelCase      ,  )
     self.parent.assertEqual(result.start_logits.shape      ,  (self.batch_size, self.seq_length)		)
     self.parent.assertEqual(result.end_logits.shape      ,  (self.batch_size, self.seq_length)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Dict:
     _lowerCAmelCase						      =   self.num_labels
     _lowerCAmelCase						      =   NezhaForSequenceClassification(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase      ,  labels=_lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.num_labels)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				str:
     _lowerCAmelCase						      =   self.num_labels
     _lowerCAmelCase						      =   NezhaForTokenClassification(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase      ,  labels=_lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.seq_length, self.num_labels)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Tuple:
     _lowerCAmelCase						      =   self.num_choices
     _lowerCAmelCase						      =   NezhaForMultipleChoice(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   input_ids.unsqueeze(1		).expand(-1      ,  self.num_choices      ,  -1		).contiguous()
     _lowerCAmelCase						      =   token_type_ids.unsqueeze(1		).expand(-1      ,  self.num_choices      ,  -1		).contiguous()
     _lowerCAmelCase						      =   input_mask.unsqueeze(1		).expand(-1      ,  self.num_choices      ,  -1		).contiguous()
     _lowerCAmelCase						      =   model(
         _lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase      ,  labels=_lowerCAmelCase      ,  )
     self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.num_choices)		)





 def 				_snake_case (						self		)     ->				Tuple:
     _lowerCAmelCase						      =   self.prepare_config_and_inputs()
     (
         (
         _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_torch
class  lowerCAmelCase_		(		__magic_name__    ,__magic_name__    ,__magic_name__    ,unittest.TestCase		):
 __lowerCamelCase    :				Dict								=       (
     (
         NezhaModel,
         NezhaForMaskedLM,
         NezhaForMultipleChoice,
         NezhaForNextSentencePrediction,
         NezhaForPreTraining,
         NezhaForQuestionAnswering,
         NezhaForSequenceClassification,
         NezhaForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 __lowerCamelCase    :				str								=       (
     {
         "feature-extraction": NezhaModel,
         "fill-mask": NezhaForMaskedLM,
         "question-answering": NezhaForQuestionAnswering,
         "text-classification": NezhaForSequenceClassification,
         "token-classification": NezhaForTokenClassification,
         "zero-shot": NezhaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowerCamelCase    :				str								=       True




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=False		)     ->				Any:
     _lowerCAmelCase						      =   super()._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase      ,  return_labels=_lowerCAmelCase		)

     if return_labels:
         if model_class in get_values(_lowerCAmelCase		):
             _lowerCAmelCase						      =   torch.zeros(
                 (self.model_tester.batch_size, self.model_tester.seq_length)      ,  dtype=torch.long      ,  device=_lowerCAmelCase		)
             _lowerCAmelCase						      =   torch.zeros(
                 self.model_tester.batch_size      ,  dtype=torch.long      ,  device=_lowerCAmelCase		)
     return inputs_dict




 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   NezhaModelTester(self		)
     _lowerCAmelCase						      =   ConfigTester(self      ,  config_class=_lowerCAmelCase      ,  hidden_size=37		)




 def 				_snake_case (						self		)     ->				Optional[Any]:
     self.config_tester.run_common_tests()




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_decoder()
     self.model_tester.create_and_check_model_as_decoder(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				str:
     # This regression test was failing with PyTorch < 1.3
     (
         (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      
     )						      =   self.model_tester.prepare_config_and_inputs_for_decoder()

     _lowerCAmelCase						      =   None

     self.model_tester.create_and_check_model_as_decoder(
         _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  )




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_next_sequence_prediction(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_pretraining(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*_lowerCAmelCase		)




 @slow
 def 				_snake_case (						self		)     ->				Optional[Any]:
     for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						      =   NezhaModel.from_pretrained(_lowerCAmelCase		)
         self.assertIsNotNone(_lowerCAmelCase		)





 @slow
 @require_torch_gpu
 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes:
         # NezhaForMultipleChoice behaves incorrectly in JIT environments.
         if model_class == NezhaForMultipleChoice:
             return

         _lowerCAmelCase						      =   True
         _lowerCAmelCase						      =   model_class(config=_lowerCAmelCase		)

         _lowerCAmelCase						      =   self._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase		)
         _lowerCAmelCase						      =   torch.jit.trace(
             _lowerCAmelCase      ,  (inputs_dict["input_ids"].to("cpu"		), inputs_dict["attention_mask"].to("cpu"		))		)

         with tempfile.TemporaryDirectory() as tmp:
             torch.jit.save(_lowerCAmelCase      ,  os.path.join(_lowerCAmelCase      ,  "bert.pt"		)		)
             _lowerCAmelCase						      =   torch.jit.load(os.path.join(_lowerCAmelCase      ,  "bert.pt"		)      ,  map_location=_lowerCAmelCase		)
             loaded(inputs_dict["input_ids"].to(_lowerCAmelCase		)      ,  inputs_dict["attention_mask"].to(_lowerCAmelCase		)		)







@require_torch
class  lowerCAmelCase_		(		unittest.TestCase		):




 @slow
 def 				_snake_case (						self		)     ->				Tuple:
     _lowerCAmelCase						      =   NezhaModel.from_pretrained("sijunhe/nezha-cn-base"		)
     _lowerCAmelCase						      =   torch.tensor([[0, 1, 2, 3, 4, 5]]		)
     _lowerCAmelCase						      =   torch.tensor([[0, 1, 1, 1, 1, 1]]		)
     with torch.no_grad():
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  attention_mask=_lowerCAmelCase		)[0]
     _lowerCAmelCase						      =   torch.Size((1, 6, 768)		)
     self.assertEqual(output.shape      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.tensor([[[0.0685, 0.2441, 0.1102], [0.0600, 0.1906, 0.1349], [0.0221, 0.0819, 0.0586]]]		)

     self.assertTrue(torch.allclose(output[:, 1:4, 1:4]      ,  _lowerCAmelCase      ,  atol=1E-4		)		)





 @slow
 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   NezhaForMaskedLM.from_pretrained("sijunhe/nezha-cn-base"		)
     _lowerCAmelCase						      =   torch.tensor([[0, 1, 2, 3, 4, 5]]		)
     _lowerCAmelCase						      =   torch.tensor([[1, 1, 1, 1, 1, 1]]		)
     with torch.no_grad():
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  attention_mask=_lowerCAmelCase		)[0]
     _lowerCAmelCase						      =   torch.Size((1, 6, 21128)		)
     self.assertEqual(output.shape      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.tensor(
         [[-2.7939, -1.7902, -2.2189], [-2.8585, -1.8908, -2.3723], [-2.6499, -1.7750, -2.2558]]		)

     self.assertTrue(torch.allclose(output[:, 1:4, 1:4]      ,  _lowerCAmelCase      ,  atol=1E-4		)		)









'''simple docstring'''


import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
       import torch

       from transformers import AutoModelForCausalLM

@require_torch
class  lowerCAmelCase_		(		unittest.TestCase		):




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   ids_tensor((1, 5)      ,  vocab_size=model.config.vocab_size		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase		)
     _lowerCAmelCase						      =   tokenizer.decode(greedy_ids[0]		)

     with CaptureStdout() as cs:
         _lowerCAmelCase						      =   TextStreamer(_lowerCAmelCase		)
         model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase      ,  streamer=_lowerCAmelCase		)
     # The greedy text should be printed to stdout, except for the final "\n" in the streamer
     _lowerCAmelCase						      =   cs.out[:-1]

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   ids_tensor((1, 5)      ,  vocab_size=model.config.vocab_size		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase		)
     _lowerCAmelCase						      =   tokenizer.decode(greedy_ids[0]		)

     _lowerCAmelCase						      =   TextIteratorStreamer(_lowerCAmelCase		)
     _lowerCAmelCase						      =   {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
     _lowerCAmelCase						      =   Thread(target=model.generate      ,  kwargs=_lowerCAmelCase		)
     thread.start()
     _lowerCAmelCase						      =   ""
     for new_text in streamer:
         streamer_text += new_text

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   ids_tensor((1, 5)      ,  vocab_size=model.config.vocab_size		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase		)
     _lowerCAmelCase						      =   greedy_ids[:, input_ids.shape[1] :]
     _lowerCAmelCase						      =   tokenizer.decode(new_greedy_ids[0]		)

     with CaptureStdout() as cs:
         _lowerCAmelCase						      =   TextStreamer(_lowerCAmelCase      ,  skip_prompt=_lowerCAmelCase		)
         model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase      ,  streamer=_lowerCAmelCase		)
     # The greedy text should be printed to stdout, except for the final "\n" in the streamer
     _lowerCAmelCase						      =   cs.out[:-1]

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Dict:
     # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
     # with actual models -- the dummy models' tokenizers are not aligned with their models, and
     # `skip_special_tokens=True` has no effect on them
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("distilgpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("distilgpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   torch.ones((1, 5)      ,  device=_lowerCAmelCase		).long() * model.config.bos_token_id
     with CaptureStdout() as cs:
         _lowerCAmelCase						      =   TextStreamer(_lowerCAmelCase      ,  skip_special_tokens=_lowerCAmelCase		)
         model.generate(_lowerCAmelCase      ,  max_new_tokens=1      ,  do_sample=_lowerCAmelCase      ,  streamer=_lowerCAmelCase		)

     # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
     # re-tokenized, must only contain one token
     _lowerCAmelCase						      =   cs.out[:-1]  # Remove the final "\n"
     _lowerCAmelCase						      =   tokenizer(_lowerCAmelCase      ,  return_tensors="pt"		)
     self.assertEqual(streamer_text_tokenized.input_ids.shape      ,  (1, 1)		)





 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   ids_tensor((1, 5)      ,  vocab_size=model.config.vocab_size		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   TextIteratorStreamer(_lowerCAmelCase      ,  timeout=0.001		)
     _lowerCAmelCase						      =   {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
     _lowerCAmelCase						      =   Thread(target=model.generate      ,  kwargs=_lowerCAmelCase		)
     thread.start()

     # The streamer will timeout after 0.001 seconds, so an exception will be raised
     with self.assertRaises(_lowerCAmelCase		):
         _lowerCAmelCase						      =   ""
         for new_text in streamer:
             streamer_text += new_text


