


"""simple docstring"""







import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
       import torch

       from transformers import (
           LiltForQuestionAnswering,
           LiltForSequenceClassification,
           LiltForTokenClassification,
           LiltModel,
       )
       from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST






class       __A				:






 """simple docstring"""




 def __init__(		self ,    __A ,    __A=13 ,    __A=7 ,    __A=True ,    __A=True ,    __A=True ,    __A=True ,    __A=99 ,    __A=24 ,    __A=2 ,    __A=6 ,    __A=37 ,    __A="gelu" ,    __A=0.1 ,    __A=0.1 ,    __A=512 ,    __A=16 ,    __A=2 ,    __A=0.02 ,    __A=3 ,    __A=None ,    __A=1000 ,    )					->     str:
     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 		=intermediate_size
     a 		=hidden_act
     a 		=hidden_dropout_prob
     a 		=attention_probs_dropout_prob
     a 		=max_position_embeddings
     a 		=type_vocab_size
     a 		=type_sequence_label_size
     a 		=initializer_range
     a 		=num_labels
     a 		=scope
     a 		=range_bbox


 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[str]:
     a 		=ids_tensor([self.batch_size, self.seq_length] ,    self.vocab_size						)

     a 		=ids_tensor([self.batch_size, self.seq_length, 4] ,    self.range_bbox						)
     # Ensure that bbox is legal
     for i in range(bbox.shape[0]						):
         for j in range(bbox.shape[1]						):
             if bbox[i, j, 3] < bbox[i, j, 1]:
                 a 		=bbox[i, j, 3]
                 a 		=bbox[i, j, 1]
                 a 		=t
             if bbox[i, j, 2] < bbox[i, j, 0]:
                 a 		=bbox[i, j, 2]
                 a 		=bbox[i, j, 0]
                 a 		=t

     a 		=None
     if self.use_input_mask:
         a 		=ids_tensor([self.batch_size, self.seq_length] ,    vocab_size=2						)

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

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

     a 		=self.get_config()

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


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Tuple:
     return LiltConfig(
         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 ,    initializer_range=self.initializer_range ,    )


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    )					->     Any:
     a 		=LiltModel(config=__A						)
     model.to(__A						)
     model.eval()
     a 		=model(__A ,    bbox=__A ,    attention_mask=__A ,    token_type_ids=__A						)
     a 		=model(__A ,    bbox=__A ,    token_type_ids=__A						)
     a 		=model(__A ,    bbox=__A						)

     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       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    )					->     List[str]:
     a 		=self.num_labels
     a 		=LiltForTokenClassification(config=__A						)
     model.to(__A						)
     model.eval()
     a 		=model(
         __A ,    bbox=__A ,    attention_mask=__A ,    token_type_ids=__A ,    labels=__A						)
     self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.seq_length, self.num_labels)						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    )					->     Any:
     a 		=LiltForQuestionAnswering(config=__A						)
     model.to(__A						)
     model.eval()
     a 		=model(
         __A ,    bbox=__A ,    attention_mask=__A ,    token_type_ids=__A ,    start_positions=__A ,    end_positions=__A ,    )
     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       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[int]:
     a 		=self.prepare_config_and_inputs()
     (
         (
         a
     )		,      (
         a
     )		,      (
         a
     )		,      (
         a
     )		,      (
         a
     )		,      (
         a
     )		,      (
         a
     )		,      
     ) 		=config_and_inputs
     a 		={
         '''input_ids''': input_ids,
         '''bbox''': bbox,
         '''token_type_ids''': token_type_ids,
         '''attention_mask''': input_mask,
     }
     return config, inputs_dict






@require_torch
class       __A				(       _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    unittest.TestCase			):






 """simple docstring"""




 __lowerCAmelCase 					=  (
     (
         LiltModel,
         LiltForSequenceClassification,
         LiltForTokenClassification,
         LiltForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 __lowerCAmelCase 					=  (
     {
         "feature-extraction": LiltModel,
         "question-answering": LiltForQuestionAnswering,
         "text-classification": LiltForSequenceClassification,
         "token-classification": LiltForTokenClassification,
         "zero-shot": LiltForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowerCAmelCase 					=  False
 __lowerCAmelCase 					=  False


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A ,    __A ,    __A						)					->     Any:
     return True


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Any:
     a 		=LiltModelTester(self						)
     a 		=ConfigTester(self ,    config_class=__A ,    hidden_size=37						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Union[str, Any]:
     self.config_tester.run_common_tests()


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Union[str, Any]:
     a 		=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     str:
     a 		=self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
         a 		=type
         self.model_tester.create_and_check_model(*__A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Dict:
     a 		=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*__A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[Any]:
     a 		=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*__A						)





 @slow
 def       SCREAMING_SNAKE_CASE							(		self						)					->     str:
     for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a 		=LiltModel.from_pretrained(__A						)
         self.assertIsNotNone(__A						)






@require_torch
@slow
class       __A				(       unittest.TestCase			):






 """simple docstring"""




 def       SCREAMING_SNAKE_CASE							(		self						)					->     Tuple:
     a 		=LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''						).to(__A						)

     a 		=torch.tensor([[1, 2]] ,    device=__A						)
     a 		=torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] ,    device=__A						)

     # forward pass
     with torch.no_grad():
         a 		=model(input_ids=__A ,    bbox=__A						)

     a 		=torch.Size([1, 2, 768]						)
     a 		=torch.tensor(
         [[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]] ,    device=__A ,    )

     self.assertTrue(outputs.last_hidden_state.shape ,    __A						)
     self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] ,    __A ,    atol=1E-3						)						)
import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
   import tensorflow as tf

   from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments





@require_tf
class 		_lowercase						(					unittest.TestCase       ):

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							Union[str, Any]			)   ->						Optional[int]:

  """simple docstring"""





  for model_result in results.values():
   for batch_size, sequence_length in zip(model_result['bs']   ,      model_result['ss']			):
    UpperCamelCase_					:						Optional[Any] =					model_result['result'][batch_size][sequence_length]
    self.assertIsNotNone(snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						Dict =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      eager_mode=snake_case   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						Tuple =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						List[str] =					benchmark.run()
  self.check_results_dict_not_empty(results.time_inference_result			)
  self.check_results_dict_not_empty(results.memory_inference_result			)

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

  """simple docstring"""





  UpperCamelCase_					:						Dict =					'sgugger/tiny-distilbert-classification'
  UpperCamelCase_					:						int =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      only_pretrain_model=snake_case   ,      )
  UpperCamelCase_					:						int =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						Tuple =					benchmark.run()
  self.check_results_dict_not_empty(results.time_inference_result			)
  self.check_results_dict_not_empty(results.memory_inference_result			)

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

  """simple docstring"""





  UpperCamelCase_					:						str =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						Any =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						List[str] =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						Optional[int] =					benchmark.run()
  self.check_results_dict_not_empty(results.time_inference_result			)
  self.check_results_dict_not_empty(results.memory_inference_result			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						str =					AutoConfig.from_pretrained(snake_case			)
  UpperCamelCase_					:						List[str] =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      eager_mode=snake_case   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						int =					TensorFlowBenchmark(snake_case   ,      [config]			)
  UpperCamelCase_					:						Optional[Any] =					benchmark.run()
  self.check_results_dict_not_empty(results.time_inference_result			)
  self.check_results_dict_not_empty(results.memory_inference_result			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						int =					AutoConfig.from_pretrained(snake_case			)
  UpperCamelCase_					:						Any =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						Tuple =					TensorFlowBenchmark(snake_case   ,      [config]			)
  UpperCamelCase_					:						Tuple =					benchmark.run()
  self.check_results_dict_not_empty(results.time_inference_result			)
  self.check_results_dict_not_empty(results.memory_inference_result			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						Tuple =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						Optional[int] =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						str =					benchmark.run()
  self.check_results_dict_not_empty(results.time_train_result			)
  self.check_results_dict_not_empty(results.memory_train_result			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						List[str] =					AutoConfig.from_pretrained(snake_case			)
  UpperCamelCase_					:						int =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						List[Any] =					TensorFlowBenchmark(snake_case   ,      [config]			)
  UpperCamelCase_					:						int =					benchmark.run()
  self.check_results_dict_not_empty(results.time_train_result			)
  self.check_results_dict_not_empty(results.memory_train_result			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'patrickvonplaten/t5-tiny-random'
  UpperCamelCase_					:						List[str] =					AutoConfig.from_pretrained(snake_case			)
  UpperCamelCase_					:						List[str] =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						int =					TensorFlowBenchmark(snake_case   ,      configs=[config]			)
  UpperCamelCase_					:						Optional[Any] =					benchmark.run()
  self.check_results_dict_not_empty(results.time_inference_result			)
  self.check_results_dict_not_empty(results.memory_inference_result			)

 @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU'			)			) == 0   ,      'Cannot do xla on CPU.'			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						int =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						Union[str, Any] =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      use_xla=snake_case   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						Union[str, Any] =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						List[str] =					benchmark.run()
  self.check_results_dict_not_empty(results.time_inference_result			)
  self.check_results_dict_not_empty(results.memory_inference_result			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					'sshleifer/tiny-gpt2'
  with tempfile.TemporaryDirectory() as tmp_dir:
   UpperCamelCase_					:						List[Any] =					TensorFlowBenchmarkArguments(
       models=[MODEL_ID]   ,      inference=snake_case   ,      save_to_csv=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      inference_time_csv_file=os.path.join(snake_case   ,      'inf_time.csv'			)   ,      inference_memory_csv_file=os.path.join(snake_case   ,      'inf_mem.csv'			)   ,      env_info_csv_file=os.path.join(snake_case   ,      'env.csv'			)   ,      multi_process=snake_case   ,      )
   UpperCamelCase_					:						List[str] =					TensorFlowBenchmark(snake_case			)
   benchmark.run()
   self.assertTrue(Path(os.path.join(snake_case   ,      'inf_time.csv'			)			).exists()			)
   self.assertTrue(Path(os.path.join(snake_case   ,      'inf_mem.csv'			)			).exists()			)
   self.assertTrue(Path(os.path.join(snake_case   ,      'env.csv'			)			).exists()			)


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

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					'sshleifer/tiny-gpt2'

  def _check_summary_is_not_empty(snake_case	:							Union[str, Any]			):
   self.assertTrue(hasattr(snake_case   ,      'sequential'			)			)
   self.assertTrue(hasattr(snake_case   ,      'cumulative'			)			)
   self.assertTrue(hasattr(snake_case   ,      'current'			)			)
   self.assertTrue(hasattr(snake_case   ,      'total'			)			)

  with tempfile.TemporaryDirectory() as tmp_dir:
   UpperCamelCase_					:						str =					TensorFlowBenchmarkArguments(
       models=[MODEL_ID]   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      log_filename=os.path.join(snake_case   ,      'log.txt'			)   ,      log_print=snake_case   ,      trace_memory_line_by_line=snake_case   ,      eager_mode=snake_case   ,      multi_process=snake_case   ,      )
   UpperCamelCase_					:						Tuple =					TensorFlowBenchmark(snake_case			)
   UpperCamelCase_					:						Union[str, Any] =					benchmark.run()
   _check_summary_is_not_empty(result.inference_summary			)
   self.assertTrue(Path(os.path.join(snake_case   ,      'log.txt'			)			).exists()			)



