


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      __lowerCAmelCase  :






  """simple docstring"""





  def __init__(  self							:							Any     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							List[str]=1_3     ,					_lowerCAmelCase							:							List[Any]=7     ,					_lowerCAmelCase							:							Any=True     ,					_lowerCAmelCase							:							Optional[Any]=True     ,					_lowerCAmelCase							:							Dict=True     ,					_lowerCAmelCase							:							Optional[Any]=True     ,					_lowerCAmelCase							:							Optional[Any]=9_9     ,					_lowerCAmelCase							:							List[Any]=2_4     ,					_lowerCAmelCase							:							int=2     ,					_lowerCAmelCase							:							Union[str, Any]=6     ,					_lowerCAmelCase							:							Tuple=3_7     ,					_lowerCAmelCase							:							List[Any]="gelu"     ,					_lowerCAmelCase							:							Union[str, Any]=0.1     ,					_lowerCAmelCase							:							Any=0.1     ,					_lowerCAmelCase							:							Optional[Any]=5_1_2     ,					_lowerCAmelCase							:							Tuple=1_6     ,					_lowerCAmelCase							:							int=2     ,					_lowerCAmelCase							:							str=0.02     ,					_lowerCAmelCase							:							Optional[int]=3     ,					_lowerCAmelCase							:							Any=None     ,					_lowerCAmelCase							:							int=1_0_0_0     ,					) ->			int:




     """simple docstring"""
     snake_case_            =       parent
     snake_case_            =       batch_size
     snake_case_            =       seq_length
     snake_case_            =       is_training
     snake_case_            =       use_input_mask
     snake_case_            =       use_token_type_ids
     snake_case_            =       use_labels
     snake_case_            =       vocab_size
     snake_case_            =       hidden_size
     snake_case_            =       num_hidden_layers
     snake_case_            =       num_attention_heads
     snake_case_            =       intermediate_size
     snake_case_            =       hidden_act
     snake_case_            =       hidden_dropout_prob
     snake_case_            =       attention_probs_dropout_prob
     snake_case_            =       max_position_embeddings
     snake_case_            =       type_vocab_size
     snake_case_            =       type_sequence_label_size
     snake_case_            =       initializer_range
     snake_case_            =       num_labels
     snake_case_            =       scope
     snake_case_            =       range_bbox



  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       ids_tensor([self.batch_size, self.seq_length]     ,					self.vocab_size	)

     snake_case_            =       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]:
              snake_case_            =       bbox[i, j, 3]
              snake_case_            =       bbox[i, j, 1]
              snake_case_            =       t
           if bbox[i, j, 2] < bbox[i, j, 0]:
              snake_case_            =       bbox[i, j, 2]
              snake_case_            =       bbox[i, j, 0]
              snake_case_            =       t

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

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

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

     snake_case_            =       self.get_config()

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



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




     """simple docstring"""
     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        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							List[str]     ,					_lowerCAmelCase							:							Tuple     ,					_lowerCAmelCase							:							List[str]     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							Any     ,					) ->			Optional[int]:




     """simple docstring"""
     snake_case_            =       LiltModel(config=lowerCAmelCase_	)
     model.to(lowerCAmelCase_	)
     model.eval()
     snake_case_            =       model(lowerCAmelCase_     ,					bbox=lowerCAmelCase_     ,					attention_mask=lowerCAmelCase_     ,					token_type_ids=lowerCAmelCase_	)
     snake_case_            =       model(lowerCAmelCase_     ,					bbox=lowerCAmelCase_     ,					token_type_ids=lowerCAmelCase_	)
     snake_case_            =       model(lowerCAmelCase_     ,					bbox=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        lowerCAmelCase__	(  self							:							Optional[int]     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							Tuple     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							Dict     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							Any     ,					) ->			Any:




     """simple docstring"""
     snake_case_            =       self.num_labels
     snake_case_            =       LiltForTokenClassification(config=lowerCAmelCase_	)
     model.to(lowerCAmelCase_	)
     model.eval()
     snake_case_            =       model(
         lowerCAmelCase_     ,					bbox=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        lowerCAmelCase__	(  self							:							Any     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							Dict     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							Union[str, Any]     ,					) ->			Dict:




     """simple docstring"""
     snake_case_            =       LiltForQuestionAnswering(config=lowerCAmelCase_	)
     model.to(lowerCAmelCase_	)
     model.eval()
     snake_case_            =       model(
         lowerCAmelCase_     ,					bbox=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        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       self.prepare_config_and_inputs()
     (
         (
         snake_case_				
     )     ,     (
         snake_case_				
     )     ,     (
         snake_case_				
     )     ,     (
         snake_case_				
     )     ,     (
         snake_case_				
     )     ,     (
         snake_case_				
     )     ,     (
         snake_case_				
     )     ,     				
     )            =       config_and_inputs
     snake_case_            =       {
         "input_ids": input_ids,
         "bbox": bbox,
         "token_type_ids": token_type_ids,
         "attention_mask": input_mask,
     }
     return config, inputs_dict






@require_torch
class      __lowerCAmelCase  (			_a			,	_a			,	_a			,	unittest.TestCase       ):






  """simple docstring"""





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



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							Optional[int]     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							List[str]	) ->			Optional[int]:




     """simple docstring"""
     return True



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




     """simple docstring"""
     snake_case_            =       LiltModelTester(self	)
     snake_case_            =       ConfigTester(self     ,					config_class=lowerCAmelCase_     ,					hidden_size=3_7	)



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




     """simple docstring"""
     self.config_tester.run_common_tests()



  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			int:




     """simple docstring"""
     snake_case_            =       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase_	)



  def        lowerCAmelCase__	(  self							:							Tuple	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
        snake_case_            =       type
        self.model_tester.create_and_check_model(*lowerCAmelCase_	)



  def        lowerCAmelCase__	(  self							:							str	) ->			Dict:




     """simple docstring"""
     snake_case_            =       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase_	)



  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			Dict:




     """simple docstring"""
     snake_case_            =       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase_	)



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




     """simple docstring"""


     for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        snake_case_            =       LiltModel.from_pretrained(lowerCAmelCase_	)
        self.assertIsNotNone(lowerCAmelCase_	)






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






  """simple docstring"""





  def        lowerCAmelCase__	(  self							:							Any	) ->			Optional[int]:




     """simple docstring"""
     snake_case_            =       LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base"	).to(lowerCAmelCase_	)

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

     # forward pass
     with torch.no_grad():
        snake_case_            =       model(input_ids=lowerCAmelCase_     ,					bbox=lowerCAmelCase_	)

     snake_case_            =       torch.Size([1, 2, 7_6_8]	)
     snake_case_            =       torch.tensor(
         [[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]]     ,					device=lowerCAmelCase_     ,					)

     self.assertTrue(outputs.last_hidden_state.shape     ,					lowerCAmelCase_	)
     self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]     ,					lowerCAmelCase_     ,					atol=1e-3	)	)










'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case : Optional[int]  				=						logging.get_logger(__name__)

_snake_case : Any  				=						{
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class      A       (						_a       ):
							lowercase_							     =							'roformer'







							def __init__(    self			:    str				,     lowerCAmelCase_			:    int=5_00_00				,     lowerCAmelCase_			:    Any=None				,     lowerCAmelCase_			:    int=7_68				,     lowerCAmelCase_			:    Tuple=12				,     lowerCAmelCase_			:    Any=12				,     lowerCAmelCase_			:    List[str]=30_72				,     lowerCAmelCase_			:    Dict="gelu"				,     lowerCAmelCase_			:    Optional[int]=0.1				,     lowerCAmelCase_			:    List[Any]=0.1				,     lowerCAmelCase_			:    int=15_36				,     lowerCAmelCase_			:    Optional[Any]=2				,     lowerCAmelCase_			:    int=0.0_2				,     lowerCAmelCase_			:    Dict=1e-12				,     lowerCAmelCase_			:    Any=0				,     lowerCAmelCase_			:    Optional[Any]=False				,     lowerCAmelCase_			:    Tuple=True				,     **lowerCAmelCase_			:    Optional[int]				,     )     ->				str:


														"""simple docstring"""


														super().__init__(pad_token_id=lowerCAmelCase_				,     **lowerCAmelCase_   )

														_a      	=							vocab_size
														_a      	=							hidden_size if embedding_size is None else embedding_size
														_a      	=							hidden_size
														_a      	=							num_hidden_layers
														_a      	=							num_attention_heads
														_a      	=							hidden_act
														_a      	=							intermediate_size
														_a      	=							hidden_dropout_prob
														_a      	=							attention_probs_dropout_prob
														_a      	=							max_position_embeddings
														_a      	=							type_vocab_size
														_a      	=							initializer_range
														_a      	=							layer_norm_eps
														_a      	=							rotary_value
														_a      	=							use_cache



class      A       (						_a       ):







							@property
							def   __lowerCAmelCase     (    self			:    Any   )     ->				Mapping[str, Mapping[int, str]]:


														"""simple docstring"""


														if self.task == "multiple-choice":
																					_a      	=							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
														else:
																					_a      	=							{0: '''batch''', 1: '''sequence'''}
														_a      	=							{0: '''batch''', 1: '''sequence'''}
														return OrderedDict(
														    [
														        ('''input_ids''', dynamic_axis),
														        ('''attention_mask''', dynamic_axis),
														        ('''token_type_ids''', dynamic_axis),
														    ]   )

