






'''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 				__SCREAMING_SNAKE_CASE							:




      def __init__(	self			, lowerCamelCase			, lowerCamelCase=13			, lowerCamelCase=7			, lowerCamelCase=True			, lowerCamelCase=True			, lowerCamelCase=True			, lowerCamelCase=True			, lowerCamelCase=99			, lowerCamelCase=24			, lowerCamelCase=2			, lowerCamelCase=6			, lowerCamelCase=37			, lowerCamelCase="gelu"			, lowerCamelCase=0.1			, lowerCamelCase=0.1			, lowerCamelCase=512			, lowerCamelCase=16			, lowerCamelCase=2			, lowerCamelCase=0.02			, lowerCamelCase=3			, lowerCamelCase=None			, lowerCamelCase=1000			, )				->List[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					  =     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 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         __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 			__UpperCamelCase				(	self   )				->Optional[Any]:



         '''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 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, )				->Dict:



         '''simple docstring'''
         __a					  =     LiltModel(config=lowerCamelCase   )
         model.to(lowerCamelCase   )
         model.eval()
         __a					  =     model(lowerCamelCase			, bbox=lowerCamelCase			, attention_mask=lowerCamelCase			, token_type_ids=lowerCamelCase   )
         __a					  =     model(lowerCamelCase			, bbox=lowerCamelCase			, token_type_ids=lowerCamelCase   )
         __a					  =     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 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, )				->str:



         '''simple docstring'''
         __a					  =     self.num_labels
         __a					  =     LiltForTokenClassification(config=lowerCamelCase   )
         model.to(lowerCamelCase   )
         model.eval()
         __a					  =     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 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, )				->Dict:



         '''simple docstring'''
         __a					  =     LiltForQuestionAnswering(config=lowerCamelCase   )
         model.to(lowerCamelCase   )
         model.eval()
         __a					  =     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 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         __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 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   ,   unittest.TestCase  ):
      __a	       =(
          (
              LiltModel,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      __a	       =(
          {
              "feature-extraction": LiltModel,
              "question-answering": LiltForQuestionAnswering,
              "text-classification": LiltForSequenceClassification,
              "token-classification": LiltForTokenClassification,
              "zero-shot": LiltForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      __a	       =False
      __a	       =False




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->Optional[int]:



         '''simple docstring'''
         return True




      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         __a					  =     LiltModelTester(self   )
         __a					  =     ConfigTester(self			, config_class=lowerCamelCase			, hidden_size=37   )




      def 			__UpperCamelCase				(	self   )				->str:



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




      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         __a					  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         __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(*lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         __a					  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         __a					  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowerCamelCase   )






      @slow
      def 			__UpperCamelCase				(	self   )				->Optional[int]:



         '''simple docstring'''
         for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __a					  =     LiltModel.from_pretrained(lowerCamelCase   )
            self.assertIsNotNone(lowerCamelCase   )







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




      def 			__UpperCamelCase				(	self   )				->str:



         '''simple docstring'''
         __a					  =     LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base'   ).to(lowerCamelCase   )

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

         # forward pass
         with torch.no_grad():
            __a					  =     model(input_ids=lowerCamelCase			, bbox=lowerCamelCase   )

         __a					  =     torch.Size([1, 2, 768]   )
         __a					  =     torch.tensor(
             [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]			, 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 __future__ import annotations

from typing import TypedDict
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =42
      __a	       =42







def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      list[str]:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter s type must be str.'     )

   return [s[i:] + s[:i] for i in range(len(SCREAMING_SNAKE_CASE__     )     )]



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      BWTTransformDict:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter s type must be str.'     )
   if not s:
      raise ValueError('The parameter s must not be empty.'     )

   __a					  =     all_rotations(SCREAMING_SNAKE_CASE__     )
   rotations.sort()  # sort the list of rotations in alphabetically order
   # make a string composed of the last char of each rotation
   __a					  =     {
       "bwt_string": "".join([word[-1] for word in rotations]     ),
       "idx_original_string": rotations.index(SCREAMING_SNAKE_CASE__     ),
   }
   return response



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						int     )	->      str:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter bwt_string type must be str.'     )
   if not bwt_string:
      raise ValueError('The parameter bwt_string must not be empty.'     )
   try:
      __a					  =     int(SCREAMING_SNAKE_CASE__     )
   except ValueError:
      raise TypeError(
          'The parameter idx_original_string type must be int or passive'
          ' of cast to int.'     )
   if idx_original_string < 0:
      raise ValueError('The parameter idx_original_string must not be lower than 0.'     )
   if idx_original_string >= len(SCREAMING_SNAKE_CASE__     ):
      raise ValueError(
          'The parameter idx_original_string must be lower than' ' len(bwt_string).'     )

   __a					  =     [''] * len(SCREAMING_SNAKE_CASE__     )
   for _ in range(len(SCREAMING_SNAKE_CASE__     )     ):
      for i in range(len(SCREAMING_SNAKE_CASE__     )     ):
         __a					  =     bwt_string[i] + ordered_rotations[i]
      ordered_rotations.sort()
   return ordered_rotations[idx_original_string]


if __name__ == "__main__":
 __UpperCamelCase   :							Any         =   """Provide a string that I will generate its BWT transform: """
 __UpperCamelCase   :							Dict         =   input(entry_msg).strip()
 __UpperCamelCase   :							Optional[int]         =   bwt_transform(s)
 print(
     f"""Burrows Wheeler transform for string '{s}' results """
     f"""in '{result["bwt_string"]}'"""
 )
 __UpperCamelCase   :							Dict         =   reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""])
 print(
     f"""Reversing Burrows Wheeler transform for entry '{result["bwt_string"]}' """
     f"""we get original string '{original_string}'"""
 )