





from __future__ import annotations

from typing import Any

class 		SCREAMING_SNAKE_CASE__				:


      def __init__(self       :      int				,			a__       :      int				,			a__       :      int				,			a__       :      float = 0      ):





         """simple docstring"""



         __snake_case       ,	__snake_case							       =   row, column
         __snake_case							       =   [[default_value for c in range(a__      )] for r in range(a__      )]


      def __str__(self       :      Any      ):





         """simple docstring"""



         __snake_case							       =   f"""Matrix consist of {self.row} rows and {self.column} columns\n"""

         # Make string identifier
         __snake_case							       =   0
         for row_vector in self.array:
            for obj in row_vector:
               __snake_case							       =   max(a__				,			len(str(a__      )      )      )
         __snake_case							       =   f"""%{max_element_length}s"""

         # Make string and return
         def single_line(a__       :      list[float]      ) -> str:
            nonlocal string_format_identifier
            __snake_case							       =   '''['''
            line += ", ".join(string_format_identifier % (obj,) for obj in row_vector      )
            line += "]"
            return line

         s += "\n".join(single_line(a__      ) for row_vector in self.array      )
         return s


      def __repr__(self       :      List[Any]      ):





         """simple docstring"""



         return str(self      )


      def    a (self       :      Union[str, Any]				,			a__       :      tuple[int, int]      ):





         """simple docstring"""



         if not (isinstance(a__				,			(list, tuple)      ) and len(a__      ) == 2):
            return False
         elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
            return False
         else:
            return True


      def __getitem__(self       :      Tuple				,			a__       :      tuple[int, int]      ):





         """simple docstring"""



         assert self.validate_indicies(a__      )
         return self.array[loc[0]][loc[1]]


      def __setitem__(self       :      Optional[int]				,			a__       :      tuple[int, int]				,			a__       :      float      ):





         """simple docstring"""



         assert self.validate_indicies(a__      )
         __snake_case							       =   value


      def __add__(self       :      Optional[Any]				,			a__       :      Matrix      ):





         """simple docstring"""



         assert isinstance(a__				,			a__      )
         assert self.row == another.row and self.column == another.column

         # Add
         __snake_case							       =   Matrix(self.row				,			self.column      )
         for r in range(self.row      ):
            for c in range(self.column      ):
               __snake_case							       =   self[r, c] + another[r, c]
         return result


      def __neg__(self       :      str      ):





         """simple docstring"""



         __snake_case							       =   Matrix(self.row				,			self.column      )
         for r in range(self.row      ):
            for c in range(self.column      ):
               __snake_case							       =   -self[r, c]
         return result


      def __sub__(self       :      Union[str, Any]				,			a__       :      Matrix      ):





         """simple docstring"""



         return self + (-another)


      def __mul__(self       :      List[str]				,			a__       :      int | float | Matrix      ):





         """simple docstring"""



         if isinstance(a__				,			(int, float)      ):  # Scalar multiplication
            __snake_case							       =   Matrix(self.row				,			self.column      )
            for r in range(self.row      ):
               for c in range(self.column      ):
                  __snake_case							       =   self[r, c] * another
            return result
         elif isinstance(a__				,			a__      ):  # Matrix multiplication
            assert self.column == another.row
            __snake_case							       =   Matrix(self.row				,			another.column      )
            for r in range(self.row      ):
               for c in range(another.column      ):
                  for i in range(self.column      ):
                     result[r, c] += self[r, i] * another[i, c]
            return result
         else:
            __snake_case							       =   f"""Unsupported type given for another ({type(a__      )})"""
            raise TypeError(a__      )


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   Matrix(self.column				,			self.row      )
         for r in range(self.row      ):
            for c in range(self.column      ):
               __snake_case							       =   self[r, c]
         return result


      def    a (self       :      List[str]				,			a__       :      Matrix				,			a__       :      Matrix      ):





         """simple docstring"""



         assert isinstance(a__				,			a__      ) and isinstance(a__				,			a__      )
         assert self.row == self.column == u.row == v.row  # u, v should be column vector
         assert u.column == v.column == 1  # u, v should be column vector

         # Calculate
         __snake_case							       =   v.transpose()
         __snake_case							       =   (v_t * self * u)[0, 0] + 1
         if numerator_factor == 0:
            return None  # It's not invertable
         return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":






    def lowerCamelCase__    (							)      ->				None:
       # a^(-1)
       __snake_case							       =   Matrix(3       ,      3       ,      0       )
       for i in range(3       ):
          __snake_case							       =   1
       print(f"""a^(-1) is {ainv}"""       )
       # u, v
       __snake_case							       =   Matrix(3       ,      1       ,      0       )
       __snake_case       ,	__snake_case       ,	__snake_case							       =   1, 2, -3
       __snake_case							       =   Matrix(3       ,      1       ,      0       )
       __snake_case       ,	__snake_case       ,	__snake_case							       =   4, -2, 5
       print(f"""u is {u}"""       )
       print(f"""v is {v}"""       )
       print(f"""uv^T is {u * v.transpose()}"""       )
       # Sherman Morrison
       print(f"""(a + uv^T)^(-1) is {ainv.sherman_morrison(snake_case_       ,      snake_case_       )}"""       )






    def lowerCamelCase__    (							)      ->				None:
       import doctest

       doctest.testmod()







    testa()








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       :      Any				,			a__       :      Union[str, Any]				,			a__       :      int=13				,			a__       :      int=7				,			a__       :      Optional[Any]=True				,			a__       :      Optional[int]=True				,			a__       :      Any=True				,			a__       :      str=True				,			a__       :      List[Any]=99				,			a__       :      Any=24				,			a__       :      List[str]=2				,			a__       :      Optional[int]=6				,			a__       :      int=37				,			a__       :      List[str]="gelu"				,			a__       :      List[Any]=0.1				,			a__       :      Optional[int]=0.1				,			a__       :      Union[str, Any]=512				,			a__       :      List[str]=16				,			a__       :      Optional[int]=2				,			a__       :      Union[str, Any]=0.0_2				,			a__       :      str=3				,			a__       :      Optional[Any]=None				,			a__       :      Any=1000				,			):





         """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    a (self       :      Optional[int]      ):





         """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    a (self       :      List[str]      ):





         """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    a (self       :      List[Any]				,			a__       :      List[Any]				,			a__       :      Optional[Any]				,			a__       :      List[str]				,			a__       :      int				,			a__       :      Optional[int]				,			a__       :      str				,			a__       :      Optional[int]				,			):





         """simple docstring"""



         __snake_case							       =   LiltModel(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(a__				,			bbox=a__				,			attention_mask=a__				,			token_type_ids=a__      )
         __snake_case							       =   model(a__				,			bbox=a__				,			token_type_ids=a__      )
         __snake_case							       =   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    a (self       :      Any				,			a__       :      Tuple				,			a__       :      Dict				,			a__       :      Optional[int]				,			a__       :      Dict				,			a__       :      Union[str, Any]				,			a__       :      str				,			a__       :      Tuple				,			):





         """simple docstring"""



         __snake_case							       =   self.num_labels
         __snake_case							       =   LiltForTokenClassification(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   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    a (self       :      int				,			a__       :      Optional[Any]				,			a__       :      int				,			a__       :      int				,			a__       :      Optional[Any]				,			a__       :      Tuple				,			a__       :      Union[str, Any]				,			a__       :      str				,			):





         """simple docstring"""



         __snake_case							       =   LiltForQuestionAnswering(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   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    a (self       :      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 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				List[Any]							=    (
          (
              LiltModel,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      A_						:				Any							=    (
          {
              'feature-extraction': LiltModel,
              'question-answering': LiltForQuestionAnswering,
              'text-classification': LiltForSequenceClassification,
              'token-classification': LiltForTokenClassification,
              'zero-shot': LiltForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      A_						:				Optional[int]							=    False
      A_						:				List[Any]							=    False


      def    a (self       :      Dict				,			a__       :      Tuple				,			a__       :      Tuple				,			a__       :      Tuple				,			a__       :      Union[str, Any]				,			a__       :      Any      ):





         """simple docstring"""



         return True


      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         __snake_case							       =   LiltModelTester(self      )
         __snake_case							       =   ConfigTester(self				,			config_class=a__				,			hidden_size=37      )


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         self.config_tester.run_common_tests()


      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__      )


      def    a (self       :      List[Any]      ):





         """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(*a__      )


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*a__      )


      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*a__      )


      @slow
      def    a (self       :      Optional[int]      ):





         """simple docstring"""



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






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


      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''      ).to(a__      )

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

         # forward pass
         with torch.no_grad():
            __snake_case							       =   model(input_ids=a__				,			bbox=a__      )

         __snake_case							       =   torch.Size([1, 2, 768]      )
         __snake_case							       =   torch.tensor(
             [[-0.0_6_5_3, 0.0_9_5_0, -0.0_0_6_1], [-0.0_5_4_5, 0.0_9_2_6, -0.0_3_2_4]]				,			device=a__				,			)

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

