


'''simple docstring'''





from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers.models.layoutlm.modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMModel,
    )




class 				UpperCAmelCase			:


 def __init__(      self							:	Tuple			,       __lowerCamelCase							:	str			,       __lowerCamelCase							:	int=1_3			,       __lowerCamelCase							:	Dict=7			,       __lowerCamelCase							:	int=True			,       __lowerCamelCase							:	Tuple=True			,       __lowerCamelCase							:	List[str]=True			,       __lowerCamelCase							:	Optional[Any]=True			,       __lowerCamelCase							:	Dict=9_9			,       __lowerCamelCase							:	int=3_2			,       __lowerCamelCase							:	str=2			,       __lowerCamelCase							:	str=4			,       __lowerCamelCase							:	str=3_7			,       __lowerCamelCase							:	int="gelu"			,       __lowerCamelCase							:	Dict=0.1			,       __lowerCamelCase							:	Optional[int]=0.1			,       __lowerCamelCase							:	Tuple=5_1_2			,       __lowerCamelCase							:	Dict=1_6			,       __lowerCamelCase							:	List[str]=2			,       __lowerCamelCase							:	Optional[int]=0.02			,       __lowerCamelCase							:	Union[str, Any]=3			,       __lowerCamelCase							:	Optional[int]=4			,       __lowerCamelCase							:	Tuple=None			,       __lowerCamelCase							:	List[Any]=1_0_0_0			,       ):
    UpperCAmelCase__	:Optional[Any]      =      parent
    UpperCAmelCase__	:List[Any]      =      batch_size
    UpperCAmelCase__	:Any      =      seq_length
    UpperCAmelCase__	:Optional[int]      =      is_training
    UpperCAmelCase__	:Union[str, Any]      =      use_input_mask
    UpperCAmelCase__	:List[str]      =      use_token_type_ids
    UpperCAmelCase__	:Union[str, Any]      =      use_labels
    UpperCAmelCase__	:Any      =      vocab_size
    UpperCAmelCase__	:str      =      hidden_size
    UpperCAmelCase__	:Tuple      =      num_hidden_layers
    UpperCAmelCase__	:Any      =      num_attention_heads
    UpperCAmelCase__	:Optional[Any]      =      intermediate_size
    UpperCAmelCase__	:Any      =      hidden_act
    UpperCAmelCase__	:str      =      hidden_dropout_prob
    UpperCAmelCase__	:Any      =      attention_probs_dropout_prob
    UpperCAmelCase__	:List[Any]      =      max_position_embeddings
    UpperCAmelCase__	:int      =      type_vocab_size
    UpperCAmelCase__	:int      =      type_sequence_label_size
    UpperCAmelCase__	:List[str]      =      initializer_range
    UpperCAmelCase__	:Dict      =      num_labels
    UpperCAmelCase__	:Any      =      num_choices
    UpperCAmelCase__	:Any      =      scope
    UpperCAmelCase__	:Dict      =      range_bbox


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:str      =      ids_tensor([self.batch_size, self.seq_length]			,       self.vocab_size )

    # convert bbox to numpy since TF does not support item assignment
    UpperCAmelCase__	:Optional[Any]      =      ids_tensor([self.batch_size, self.seq_length, 4]			,       self.range_bbox ).numpy()
    # 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]:
             UpperCAmelCase__	:Union[str, Any]      =      bbox[i, j, 3]
             UpperCAmelCase__	:Any      =      bbox[i, j, 1]
             UpperCAmelCase__	:Any      =      t
          if bbox[i, j, 2] < bbox[i, j, 0]:
             UpperCAmelCase__	:int      =      bbox[i, j, 2]
             UpperCAmelCase__	:Union[str, Any]      =      bbox[i, j, 0]
             UpperCAmelCase__	:Tuple      =      t
    UpperCAmelCase__	:Optional[int]      =      tf.convert_to_tensor(__lowerCamelCase )

    UpperCAmelCase__	:str      =      None
    if self.use_input_mask:
       UpperCAmelCase__	:List[Any]      =      random_attention_mask([self.batch_size, self.seq_length] )

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

    UpperCAmelCase__	:Tuple      =      None
    UpperCAmelCase__	:Union[str, Any]      =      None
    UpperCAmelCase__	:List[Any]      =      None
    if self.use_labels:
       UpperCAmelCase__	:Dict      =      ids_tensor([self.batch_size]			,       self.type_sequence_label_size )
       UpperCAmelCase__	:Tuple      =      ids_tensor([self.batch_size, self.seq_length]			,       self.num_labels )
       UpperCAmelCase__	:str      =      ids_tensor([self.batch_size]			,       self.num_choices )

    UpperCAmelCase__	:Union[str, Any]      =      LayoutLMConfig(
        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			,       )

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	str			,       __lowerCamelCase							:	List[str]			,       __lowerCamelCase							:	int			,       __lowerCamelCase							:	Union[str, Any]			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	Tuple			,       __lowerCamelCase							:	Any ):
    UpperCAmelCase__	:Any      =      TFLayoutLMModel(config=__lowerCamelCase )

    UpperCAmelCase__	:Union[str, Any]      =      model(__lowerCamelCase			,       __lowerCamelCase			,       attention_mask=__lowerCamelCase			,       token_type_ids=__lowerCamelCase )
    UpperCAmelCase__	:int      =      model(__lowerCamelCase			,       __lowerCamelCase			,       token_type_ids=__lowerCamelCase )
    UpperCAmelCase__	:Optional[Any]      =      model(__lowerCamelCase			,       __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        __SCREAMING_SNAKE_CASE					(      self							:	Tuple			,       __lowerCamelCase							:	List[Any]			,       __lowerCamelCase							:	Optional[int]			,       __lowerCamelCase							:	int			,       __lowerCamelCase							:	Optional[Any]			,       __lowerCamelCase							:	Optional[Any]			,       __lowerCamelCase							:	int			,       __lowerCamelCase							:	Optional[Any]			,       __lowerCamelCase							:	List[str] ):
    UpperCAmelCase__	:Any      =      TFLayoutLMForMaskedLM(config=__lowerCamelCase )

    UpperCAmelCase__	:Dict      =      model(__lowerCamelCase			,       __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        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int]			,       __lowerCamelCase							:	str			,       __lowerCamelCase							:	int			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	Optional[Any]			,       __lowerCamelCase							:	List[str]			,       __lowerCamelCase							:	Dict			,       __lowerCamelCase							:	int ):
    UpperCAmelCase__	:Union[str, Any]      =      self.num_labels
    UpperCAmelCase__	:List[str]      =      TFLayoutLMForSequenceClassification(config=__lowerCamelCase )

    UpperCAmelCase__	:Optional[Any]      =      model(__lowerCamelCase			,       __lowerCamelCase			,       attention_mask=__lowerCamelCase			,       token_type_ids=__lowerCamelCase )
    self.parent.assertEqual(result.logits.shape			,       (self.batch_size, self.num_labels) )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple			,       __lowerCamelCase							:	Optional[int]			,       __lowerCamelCase							:	Optional[Any]			,       __lowerCamelCase							:	List[Any]			,       __lowerCamelCase							:	Optional[Any]			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	List[str]			,       __lowerCamelCase							:	Tuple ):
    UpperCAmelCase__	:int      =      self.num_labels
    UpperCAmelCase__	:Any      =      TFLayoutLMForTokenClassification(config=__lowerCamelCase )

    UpperCAmelCase__	:Any      =      model(__lowerCamelCase			,       __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        __SCREAMING_SNAKE_CASE					(      self							:	List[Any]			,       __lowerCamelCase							:	Union[str, Any]			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	int			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	Dict			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	str			,       __lowerCamelCase							:	Optional[Any] ):
    UpperCAmelCase__	:Any      =      TFLayoutLMForQuestionAnswering(config=__lowerCamelCase )

    UpperCAmelCase__	:Dict      =      model(__lowerCamelCase			,       __lowerCamelCase			,       attention_mask=__lowerCamelCase			,       token_type_ids=__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        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:Dict      =      self.prepare_config_and_inputs()
    (
        (
        UpperCAmelCase__
    )   ,						(
        UpperCAmelCase__
    )   ,						(
        UpperCAmelCase__
    )   ,						(
        UpperCAmelCase__
    )   ,						(
        UpperCAmelCase__
    )   ,						(
        UpperCAmelCase__
    )   ,						(
        UpperCAmelCase__
    )   ,						(
        UpperCAmelCase__
    )   ,						
    )	:Union[str, Any]      =      config_and_inputs
    UpperCAmelCase__	:Optional[Any]      =      {
        '''input_ids''': input_ids,
        '''bbox''': bbox,
        '''token_type_ids''': token_type_ids,
        '''attention_mask''': input_mask,
    }
    return config, inputs_dict




@require_tf
class 				UpperCAmelCase			(			_snake_case						, _snake_case						, unittest.TestCase						):
 UpperCAmelCase				=					(
     (
         TFLayoutLMModel,
         TFLayoutLMForMaskedLM,
         TFLayoutLMForTokenClassification,
         TFLayoutLMForSequenceClassification,
         TFLayoutLMForQuestionAnswering,
     )
     if is_tf_available()
     else ()
 )
 UpperCAmelCase				=					(
     {
         "feature-extraction": TFLayoutLMModel,
         "fill-mask": TFLayoutLMForMaskedLM,
         "text-classification": TFLayoutLMForSequenceClassification,
         "token-classification": TFLayoutLMForTokenClassification,
         "zero-shot": TFLayoutLMForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 UpperCAmelCase				=					False
 UpperCAmelCase				=					True
 UpperCAmelCase				=					10


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any ):
    UpperCAmelCase__	:Union[str, Any]      =      TFLayoutLMModelTester(self )
    UpperCAmelCase__	:Tuple      =      ConfigTester(self			,       config_class=__lowerCamelCase			,       hidden_size=3_7 )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    self.config_tester.run_common_tests()


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    UpperCAmelCase__	:Dict      =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    UpperCAmelCase__	:Union[str, Any]      =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:List[Any]      =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:int      =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any ):
    UpperCAmelCase__	:Union[str, Any]      =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase )


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCAmelCase__	:Optional[int]      =      TFLayoutLMModel.from_pretrained(__lowerCamelCase )
       self.assertIsNotNone(__lowerCamelCase )





 @unittest.skip('''Onnx compliancy broke with TF 2.10''' )
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    pass


def        a__     (					):
   # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
   # fmt: off
   UpperCAmelCase__	:int      =      tf.convert_to_tensor([[101,1_019,1_014,1_016,1_037,12_849,4_747,1_004,14_246,2_278,5_439,4_524,5_002,2_930,2_193,2_930,4_341,3_208,1_005,1_055,2_171,2_848,11_300,3_531,102],[101,4_070,4_034,7_020,1_024,3_058,1_015,1_013,2_861,1_013,6_070,19_274,2_772,6_205,27_814,16_147,16_147,4_343,2_047,10_283,10_969,14_389,1_012,2_338,102]]						)  # noqa: E231
   UpperCAmelCase__	:Union[str, Any]      =      tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]						)  # noqa: E231
   UpperCAmelCase__	:str      =      tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1_000,1_000,1_000,1_000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1_000,1_000,1_000,1_000]]]						)  # noqa: E231
   UpperCAmelCase__	:Dict      =      tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]						)  # noqa: E231
   # these are sequence labels (i.e. at the token level)
   UpperCAmelCase__	:Optional[int]      =      tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]						)  # noqa: E231
   # fmt: on

   return input_ids, attention_mask, bbox, token_type_ids, labels




@require_tf
class 				UpperCAmelCase			(			unittest.TestCase						):


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    UpperCAmelCase__	:Optional[Any]      =      TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased''' )

    UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__	:List[Any]      =      prepare_layoutlm_batch_inputs()

    # forward pass
    UpperCAmelCase__	:List[str]      =      model(input_ids=__lowerCamelCase			,       bbox=__lowerCamelCase			,       attention_mask=__lowerCamelCase			,       token_type_ids=__lowerCamelCase )

    # test the sequence output on [0, :3, :3]
    UpperCAmelCase__	:Optional[Any]      =      tf.convert_to_tensor(
        [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]			,       )

    self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]			,       __lowerCamelCase			,       atol=1e-3 ) )

    # test the pooled output on [1, :3]
    UpperCAmelCase__	:Dict      =      tf.convert_to_tensor([-0.65_80, -0.02_14, 0.85_52] )

    self.assertTrue(np.allclose(outputs.pooler_output[1, :3]			,       __lowerCamelCase			,       atol=1e-3 ) )


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    # initialize model with randomly initialized sequence classification head
    UpperCAmelCase__	:str      =      TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''			,       num_labels=2 )

    UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__	:Any      =      prepare_layoutlm_batch_inputs()

    # forward pass
    UpperCAmelCase__	:Any      =      model(
        input_ids=__lowerCamelCase			,       bbox=__lowerCamelCase			,       attention_mask=__lowerCamelCase			,       token_type_ids=__lowerCamelCase			,       labels=tf.convert_to_tensor([1, 1] )			,       )

    # test whether we get a loss as a scalar
    UpperCAmelCase__	:Tuple      =      outputs.loss
    UpperCAmelCase__	:Dict      =      (2,)
    self.assertEqual(loss.shape			,       __lowerCamelCase )

    # test the shape of the logits
    UpperCAmelCase__	:Dict      =      outputs.logits
    UpperCAmelCase__	:List[Any]      =      (2, 2)
    self.assertEqual(logits.shape			,       __lowerCamelCase )


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    # initialize model with randomly initialized token classification head
    UpperCAmelCase__	:Tuple      =      TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''			,       num_labels=1_3 )

    UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__	:Tuple      =      prepare_layoutlm_batch_inputs()

    # forward pass
    UpperCAmelCase__	:Tuple      =      model(
        input_ids=__lowerCamelCase			,       bbox=__lowerCamelCase			,       attention_mask=__lowerCamelCase			,       token_type_ids=__lowerCamelCase			,       labels=__lowerCamelCase )

    # test the shape of the logits
    UpperCAmelCase__	:Optional[int]      =      outputs.logits
    UpperCAmelCase__	:Union[str, Any]      =      tf.convert_to_tensor((2, 2_5, 1_3) )
    self.assertEqual(logits.shape			,       __lowerCamelCase )





 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	Any ):
    # initialize model with randomly initialized token classification head
    UpperCAmelCase__	:List[Any]      =      TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased''' )

    UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__	:str      =      prepare_layoutlm_batch_inputs()

    # forward pass
    UpperCAmelCase__	:Optional[int]      =      model(input_ids=__lowerCamelCase			,       bbox=__lowerCamelCase			,       attention_mask=__lowerCamelCase			,       token_type_ids=__lowerCamelCase )

    # test the shape of the logits
    UpperCAmelCase__	:List[Any]      =      tf.convert_to_tensor((2, 2_5) )
    self.assertEqual(outputs.start_logits.shape			,       __lowerCamelCase )
    self.assertEqual(outputs.end_logits.shape			,       __lowerCamelCase )







'''simple docstring'''





from __future__ import annotations

from collections.abc import Sequence
from typing import Literal


def        a__     (					UpperCamelCase_						:      str,   UpperCamelCase_						:      str						):
   UpperCAmelCase__	:Any      =      list(UpperCamelCase_						)
   UpperCAmelCase__	:Optional[int]      =      list(UpperCamelCase_						)
   UpperCAmelCase__	:str      =      0
   for i in range(len(UpperCamelCase_						)						):
      if lista[i] != lista[i]:
         count += 1
         UpperCAmelCase__	:Union[str, Any]      =      '''_'''
   if count > 1:
      return False
   else:
      return "".join(UpperCamelCase_						)


def        a__     (					UpperCamelCase_						:      list[str]						):
   UpperCAmelCase__	:int      =      []
   while True:
      UpperCAmelCase__	:Dict      =      ['''$'''] * len(UpperCamelCase_						)
      UpperCAmelCase__	:List[str]      =      []
      for i in range(len(UpperCamelCase_						)						):
         for j in range(i + 1,   len(UpperCamelCase_						)						):
            UpperCAmelCase__	:Optional[Any]      =      compare_string(binary[i],   binary[j]						)
            if k is False:
               UpperCAmelCase__	:Any      =      '''*'''
               UpperCAmelCase__	:List[Any]      =      '''*'''
               temp.append('''X'''						)
      for i in range(len(UpperCamelCase_						)						):
         if checka[i] == "$":
            pi.append(binary[i]						)
      if len(UpperCamelCase_						) == 0:
         return pi
      UpperCAmelCase__	:Tuple      =      list(set(UpperCamelCase_						)						)


def        a__     (					UpperCamelCase_						:      int,   UpperCamelCase_						:      Sequence[float]						):
   UpperCAmelCase__	:int      =      []
   for minterm in minterms:
      UpperCAmelCase__	:int      =      ''''''
      for _ in range(UpperCamelCase_						):
         UpperCAmelCase__	:Optional[int]      =      str(minterm % 2						) + string
         minterm //= 2
      temp.append(UpperCamelCase_						)
   return temp


def        a__     (					UpperCamelCase_						:      str,   UpperCamelCase_						:      str,   UpperCamelCase_						:      int						):
   UpperCAmelCase__	:Dict      =      list(UpperCamelCase_						)
   UpperCAmelCase__	:str      =      list(UpperCamelCase_						)
   UpperCAmelCase__	:str      =      0
   for i in range(len(UpperCamelCase_						)						):
      if lista[i] != lista[i]:
         count_n += 1
   return count_n == count


def        a__     (					UpperCamelCase_						:      list[list[int]],   UpperCamelCase_						:      list[str]						):
   UpperCAmelCase__	:Optional[Any]      =      []
   UpperCAmelCase__	:List[Any]      =      [0] * len(UpperCamelCase_						)
   for i in range(len(chart[0]						)						):
      UpperCAmelCase__	:Optional[Any]      =      0
      UpperCAmelCase__	:Union[str, Any]      =      -1
      for j in range(len(UpperCamelCase_						)						):
         if chart[j][i] == 1:
            count += 1
            UpperCAmelCase__	:Any      =      j
      if count == 1:
         UpperCAmelCase__	:Any      =      1
   for i in range(len(UpperCamelCase_						)						):
      if select[i] == 1:
         for j in range(len(chart[0]						)						):
            if chart[i][j] == 1:
               for k in range(len(UpperCamelCase_						)						):
                  UpperCAmelCase__	:int      =      0
         temp.append(prime_implicants[i]						)
   while True:
      UpperCAmelCase__	:Optional[int]      =      0
      UpperCAmelCase__	:Dict      =      -1
      UpperCAmelCase__	:Optional[Any]      =      0
      for i in range(len(UpperCamelCase_						)						):
         UpperCAmelCase__	:str      =      chart[i].count(1						)
         if count_n > max_n:
            UpperCAmelCase__	:Any      =      count_n
            UpperCAmelCase__	:List[Any]      =      i

      if max_n == 0:
         return temp

      temp.append(prime_implicants[rem]						)

      for i in range(len(chart[0]						)						):
         if chart[rem][i] == 1:
            for j in range(len(UpperCamelCase_						)						):
               UpperCAmelCase__	:Optional[int]      =      0


def        a__     (					UpperCamelCase_						:      list[str],   UpperCamelCase_						:      list[str]						):
   UpperCAmelCase__	:List[str]      =      [[0 for x in range(len(UpperCamelCase_						)						)] for x in range(len(UpperCamelCase_						)						)]
   for i in range(len(UpperCamelCase_						)						):
      UpperCAmelCase__	:Tuple      =      prime_implicants[i].count('''_'''						)
      for j in range(len(UpperCamelCase_						)						):
         if is_for_table(prime_implicants[i],   binary[j],   UpperCamelCase_						):
            UpperCAmelCase__	:List[str]      =      1

   return chart


def        a__     (					):
   UpperCAmelCase__	:int      =      int(input('''Enter the no. of variables\n'''						)						)
   UpperCAmelCase__	:Tuple      =      [
       float(UpperCamelCase_						)
       for x in input(
           '''Enter the decimal representation of Minterms \'Spaces Separated\'\n'''						).split()
   ]
   UpperCAmelCase__	:Union[str, Any]      =      decimal_to_binary(UpperCamelCase_,   UpperCamelCase_						)

   UpperCAmelCase__	:Optional[Any]      =      check(UpperCamelCase_						)
   print('''Prime Implicants are:'''						)
   print(UpperCamelCase_						)
   UpperCAmelCase__	:Optional[int]      =      prime_implicant_chart(UpperCamelCase_,   UpperCamelCase_						)

   UpperCAmelCase__	:Dict      =      selection(UpperCamelCase_,   UpperCamelCase_						)
   print('''Essential Prime Implicants are:'''						)
   print(UpperCamelCase_						)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()



