





"""simple docstring"""
from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str			):
 lowerCAmelCase				,lowerCAmelCase    				= analyze_text(_UpperCAmelCase			)
 lowerCAmelCase    				= list(' ' + ascii_lowercase			)
 # what is our total sum of probabilities.
 lowerCAmelCase    				= sum(single_char_strings.values()			)

 # one length string
 lowerCAmelCase    				= 0
 # for each alpha we go in our dict and if it is in it we calculate entropy
 for ch in my_alphas:
  if ch in single_char_strings:
   lowerCAmelCase    				= single_char_strings[ch]
   lowerCAmelCase    				= my_str / all_sum
   my_fir_sum += prob * math.loga(_UpperCAmelCase			)  # entropy formula.

    # print entropy
 print(F'{round(-1 * my_fir_sum			):.1f}'			)

 # two len string
 lowerCAmelCase    				= sum(two_char_strings.values()			)
 lowerCAmelCase    				= 0
 # for each alpha (two in size) calculate entropy.
 for cha in my_alphas:
  for cha in my_alphas:
   lowerCAmelCase    				= cha + cha
   if sequence in two_char_strings:
    lowerCAmelCase    				= two_char_strings[sequence]
    lowerCAmelCase    				= int(_UpperCAmelCase			) / all_sum
    my_sec_sum += prob * math.loga(_UpperCAmelCase			)

    # print second entropy
 print(F'{round(-1 * my_sec_sum			):.1f}'			)

 # print the difference between them
 print(F'{round((-1 * my_sec_sum) - (-1 * my_fir_sum)			):.1f}'			)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str			):
 lowerCAmelCase    				= Counter()  # type: ignore
 lowerCAmelCase    				= Counter()  # type: ignore
 single_char_strings[text[-1]] += 1

 # first case when we have space at start.
 two_char_strings[" " + text[0]] += 1
 for i in range(0  ,     len(_UpperCAmelCase			) - 1			):
  single_char_strings[text[i]] += 1
  two_char_strings[text[i : i + 2]] += 1
 return single_char_strings, two_char_strings

def 		_SCREAMING_SNAKE_CASE			():
 import doctest

 doctest.testmod()
 # text = (
 #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
 #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
 #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
 #     "beloved evident through no service elderly is. Blind there if every no so "
 #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
 #     "of message cottage windows do besides against uncivil.  Delightful "
 #     "unreserved impossible few estimating men favourable see entreaties. She "
 #     "propriety immediate was improving. He or entrance humoured likewise "
 #     "moderate. Much nor game son say feel. Fat make met can must form into "
 #     "gate. Me we offending prevailed discovery. "
 # )

 # calculate_prob(text)


if __name__ == "__main__":
       main()












"""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       a							:



 def __init__(     self				,       _snake_case				,       _snake_case=13				,       _snake_case=7				,       _snake_case=True				,       _snake_case=True				,       _snake_case=True				,       _snake_case=True				,       _snake_case=99				,       _snake_case=32				,       _snake_case=2				,       _snake_case=4				,       _snake_case=37				,       _snake_case="gelu"				,       _snake_case=0.1				,       _snake_case=0.1				,       _snake_case=5_12				,       _snake_case=16				,       _snake_case=2				,       _snake_case=0.02				,       _snake_case=3				,       _snake_case=4				,       _snake_case=None				,       _snake_case=10_00				,       ):







  """simple docstring"""






  lowerCAmelCase    				= parent
  lowerCAmelCase    				= batch_size
  lowerCAmelCase    				= seq_length
  lowerCAmelCase    				= is_training
  lowerCAmelCase    				= use_input_mask
  lowerCAmelCase    				= use_token_type_ids
  lowerCAmelCase    				= use_labels
  lowerCAmelCase    				= vocab_size
  lowerCAmelCase    				= hidden_size
  lowerCAmelCase    				= num_hidden_layers
  lowerCAmelCase    				= num_attention_heads
  lowerCAmelCase    				= intermediate_size
  lowerCAmelCase    				= hidden_act
  lowerCAmelCase    				= hidden_dropout_prob
  lowerCAmelCase    				= attention_probs_dropout_prob
  lowerCAmelCase    				= max_position_embeddings
  lowerCAmelCase    				= type_vocab_size
  lowerCAmelCase    				= type_sequence_label_size
  lowerCAmelCase    				= initializer_range
  lowerCAmelCase    				= num_labels
  lowerCAmelCase    				= num_choices
  lowerCAmelCase    				= scope
  lowerCAmelCase    				= range_bbox



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size     )

  # convert bbox to numpy since TF does not support item assignment
  lowerCAmelCase    				= 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]:
     lowerCAmelCase    				= bbox[i, j, 3]
     lowerCAmelCase    				= bbox[i, j, 1]
     lowerCAmelCase    				= t
    if bbox[i, j, 2] < bbox[i, j, 0]:
     lowerCAmelCase    				= bbox[i, j, 2]
     lowerCAmelCase    				= bbox[i, j, 0]
     lowerCAmelCase    				= t
  lowerCAmelCase    				= tf.convert_to_tensor(_snake_case     )

  lowerCAmelCase    				= None
  if self.use_input_mask:
   lowerCAmelCase    				= random_attention_mask([self.batch_size, self.seq_length]     )

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

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

  lowerCAmelCase    				= 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    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMModel(config=_snake_case     )

  lowerCAmelCase    				= model(_snake_case				,       _snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case     )
  lowerCAmelCase    				= model(_snake_case				,       _snake_case				,       token_type_ids=_snake_case     )
  lowerCAmelCase    				= model(_snake_case				,       _snake_case     )
  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				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMForMaskedLM(config=_snake_case     )

  lowerCAmelCase    				= model(_snake_case				,       _snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case				,       labels=_snake_case     )
  self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.num_labels
  lowerCAmelCase    				= TFLayoutLMForSequenceClassification(config=_snake_case     )

  lowerCAmelCase    				= model(_snake_case				,       _snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case     )
  self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.num_labels
  lowerCAmelCase    				= TFLayoutLMForTokenClassification(config=_snake_case     )

  lowerCAmelCase    				= model(_snake_case				,       _snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case				,       labels=_snake_case     )
  self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.num_labels)     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMForQuestionAnswering(config=_snake_case     )

  lowerCAmelCase    				= model(_snake_case				,       _snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case     )
  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     ):







  """simple docstring"""






  lowerCAmelCase    				= self.prepare_config_and_inputs()
  (
      (
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,
  )    				= config_and_inputs
  lowerCAmelCase    				= {
      'input_ids': input_ids,
      'bbox': bbox,
      'token_type_ids': token_type_ids,
      'attention_mask': input_mask,
  }
  return config, inputs_dict







@require_tf
class       a							(   a__   , a__   , unittest.TestCase  ):
 snake_case__             =       (
     (
         TFLayoutLMModel,
         TFLayoutLMForMaskedLM,
         TFLayoutLMForTokenClassification,
         TFLayoutLMForSequenceClassification,
         TFLayoutLMForQuestionAnswering,
     )
     if is_tf_available()
     else ()
 )
 snake_case__             =       (
     {
         '''feature-extraction''': TFLayoutLMModel,
         '''fill-mask''': TFLayoutLMForMaskedLM,
         '''text-classification''': TFLayoutLMForSequenceClassification,
         '''token-classification''': TFLayoutLMForTokenClassification,
         '''zero-shot''': TFLayoutLMForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 snake_case__             =       False
 snake_case__             =       True
 snake_case__             =       1_0



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMModelTester(self     )
  lowerCAmelCase    				= ConfigTester(self				,       config_class=_snake_case				,       hidden_size=37     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self.config_tester.run_common_tests()



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_masked_lm(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_sequence_classification(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_token_classification(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_question_answering(*_snake_case     )



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   lowerCAmelCase    				= TFLayoutLMModel.from_pretrained(_snake_case     )
   self.assertIsNotNone(_snake_case     )



 @unittest.skip('Onnx compliancy broke with TF 2.10'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  pass


def 		_SCREAMING_SNAKE_CASE			():
 # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
 # fmt: off
 lowerCAmelCase    				= tf.convert_to_tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]			)  # noqa: E231
 lowerCAmelCase    				= 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
 lowerCAmelCase    				= 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],[1000,1000,1000,1000]],[[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],[1000,1000,1000,1000]]]			)  # noqa: E231
 lowerCAmelCase    				= 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)
 lowerCAmelCase    				= 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       a							(   unittest.TestCase  ):



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase    				= model(input_ids=_snake_case				,       bbox=_snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case     )

  # test the sequence output on [0, :3, :3]
  lowerCAmelCase    				= tf.convert_to_tensor(
      [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]				,       )

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

  # test the pooled output on [1, :3]
  lowerCAmelCase    				= tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552]     )

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



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'				,       num_labels=2     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase    				= model(
      input_ids=_snake_case				,       bbox=_snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case				,       labels=tf.convert_to_tensor([1, 1]     )				,       )

  # test whether we get a loss as a scalar
  lowerCAmelCase    				= outputs.loss
  lowerCAmelCase    				= (2,)
  self.assertEqual(loss.shape				,       _snake_case     )

  # test the shape of the logits
  lowerCAmelCase    				= outputs.logits
  lowerCAmelCase    				= (2, 2)
  self.assertEqual(logits.shape				,       _snake_case     )



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'				,       num_labels=13     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase    				= model(
      input_ids=_snake_case				,       bbox=_snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case				,       labels=_snake_case     )

  # test the shape of the logits
  lowerCAmelCase    				= outputs.logits
  lowerCAmelCase    				= tf.convert_to_tensor((2, 25, 13)     )
  self.assertEqual(logits.shape				,       _snake_case     )



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase    				= model(input_ids=_snake_case				,       bbox=_snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case     )

  # test the shape of the logits
  lowerCAmelCase    				= tf.convert_to_tensor((2, 25)     )
  self.assertEqual(outputs.start_logits.shape				,       _snake_case     )
  self.assertEqual(outputs.end_logits.shape				,       _snake_case     )





