





'''simple docstring'''

from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase





def 						UpperCamelCase    (							_lowerCamelCase : str	):
    A__,     A__    =      analyze_text(_lowerCamelCase	)
    A__    =      list(" " + ascii_lowercase	)
    # what is our total sum of probabilities.
    A__    =      sum(single_char_strings.values()	)

    # one length string
    A__    =      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:
            A__    =      single_char_strings[ch]
            A__    =      my_str / all_sum
            my_fir_sum += prob * math.loga(_lowerCamelCase	)  # entropy formula.

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

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

    # 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 						UpperCamelCase    (							_lowerCamelCase : str	):
    A__    =      Counter()  # type: ignore
    A__    =      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(_lowerCamelCase	) - 1	):
        single_char_strings[text[i]] += 1
        two_char_strings[text[i : i + 2]] += 1
    return single_char_strings, two_char_strings





def 						UpperCamelCase    (							):
    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'''

import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
      TimesformerForVideoClassification,
      TimesformerModel,
  )
  from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from transformers import VideoMAEImageProcessor






class        UpperCAmelCase     :
 def __init__(    self				:Optional[Any]						,	lowercase_				:int						,	lowercase_				:Union[str, Any]=13						,	lowercase_				:Union[str, Any]=10						,	lowercase_				:Any=3						,	lowercase_				:Tuple=2						,	lowercase_				:List[Any]=2						,	lowercase_				:int=True						,	lowercase_				:int=True						,	lowercase_				:List[str]=32						,	lowercase_				:Dict=5						,	lowercase_				:List[Any]=4						,	lowercase_				:List[Any]=37						,	lowercase_				:List[Any]="gelu"						,	lowercase_				:int=0.1						,	lowercase_				:List[Any]=0.1						,	lowercase_				:List[Any]=10						,	lowercase_				:int=0.0_2						,	lowercase_				:Union[str, Any]="divided_space_time"						,	lowercase_				:Tuple=None						,	)->							Tuple:
     A__    =      parent
     A__    =      batch_size
     A__    =      image_size
     A__    =      num_channels
     A__    =      patch_size
     A__    =      num_frames
     A__    =      is_training
     A__    =      use_labels
     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__    =      attention_type
     A__    =      initializer_range
     A__    =      scope
     A__    =      num_labels

     # in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
     A__    =      (image_size // patch_size) ** 2
     A__    =      (num_frames) * self.num_patches_per_frame + 1
 def 					UpperCAmelCase_							(    self				:str							)->							str:
     A__    =      floats_tensor(
         [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]							)

     A__    =      None
     if self.use_labels:
         A__    =      ids_tensor([self.batch_size]						,	self.num_labels							)

     A__    =      self.get_config()

     return config, pixel_values, labels
 def 					UpperCAmelCase_							(    self				:int							)->							Any:
     A__    =      TimesformerConfig(
         image_size=self.image_size						,	patch_size=self.patch_size						,	num_channels=self.num_channels						,	num_frames=self.num_frames						,	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						,	initializer_range=self.initializer_range						,	attention_type=self.attention_type						,	)
     A__    =      self.num_labels
     return config
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:List[str]						,	lowercase_				:List[Any]						,	lowercase_				:Tuple							)->							Optional[int]:
     A__    =      TimesformerModel(config=lowercase_							)
     model.to(lowercase_							)
     model.eval()
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)							)
 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:Tuple						,	lowercase_				:Tuple						,	lowercase_				:Dict							)->							Tuple:
     A__    =      TimesformerForVideoClassification(lowercase_							)
     model.to(lowercase_							)
     model.eval()

     A__    =      model(lowercase_							)

     # verify the logits shape
     A__    =      torch.Size((self.batch_size, self.num_labels)							)
     self.parent.assertEqual(result.logits.shape						,	lowercase_							)



 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							str:
     A__    =      self.prepare_config_and_inputs()
     A__,     A__,     A__    =      config_and_inputs
     A__    =      {"pixel_values": pixel_values}
     return config, inputs_dict






@require_torch
class        UpperCAmelCase     (	UpperCamelCase__      ,	UpperCamelCase__      ,	unittest.TestCase    ):
 __lowercase       		=    (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
 __lowercase       		=    (
     {"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
     if is_torch_available()
     else {}
 )

 __lowercase       		=    False
 __lowercase       		=    False
 __lowercase       		=    False
 __lowercase       		=    False
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Optional[int]:
     A__    =      TimesformerModelTester(self							)
     A__    =      ConfigTester(
         self						,	config_class=lowercase_						,	has_text_modality=lowercase_						,	hidden_size=37							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:int						,	lowercase_				:Dict						,	lowercase_				:int=False							)->							str:
     A__    =      copy.deepcopy(lowercase_							)

     if return_labels:
         if model_class in get_values(lowercase_							):
             A__    =      torch.zeros(
                 self.model_tester.batch_size						,	dtype=torch.long						,	device=lowercase_							)

     return inputs_dict
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							List[Any]:
     self.config_tester.run_common_tests()
 @unittest.skip(reason="TimeSformer does not use inputs_embeds"							)
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Tuple:
     pass
 def 					UpperCAmelCase_							(    self				:Dict							)->							Optional[Any]:
     A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         A__    =      model_class(lowercase_							)
         self.assertIsInstance(model.get_input_embeddings()						,	(nn.Module)							)
         A__    =      model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(lowercase_						,	nn.Linear							)							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Dict:
     A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         A__    =      model_class(lowercase_							)
         A__    =      inspect.signature(model.forward							)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         A__    =      [*signature.parameters.keys()]

         A__    =      ["pixel_values"]
         self.assertListEqual(arg_names[:1]						,	lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Optional[int]:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:Dict							)->							Optional[int]:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_video_classification(*lowercase_							)
 @slow
 def 					UpperCAmelCase_							(    self				:Any							)->							List[Any]:
     for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         A__    =      TimesformerModel.from_pretrained(lowercase_							)
         self.assertIsNotNone(lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     if not self.has_attentions:
         pass

     else:
         A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()
         A__    =      True

         for model_class in self.all_model_classes:
             A__    =      self.model_tester.seq_length
             A__    =      self.model_tester.num_frames

             A__    =      True
             A__    =      False
             A__    =      True
             A__    =      model_class(lowercase_							)
             model.to(lowercase_							)
             model.eval()
             with torch.no_grad():
                 A__    =      model(**self._prepare_for_class(lowercase_						,	lowercase_							)							)
             A__    =      outputs.attentions
             self.assertEqual(len(lowercase_							)						,	self.model_tester.num_hidden_layers							)

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             A__    =      True
             A__    =      model_class(lowercase_							)
             model.to(lowercase_							)
             model.eval()
             with torch.no_grad():
                 A__    =      model(**self._prepare_for_class(lowercase_						,	lowercase_							)							)
             A__    =      outputs.attentions
             self.assertEqual(len(lowercase_							)						,	self.model_tester.num_hidden_layers							)

             # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
             self.assertListEqual(
                 list(attentions[0].shape[-3:]							)						,	[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]						,	)
             A__    =      len(lowercase_							)

             # Check attention is always last and order is fine
             A__    =      True
             A__    =      True
             A__    =      model_class(lowercase_							)
             model.to(lowercase_							)
             model.eval()
             with torch.no_grad():
                 A__    =      model(**self._prepare_for_class(lowercase_						,	lowercase_							)							)

             self.assertEqual(out_len + 1						,	len(lowercase_							)							)

             A__    =      outputs.attentions

             self.assertEqual(len(lowercase_							)						,	self.model_tester.num_hidden_layers							)

             # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]							)						,	[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]						,	)



 def 					UpperCAmelCase_							(    self				:List[Any]							)->							List[str]:
     def check_hidden_states_output(lowercase_				:Dict						,	lowercase_				:int						,	lowercase_				:List[Any]							):
         A__    =      model_class(lowercase_							)
         model.to(lowercase_							)
         model.eval()

         with torch.no_grad():
             A__    =      model(**self._prepare_for_class(lowercase_						,	lowercase_							)							)

         A__    =      outputs.hidden_states
         A__    =      self.model_tester.num_hidden_layers + 1
         self.assertEqual(len(lowercase_							)						,	lowercase_							)

         A__    =      self.model_tester.seq_length

         self.assertListEqual(
             list(hidden_states[0].shape[-2:]							)						,	[seq_length, self.model_tester.hidden_size]						,	)

     A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         A__    =      True
         check_hidden_states_output(lowercase_						,	lowercase_						,	lowercase_							)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         A__    =      True

         check_hidden_states_output(lowercase_						,	lowercase_						,	lowercase_							)





def 						UpperCamelCase    (							):
    A__    =      hf_hub_download(
        repo_id="hf-internal-testing/spaghetti-video"						,	filename="eating_spaghetti.npy"						,	repo_type="dataset"	)
    A__    =      np.load(_lowerCamelCase	)
    return list(_lowerCamelCase	)






@require_torch
@require_vision
class        UpperCAmelCase     (	unittest.TestCase    ):
 @cached_property
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							int:
     # logits were tested with a different mean and std, so we use the same here
     return (
         VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]						,	image_std=[0.5, 0.5, 0.5]							)
         if is_vision_available()
         else None
     )



 @slow
 def 					UpperCAmelCase_							(    self				:int							)->							Any:
     A__    =      TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400"							).to(
         lowercase_							)

     A__    =      self.default_image_processor
     A__    =      prepare_video()
     A__    =      image_processor(video[:8]						,	return_tensors="pt"							).to(lowercase_							)

     # forward pass
     with torch.no_grad():
         A__    =      model(**lowercase_							)

     # verify the logits
     A__    =      torch.Size((1, 4_00)							)
     self.assertEqual(outputs.logits.shape						,	lowercase_							)

     A__    =      torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]							).to(lowercase_							)

     self.assertTrue(torch.allclose(outputs.logits[0, :3]						,	lowercase_						,	atol=1E-4							)							)



