




"""simple docstring"""




from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

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


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
      TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
      TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
      TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
      TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
      LayoutLMvaConfig,
      TFLayoutLMvaForQuestionAnswering,
      TFLayoutLMvaForSequenceClassification,
      TFLayoutLMvaForTokenClassification,
      TFLayoutLMvaModel,
  )

if is_vision_available():
  from PIL import Image

  from transformers import LayoutLMvaImageProcessor


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     Tuple ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     Optional[int]=2 ,			UpperCAmelCase__				:     Optional[Any]=3 ,			UpperCAmelCase__				:     Any=4 ,			UpperCAmelCase__				:     List[Any]=2 ,			UpperCAmelCase__				:     List[Any]=7 ,			UpperCAmelCase__				:     Union[str, Any]=True ,			UpperCAmelCase__				:     List[str]=True ,			UpperCAmelCase__				:     List[str]=True ,			UpperCAmelCase__				:     Any=True ,			UpperCAmelCase__				:     Optional[Any]=99 ,			UpperCAmelCase__				:     Any=36 ,			UpperCAmelCase__				:     int=2 ,			UpperCAmelCase__				:     Tuple=4 ,			UpperCAmelCase__				:     str=37 ,			UpperCAmelCase__				:     Dict="gelu" ,			UpperCAmelCase__				:     List[str]=0.1 ,			UpperCAmelCase__				:     Optional[int]=0.1 ,			UpperCAmelCase__				:     List[str]=512 ,			UpperCAmelCase__				:     str=16 ,			UpperCAmelCase__				:     Union[str, Any]=2 ,			UpperCAmelCase__				:     List[Any]=0.02 ,			UpperCAmelCase__				:     int=6 ,			UpperCAmelCase__				:     Optional[Any]=6 ,			UpperCAmelCase__				:     Optional[Any]=3 ,			UpperCAmelCase__				:     Union[str, Any]=4 ,			UpperCAmelCase__				:     List[Any]=None ,			UpperCAmelCase__				:     Tuple=1_000 ,			)						->Union[str, Any]:





           '''simple docstring'''

           A__							=   parent
           A__							=   batch_size
           A__							=   num_channels
           A__							=   image_size
           A__							=   patch_size
           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__							=   coordinate_size
           A__							=   shape_size
           A__							=   num_labels
           A__							=   num_choices
           A__							=   scope
           A__							=   range_bbox

           # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
           A__							=   text_seq_length
           A__							=   (image_size // patch_size) ** 2 + 1
           A__							=   self.text_seq_length + self.image_seq_length


    def 	SCREAMING_SNAKE_CASE							(      self				:     int)						->str:





           '''simple docstring'''

           A__							=   ids_tensor([self.batch_size, self.text_seq_length] ,			self.vocab_size)

           A__							=   ids_tensor([self.batch_size, self.text_seq_length, 4] ,			self.range_bbox)
           A__							=   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]:
                                A__							=   bbox[i, j, 3]
                                A__							=   bbox[i, j, 1]
                                A__							=   tmp_coordinate
                         if bbox[i, j, 2] < bbox[i, j, 0]:
                                A__							=   bbox[i, j, 2]
                                A__							=   bbox[i, j, 0]
                                A__							=   tmp_coordinate
           A__							=   tf.constant(UpperCAmelCase__)

           A__							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

           A__							=   None
           if self.use_input_mask:
                  A__							=   random_attention_mask([self.batch_size, self.text_seq_length])

           A__							=   None
           if self.use_token_type_ids:
                  A__							=   ids_tensor([self.batch_size, self.text_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.text_seq_length] ,			self.num_labels)

           A__							=   LayoutLMvaConfig(
               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 ,			coordinate_size=self.coordinate_size ,			shape_size=self.shape_size ,			input_size=self.image_size ,			patch_size=self.patch_size ,			)

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     Tuple)						->Any:





           '''simple docstring'''

           A__							=   TFLayoutLMvaModel(config=UpperCAmelCase__)

           # text + image
           A__							=   model(UpperCAmelCase__ ,			pixel_values=UpperCAmelCase__ ,			training=UpperCAmelCase__)
           A__							=   model(
               UpperCAmelCase__ ,			bbox=UpperCAmelCase__ ,			pixel_values=UpperCAmelCase__ ,			attention_mask=UpperCAmelCase__ ,			token_type_ids=UpperCAmelCase__ ,			training=UpperCAmelCase__ ,			)
           A__							=   model(UpperCAmelCase__ ,			bbox=UpperCAmelCase__ ,			pixel_values=UpperCAmelCase__ ,			training=UpperCAmelCase__)

           self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size))

           # text only
           A__							=   model(UpperCAmelCase__ ,			training=UpperCAmelCase__)
           self.parent.assertEqual(
               result.last_hidden_state.shape ,			(self.batch_size, self.text_seq_length, self.hidden_size))

           # image only
           A__							=   model({'''pixel_values''': pixel_values} ,			training=UpperCAmelCase__)
           self.parent.assertEqual(
               result.last_hidden_state.shape ,			(self.batch_size, self.image_seq_length, self.hidden_size))


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     Optional[Any])						->str:





           '''simple docstring'''

           A__							=   self.num_labels
           A__							=   TFLayoutLMvaForSequenceClassification(config=UpperCAmelCase__)
           A__							=   model(
               UpperCAmelCase__ ,			bbox=UpperCAmelCase__ ,			pixel_values=UpperCAmelCase__ ,			attention_mask=UpperCAmelCase__ ,			token_type_ids=UpperCAmelCase__ ,			labels=UpperCAmelCase__ ,			training=UpperCAmelCase__ ,			)
           self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.num_labels))


    def 	SCREAMING_SNAKE_CASE							(      self				:     int ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     Tuple)						->Tuple:





           '''simple docstring'''

           A__							=   self.num_labels
           A__							=   TFLayoutLMvaForTokenClassification(config=UpperCAmelCase__)
           A__							=   model(
               UpperCAmelCase__ ,			bbox=UpperCAmelCase__ ,			pixel_values=UpperCAmelCase__ ,			attention_mask=UpperCAmelCase__ ,			token_type_ids=UpperCAmelCase__ ,			labels=UpperCAmelCase__ ,			training=UpperCAmelCase__ ,			)
           self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.text_seq_length, self.num_labels))


    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     List[Any])						->List[Any]:





           '''simple docstring'''

           A__							=   2
           A__							=   TFLayoutLMvaForQuestionAnswering(config=UpperCAmelCase__)
           A__							=   model(
               UpperCAmelCase__ ,			bbox=UpperCAmelCase__ ,			pixel_values=UpperCAmelCase__ ,			attention_mask=UpperCAmelCase__ ,			token_type_ids=UpperCAmelCase__ ,			start_positions=UpperCAmelCase__ ,			end_positions=UpperCAmelCase__ ,			training=UpperCAmelCase__ ,			)
           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[str])						->Optional[Any]:





           '''simple docstring'''

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


@require_tf
class     UpperCamelCase_   (			snake_case_		, snake_case_		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				(
        (
            TFLayoutLMvaModel,
            TFLayoutLMvaForQuestionAnswering,
            TFLayoutLMvaForSequenceClassification,
            TFLayoutLMvaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    UpperCAmelCase__							  =				(
        {'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
        if is_tf_available()
        else {}
    )

    UpperCAmelCase__							  =				False
    UpperCAmelCase__							  =				False
    UpperCAmelCase__							  =				False


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     Optional[int] ,			UpperCAmelCase__				:     List[Any])						->Any:





           '''simple docstring'''

           return True


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     str=False)						->dict:





           '''simple docstring'''

           A__							=   copy.deepcopy(UpperCAmelCase__)

           if model_class in get_values(UpperCAmelCase__):
                  A__							=   {
                      k: tf.tile(tf.expand_dims(UpperCAmelCase__ ,			1) ,			(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1))
                      if isinstance(UpperCAmelCase__ ,			tf.Tensor) and v.ndim > 0
                      else v
                      for k, v in inputs_dict.items()
                  }

           if return_labels:
                  if model_class in get_values(UpperCAmelCase__):
                         A__							=   tf.ones(self.model_tester.batch_size ,			dtype=tf.intaa)
                  elif model_class in get_values(UpperCAmelCase__):
                         A__							=   tf.zeros(self.model_tester.batch_size ,			dtype=tf.intaa)
                         A__							=   tf.zeros(self.model_tester.batch_size ,			dtype=tf.intaa)
                  elif model_class in get_values(UpperCAmelCase__):
                         A__							=   tf.zeros(self.model_tester.batch_size ,			dtype=tf.intaa)
                  elif model_class in get_values(UpperCAmelCase__):
                         A__							=   tf.zeros(
                             (self.model_tester.batch_size, self.model_tester.text_seq_length) ,			dtype=tf.intaa)

           return inputs_dict


    def 	SCREAMING_SNAKE_CASE							(      self				:     int)						->int:





           '''simple docstring'''

           A__							=   TFLayoutLMvaModelTester(self)
           A__							=   ConfigTester(self ,			config_class=UpperCAmelCase__ ,			hidden_size=37)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->Dict:





           '''simple docstring'''

           self.config_tester.run_common_tests()


    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->Optional[Any]:





           '''simple docstring'''

           A__			,							A__							=   self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                  A__							=   model_class(UpperCAmelCase__)
                  if getattr(UpperCAmelCase__ ,			'''hf_compute_loss''' ,			UpperCAmelCase__):
                         # The number of elements in the loss should be the same as the number of elements in the label
                         A__							=   self._prepare_for_class(inputs_dict.copy() ,			UpperCAmelCase__ ,			return_labels=UpperCAmelCase__)
                         A__							=   prepared_for_class[
                             sorted(prepared_for_class.keys() - inputs_dict.keys() ,			reverse=UpperCAmelCase__)[0]
                         ]
                         A__							=   added_label.shape.as_list()[:1]

                         # Test that model correctly compute the loss with kwargs
                         A__							=   self._prepare_for_class(inputs_dict.copy() ,			UpperCAmelCase__ ,			return_labels=UpperCAmelCase__)
                         A__							=   prepared_for_class.pop('''input_ids''')

                         A__							=   model(UpperCAmelCase__ ,			**UpperCAmelCase__)[0]
                         self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])

                         # Test that model correctly compute the loss when we mask some positions
                         A__							=   self._prepare_for_class(inputs_dict.copy() ,			UpperCAmelCase__ ,			return_labels=UpperCAmelCase__)
                         A__							=   prepared_for_class.pop('''input_ids''')
                         if "labels" in prepared_for_class:
                                A__							=   prepared_for_class['''labels'''].numpy()
                                if len(labels.shape) > 1 and labels.shape[1] != 1:
                                       A__							=   -100
                                       A__							=   tf.convert_to_tensor(UpperCAmelCase__)
                                       A__							=   model(UpperCAmelCase__ ,			**UpperCAmelCase__)[0]
                                       self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
                                       self.assertTrue(not np.any(np.isnan(loss.numpy())))

                # Test that model correctly compute the loss with a dict
                         A__							=   self._prepare_for_class(inputs_dict.copy() ,			UpperCAmelCase__ ,			return_labels=UpperCAmelCase__)
                         A__							=   model(UpperCAmelCase__)[0]
                         self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])

                         # Test that model correctly compute the loss with a tuple
                         A__							=   self._prepare_for_class(inputs_dict.copy() ,			UpperCAmelCase__ ,			return_labels=UpperCAmelCase__)

                         # Get keys that were added with the _prepare_for_class function
                         A__							=   prepared_for_class.keys() - inputs_dict.keys()
                         A__							=   inspect.signature(model.call).parameters
                         A__							=   list(signature.keys())

                         # Create a dictionary holding the location of the tensors in the tuple
                         A__							=   {0: '''input_ids'''}
                         for label_key in label_keys:
                                A__							=   signature_names.index(UpperCAmelCase__)
                                A__							=   label_key
                         A__							=   sorted(tuple_index_mapping.items())
                         # Initialize a list with their default values, update the values and convert to a tuple
                         A__							=   []

                         for name in signature_names:
                                if name != "kwargs":
                                       list_input.append(signature[name].default)

                         for index, value in sorted_tuple_index_mapping:
                                A__							=   prepared_for_class[value]

                         A__							=   tuple(UpperCAmelCase__)

                         # Send to model
                         A__							=   model(tuple_input[:-1])[0]

                         self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->List[Any]:





           '''simple docstring'''

           (
               (
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							   
           )							=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->Any:





           '''simple docstring'''

           (
               (
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               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(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Any:





           '''simple docstring'''

           (
               (
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							   
           )							=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(
               UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->List[str]:





           '''simple docstring'''

           (
               (
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							   
           )							=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(
               UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->List[str]:





           '''simple docstring'''

           (
               (
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							(
               A__   
           )			,							   
           )							=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(
               UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)





    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Any:





           '''simple docstring'''

           for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A__							=   TFLayoutLMvaModel.from_pretrained(UpperCAmelCase__)
                  self.assertIsNotNone(UpperCAmelCase__)







def SCREAMING_SNAKE_CASE    (	)       ->	List[str]:



       """simple docstring"""

       A__							=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
       return image


@require_tf
class     UpperCamelCase_   (			unittest.TestCase					):

    '''simple docstring'''






    @cached_property
    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Optional[Any]:





           '''simple docstring'''

           return LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase__) if is_vision_available() else None





    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->List[str]:





           '''simple docstring'''

           A__							=   TFLayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base''')

           A__							=   self.default_image_processor
           A__							=   prepare_img()
           A__							=   image_processor(images=UpperCAmelCase__ ,			return_tensors='''tf''').pixel_values

           A__							=   tf.constant([[1, 2]])
           A__							=   tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]) ,			axis=0)

           # forward pass
           A__							=   model(input_ids=UpperCAmelCase__ ,			bbox=UpperCAmelCase__ ,			pixel_values=UpperCAmelCase__ ,			training=UpperCAmelCase__)

           # verify the logits
           A__							=   (1, 199, 768)
           self.assertEqual(outputs.last_hidden_state.shape ,			UpperCAmelCase__)

           A__							=   tf.constant(
               [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]])

           self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] ,			UpperCAmelCase__ ,			atol=1e-4))








from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     float)						->float:





           '''simple docstring'''

           return 0.0







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	tuple[int | float, int | float]:



       """simple docstring"""

       A__							=   min([-20, np.min(fft_results[1 : samplerate // 2 - 1]						)]						)
       A__							=   max([20, np.max(fft_results[1 : samplerate // 2 - 1]						)]						)
       return lowest, highest







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	None:



       """simple docstring"""

       A__							=   512
       A__							=   [1] + [0] * (size - 1)
       A__							=   [filter_type.process(lowercase_						) for item in inputs]

       A__							=   [0] * (samplerate - size)  # zero-padding
       outputs += filler
       A__							=   np.abs(np.fft.fft(lowercase_						)						)
       A__							=   20 * np.logaa(lowercase_						)

       # Frequencies on log scale from 24 to nyquist frequency
       plt.xlim(24			,    samplerate / 2 - 1						)
       plt.xlabel('''Frequency (Hz)'''						)
       plt.xscale('''log'''						)

       # Display within reasonable bounds
       A__							=   get_bounds(lowercase_			,    lowercase_						)
       plt.ylim(max([-80, bounds[0]]						)			,    min([80, bounds[1]]						)						)
       plt.ylabel('''Gain (dB)'''						)

       plt.plot(lowercase_						)
       plt.show()







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	None:



       """simple docstring"""

       A__							=   512
       A__							=   [1] + [0] * (size - 1)
       A__							=   [filter_type.process(lowercase_						) for item in inputs]

       A__							=   [0] * (samplerate - size)  # zero-padding
       outputs += filler
       A__							=   np.angle(np.fft.fft(lowercase_						)						)

       # Frequencies on log scale from 24 to nyquist frequency
       plt.xlim(24			,    samplerate / 2 - 1						)
       plt.xlabel('''Frequency (Hz)'''						)
       plt.xscale('''log'''						)

       plt.ylim(-2 * pi			,    2 * pi						)
       plt.ylabel('''Phase shift (Radians)'''						)
       plt.plot(np.unwrap(lowercase_			,    -2 * pi						)						)
       plt.show()


