




"""simple docstring"""




import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      LiltForQuestionAnswering,
      LiltForSequenceClassification,
      LiltForTokenClassification,
      LiltModel,
  )
  from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     Any ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     Tuple=13 ,			UpperCAmelCase__				:     str=7 ,			UpperCAmelCase__				:     List[str]=True ,			UpperCAmelCase__				:     List[Any]=True ,			UpperCAmelCase__				:     Optional[Any]=True ,			UpperCAmelCase__				:     List[Any]=True ,			UpperCAmelCase__				:     List[str]=99 ,			UpperCAmelCase__				:     Optional[Any]=24 ,			UpperCAmelCase__				:     Optional[Any]=2 ,			UpperCAmelCase__				:     int=6 ,			UpperCAmelCase__				:     Any=37 ,			UpperCAmelCase__				:     Any="gelu" ,			UpperCAmelCase__				:     List[Any]=0.1 ,			UpperCAmelCase__				:     Optional[int]=0.1 ,			UpperCAmelCase__				:     Dict=512 ,			UpperCAmelCase__				:     List[Any]=16 ,			UpperCAmelCase__				:     List[str]=2 ,			UpperCAmelCase__				:     List[Any]=0.02 ,			UpperCAmelCase__				:     List[str]=3 ,			UpperCAmelCase__				:     Any=None ,			UpperCAmelCase__				:     Dict=1_000 ,			)						->Any:





           '''simple docstring'''

           A__							=   parent
           A__							=   batch_size
           A__							=   seq_length
           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__							=   num_labels
           A__							=   scope
           A__							=   range_bbox


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





           '''simple docstring'''

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

           A__							=   ids_tensor([self.batch_size, self.seq_length, 4] ,			self.range_bbox)
           # Ensure that bbox is legal
           for i in range(bbox.shape[0]):
                  for j in range(bbox.shape[1]):
                         if bbox[i, j, 3] < bbox[i, j, 1]:
                                A__							=   bbox[i, j, 3]
                                A__							=   bbox[i, j, 1]
                                A__							=   t
                         if bbox[i, j, 2] < bbox[i, j, 0]:
                                A__							=   bbox[i, j, 2]
                                A__							=   bbox[i, j, 0]
                                A__							=   t

           A__							=   None
           if self.use_input_mask:
                  A__							=   ids_tensor([self.batch_size, self.seq_length] ,			vocab_size=2)

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

           A__							=   self.get_config()

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->str:





           '''simple docstring'''

           return LiltConfig(
               vocab_size=self.vocab_size ,			hidden_size=self.hidden_size ,			num_hidden_layers=self.num_hidden_layers ,			num_attention_heads=self.num_attention_heads ,			intermediate_size=self.intermediate_size ,			hidden_act=self.hidden_act ,			hidden_dropout_prob=self.hidden_dropout_prob ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob ,			max_position_embeddings=self.max_position_embeddings ,			type_vocab_size=self.type_vocab_size ,			initializer_range=self.initializer_range ,			)


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





           '''simple docstring'''

           A__							=   LiltModel(config=UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__							=   model(UpperCAmelCase__ ,			bbox=UpperCAmelCase__ ,			attention_mask=UpperCAmelCase__ ,			token_type_ids=UpperCAmelCase__)
           A__							=   model(UpperCAmelCase__ ,			bbox=UpperCAmelCase__ ,			token_type_ids=UpperCAmelCase__)
           A__							=   model(UpperCAmelCase__ ,			bbox=UpperCAmelCase__)

           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				:     List[Any] ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     Any ,			UpperCAmelCase__				:     Optional[Any] ,			)						->Any:





           '''simple docstring'''

           A__							=   self.num_labels
           A__							=   LiltForTokenClassification(config=UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__							=   model(
               UpperCAmelCase__ ,			bbox=UpperCAmelCase__ ,			attention_mask=UpperCAmelCase__ ,			token_type_ids=UpperCAmelCase__ ,			labels=UpperCAmelCase__)
           self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.num_labels))


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





           '''simple docstring'''

           A__							=   LiltForQuestionAnswering(config=UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__							=   model(
               UpperCAmelCase__ ,			bbox=UpperCAmelCase__ ,			attention_mask=UpperCAmelCase__ ,			token_type_ids=UpperCAmelCase__ ,			start_positions=UpperCAmelCase__ ,			end_positions=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				:     Union[str, Any])						->Tuple:





           '''simple docstring'''

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


@require_torch
class     UpperCamelCase_   (			UpperCAmelCase__		, UpperCAmelCase__		, UpperCAmelCase__		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				(
        (
            LiltModel,
            LiltForSequenceClassification,
            LiltForTokenClassification,
            LiltForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    UpperCAmelCase__							  =				(
        {
            "feature-extraction": LiltModel,
            "question-answering": LiltForQuestionAnswering,
            "text-classification": LiltForSequenceClassification,
            "token-classification": LiltForTokenClassification,
            "zero-shot": LiltForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    UpperCAmelCase__							  =				False
    UpperCAmelCase__							  =				False


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





           '''simple docstring'''

           return True


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





           '''simple docstring'''

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


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





           '''simple docstring'''

           self.config_tester.run_common_tests()


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Any:





           '''simple docstring'''

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


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





           '''simple docstring'''

           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__)


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





           '''simple docstring'''

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


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





           '''simple docstring'''

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





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





           '''simple docstring'''

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


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

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     int)						->str:





           '''simple docstring'''

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

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

           # forward pass
           with torch.no_grad():
                  A__							=   model(input_ids=UpperCAmelCase__ ,			bbox=UpperCAmelCase__)

           A__							=   torch.Size([1, 2, 768])
           A__							=   torch.tensor(
               [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]] ,			device=UpperCAmelCase__ ,			)

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








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()


