




"""simple docstring"""




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import (
      AutoProcessor,
      BertTokenizerFast,
      BlipImageProcessor,
      GPTaTokenizer,
      InstructBlipProcessor,
      PreTrainedTokenizerFast,
  )


@require_vision
class     UpperCamelCase_   (			unittest.TestCase					):

    '''simple docstring'''






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





           '''simple docstring'''

           A__							=   tempfile.mkdtemp()

           A__							=   BlipImageProcessor()
           A__							=   GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model''')
           A__							=   BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert''')

           A__							=   InstructBlipProcessor(snake_case__ ,			snake_case__ ,			snake_case__)

           processor.save_pretrained(self.tmpdirname)


    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			**UpperCAmelCase__				:     Dict)						->Tuple:





           '''simple docstring'''

           return AutoProcessor.from_pretrained(self.tmpdirname ,			**snake_case__).tokenizer


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





           '''simple docstring'''

           return AutoProcessor.from_pretrained(self.tmpdirname ,			**snake_case__).image_processor


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict ,			**UpperCAmelCase__				:     int)						->Tuple:





           '''simple docstring'''

           return AutoProcessor.from_pretrained(self.tmpdirname ,			**snake_case__).qformer_tokenizer


    def 	SCREAMING_SNAKE_CASE							(      self				:     int)						->int:





           '''simple docstring'''

           shutil.rmtree(self.tmpdirname)


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





           '''simple docstring'''

           A__							=   [np.random.randint(255 ,			size=(3, 30, 400) ,			dtype=np.uinta)]

           A__							=   [Image.fromarray(np.moveaxis(snake_case__ ,			0 ,			-1)) for x in image_inputs]

           return image_inputs


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Dict:





           '''simple docstring'''

           A__							=   InstructBlipProcessor(
               tokenizer=self.get_tokenizer() ,			image_processor=self.get_image_processor() ,			qformer_tokenizer=self.get_qformer_tokenizer() ,			)
           processor.save_pretrained(self.tmpdirname)

           A__							=   self.get_tokenizer(bos_token='''(BOS)''' ,			eos_token='''(EOS)''')
           A__							=   self.get_image_processor(do_normalize=snake_case__ ,			padding_value=1.0)

           A__							=   InstructBlipProcessor.from_pretrained(
               self.tmpdirname ,			bos_token='''(BOS)''' ,			eos_token='''(EOS)''' ,			do_normalize=snake_case__ ,			padding_value=1.0)

           self.assertEqual(processor.tokenizer.get_vocab() ,			tokenizer_add_kwargs.get_vocab())
           self.assertIsInstance(processor.tokenizer ,			snake_case__)

           self.assertEqual(processor.image_processor.to_json_string() ,			image_processor_add_kwargs.to_json_string())
           self.assertIsInstance(processor.image_processor ,			snake_case__)
           self.assertIsInstance(processor.qformer_tokenizer ,			snake_case__)


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





           '''simple docstring'''

           A__							=   self.get_image_processor()
           A__							=   self.get_tokenizer()
           A__							=   self.get_qformer_tokenizer()

           A__							=   InstructBlipProcessor(
               tokenizer=snake_case__ ,			image_processor=snake_case__ ,			qformer_tokenizer=snake_case__)

           A__							=   self.prepare_image_inputs()

           A__							=   image_processor(snake_case__ ,			return_tensors='''np''')
           A__							=   processor(images=snake_case__ ,			return_tensors='''np''')

           for key in input_feat_extract.keys():
                  self.assertAlmostEqual(input_feat_extract[key].sum() ,			input_processor[key].sum() ,			delta=1e-2)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->int:





           '''simple docstring'''

           A__							=   self.get_image_processor()
           A__							=   self.get_tokenizer()
           A__							=   self.get_qformer_tokenizer()

           A__							=   InstructBlipProcessor(
               tokenizer=snake_case__ ,			image_processor=snake_case__ ,			qformer_tokenizer=snake_case__)

           A__							=   'lower newer'

           A__							=   processor(text=snake_case__)

           A__							=   tokenizer(snake_case__ ,			return_token_type_ids=snake_case__)
           A__							=   qformer_tokenizer(snake_case__ ,			return_token_type_ids=snake_case__)

           for key in encoded_tokens.keys():
                  self.assertListEqual(encoded_tokens[key] ,			encoded_processor[key])

           for key in encoded_tokens_qformer.keys():
                  self.assertListEqual(encoded_tokens_qformer[key] ,			encoded_processor['''qformer_''' + key])


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





           '''simple docstring'''

           A__							=   self.get_image_processor()
           A__							=   self.get_tokenizer()
           A__							=   self.get_qformer_tokenizer()

           A__							=   InstructBlipProcessor(
               tokenizer=snake_case__ ,			image_processor=snake_case__ ,			qformer_tokenizer=snake_case__)

           A__							=   'lower newer'
           A__							=   self.prepare_image_inputs()

           A__							=   processor(text=snake_case__ ,			images=snake_case__)

           self.assertListEqual(
               list(inputs.keys()) ,			['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values'''] ,			)

           # test if it raises when no input is passed
           with pytest.raises(snake_case__):
                  processor()


    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->str:





           '''simple docstring'''

           A__							=   self.get_image_processor()
           A__							=   self.get_tokenizer()
           A__							=   self.get_qformer_tokenizer()

           A__							=   InstructBlipProcessor(
               tokenizer=snake_case__ ,			image_processor=snake_case__ ,			qformer_tokenizer=snake_case__)

           A__							=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           A__							=   processor.batch_decode(snake_case__)
           A__							=   tokenizer.batch_decode(snake_case__)

           self.assertListEqual(snake_case__ ,			snake_case__)





    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->str:





           '''simple docstring'''

           A__							=   self.get_image_processor()
           A__							=   self.get_tokenizer()
           A__							=   self.get_qformer_tokenizer()

           A__							=   InstructBlipProcessor(
               tokenizer=snake_case__ ,			image_processor=snake_case__ ,			qformer_tokenizer=snake_case__)

           A__							=   'lower newer'
           A__							=   self.prepare_image_inputs()

           A__							=   processor(text=snake_case__ ,			images=snake_case__)

           self.assertListEqual(
               list(inputs.keys()) ,			['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values'''] ,			)








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()


