




import os
import sys
import unittest


_lowerCamelCase     :		Any								=       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
_lowerCamelCase     :		Dict								=       os.path.join(git_repo_path, """src""", """transformers""")

_lowerCamelCase     :		Optional[Any]								=       """
{0} = None
"""

_lowerCamelCase     :		Dict								=       """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


_lowerCamelCase     :		Union[str, Any]								=       """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""


class     UpperCamelCase_   (			unittest.TestCase					):

    '''simple docstring'''






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





           '''simple docstring'''

           A__							=   find_backend('''    _import_structure["models.albert"].append("AlbertTokenizerFast")''')
           self.assertIsNone(UpperCAmelCase__)

           A__							=   find_backend('''    if not is_tokenizers_available():''')
           self.assertEqual(UpperCAmelCase__ ,			'''tokenizers''')

           A__							=   find_backend('''    if not is_tensorflow_text_available():''')
           self.assertEqual(UpperCAmelCase__ ,			'''tensorflow_text''')

           A__							=   find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):''')
           self.assertEqual(UpperCAmelCase__ ,			'''sentencepiece_and_tokenizers''')

           A__							=   find_backend(
               '''    if not (is_sentencepiece_available() and is_tensorflow_text_available()):''')
           self.assertEqual(UpperCAmelCase__ ,			'''sentencepiece_and_tensorflow_text''')

           A__							=   find_backend(
               '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):''')
           self.assertEqual(UpperCAmelCase__ ,			'''sentencepiece_and_tokenizers_and_vision''')


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Any:





           '''simple docstring'''

           A__							=   read_init()
           # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
           self.assertIn('''torch''' ,			UpperCAmelCase__)
           self.assertIn('''tensorflow_text''' ,			UpperCAmelCase__)
           self.assertIn('''sentencepiece_and_tokenizers''' ,			UpperCAmelCase__)

           # Likewise, we can't assert on the exact content of a key
           self.assertIn('''BertModel''' ,			objects['''torch'''])
           self.assertIn('''TFBertModel''' ,			objects['''tf'''])
           self.assertIn('''FlaxBertModel''' ,			objects['''flax'''])
           self.assertIn('''BertModel''' ,			objects['''torch'''])
           self.assertIn('''TFBertTokenizer''' ,			objects['''tensorflow_text'''])
           self.assertIn('''convert_slow_tokenizer''' ,			objects['''sentencepiece_and_tokenizers'''])


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





           '''simple docstring'''

           A__							=   create_dummy_object('''CONSTANT''' ,			'''\'torch\'''')
           self.assertEqual(UpperCAmelCase__ ,			'''\nCONSTANT = None\n''')

           A__							=   create_dummy_object('''function''' ,			'''\'torch\'''')
           self.assertEqual(
               UpperCAmelCase__ ,			'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n''')

           A__							=   '''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')
'''
           A__							=   create_dummy_object('''FakeClass''' ,			'''\'torch\'''')
           self.assertEqual(UpperCAmelCase__ ,			UpperCAmelCase__)





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





           '''simple docstring'''

           A__							=   '''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
'''
           A__							=   create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']})
           self.assertEqual(dummy_files['''torch'''] ,			UpperCAmelCase__)








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()


