
'''simple docstring'''







import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
					from test_module.custom_tokenization_fast import CustomTokenizerFast


class 						__UpperCAmelCase      (					unittest.TestCase						):


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		mock.Mock()
								_snake_case   						=		5_00
								_snake_case   						=		{}
								_snake_case   						=		HTTPError
								_snake_case   						=		{}

								# Download this model to make sure it's in the cache.
								_snake_case   						=		BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'  )

								# Under the mock environment we get a 500 error when trying to reach the tokenizer.
								with mock.patch('requests.Session.request'    ,   return_value=lowerCAmelCase_  ) as mock_head:
									_snake_case   						=		BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'  )
									# This check we did call the fake head request
									mock_head.assert_called()


							@require_tokenizers
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		mock.Mock()
								_snake_case   						=		5_00
								_snake_case   						=		{}
								_snake_case   						=		HTTPError
								_snake_case   						=		{}

								# Download this model to make sure it's in the cache.
								_snake_case   						=		GPTaTokenizerFast.from_pretrained('gpt2'  )

								# Under the mock environment we get a 500 error when trying to reach the tokenizer.
								with mock.patch('requests.Session.request'    ,   return_value=lowerCAmelCase_  ) as mock_head:
									_snake_case   						=		GPTaTokenizerFast.from_pretrained('gpt2'  )
									# This check we did call the fake head request
									mock_head.assert_called()


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								try:
									_snake_case   						=		tempfile.mktemp()
									with open(lowerCAmelCase_    ,   'wb'  ) as f:
										http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'    ,   lowerCAmelCase_  )

									_snake_case   						=		AlbertTokenizer.from_pretrained(lowerCAmelCase_  )
								finally:
									os.remove(lowerCAmelCase_  )

								# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
								# the current folder and have the right name.
								if os.path.isfile('tokenizer.json'  ):
									# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
									return
								try:
									with open('tokenizer.json'    ,   'wb'  ) as f:
										http_get('https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json'    ,   lowerCAmelCase_  )
									_snake_case   						=		AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'  )
									# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
									self.assertEqual(tokenizer.vocab_size    ,   10_00  )
									# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

								finally:
									os.remove('tokenizer.json'  )




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'  )


@is_staging_test
class 						__UpperCAmelCase      (					unittest.TestCase						):
							__lowercase      		=       ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]


							@classmethod
							def  lowerCamelCase				(       cls  ):





								"""simple docstring"""






								_snake_case   						=		TOKEN
								HfFolder.save_token(lowerCAmelCase_  )


							@classmethod
							def  lowerCamelCase				(       cls  ):





								"""simple docstring"""






								try:
									delete_repo(token=cls._token    ,   repo_id='test-tokenizer'  )
								except HTTPError:
									pass

								try:
									delete_repo(token=cls._token    ,   repo_id='valid_org/test-tokenizer-org'  )
								except HTTPError:
									pass

								try:
									delete_repo(token=cls._token    ,   repo_id='test-dynamic-tokenizer'  )
								except HTTPError:
									pass


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								with tempfile.TemporaryDirectory() as tmp_dir:
									_snake_case   						=		os.path.join(lowerCAmelCase_    ,   'vocab.txt'  )
									with open(lowerCAmelCase_    ,   'w'    ,   encoding='utf-8'  ) as vocab_writer:
										vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]  )  )
									_snake_case   						=		BertTokenizer(lowerCAmelCase_  )

								tokenizer.push_to_hub('test-tokenizer'    ,   use_auth_token=self._token  )
								_snake_case   						=		BertTokenizer.from_pretrained(F'{USER}/test-tokenizer'  )
								self.assertDictEqual(new_tokenizer.vocab    ,   tokenizer.vocab  )

								# Reset repo
								delete_repo(token=self._token    ,   repo_id='test-tokenizer'  )

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer.save_pretrained(lowerCAmelCase_    ,   repo_id='test-tokenizer'    ,   push_to_hub=lowerCAmelCase_    ,   use_auth_token=self._token  )

								_snake_case   						=		BertTokenizer.from_pretrained(F'{USER}/test-tokenizer'  )
								self.assertDictEqual(new_tokenizer.vocab    ,   tokenizer.vocab  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								with tempfile.TemporaryDirectory() as tmp_dir:
									_snake_case   						=		os.path.join(lowerCAmelCase_    ,   'vocab.txt'  )
									with open(lowerCAmelCase_    ,   'w'    ,   encoding='utf-8'  ) as vocab_writer:
										vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]  )  )
									_snake_case   						=		BertTokenizer(lowerCAmelCase_  )

								tokenizer.push_to_hub('valid_org/test-tokenizer-org'    ,   use_auth_token=self._token  )
								_snake_case   						=		BertTokenizer.from_pretrained('valid_org/test-tokenizer-org'  )
								self.assertDictEqual(new_tokenizer.vocab    ,   tokenizer.vocab  )

								# Reset repo
								delete_repo(token=self._token    ,   repo_id='valid_org/test-tokenizer-org'  )

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer.save_pretrained(
									    lowerCAmelCase_    ,   repo_id='valid_org/test-tokenizer-org'    ,   push_to_hub=lowerCAmelCase_    ,   use_auth_token=self._token  )

								_snake_case   						=		BertTokenizer.from_pretrained('valid_org/test-tokenizer-org'  )
								self.assertDictEqual(new_tokenizer.vocab    ,   tokenizer.vocab  )




							@require_tokenizers
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								CustomTokenizer.register_for_auto_class()
								with tempfile.TemporaryDirectory() as tmp_dir:
									_snake_case   						=		os.path.join(lowerCAmelCase_    ,   'vocab.txt'  )
									with open(lowerCAmelCase_    ,   'w'    ,   encoding='utf-8'  ) as vocab_writer:
										vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]  )  )
									_snake_case   						=		CustomTokenizer(lowerCAmelCase_  )

								# No fast custom tokenizer
								tokenizer.push_to_hub('test-dynamic-tokenizer'    ,   use_auth_token=self._token  )

								_snake_case   						=		AutoTokenizer.from_pretrained(F'{USER}/test-dynamic-tokenizer'    ,   trust_remote_code=lowerCAmelCase_  )
								# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
								self.assertEqual(tokenizer.__class__.__name__    ,   'CustomTokenizer'  )

								# Fast and slow custom tokenizer
								CustomTokenizerFast.register_for_auto_class()
								with tempfile.TemporaryDirectory() as tmp_dir:
									_snake_case   						=		os.path.join(lowerCAmelCase_    ,   'vocab.txt'  )
									with open(lowerCAmelCase_    ,   'w'    ,   encoding='utf-8'  ) as vocab_writer:
										vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]  )  )

									_snake_case   						=		BertTokenizerFast.from_pretrained(lowerCAmelCase_  )
									bert_tokenizer.save_pretrained(lowerCAmelCase_  )
									_snake_case   						=		CustomTokenizerFast.from_pretrained(lowerCAmelCase_  )

								tokenizer.push_to_hub('test-dynamic-tokenizer'    ,   use_auth_token=self._token  )

								_snake_case   						=		AutoTokenizer.from_pretrained(F'{USER}/test-dynamic-tokenizer'    ,   trust_remote_code=lowerCAmelCase_  )
								# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
								self.assertEqual(tokenizer.__class__.__name__    ,   'CustomTokenizerFast'  )
								_snake_case   						=		AutoTokenizer.from_pretrained(
								    F'{USER}/test-dynamic-tokenizer'    ,   use_fast=lowerCAmelCase_    ,   trust_remote_code=lowerCAmelCase_  )
								# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
								self.assertEqual(tokenizer.__class__.__name__    ,   'CustomTokenizer'  )


class 						__UpperCAmelCase      (					unittest.TestCase						):


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		Trie()
								trie.add('Hello 友達'  )
								self.assertEqual(trie.data    ,   {'H': {'e': {'l': {'l': {'o': {' ': {'友': {'達': {'': 1}}}}}}}}}  )
								trie.add('Hello'  )
								trie.data
								self.assertEqual(trie.data    ,   {'H': {'e': {'l': {'l': {'o': {'': 1, ' ': {'友': {'達': {'': 1}}}}}}}}}  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		Trie()
								self.assertEqual(trie.split('[CLS] This is a extra_id_100'  )    ,   ['[CLS] This is a extra_id_100']  )
								trie.add('[CLS]'  )
								trie.add('extra_id_1'  )
								trie.add('extra_id_100'  )
								self.assertEqual(trie.split('[CLS] This is a extra_id_100'  )    ,   ['[CLS]', ' This is a ', 'extra_id_100']  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		Trie()
								trie.add('A'  )
								self.assertEqual(trie.split('ABC'  )    ,   ['A', 'BC']  )
								self.assertEqual(trie.split('BCA'  )    ,   ['BC', 'A']  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		Trie()
								trie.add('TOKEN]'  )
								trie.add('[SPECIAL_TOKEN]'  )
								self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]'  )    ,   ['This is something ', '[SPECIAL_TOKEN]']  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		Trie()
								trie.add('A'  )
								trie.add('P'  )
								trie.add('[SPECIAL_TOKEN]'  )
								self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]'  )    ,   ['This is something ', '[SPECIAL_TOKEN]']  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		Trie()
								trie.add('AB'  )
								trie.add('B'  )
								trie.add('C'  )
								self.assertEqual(trie.split('ABC'  )    ,   ['AB', 'C']  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		Trie()
								trie.add('ABC'  )
								trie.add('B'  )
								trie.add('CD'  )
								self.assertEqual(trie.split('ABCD'  )    ,   ['ABC', 'D']  )




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		Trie()
								_snake_case   						=		trie.cut_text('ABC'    ,   [0, 0, 2, 1, 2, 3]  )
								self.assertEqual(lowerCAmelCase_    ,   ['AB', 'C']  )



'''simple docstring'''







from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np


class 						__UpperCAmelCase      (					_lowerCamelCase						):


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								return 0.0







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	tuple[int | float, int | float]:
	_snake_case   						=		min([-20, np.min(fft_results[1 : samplerate // 2 - 1]	)]	)
	_snake_case   						=		max([20, np.max(fft_results[1 : samplerate // 2 - 1]	)]	)
	return lowest, highest







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	None:
	_snake_case   						=		512
	_snake_case   						=		[1] + [0] * (size - 1)
	_snake_case   						=		[filter_type.process(__A	) for item in inputs]

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

	# 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
	_snake_case   						=		get_bounds(__A			,				__A	)
	plt.ylim(max([-80, bounds[0]]	)			,				min([80, bounds[1]]	)	)
	plt.ylabel('Gain (dB)'	)

	plt.plot(__A	)
	plt.show()







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	None:
	_snake_case   						=		512
	_snake_case   						=		[1] + [0] * (size - 1)
	_snake_case   						=		[filter_type.process(__A	) for item in inputs]

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

	# 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(__A			,				-2 * pi	)	)
	plt.show()

