
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
					from test_module.custom_tokenization_fast import CustomTokenizerFast


class 				lowercase					(		unittest.TestCase   ):



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     0



		@slow
		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
						UpperCamelCase		=     AutoTokenizer.from_pretrained(A_       )
						self.assertIsNotNone(A_       )
						self.assertIsInstance(A_				,      (BertTokenizer, BertTokenizerFast)       )
						self.assertGreater(len(A_       )				,      0       )

				for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
						UpperCamelCase		=     AutoTokenizer.from_pretrained(A_       )
						self.assertIsNotNone(A_       )
						self.assertIsInstance(A_				,      (GPTaTokenizer, GPTaTokenizerFast)       )
						self.assertGreater(len(A_       )				,      0       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     AutoTokenizer.from_pretrained(A_       )
				self.assertIsInstance(A_				,      (BertTokenizer, BertTokenizerFast)       )
				self.assertEqual(tokenizer.vocab_size				,      12       )



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     AutoTokenizer.from_pretrained(A_       )
				self.assertIsInstance(A_				,      (RobertaTokenizer, RobertaTokenizerFast)       )
				self.assertEqual(tokenizer.vocab_size				,      20       )



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     AutoConfig.from_pretrained(A_       )
				self.assertIsInstance(A_				,      A_       )
				# Check that tokenizer_type ≠ model_type
				UpperCamelCase		=     AutoTokenizer.from_pretrained(A_				,      config=A_       )
				self.assertIsInstance(A_				,      (BertTokenizer, BertTokenizerFast)       )
				self.assertEqual(tokenizer.vocab_size				,      12       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				with tempfile.TemporaryDirectory() as tmp_dir:
						shutil.copy('./tests/fixtures/vocab.txt'				,      os.path.join(A_				,      'vocab.txt'       )       )

						UpperCamelCase		=     AutoTokenizer.from_pretrained(A_				,      tokenizer_type='bert'				,      use_fast=A_       )
						self.assertIsInstance(A_				,      A_       )

				with tempfile.TemporaryDirectory() as tmp_dir:
						shutil.copy('./tests/fixtures/vocab.json'				,      os.path.join(A_				,      'vocab.json'       )       )
						shutil.copy('./tests/fixtures/merges.txt'				,      os.path.join(A_				,      'merges.txt'       )       )

						UpperCamelCase		=     AutoTokenizer.from_pretrained(A_				,      tokenizer_type='gpt2'				,      use_fast=A_       )
						self.assertIsInstance(A_				,      A_       )



		@require_tokenizers
		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				with tempfile.TemporaryDirectory() as tmp_dir:
						shutil.copy('./tests/fixtures/vocab.txt'				,      os.path.join(A_				,      'vocab.txt'       )       )

						UpperCamelCase		=     AutoTokenizer.from_pretrained(A_				,      tokenizer_type='bert'       )
						self.assertIsInstance(A_				,      A_       )

				with tempfile.TemporaryDirectory() as tmp_dir:
						shutil.copy('./tests/fixtures/vocab.json'				,      os.path.join(A_				,      'vocab.json'       )       )
						shutil.copy('./tests/fixtures/merges.txt'				,      os.path.join(A_				,      'merges.txt'       )       )

						UpperCamelCase		=     AutoTokenizer.from_pretrained(A_				,      tokenizer_type='gpt2'       )
						self.assertIsInstance(A_				,      A_       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				with pytest.raises(A_       ):
						AutoTokenizer.from_pretrained('./'				,      tokenizer_type='xxx'       )



		@require_tokenizers
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
						UpperCamelCase		=     tokenizer_class.from_pretrained('wietsedv/bert-base-dutch-cased'       )
						self.assertIsInstance(A_				,      (BertTokenizer, BertTokenizerFast)       )

						if isinstance(A_				,      A_       ):
								self.assertEqual(tokenizer.basic_tokenizer.do_lower_case				,      A_       )
						else:
								self.assertEqual(tokenizer.do_lower_case				,      A_       )

						self.assertEqual(tokenizer.model_max_length				,      512       )



		@require_tokenizers
		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
						with self.assertRaisesRegex(
						    A_				,      'julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier'				,      ):
								UpperCamelCase		=     tokenizer_class.from_pretrained('julien-c/herlolip-not-exists'       )



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				# tests: https://github.com/huggingface/transformers/pull/13251
				# 1. models with `-`, e.g. xlm-roberta -> xlm_roberta
				# 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai
				UpperCamelCase		=     TOKENIZER_MAPPING.values()
				UpperCamelCase		=     []

				for slow_tok, fast_tok in tokenizers:
						if slow_tok is not None:
								tokenizer_names.append(slow_tok.__name__       )

						if fast_tok is not None:
								tokenizer_names.append(fast_tok.__name__       )

				for tokenizer_name in tokenizer_names:
						# must find the right class
						tokenizer_class_from_name(A_       )



		@require_tokenizers
		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				self.assertIsInstance(AutoTokenizer.from_pretrained('bert-base-cased'				,      use_fast=A_       )				,      A_       )
				self.assertIsInstance(AutoTokenizer.from_pretrained('bert-base-cased'       )				,      A_       )



		@require_tokenizers
		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     AutoTokenizer.from_pretrained('distilbert-base-uncased'				,      do_lower_case=A_       )
				UpperCamelCase		=     'Hello, world. How are you?'
				UpperCamelCase		=     tokenizer.tokenize(A_       )
				self.assertEqual('[UNK]'				,      tokens[0]       )

				UpperCamelCase		=     AutoTokenizer.from_pretrained('microsoft/mpnet-base'				,      do_lower_case=A_       )
				UpperCamelCase		=     tokenizer.tokenize(A_       )
				self.assertEqual('[UNK]'				,      tokens[0]       )



		@require_tokenizers
		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     AutoTokenizer.from_pretrained('robot-test/dummy-tokenizer-fast-with-model-config'       )
				self.assertEqual(type(A_       )				,      A_       )
				self.assertEqual(tokenizer.model_max_length				,      512       )
				self.assertEqual(tokenizer.vocab_size				,      30_000       )
				self.assertEqual(tokenizer.unk_token				,      '[UNK]'       )
				self.assertEqual(tokenizer.padding_side				,      'right'       )
				self.assertEqual(tokenizer.truncation_side				,      'right'       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     AutoTokenizer.from_pretrained(A_       )
				self.assertIsInstance(A_				,      (BertTokenizer, BertTokenizerFast)       )
				with tempfile.TemporaryDirectory() as tmp_dir:
						tokenizer.save_pretrained(A_       )
						UpperCamelCase		=     AutoTokenizer.from_pretrained(A_       )

				self.assertIsInstance(A_				,      tokenizer.__class__       )
				self.assertEqual(tokenizera.vocab_size				,      12       )



		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     AutoTokenizer.from_pretrained('ctrl'       )
				# There is no fast CTRL so this always gives us a slow tokenizer.
				self.assertIsInstance(A_				,      A_       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				# Check we can load the tokenizer config of an online model.
				UpperCamelCase		=     get_tokenizer_config('bert-base-cased'       )
				UpperCamelCase		=     config.pop('_commit_hash'				,      A_       )
				# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
				self.assertEqual(A_				,      {'do_lower_case': False}       )

				# This model does not have a tokenizer_config so we get back an empty dict.
				UpperCamelCase		=     get_tokenizer_config(A_       )
				self.assertDictEqual(A_				,      {}       )

				# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
				UpperCamelCase		=     AutoTokenizer.from_pretrained(A_       )
				with tempfile.TemporaryDirectory() as tmp_dir:
						tokenizer.save_pretrained(A_       )
						UpperCamelCase		=     get_tokenizer_config(A_       )

				# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
				self.assertEqual(config['tokenizer_class']				,      'BertTokenizer'       )



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				try:
						AutoConfig.register('custom'				,      A_       )

						AutoTokenizer.register(A_				,      slow_tokenizer_class=A_       )
						# Trying to register something existing in the Transformers library will raise an error
						with self.assertRaises(A_       ):
								AutoTokenizer.register(A_				,      slow_tokenizer_class=A_       )

						UpperCamelCase		=     CustomTokenizer.from_pretrained(A_       )
						with tempfile.TemporaryDirectory() as tmp_dir:
								tokenizer.save_pretrained(A_       )

								UpperCamelCase		=     AutoTokenizer.from_pretrained(A_       )
								self.assertIsInstance(A_				,      A_       )

				finally:
						if "custom" in CONFIG_MAPPING._extra_content:
								del CONFIG_MAPPING._extra_content["custom"]
						if CustomConfig in TOKENIZER_MAPPING._extra_content:
								del TOKENIZER_MAPPING._extra_content[CustomConfig]



		@require_tokenizers
		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				try:
						AutoConfig.register('custom'				,      A_       )

						# Can register in two steps
						AutoTokenizer.register(A_				,      slow_tokenizer_class=A_       )
						self.assertEqual(TOKENIZER_MAPPING[CustomConfig]				,      (CustomTokenizer, None)       )
						AutoTokenizer.register(A_				,      fast_tokenizer_class=A_       )
						self.assertEqual(TOKENIZER_MAPPING[CustomConfig]				,      (CustomTokenizer, CustomTokenizerFast)       )

						del TOKENIZER_MAPPING._extra_content[CustomConfig]
						# Can register in one step
						AutoTokenizer.register(
						    A_				,      slow_tokenizer_class=A_				,      fast_tokenizer_class=A_       )
						self.assertEqual(TOKENIZER_MAPPING[CustomConfig]				,      (CustomTokenizer, CustomTokenizerFast)       )

						# Trying to register something existing in the Transformers library will raise an error
						with self.assertRaises(A_       ):
								AutoTokenizer.register(A_				,      fast_tokenizer_class=A_       )

						# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
						# and that model does not have a tokenizer.json
						with tempfile.TemporaryDirectory() as tmp_dir:
								UpperCamelCase		=     BertTokenizerFast.from_pretrained(A_       )
								bert_tokenizer.save_pretrained(A_       )
								UpperCamelCase		=     CustomTokenizerFast.from_pretrained(A_       )

						with tempfile.TemporaryDirectory() as tmp_dir:
								tokenizer.save_pretrained(A_       )

								UpperCamelCase		=     AutoTokenizer.from_pretrained(A_       )
								self.assertIsInstance(A_				,      A_       )

								UpperCamelCase		=     AutoTokenizer.from_pretrained(A_				,      use_fast=A_       )
								self.assertIsInstance(A_				,      A_       )

				finally:
						if "custom" in CONFIG_MAPPING._extra_content:
								del CONFIG_MAPPING._extra_content["custom"]
						if CustomConfig in TOKENIZER_MAPPING._extra_content:
								del TOKENIZER_MAPPING._extra_content[CustomConfig]



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				# If remote code is not set, we will time out when asking whether to load the model.
				with self.assertRaises(A_       ):
						UpperCamelCase		=     AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'       )
				# If remote code is disabled, we can't load this config.
				with self.assertRaises(A_       ):
						UpperCamelCase		=     AutoTokenizer.from_pretrained(
						    'hf-internal-testing/test_dynamic_tokenizer'				,      trust_remote_code=A_       )

				UpperCamelCase		=     AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'				,      trust_remote_code=A_       )
				self.assertTrue(tokenizer.special_attribute_present       )
				# Test tokenizer can be reloaded.
				with tempfile.TemporaryDirectory() as tmp_dir:
						tokenizer.save_pretrained(A_       )
						UpperCamelCase		=     AutoTokenizer.from_pretrained(A_				,      trust_remote_code=A_       )
				self.assertTrue(reloaded_tokenizer.special_attribute_present       )

				if is_tokenizers_available():
						self.assertEqual(tokenizer.__class__.__name__				,      'NewTokenizerFast'       )
						self.assertEqual(reloaded_tokenizer.__class__.__name__				,      'NewTokenizerFast'       )

						# Test we can also load the slow version
						UpperCamelCase		=     AutoTokenizer.from_pretrained(
						    'hf-internal-testing/test_dynamic_tokenizer'				,      trust_remote_code=A_				,      use_fast=A_       )
						self.assertTrue(tokenizer.special_attribute_present       )
						self.assertEqual(tokenizer.__class__.__name__				,      'NewTokenizer'       )
						# Test tokenizer can be reloaded.
						with tempfile.TemporaryDirectory() as tmp_dir:
								tokenizer.save_pretrained(A_       )
								UpperCamelCase		=     AutoTokenizer.from_pretrained(A_				,      trust_remote_code=A_				,      use_fast=A_       )
						self.assertEqual(reloaded_tokenizer.__class__.__name__				,      'NewTokenizer'       )
						self.assertTrue(reloaded_tokenizer.special_attribute_present       )
				else:
						self.assertEqual(tokenizer.__class__.__name__				,      'NewTokenizer'       )
						self.assertEqual(reloaded_tokenizer.__class__.__name__				,      'NewTokenizer'       )



		@require_tokenizers
		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""


				class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
						__lowercase       :       List[str]	     =				False






				class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
						__lowercase       :       int	     =				NewTokenizer
						__lowercase       :       Optional[Any]	     =				False

				try:
						AutoConfig.register('custom'				,      A_       )
						AutoTokenizer.register(A_				,      slow_tokenizer_class=A_       )
						AutoTokenizer.register(A_				,      fast_tokenizer_class=A_       )
						# If remote code is not set, the default is to use local
						UpperCamelCase		=     AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'       )
						self.assertEqual(tokenizer.__class__.__name__				,      'NewTokenizerFast'       )
						self.assertFalse(tokenizer.special_attribute_present       )
						UpperCamelCase		=     AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'				,      use_fast=A_       )
						self.assertEqual(tokenizer.__class__.__name__				,      'NewTokenizer'       )
						self.assertFalse(tokenizer.special_attribute_present       )

						# If remote code is disabled, we load the local one.
						UpperCamelCase		=     AutoTokenizer.from_pretrained(
						    'hf-internal-testing/test_dynamic_tokenizer'				,      trust_remote_code=A_       )
						self.assertEqual(tokenizer.__class__.__name__				,      'NewTokenizerFast'       )
						self.assertFalse(tokenizer.special_attribute_present       )
						UpperCamelCase		=     AutoTokenizer.from_pretrained(
						    'hf-internal-testing/test_dynamic_tokenizer'				,      trust_remote_code=A_				,      use_fast=A_       )
						self.assertEqual(tokenizer.__class__.__name__				,      'NewTokenizer'       )
						self.assertFalse(tokenizer.special_attribute_present       )

						# If remote is enabled, we load from the Hub
						UpperCamelCase		=     AutoTokenizer.from_pretrained(
						    'hf-internal-testing/test_dynamic_tokenizer'				,      trust_remote_code=A_       )
						self.assertEqual(tokenizer.__class__.__name__				,      'NewTokenizerFast'       )
						self.assertTrue(tokenizer.special_attribute_present       )
						UpperCamelCase		=     AutoTokenizer.from_pretrained(
						    'hf-internal-testing/test_dynamic_tokenizer'				,      trust_remote_code=A_				,      use_fast=A_       )
						self.assertEqual(tokenizer.__class__.__name__				,      'NewTokenizer'       )
						self.assertTrue(tokenizer.special_attribute_present       )

				finally:
						if "custom" in CONFIG_MAPPING._extra_content:
								del CONFIG_MAPPING._extra_content["custom"]
						if CustomConfig in TOKENIZER_MAPPING._extra_content:
								del TOKENIZER_MAPPING._extra_content[CustomConfig]



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     AutoTokenizer.from_pretrained(
				    'hf-internal-testing/test_dynamic_tokenizer_legacy'				,      trust_remote_code=A_       )
				self.assertTrue(tokenizer.special_attribute_present       )
				if is_tokenizers_available():
						self.assertEqual(tokenizer.__class__.__name__				,      'NewTokenizerFast'       )

						# Test we can also load the slow version
						UpperCamelCase		=     AutoTokenizer.from_pretrained(
						    'hf-internal-testing/test_dynamic_tokenizer_legacy'				,      trust_remote_code=A_				,      use_fast=A_       )
						self.assertTrue(tokenizer.special_attribute_present       )
						self.assertEqual(tokenizer.__class__.__name__				,      'NewTokenizer'       )
				else:
						self.assertEqual(tokenizer.__class__.__name__				,      'NewTokenizer'       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				with self.assertRaisesRegex(
				    A_				,      'bert-base is not a local folder and is not a valid model identifier'       ):
						UpperCamelCase		=     AutoTokenizer.from_pretrained('bert-base'       )



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				with self.assertRaisesRegex(
				    A_				,      r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'       ):
						UpperCamelCase		=     AutoTokenizer.from_pretrained(A_				,      revision='aaaaaa'       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				# Make sure we have cached the tokenizer.
				UpperCamelCase		=     AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'       )
				with RequestCounter() as counter:
						UpperCamelCase		=     AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'       )
						self.assertEqual(counter.get_request_count				,      0       )
						self.assertEqual(counter.head_request_count				,      1       )
						self.assertEqual(counter.other_request_count				,      0       )


from random import randint
from tempfile import TemporaryFile

import numpy as np


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  str:
		'''simple docstring'''

		UpperCamelCase		=     0
		if start < end:
				UpperCamelCase		=     randint(lowercase    ,       lowercase )
				UpperCamelCase		=     a[end]
				UpperCamelCase		=     a[pivot]
				UpperCamelCase		=     temp

				UpperCamelCase	,  UpperCamelCase		=     _in_place_partition(lowercase    ,       lowercase    ,       lowercase )
				count += _in_place_quick_sort(lowercase    ,       lowercase    ,       p - 1 )
				count += _in_place_quick_sort(lowercase    ,       p + 1    ,       lowercase )
		return count


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     0
		UpperCamelCase		=     randint(lowercase    ,       lowercase )
		UpperCamelCase		=     a[end]
		UpperCamelCase		=     a[pivot]
		UpperCamelCase		=     temp
		UpperCamelCase		=     start - 1
		for index in range(lowercase    ,       lowercase ):
				count += 1
				if a[index] < a[end]:  # check if current val is less than pivot value
						UpperCamelCase		=     new_pivot_index + 1
						UpperCamelCase		=     a[new_pivot_index]
						UpperCamelCase		=     a[index]
						UpperCamelCase		=     temp

		UpperCamelCase		=     a[new_pivot_index + 1]
		UpperCamelCase		=     a[end]
		UpperCamelCase		=     temp
		return new_pivot_index + 1, count


_UpperCAmelCase					: Union[str, Any]      =       TemporaryFile()
_UpperCAmelCase					: List[Any]      =       100  # 1000 elements are to be sorted


_UpperCAmelCase   ,_UpperCAmelCase					: Any      =       0, 1  # mean and standard deviation
_UpperCAmelCase					: Any      =       np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)


outfile.seek(0)  # using the same array
_UpperCAmelCase					: Any      =       np.load(outfile)
_UpperCAmelCase					: str      =       len(M) - 1
_UpperCAmelCase					: List[str]      =       _in_place_quick_sort(M, 0, r)

print(
    "No of Comparisons for 100 elements selected from a standard normal distribution"
    "is :"
)


print(z)
