"""simple docstring"""




import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

__lowerCamelCase											=  logging.getLogger()






def   UpperCAmelCase					(	):






       """simple docstring"""





       A__	     =							argparse.ArgumentParser()
       parser.add_argument('-f'				)
       A__	     =							parser.parse_args()
       return args.f


class     UpperCamelCase__(			lowercase_ ):





      def 				snake_case__	(       self     )					->				Optional[int]:
             A__	     =							logging.StreamHandler(sys.stdout     )
             logger.addHandler(UpperCamelCase__     )





      def 				snake_case__	(       self		,__UpperCAmelCase     )					->				Optional[int]:
             A__	     =							get_gpu_count()

             if n_gpu > 1:
                    pass
                    # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
                    # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
                    # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
                    # cmd = [sys.executable] + distributed_args + args
                    # execute_subprocess_async(cmd, env=self.get_env())
                    # XXX: test the results - need to save them first into .json file
             else:
                    args.insert(0		,'run_glue_deebert.py'     )
                    with patch.object(UpperCamelCase__		,'argv'		,UpperCamelCase__     ):
                           A__	     =							run_glue_deebert.main()
                           for value in result.values():
                                  self.assertGreaterEqual(UpperCamelCase__		,0.6_6_6     )





      @slow
      @require_torch_non_multi_gpu
      def 				snake_case__	(       self     )					->				Optional[int]:
             A__	     =							'\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            '.split()
             self.run_and_check(UpperCamelCase__     )

             A__	     =							'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
             self.run_and_check(UpperCamelCase__     )

             A__	     =							'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
             self.run_and_check(UpperCamelCase__     )






"""simple docstring"""




import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
				__lowerCamelCase											=  "pt"
elif is_tf_available():
				__lowerCamelCase											=  "tf"
else:
				__lowerCamelCase											=  "jax"
class     UpperCamelCase__(			__A		,						unittest.TestCase ):
						lowerCAmelCase__  :						List[Any]													=		ByTaTokenizer
						lowerCAmelCase__  :						str													=		False





						def 				snake_case__	(       self     )					->				int:
													super().setUp()
													A__	     =							ByTaTokenizer()
													tokenizer.save_pretrained(self.tmpdirname     )





						@cached_property
						def 				snake_case__	(       self     )					->				List[Any]:
													return ByTaTokenizer.from_pretrained('google/byt5-small'     )





						def 				snake_case__	(       self		,**__UpperCAmelCase     )					->				ByTaTokenizer:
													return self.tokenizer_class.from_pretrained(self.tmpdirname		,**__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=False		,__UpperCAmelCase=20		,__UpperCAmelCase=5     )					->				Tuple[str, list]:
													# XXX The default common tokenizer tests assume that every ID is decodable on its own.
													# This assumption is invalid for ByT5 because single bytes might not be
													# valid utf-8 (byte 128 for instance).
													# Here we're overriding the smallest possible method to provide
													# a clean sequence without making the same assumption.

													A__	     =							[]
													for i in range(len(__UpperCAmelCase     )     ):
																				try:
																											A__	     =							tokenizer.decode([i]		,clean_up_tokenization_spaces=__UpperCAmelCase     )
																				except UnicodeDecodeError:
																											pass
																				toks.append((i, tok)     )

													A__	     =							list(filter(lambda __UpperCAmelCase     : re.match(R'^[ a-zA-Z]+$'		,t[1]     )		,__UpperCAmelCase     )     )
													A__	     =							list(filter(lambda __UpperCAmelCase     : [t[0]] == tokenizer.encode(t[1]		,add_special_tokens=__UpperCAmelCase     )		,__UpperCAmelCase     )     )
													if max_length is not None and len(__UpperCAmelCase     ) > max_length:
																				A__	     =							toks[:max_length]
													if min_length is not None and len(__UpperCAmelCase     ) < min_length and len(__UpperCAmelCase     ) > 0:
																				while len(__UpperCAmelCase     ) < min_length:
																											A__	     =							toks + toks
        # toks_str = [t[1] for t in toks]
													A__	     =							[t[0] for t in toks]

													# Ensure consistency
													A__	     =							tokenizer.decode(__UpperCAmelCase		,clean_up_tokenization_spaces=__UpperCAmelCase     )
													if " " not in output_txt and len(__UpperCAmelCase     ) > 1:
																				A__	     =							(
																				    tokenizer.decode([toks_ids[0]]		,clean_up_tokenization_spaces=__UpperCAmelCase     )
																				    + ' '
																				    + tokenizer.decode(toks_ids[1:]		,clean_up_tokenization_spaces=__UpperCAmelCase     )
																				)
													if with_prefix_space:
																				A__	     =							' ' + output_txt
													A__	     =							tokenizer.encode(__UpperCAmelCase		,add_special_tokens=__UpperCAmelCase     )
													return output_txt, output_ids





						def 				snake_case__	(       self     )					->				Dict:
													A__	     =							self.ta_base_tokenizer
													A__	     =							tokenizer(['hi</s>', 'I went to the gym</s>', '</s>']     )
													A__	     =							tokenizer(['hi', 'I went to the gym', '']     )
													self.assertListEqual(batch_with_eos_added['input_ids']		,batch_without_eos_added['input_ids']     )





						def 				snake_case__	(       self     )					->				Dict:
													A__	     =							self.ta_base_tokenizer
													A__	     =							'Unicode €.'
													A__	     =							tokenizer(__UpperCAmelCase     )
													A__	     =							[88, 1_13, 1_08, 1_02, 1_14, 1_03, 1_04, 35, 2_29, 1_33, 1_75, 49, 1]
													self.assertEqual(encoded['input_ids']		,__UpperCAmelCase     )

													# decoding
													A__	     =							tokenizer.decode(__UpperCAmelCase     )
													self.assertEqual(__UpperCAmelCase		,'Unicode €.</s>'     )

													A__	     =							tokenizer('e è é ê ë'     )
													A__	     =							[1_04, 35, 1_98, 1_71, 35, 1_98, 1_72, 35, 1_98, 1_73, 35, 1_98, 1_74, 1]
													self.assertEqual(encoded['input_ids']		,__UpperCAmelCase     )
													# decoding
													A__	     =							tokenizer.decode(__UpperCAmelCase     )
													self.assertEqual(__UpperCAmelCase		,'e è é ê ë</s>'     )

													# encode/decode, but with `encode` instead of `__call__`
													self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'     )     )		,'e è é ê ë</s>'     )





						def 				snake_case__	(       self     )					->				Optional[int]:
													A__	     =							self.ta_base_tokenizer
													A__	     =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
													# fmt: off
													A__	     =							[68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 1, 0]
													# fmt: on
													A__	     =							tokenizer(__UpperCAmelCase		,padding=__UpperCAmelCase		,return_tensors=__UpperCAmelCase     )
													self.assertIsInstance(__UpperCAmelCase		,__UpperCAmelCase     )

													if FRAMEWORK != "jax":
																				A__	     =							list(batch.input_ids.numpy()[0]     )
													else:
																				A__	     =							list(batch.input_ids.tolist()[0]     )

													self.assertListEqual(__UpperCAmelCase		,__UpperCAmelCase     )

													self.assertEqual((2, 37)		,batch.input_ids.shape     )
													self.assertEqual((2, 37)		,batch.attention_mask.shape     )





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							self.ta_base_tokenizer
													A__	     =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
													A__	     =							tokenizer(__UpperCAmelCase		,padding=__UpperCAmelCase		,return_tensors=__UpperCAmelCase     )
													# check if input_ids are returned and no decoder_input_ids
													self.assertIn('input_ids'		,__UpperCAmelCase     )
													self.assertIn('attention_mask'		,__UpperCAmelCase     )
													self.assertNotIn('decoder_input_ids'		,__UpperCAmelCase     )
													self.assertNotIn('decoder_attention_mask'		,__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				Any:
													A__	     =							self.ta_base_tokenizer
													A__	     =							[
													    'Summary of the text.',
													    'Another summary.',
													]
													A__	     =							tokenizer(
													    text_target=__UpperCAmelCase		,max_length=32		,padding='max_length'		,truncation=__UpperCAmelCase		,return_tensors=__UpperCAmelCase     )
													self.assertEqual(32		,targets['input_ids'].shape[1]     )





						def 				snake_case__	(       self     )					->				Optional[int]:
													A__	     =							self.ta_base_tokenizer
													A__	     =							['A long paragraph for summarization. </s>']
													A__	     =							['Summary of the text. </s>']
													# fmt: off
													A__	     =							[68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 35, 1]
													A__	     =							[86, 1_20, 1_12, 1_12, 1_00, 1_17, 1_24, 35, 1_14, 1_05, 35, 1_19, 1_07, 1_04, 35, 1_19, 1_04, 1_23, 1_19, 49, 35, 1]
													# fmt: on

													A__	     =							tokenizer(__UpperCAmelCase		,text_target=__UpperCAmelCase     )

													self.assertEqual(__UpperCAmelCase		,batch['input_ids'][0]     )
													self.assertEqual(__UpperCAmelCase		,batch['labels'][0]     )





						def 				snake_case__	(       self     )					->				Tuple:
													# safety check on max_len default value so we are sure the test works
													A__	     =							self.get_tokenizers()
													for tokenizer in tokenizers:
																				with self.subTest(f'''{tokenizer.__class__.__name__}'''     ):
																											self.assertNotEqual(tokenizer.model_max_length		,42     )

        # Now let's start the test
													A__	     =							self.get_tokenizers()
													for tokenizer in tokenizers:
																				with self.subTest(f'''{tokenizer.__class__.__name__}'''     ):
																											# Isolate this from the other tests because we save additional tokens/etc
																											A__	     =							tempfile.mkdtemp()

																											A__	     =							' He is very happy, UNwant\u00E9d,running'
																											A__	     =							tokenizer.encode(__UpperCAmelCase		,add_special_tokens=__UpperCAmelCase     )
																											tokenizer.save_pretrained(__UpperCAmelCase     )

																											A__	     =							tokenizer.__class__.from_pretrained(__UpperCAmelCase     )
																											A__	     =							after_tokenizer.encode(__UpperCAmelCase		,add_special_tokens=__UpperCAmelCase     )
																											self.assertListEqual(__UpperCAmelCase		,__UpperCAmelCase     )

																											shutil.rmtree(__UpperCAmelCase     )

													A__	     =							self.get_tokenizers(model_max_length=42     )
													for tokenizer in tokenizers:
																				with self.subTest(f'''{tokenizer.__class__.__name__}'''     ):
																											# Isolate this from the other tests because we save additional tokens/etc
																											A__	     =							tempfile.mkdtemp()

																											A__	     =							' He is very happy, UNwant\u00E9d,running'
																											tokenizer.add_tokens(['bim', 'bambam']     )
																											A__	     =							tokenizer.additional_special_tokens
																											additional_special_tokens.append('new_additional_special_token'     )
																											tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}     )
																											A__	     =							tokenizer.encode(__UpperCAmelCase		,add_special_tokens=__UpperCAmelCase     )
																											tokenizer.save_pretrained(__UpperCAmelCase     )

																											A__	     =							tokenizer.__class__.from_pretrained(__UpperCAmelCase     )
																											A__	     =							after_tokenizer.encode(__UpperCAmelCase		,add_special_tokens=__UpperCAmelCase     )
																											self.assertListEqual(__UpperCAmelCase		,__UpperCAmelCase     )
																											self.assertIn('new_additional_special_token'		,after_tokenizer.additional_special_tokens     )
																											self.assertEqual(after_tokenizer.model_max_length		,42     )

																											A__	     =							tokenizer.__class__.from_pretrained(__UpperCAmelCase		,model_max_length=43     )
																											self.assertEqual(tokenizer.model_max_length		,43     )

																											shutil.rmtree(__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				Optional[int]:
													A__	     =							[]
													if self.test_slow_tokenizer:
																				tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())     )

													if self.test_rust_tokenizer:
																				tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())     )

													for tokenizer_class, tokenizer_utils in tokenizer_list:
																				with tempfile.TemporaryDirectory() as tmp_dir:
																											tokenizer_utils.save_pretrained(__UpperCAmelCase     )

																											with open(os.path.join(__UpperCAmelCase		,'special_tokens_map.json'     )		,encoding='utf-8'     ) as json_file:
																																		A__	     =							json.load(__UpperCAmelCase     )

																											with open(os.path.join(__UpperCAmelCase		,'tokenizer_config.json'     )		,encoding='utf-8'     ) as json_file:
																																		A__	     =							json.load(__UpperCAmelCase     )

																											A__	     =							[f'''<extra_id_{i}>''' for i in range(1_25     )]

																											A__	     =							added_tokens_extra_ids + [
																											    'an_additional_special_token'
																											]
																											A__	     =							added_tokens_extra_ids + [
																											    'an_additional_special_token'
																											]

																											with open(os.path.join(__UpperCAmelCase		,'special_tokens_map.json'     )		,'w'		,encoding='utf-8'     ) as outfile:
																																		json.dump(__UpperCAmelCase		,__UpperCAmelCase     )
																											with open(os.path.join(__UpperCAmelCase		,'tokenizer_config.json'     )		,'w'		,encoding='utf-8'     ) as outfile:
																																		json.dump(__UpperCAmelCase		,__UpperCAmelCase     )

																											# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																											# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																											# "special_tokens_map.json" files
																											A__	     =							tokenizer_class.from_pretrained(
																											    __UpperCAmelCase		,)
																											self.assertIn(
																											    'an_additional_special_token'		,tokenizer_without_change_in_init.additional_special_tokens     )
																											# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
																											self.assertEqual(
																											    ['an_additional_special_token']		,tokenizer_without_change_in_init.convert_ids_to_tokens(
																											        tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']     )     )		,)

																											# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																											A__	     =							added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'		,lstrip=__UpperCAmelCase     )]
																											A__	     =							tokenizer_class.from_pretrained(
																											    __UpperCAmelCase		,additional_special_tokens=__UpperCAmelCase		,)

																											self.assertIn('a_new_additional_special_token'		,tokenizer.additional_special_tokens     )
																											self.assertEqual(
																											    ['a_new_additional_special_token']		,tokenizer.convert_ids_to_tokens(
																											        tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']     )     )		,)





						def 				snake_case__	(       self     )					->				str:
													A__	     =							[]
													if self.test_slow_tokenizer:
																				tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())     )

													if self.test_rust_tokenizer:
																				tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())     )

													for tokenizer_class, tokenizer_utils in tokenizer_list:
																				with tempfile.TemporaryDirectory() as tmp_dir:
																											tokenizer_utils.save_pretrained(__UpperCAmelCase     )

																											A__	     =							tokenizer_class.from_pretrained(__UpperCAmelCase     )

																											self.assertTrue(tokenizer.decode([2_55]     ) == ''     )





						def 				snake_case__	(       self     )					->				Any:
													pass





						def 				snake_case__	(       self     )					->				Any:
													pass





						def 				snake_case__	(       self     )					->				str:
													pass





						def 				snake_case__	(       self     )					->				int:
													pass





						def 				snake_case__	(       self     )					->				Dict:
													# The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
													# and special added tokens as tokens
													A__	     =							self.get_tokenizers(fast=__UpperCAmelCase		,do_lower_case=__UpperCAmelCase     )
													for tokenizer in tokenizers:
																				with self.subTest(f'''{tokenizer.__class__.__name__}'''     ):
																											A__	     =							['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
																											A__	     =							tokenizer.convert_tokens_to_string(__UpperCAmelCase     )

																											self.assertIsInstance(__UpperCAmelCase		,__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				Tuple:
													A__	     =							self.get_tokenizers()
													for tokenizer in tokenizers:
																				with self.subTest(f'''{tokenizer.__class__.__name__}'''     ):
																											A__	     =							[
																											    'bos_token',
																											    'eos_token',
																											    'unk_token',
																											    'sep_token',
																											    'pad_token',
																											    'cls_token',
																											    'mask_token',
																											]

																											A__	     =							0
																											A__	     =							tokenizer.convert_ids_to_tokens(
																											    __UpperCAmelCase		,skip_special_tokens=__UpperCAmelCase     )

																											for attr in attributes_list:
																																		setattr(__UpperCAmelCase		,attr + '_id'		,__UpperCAmelCase     )
																																		self.assertEqual(getattr(__UpperCAmelCase		,__UpperCAmelCase     )		,__UpperCAmelCase     )
																																		self.assertEqual(getattr(__UpperCAmelCase		,attr + '_id'     )		,__UpperCAmelCase     )

																																		setattr(__UpperCAmelCase		,attr + '_id'		,__UpperCAmelCase     )
																																		self.assertEqual(getattr(__UpperCAmelCase		,__UpperCAmelCase     )		,__UpperCAmelCase     )
																																		self.assertEqual(getattr(__UpperCAmelCase		,attr + '_id'     )		,__UpperCAmelCase     )

																											setattr(__UpperCAmelCase		,'additional_special_tokens_ids'		,[]     )
																											self.assertListEqual(getattr(__UpperCAmelCase		,'additional_special_tokens'     )		,[]     )
																											self.assertListEqual(getattr(__UpperCAmelCase		,'additional_special_tokens_ids'     )		,[]     )

																											setattr(__UpperCAmelCase		,'additional_special_tokens_ids'		,[token_id_to_test_setters]     )
																											self.assertListEqual(getattr(__UpperCAmelCase		,'additional_special_tokens'     )		,[token_to_test_setters]     )
																											self.assertListEqual(getattr(__UpperCAmelCase		,'additional_special_tokens_ids'     )		,[token_id_to_test_setters]     )





