

"""simple docstring"""




import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

_UpperCAmelCase		:       int						    =	logging.getLogger()





def  __lowerCamelCase     (			):







							'''simple docstring'''






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





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   {}
							snake_case_          =   os.path.join(UpperCamelCase__       ,							'all_results.json'				)
							if os.path.exists(UpperCamelCase__				):
														with open(UpperCamelCase__       ,							'r'				) as f:
																					snake_case_          =   json.load(UpperCamelCase__				)
							else:
														raise ValueError(F'''can\'t find {path}'''				)
							return results





def  __lowerCamelCase     (			):







							'''simple docstring'''






							snake_case_          =   torch.cuda.is_available() and torch_device == 'cuda'
							return is_using_cuda and is_apex_available()


_UpperCAmelCase		:       Union[str, Any]						    =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 			lowercase    (	lowercase_      ):




				@classmethod
				def    a							( cls	):
											# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
											snake_case_          =   tempfile.mkdtemp()
											snake_case_          =   os.path.join(cls.tmpdir				,       'default_config.yml'	)
											write_basic_config(save_location=cls.configPath	)
											snake_case_          =   ['accelerate', 'launch', '--config_file', cls.configPath]




				@classmethod
				def    a							( cls	):
											shutil.rmtree(cls.tmpdir	)




				@mock.patch.dict(os.environ				,       {'WANDB_MODE': 'offline'}	)
				def    a							( self	):
											snake_case_          =   self.get_auto_remove_tmp_dir()
											snake_case_          =   F'''
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

											if is_cuda_and_apex_available():
																		testargs.append('--fp16'	)

											run_command(self._launch_args + testargs	)
											snake_case_          =   get_results(snake_case	)
											self.assertGreaterEqual(result['eval_accuracy']				,       0.75	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'epoch_0'	)	)	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'glue_no_trainer'	)	)	)




				@mock.patch.dict(os.environ				,       {'WANDB_MODE': 'offline'}	)
				def    a							( self	):
											snake_case_          =   self.get_auto_remove_tmp_dir()
											snake_case_          =   F'''
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

											if torch.cuda.device_count() > 1:
																		# Skipping because there are not enough batches to train the model + would need a drop_last to work.
																		return

											run_command(self._launch_args + testargs	)
											snake_case_          =   get_results(snake_case	)
											self.assertLess(result['perplexity']				,       100	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'epoch_0'	)	)	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'clm_no_trainer'	)	)	)




				@mock.patch.dict(os.environ				,       {'WANDB_MODE': 'offline'}	)
				def    a							( self	):
											snake_case_          =   self.get_auto_remove_tmp_dir()
											snake_case_          =   F'''
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

											run_command(self._launch_args + testargs	)
											snake_case_          =   get_results(snake_case	)
											self.assertLess(result['perplexity']				,       42	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'epoch_0'	)	)	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'mlm_no_trainer'	)	)	)




				@mock.patch.dict(os.environ				,       {'WANDB_MODE': 'offline'}	)
				def    a							( self	):
											# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
											snake_case_          =   7 if get_gpu_count() > 1 else 2

											snake_case_          =   self.get_auto_remove_tmp_dir()
											snake_case_          =   F'''
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

											run_command(self._launch_args + testargs	)
											snake_case_          =   get_results(snake_case	)
											self.assertGreaterEqual(result['eval_accuracy']				,       0.75	)
											self.assertLess(result['train_loss']				,       0.5	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'epoch_0'	)	)	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'ner_no_trainer'	)	)	)




				@unittest.skip(reason='Fix me @muellerzr'	)
				@mock.patch.dict(os.environ				,       {'WANDB_MODE': 'offline'}	)
				def    a							( self	):
											snake_case_          =   self.get_auto_remove_tmp_dir()
											snake_case_          =   F'''
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

											run_command(self._launch_args + testargs	)
											snake_case_          =   get_results(snake_case	)
											# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
											self.assertGreaterEqual(result['eval_f1']				,       28	)
											self.assertGreaterEqual(result['eval_exact']				,       28	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'epoch_0'	)	)	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'qa_no_trainer'	)	)	)




				@mock.patch.dict(os.environ				,       {'WANDB_MODE': 'offline'}	)
				def    a							( self	):
											snake_case_          =   self.get_auto_remove_tmp_dir()
											snake_case_          =   F'''
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        '''.split()

											run_command(self._launch_args + testargs	)
											snake_case_          =   get_results(snake_case	)
											self.assertGreaterEqual(result['eval_accuracy']				,       0.8	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'swag_no_trainer'	)	)	)




				@slow
				@mock.patch.dict(os.environ				,       {'WANDB_MODE': 'offline'}	)
				def    a							( self	):
											snake_case_          =   self.get_auto_remove_tmp_dir()
											snake_case_          =   F'''
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

											run_command(self._launch_args + testargs	)
											snake_case_          =   get_results(snake_case	)
											self.assertGreaterEqual(result['eval_rouge1']				,       10	)
											self.assertGreaterEqual(result['eval_rouge2']				,       2	)
											self.assertGreaterEqual(result['eval_rougeL']				,       7	)
											self.assertGreaterEqual(result['eval_rougeLsum']				,       7	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'epoch_0'	)	)	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'summarization_no_trainer'	)	)	)




				@slow
				@mock.patch.dict(os.environ				,       {'WANDB_MODE': 'offline'}	)
				def    a							( self	):
											snake_case_          =   self.get_auto_remove_tmp_dir()
											snake_case_          =   F'''
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

											run_command(self._launch_args + testargs	)
											snake_case_          =   get_results(snake_case	)
											self.assertGreaterEqual(result['eval_bleu']				,       30	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'epoch_0'	)	)	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'translation_no_trainer'	)	)	)




				@slow
				def    a							( self	):
											snake_case_          =   logging.StreamHandler(sys.stdout	)
											logger.addHandler(snake_case	)

											snake_case_          =   self.get_auto_remove_tmp_dir()
											snake_case_          =   F'''
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        '''.split()

											run_command(self._launch_args + testargs	)
											snake_case_          =   get_results(snake_case	)
											self.assertGreaterEqual(result['eval_overall_accuracy']				,       0.10	)




				@mock.patch.dict(os.environ				,       {'WANDB_MODE': 'offline'}	)
				def    a							( self	):
											snake_case_          =   self.get_auto_remove_tmp_dir()
											snake_case_          =   F'''
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        '''.split()

											if is_cuda_and_apex_available():
																		testargs.append('--fp16'	)

											run_command(self._launch_args + testargs	)
											snake_case_          =   get_results(snake_case	)
											# The base model scores a 25%
											self.assertGreaterEqual(result['eval_accuracy']				,       0.6	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'step_1'	)	)	)
											self.assertTrue(os.path.exists(os.path.join(snake_case				,       'image_classification_no_trainer'	)	)	)







import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 			lowercase    (	lowercase_     ,  unittest.TestCase      ):
				__SCREAMING_SNAKE_CASE :			List[str]	      =      BarthezTokenizer
				__SCREAMING_SNAKE_CASE :			str	      =      BarthezTokenizerFast
				__SCREAMING_SNAKE_CASE :			Optional[Any]	      =      True
				__SCREAMING_SNAKE_CASE :			str	      =      True




				def    a							( self	):
											super().setUp()

											snake_case_          =   BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'	)
											tokenizer.save_pretrained(self.tmpdirname	)
											tokenizer.save_pretrained(self.tmpdirname				,       legacy_format=snake_case	)
											snake_case_          =   tokenizer




				def    a							( self	):
											snake_case_          =   '<pad>'
											snake_case_          =   1

											self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case	)				,       snake_case	)
											self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case	)				,       snake_case	)




				def    a							( self	):
											snake_case_          =   list(self.get_tokenizer().get_vocab().keys()	)

											self.assertEqual(vocab_keys[0]				,       '<s>'	)
											self.assertEqual(vocab_keys[1]				,       '<pad>'	)
											self.assertEqual(vocab_keys[-1]				,       '<mask>'	)
											self.assertEqual(len(snake_case	)				,       10_1122	)




				def    a							( self	):
											self.assertEqual(self.get_tokenizer().vocab_size				,       10_1122	)




				@require_torch
				def    a							( self	):
											snake_case_          =   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											snake_case_          =   [0, 57, 3018, 7_0307, 91, 2]

											snake_case_          =   self.tokenizer(
											    snake_case				,       max_length=len(snake_case	)				,       padding=snake_case				,       truncation=snake_case				,       return_tensors='pt'	)
											self.assertIsInstance(snake_case				,       snake_case	)

											self.assertEqual((2, 6)				,       batch.input_ids.shape	)
											self.assertEqual((2, 6)				,       batch.attention_mask.shape	)
											snake_case_          =   batch.input_ids.tolist()[0]
											self.assertListEqual(snake_case				,       snake_case	)




				def    a							( self	):
											if not self.test_rust_tokenizer:
																		return

											snake_case_          =   self.get_tokenizer()
											snake_case_          =   self.get_rust_tokenizer()

											snake_case_          =   'I was born in 92000, and this is falsé.'

											snake_case_          =   tokenizer.tokenize(snake_case	)
											snake_case_          =   rust_tokenizer.tokenize(snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)

											snake_case_          =   tokenizer.encode(snake_case				,       add_special_tokens=snake_case	)
											snake_case_          =   rust_tokenizer.encode(snake_case				,       add_special_tokens=snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)

											snake_case_          =   self.get_rust_tokenizer()
											snake_case_          =   tokenizer.encode(snake_case	)
											snake_case_          =   rust_tokenizer.encode(snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)




				@slow
				def    a							( self	):
											# fmt: off
											snake_case_          =   {'input_ids': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
											# fmt: on

											# moussaKam/mbarthez is a french model. So we also use french texts.
											snake_case_          =   [
											    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
											    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
											    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
											    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
											    'telles que la traduction et la synthèse de texte.',
											]

											self.tokenizer_integration_test_util(
											    expected_encoding=snake_case				,       model_name='moussaKam/mbarthez'				,       revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'				,       sequences=snake_case				,       )




