


import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


UpperCamelCase			:					Tuple         =							[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        '''text-generation''',
        '''text-classification''',
        '''token-classification''',
        '''language-modeling''',
        '''multiple-choice''',
        '''question-answering''',
        '''summarization''',
        '''translation''',
        '''image-classification''',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
					import run_clm
					import run_image_classification
					import run_mlm
					import run_ner
					import run_qa as run_squad
					import run_summarization
					import run_swag
					import run_text_classification
					import run_translation


logging.basicConfig(level=logging.DEBUG)

UpperCamelCase			:					List[Any]         =							logging.getLogger()
def 				UpperCamelCase_	(					)->   Dict:




						'''simple docstring'''
						__magic_name__					=      argparse.ArgumentParser()
						parser.add_argument('-f'  )
						__magic_name__					=      parser.parse_args()
						return args.f
def 				UpperCamelCase_	(					__snake_case      :    Optional[Any]  )->   Tuple:




						'''simple docstring'''
						__magic_name__					=      {}
						__magic_name__					=      os.path.join(__snake_case						,      'all_results.json'  )
						if os.path.exists(__snake_case  ):
												with open(__snake_case						,      'r'  ) as f:
																		__magic_name__					=      json.load(__snake_case  )
						else:
												raise ValueError(F'''can\'t find {path}'''  )
						return results
def 				UpperCamelCase_	(					)->   List[str]:




						'''simple docstring'''
						return bool(tf.config.list_physical_devices('GPU'  )  )


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




class      __snake_case				(   _UpperCAmelCase):
					@skip('Skipping until shape inference for to_tf_dataset PR is merged.'  )
					def 	a			(						self       :						int  )							->					List[str]:



											'''simple docstring'''





											__magic_name__					=      self.get_auto_remove_tmp_dir()
											__magic_name__					=      F'''
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

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

											with patch.object(_lowerCAmelCase	,      'argv'	,      _lowerCAmelCase  ):
																	run_text_classification.main()
																	# Reset the mixed precision policy so we don't break other tests
																	tf.keras.mixed_precision.set_global_policy('float32'  )
																	__magic_name__					=      get_results(_lowerCAmelCase  )
																	self.assertGreaterEqual(result['eval_accuracy']	,      0.75  )
					def 	a			(						self       :						Union[str, Any]  )							->					List[str]:



											'''simple docstring'''





											__magic_name__					=      self.get_auto_remove_tmp_dir()
											__magic_name__					=      F'''
            run_clm.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

											if len(tf.config.list_physical_devices('GPU'  )  ) > 1:
																	# Skipping because there are not enough batches to train the model + would need a drop_last to work.
																	return

											with patch.object(_lowerCAmelCase	,      'argv'	,      _lowerCAmelCase  ):
																	run_clm.main()
																	__magic_name__					=      get_results(_lowerCAmelCase  )
																	self.assertLess(result['eval_perplexity']	,      1_0_0  )
					def 	a			(						self       :						Any  )							->					str:



											'''simple docstring'''





											__magic_name__					=      self.get_auto_remove_tmp_dir()
											__magic_name__					=      F'''
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        '''.split()

											with patch.object(_lowerCAmelCase	,      'argv'	,      _lowerCAmelCase  ):
																	run_mlm.main()
																	__magic_name__					=      get_results(_lowerCAmelCase  )
																	self.assertLess(result['eval_perplexity']	,      4_2  )
					def 	a			(						self       :						str  )							->					Dict:



											'''simple docstring'''





											__magic_name__					=      7 if get_gpu_count() > 1 else 2

											__magic_name__					=      self.get_auto_remove_tmp_dir()
											__magic_name__					=      F'''
            run_ner.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}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        '''.split()

											with patch.object(_lowerCAmelCase	,      'argv'	,      _lowerCAmelCase  ):
																	run_ner.main()
																	__magic_name__					=      get_results(_lowerCAmelCase  )
																	self.assertGreaterEqual(result['accuracy']	,      0.75  )
					def 	a			(						self       :						Any  )							->					Optional[int]:



											'''simple docstring'''





											__magic_name__					=      self.get_auto_remove_tmp_dir()
											__magic_name__					=      F'''
            run_qa.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}
            --overwrite_output_dir
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

											with patch.object(_lowerCAmelCase	,      'argv'	,      _lowerCAmelCase  ):
																	run_squad.main()
																	__magic_name__					=      get_results(_lowerCAmelCase  )
																	self.assertGreaterEqual(result['f1']	,      3_0  )
																	self.assertGreaterEqual(result['exact']	,      3_0  )
					def 	a			(						self       :						Any  )							->					List[str]:



											'''simple docstring'''





											__magic_name__					=      self.get_auto_remove_tmp_dir()
											__magic_name__					=      F'''
            run_swag.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}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

											with patch.object(_lowerCAmelCase	,      'argv'	,      _lowerCAmelCase  ):
																	run_swag.main()
																	__magic_name__					=      get_results(_lowerCAmelCase  )
																	self.assertGreaterEqual(result['val_accuracy']	,      0.8  )
					@slow
					def 	a			(						self       :						Any  )							->					str:



											'''simple docstring'''





											__magic_name__					=      self.get_auto_remove_tmp_dir()
											__magic_name__					=      F'''
            run_summarization.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}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

											with patch.object(_lowerCAmelCase	,      'argv'	,      _lowerCAmelCase  ):
																	run_summarization.main()
																	__magic_name__					=      get_results(_lowerCAmelCase  )
																	self.assertGreaterEqual(result['rouge1']	,      1_0  )
																	self.assertGreaterEqual(result['rouge2']	,      2  )
																	self.assertGreaterEqual(result['rougeL']	,      7  )
																	self.assertGreaterEqual(result['rougeLsum']	,      7  )
					@slow
					def 	a			(						self       :						Optional[Any]  )							->					Optional[Any]:



											'''simple docstring'''





											__magic_name__					=      self.get_auto_remove_tmp_dir()
											__magic_name__					=      F'''
            run_translation.py
            --model_name_or_path Rocketknight1/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}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        '''.split()

											with patch.object(_lowerCAmelCase	,      'argv'	,      _lowerCAmelCase  ):
																	run_translation.main()
																	__magic_name__					=      get_results(_lowerCAmelCase  )
																	self.assertGreaterEqual(result['bleu']	,      3_0  )







					def 	a			(						self       :						str  )							->					Optional[Any]:



											'''simple docstring'''





											__magic_name__					=      self.get_auto_remove_tmp_dir()
											__magic_name__					=      F'''
            run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --model_name_or_path microsoft/resnet-18
            --do_train
            --do_eval
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --dataloader_num_workers 16
            --num_train_epochs 2
            --train_val_split 0.1
            --seed 42
            --ignore_mismatched_sizes True
            '''.split()

											with patch.object(_lowerCAmelCase	,      'argv'	,      _lowerCAmelCase  ):
																	run_image_classification.main()
																	__magic_name__					=      get_results(_lowerCAmelCase  )
																	self.assertGreaterEqual(result['accuracy']	,      0.7  )





import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


UpperCamelCase			:					List[str]         =							logging.getLogger(__name__)
def 				UpperCamelCase_	(					)->   Dict:




						'''simple docstring'''
						__magic_name__					=      argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'  )
						parser.add_argument(
						    '--dataset_name'						,      type=__snake_case						,      default='wikitext'						,      help='Name of the training. Explore datasets at: hf.co/datasets.'						,      )
						parser.add_argument(
						    '--dataset_config'						,      type=__snake_case						,      default='wikitext-103-raw-v1'						,      help='Configuration name of the dataset.'  )
						parser.add_argument(
						    '--batch_size'						,      type=__snake_case						,      default=10_00						,      help='Batch size during training.'						,      )
						parser.add_argument(
						    '--vocab_size'						,      type=__snake_case						,      default=1_00_48						,      help='Size of the desired vocabulary.'						,      )
						parser.add_argument(
						    '--limit'						,      default=__snake_case						,      type=__snake_case						,      help='Limit the number of shards (used for debugging).'						,      )
						parser.add_argument(
						    '--export_to_hub'						,      action='store_true'						,      )

						__magic_name__					=      parser.parse_args()
						return args
def 				UpperCamelCase_	(					__snake_case      :    Any  )->   Optional[int]:




						'''simple docstring'''
						__magic_name__					=      datasets.load_dataset(args.dataset_name						,      args.dataset_config						,      split='train'  )

						if args.limit is not None:
												__magic_name__					=      min(len(__snake_case  )						,      args.limit  )
												__magic_name__					=      dataset.select(range(__snake_case  )  )
												logger.info(F'''Limiting the dataset to {args.limit} entries.'''  )

						def batch_iterator():
												for i in range(0						,      len(__snake_case  )						,      args.batch_size  ):
																		yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
						__magic_name__					=      Tokenizer(Unigram()  )
						__magic_name__					=      normalizers.Sequence([normalizers.Replace('``'						,      '"'  ), normalizers.Replace('\'\''						,      '"'  )]  )
						__magic_name__					=      pre_tokenizers.Metaspace()

						# Prepare the trainer.
						__magic_name__					=      UnigramTrainer(
						    unk_token='<unk>'						,      special_tokens=['[CLS]', '[SEP]', '<unk>', '<pad>', '[MASK]']						,      vocab_size=args.vocab_size						,      )

						logger.info('Training the tokenizer.'  )
						tokenizer.train_from_iterator(batch_iterator()						,      trainer=__snake_case  )
						logger.info('Tokenizer training complete!'  )

						__magic_name__					=      tokenizer.token_to_id('[CLS]'  )
						__magic_name__					=      tokenizer.token_to_id('[SEP]'  )
						__magic_name__					=      processors.TemplateProcessing(
						    single='[CLS]:0 $A:0 [SEP]:0'						,      pair='[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1'						,      special_tokens=[
						        ('[CLS]', cls_token_id),
						        ('[SEP]', sep_token_id),
						    ]						,      )
						__magic_name__					=      decoders.Metaspace()

						if args.export_to_hub:
												logger.info('Exporting the trained tokenzier to Hub.'  )
												__magic_name__					=      AlbertTokenizerFast(tokenizer_object=__snake_case  )
												new_tokenizer.push_to_hub('unigram-tokenizer-dataset'  )


if __name__ == "__main__":
					UpperCamelCase			:					Dict         =							parse_args()
					main(args)

