






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


_a				     =  [
    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)

_a				     =  logging.getLogger()


def __A   (				)->  Tuple:





						"""simple docstring"""



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


def __A   (				__lowerCAmelCase     )->  Tuple:





						"""simple docstring"""



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


def __A   (				)->  Union[str, Any]:





						"""simple docstring"""



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


_a				     =  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class __lowerCamelCase			(				snake_case__):





	"""simple docstring"""






	@skip('Skipping until shape inference for to_tf_dataset PR is merged.'   )
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_auto_remove_tmp_dir()
							_UpperCAmelCase     =      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(UpperCAmelCase	,   'argv'	,   UpperCAmelCase   ):
													run_text_classification.main()
													# Reset the mixed precision policy so we don't break other tests
													tf.keras.mixed_precision.set_global_policy('float32'   )
													_UpperCAmelCase     =      get_results(UpperCAmelCase   )
													self.assertGreaterEqual(result['eval_accuracy']	,   0.75   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_auto_remove_tmp_dir()
							_UpperCAmelCase     =      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(UpperCAmelCase	,   'argv'	,   UpperCAmelCase   ):
													run_clm.main()
													_UpperCAmelCase     =      get_results(UpperCAmelCase   )
													self.assertLess(result['eval_perplexity']	,   100   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_auto_remove_tmp_dir()
							_UpperCAmelCase     =      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(UpperCAmelCase	,   'argv'	,   UpperCAmelCase   ):
													run_mlm.main()
													_UpperCAmelCase     =      get_results(UpperCAmelCase   )
													self.assertLess(result['eval_perplexity']	,   42   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      7 if get_gpu_count() > 1 else 2

							_UpperCAmelCase     =      self.get_auto_remove_tmp_dir()
							_UpperCAmelCase     =      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(UpperCAmelCase	,   'argv'	,   UpperCAmelCase   ):
													run_ner.main()
													_UpperCAmelCase     =      get_results(UpperCAmelCase   )
													self.assertGreaterEqual(result['accuracy']	,   0.75   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_auto_remove_tmp_dir()
							_UpperCAmelCase     =      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(UpperCAmelCase	,   'argv'	,   UpperCAmelCase   ):
													run_squad.main()
													_UpperCAmelCase     =      get_results(UpperCAmelCase   )
													self.assertGreaterEqual(result['f1']	,   30   )
													self.assertGreaterEqual(result['exact']	,   30   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_auto_remove_tmp_dir()
							_UpperCAmelCase     =      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(UpperCAmelCase	,   'argv'	,   UpperCAmelCase   ):
													run_swag.main()
													_UpperCAmelCase     =      get_results(UpperCAmelCase   )
													self.assertGreaterEqual(result['val_accuracy']	,   0.8   )

	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_auto_remove_tmp_dir()
							_UpperCAmelCase     =      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(UpperCAmelCase	,   'argv'	,   UpperCAmelCase   ):
													run_summarization.main()
													_UpperCAmelCase     =      get_results(UpperCAmelCase   )
													self.assertGreaterEqual(result['rouge1']	,   10   )
													self.assertGreaterEqual(result['rouge2']	,   2   )
													self.assertGreaterEqual(result['rougeL']	,   7   )
													self.assertGreaterEqual(result['rougeLsum']	,   7   )

	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_auto_remove_tmp_dir()
							_UpperCAmelCase     =      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(UpperCAmelCase	,   'argv'	,   UpperCAmelCase   ):
													run_translation.main()
													_UpperCAmelCase     =      get_results(UpperCAmelCase   )
													self.assertGreaterEqual(result['bleu']	,   30   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_auto_remove_tmp_dir()
							_UpperCAmelCase     =      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(UpperCAmelCase	,   'argv'	,   UpperCAmelCase   ):
													run_image_classification.main()
													_UpperCAmelCase     =      get_results(UpperCAmelCase   )
													self.assertGreaterEqual(result['accuracy']	,   0.7   )








import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir





@dataclass
class __lowerCamelCase			:





	"""simple docstring"""






	UpperCamelCase__								=				42





class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      get_tests_dir('fixtures/test_sentencepiece.model'   )
							_UpperCAmelCase     =      get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model'   )

							_UpperCAmelCase     =      FakeOriginalTokenizer(vocab_file=UpperCAmelCase   )

							with warnings.catch_warnings(record=UpperCAmelCase   ) as w:
													_UpperCAmelCase     =      SpmConverter(UpperCAmelCase   )
							self.assertEqual(len(UpperCAmelCase   )	,   0   )

							_UpperCAmelCase     =      FakeOriginalTokenizer(vocab_file=UpperCAmelCase   )

							with warnings.catch_warnings(record=UpperCAmelCase   ) as w:
													_UpperCAmelCase     =      SpmConverter(UpperCAmelCase   )
							self.assertEqual(len(UpperCAmelCase   )	,   1   )

							self.assertIn(
							    'The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'
							    ' which is not implemented in the fast tokenizers.'	,   str(w[0].message   )	,   )
