




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


lowercase_      :							List[Any]    							=					[
    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)

lowercase_      :							int    							=					logging.getLogger()


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


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


def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


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


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


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


lowercase_      :							str    							=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class        __lowerCAmelCase					(			UpperCAmelCase__					):







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




									"""simple docstring"""


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

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

									with patch.object(__snake_case       ,       "argv"       ,       __snake_case ):
															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(__snake_case )
															self.assertGreaterEqual(result["eval_accuracy"]       ,       0.75 )







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.get_auto_remove_tmp_dir()
									_UpperCAmelCase						=	F"""\n            run_clm.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            """.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(__snake_case       ,       "argv"       ,       __snake_case ):
															run_clm.main()
															_UpperCAmelCase						=	get_results(__snake_case )
															self.assertLess(result["eval_perplexity"]       ,       100 )







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


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

									with patch.object(__snake_case       ,       "argv"       ,       __snake_case ):
															run_mlm.main()
															_UpperCAmelCase						=	get_results(__snake_case )
															self.assertLess(result["eval_perplexity"]       ,       42 )







			def UpperCamelCase     (			self       :     Union[str, Any] ):




									"""simple docstring"""


									_UpperCAmelCase						=	7 if get_gpu_count() > 1 else 2

									_UpperCAmelCase						=	self.get_auto_remove_tmp_dir()
									_UpperCAmelCase						=	F"""\n            run_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        """.split()

									with patch.object(__snake_case       ,       "argv"       ,       __snake_case ):
															run_ner.main()
															_UpperCAmelCase						=	get_results(__snake_case )
															self.assertGreaterEqual(result["accuracy"]       ,       0.75 )







			def UpperCamelCase     (			self       :     List[Any] ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.get_auto_remove_tmp_dir()
									_UpperCAmelCase						=	F"""\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        """.split()

									with patch.object(__snake_case       ,       "argv"       ,       __snake_case ):
															run_squad.main()
															_UpperCAmelCase						=	get_results(__snake_case )
															self.assertGreaterEqual(result["f1"]       ,       30 )
															self.assertGreaterEqual(result["exact"]       ,       30 )







			def UpperCamelCase     (			self       :     Any ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.get_auto_remove_tmp_dir()
									_UpperCAmelCase						=	F"""\n            run_swag.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=20\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        """.split()

									with patch.object(__snake_case       ,       "argv"       ,       __snake_case ):
															run_swag.main()
															_UpperCAmelCase						=	get_results(__snake_case )
															self.assertGreaterEqual(result["val_accuracy"]       ,       0.8 )







			@slow
			def UpperCamelCase     (			self       :     Any ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.get_auto_remove_tmp_dir()
									_UpperCAmelCase						=	F"""\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        """.split()

									with patch.object(__snake_case       ,       "argv"       ,       __snake_case ):
															run_summarization.main()
															_UpperCAmelCase						=	get_results(__snake_case )
															self.assertGreaterEqual(result["rouge1"]       ,       10 )
															self.assertGreaterEqual(result["rouge2"]       ,       2 )
															self.assertGreaterEqual(result["rougeL"]       ,       7 )
															self.assertGreaterEqual(result["rougeLsum"]       ,       7 )







			@slow
			def UpperCamelCase     (			self       :     str ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.get_auto_remove_tmp_dir()
									_UpperCAmelCase						=	F"""\n            run_translation.py\n            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n        """.split()

									with patch.object(__snake_case       ,       "argv"       ,       __snake_case ):
															run_translation.main()
															_UpperCAmelCase						=	get_results(__snake_case )
															self.assertGreaterEqual(result["bleu"]       ,       30 )







			def UpperCamelCase     (			self       :     List[str] ):




									"""simple docstring"""


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

									with patch.object(__snake_case       ,       "argv"       ,       __snake_case ):
															run_image_classification.main()
															_UpperCAmelCase						=	get_results(__snake_case )
															self.assertGreaterEqual(result["accuracy"]       ,       0.7 )






from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 		__lowerCamelCase     (   lowercase		):
							lowerCamelCase__:				Any      =							['''image_processor''', '''tokenizer''']
							lowerCamelCase__:				List[Any]      =							'''EfficientNetImageProcessor'''
							lowerCamelCase__:				str      =							('''BertTokenizer''', '''BertTokenizerFast''')



							def __init__(     self	,				__snake_case	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								super().__init__(__snake_case	,				__snake_case							)



							def __call__(     self	,				__snake_case=None	,				__snake_case=None	,				__snake_case="max_length"	,				__snake_case=6_4	,				__snake_case=None	,				**__snake_case							)						->     Any:






								"""simple docstring"""



								if text is None and images is None:
									raise ValueError("You have to specify either text or images. Both cannot be none."							)

								if text is not None:
									UpperCAmelCase:					Dict 						=     self.tokenizer(
									    __snake_case	,				padding=__snake_case	,				max_length=__snake_case	,				return_tensors=__snake_case	,				**__snake_case							)

								if images is not None:
									UpperCAmelCase:					List[str] 						=     self.image_processor(__snake_case	,				return_tensors=__snake_case	,				**__snake_case							)

								if text is not None and images is not None:
									UpperCAmelCase:					Optional[int] 						=     image_features.pixel_values
									return encoding
								elif text is not None:
									return encoding
								else:
									return BatchEncoding(data=dict(**__snake_case							)	,				tensor_type=__snake_case							)



							def     A__ (     self	,				*__snake_case	,				**__snake_case							)						->     Any:






								"""simple docstring"""



								return self.tokenizer.batch_decode(*__snake_case	,				**__snake_case							)



							def     A__ (     self	,				*__snake_case	,				**__snake_case							)						->     List[str]:






								"""simple docstring"""



								return self.tokenizer.decode(*__snake_case	,				**__snake_case							)




							@property
							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     self.tokenizer.model_input_names
								UpperCAmelCase:					int 						=     self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)
