
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


snake_case_      :					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)

snake_case_      :					int				      =     logging.getLogger()



def      __UpperCAmelCase    (  ):



	'''simple docstring'''



	UpperCAmelCase:					str 						=     argparse.ArgumentParser()
	parser.add_argument("-f"				)
	UpperCAmelCase:					int 						=     parser.parse_args()
	return args.f



def      __UpperCAmelCase    (  snake_case_							:		Any				):



	'''simple docstring'''



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



def      __UpperCAmelCase    (  ):



	'''simple docstring'''



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


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





class 		__lowerCamelCase     (   lowercase		):



							@skip("Skipping until shape inference for to_tf_dataset PR is merged."							)
							def     A__ (     self							)						->     Union[str, Any]:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     self.get_auto_remove_tmp_dir()
								UpperCAmelCase:					Optional[int] 						=     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:					int 						=     get_results(__snake_case							)
									self.assertGreaterEqual(result["eval_accuracy"]	,				0.75							)



							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					Any 						=     self.get_auto_remove_tmp_dir()
								UpperCAmelCase:					List[str] 						=     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:					Any 						=     get_results(__snake_case							)
									self.assertLess(result["eval_perplexity"]	,				1_0_0							)



							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					List[Any] 						=     self.get_auto_remove_tmp_dir()
								UpperCAmelCase:					Tuple 						=     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:					int 						=     get_results(__snake_case							)
									self.assertLess(result["eval_perplexity"]	,				4_2							)



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






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     7 if get_gpu_count() > 1 else 2

								UpperCAmelCase:					Tuple 						=     self.get_auto_remove_tmp_dir()
								UpperCAmelCase:					int 						=     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:					Any 						=     get_results(__snake_case							)
									self.assertGreaterEqual(result["accuracy"]	,				0.75							)



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Any 						=     self.get_auto_remove_tmp_dir()
								UpperCAmelCase:					Tuple 						=     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:					int 						=     get_results(__snake_case							)
									self.assertGreaterEqual(result["f1"]	,				3_0							)
									self.assertGreaterEqual(result["exact"]	,				3_0							)



							def     A__ (     self							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     self.get_auto_remove_tmp_dir()
								UpperCAmelCase:					Tuple 						=     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:					Tuple 						=     get_results(__snake_case							)
									self.assertGreaterEqual(result["val_accuracy"]	,				0.8							)



							@slow
							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					Any 						=     self.get_auto_remove_tmp_dir()
								UpperCAmelCase:					int 						=     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:					Tuple 						=     get_results(__snake_case							)
									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							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					List[Any] 						=     self.get_auto_remove_tmp_dir()
								UpperCAmelCase:					Any 						=     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:					Optional[int] 						=     get_results(__snake_case							)
									self.assertGreaterEqual(result["bleu"]	,				3_0							)




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






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     self.get_auto_remove_tmp_dir()
								UpperCAmelCase:					Optional[Any] 						=     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:					int 						=     get_results(__snake_case							)
									self.assertGreaterEqual(result["accuracy"]	,				0.7							)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_      :					Dict				      =     {
    'configuration_big_bird': ['BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BigBirdConfig', 'BigBirdOnnxConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					str				      =     ['BigBirdTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Any				      =     ['BigBirdTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Optional[Any]				      =     [
	    'BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BigBirdForCausalLM',
	    'BigBirdForMaskedLM',
	    'BigBirdForMultipleChoice',
	    'BigBirdForPreTraining',
	    'BigBirdForQuestionAnswering',
	    'BigBirdForSequenceClassification',
	    'BigBirdForTokenClassification',
	    'BigBirdLayer',
	    'BigBirdModel',
	    'BigBirdPreTrainedModel',
	    'load_tf_weights_in_big_bird',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Any				      =     [
	    'FlaxBigBirdForCausalLM',
	    'FlaxBigBirdForMaskedLM',
	    'FlaxBigBirdForMultipleChoice',
	    'FlaxBigBirdForPreTraining',
	    'FlaxBigBirdForQuestionAnswering',
	    'FlaxBigBirdForSequenceClassification',
	    'FlaxBigBirdForTokenClassification',
	    'FlaxBigBirdModel',
	    'FlaxBigBirdPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_big_bird import BigBirdTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_big_bird_fast import BigBirdTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_big_bird import (
		    BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BigBirdForCausalLM,
		    BigBirdForMaskedLM,
		    BigBirdForMultipleChoice,
		    BigBirdForPreTraining,
		    BigBirdForQuestionAnswering,
		    BigBirdForSequenceClassification,
		    BigBirdForTokenClassification,
		    BigBirdLayer,
		    BigBirdModel,
		    BigBirdPreTrainedModel,
		    load_tf_weights_in_big_bird,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_big_bird import (
		    FlaxBigBirdForCausalLM,
		    FlaxBigBirdForMaskedLM,
		    FlaxBigBirdForMultipleChoice,
		    FlaxBigBirdForPreTraining,
		    FlaxBigBirdForQuestionAnswering,
		    FlaxBigBirdForSequenceClassification,
		    FlaxBigBirdForTokenClassification,
		    FlaxBigBirdModel,
		    FlaxBigBirdPreTrainedModel,
		)

else:
	import sys

	snake_case_      :					int				      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
