


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      :						str    =		logging.getLogger()






def   __lowerCamelCase	(       ):





			'''simple docstring'''




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






def   __lowerCamelCase	(       lowerCamelCase__		:	str			):





			'''simple docstring'''




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






def   __lowerCamelCase	(       ):





			'''simple docstring'''




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


UpperCAmelCase      :						Any    =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 					__lowercase					(       __lowerCamelCase     ):



		"""simple docstring"""

		@skip("""Skipping until shape inference for to_tf_dataset PR is merged."""  )
		def 				__A      (   self  )     ->   List[Any]:







					'''simple docstring'''







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







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







					'''simple docstring'''







					lowerCamelCase								=     self.get_auto_remove_tmp_dir()
					lowerCamelCase								=     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(A		,			"""argv"""		,			A  ):
								run_clm.main()
								lowerCamelCase								=     get_results(A  )
								self.assertLess(result["""eval_perplexity"""]		,			1_00  )







		def 				__A      (   self  )     ->   Any:







					'''simple docstring'''







					lowerCamelCase								=     self.get_auto_remove_tmp_dir()
					lowerCamelCase								=     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(A		,			"""argv"""		,			A  ):
								run_mlm.main()
								lowerCamelCase								=     get_results(A  )
								self.assertLess(result["""eval_perplexity"""]		,			42  )







		def 				__A      (   self  )     ->   Any:







					'''simple docstring'''







					lowerCamelCase								=     7 if get_gpu_count() > 1 else 2

					lowerCamelCase								=     self.get_auto_remove_tmp_dir()
					lowerCamelCase								=     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(A		,			"""argv"""		,			A  ):
								run_ner.main()
								lowerCamelCase								=     get_results(A  )
								self.assertGreaterEqual(result["""accuracy"""]		,			0.75  )







		def 				__A      (   self  )     ->   Tuple:







					'''simple docstring'''







					lowerCamelCase								=     self.get_auto_remove_tmp_dir()
					lowerCamelCase								=     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(A		,			"""argv"""		,			A  ):
								run_squad.main()
								lowerCamelCase								=     get_results(A  )
								self.assertGreaterEqual(result["""f1"""]		,			30  )
								self.assertGreaterEqual(result["""exact"""]		,			30  )







		def 				__A      (   self  )     ->   Dict:







					'''simple docstring'''







					lowerCamelCase								=     self.get_auto_remove_tmp_dir()
					lowerCamelCase								=     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(A		,			"""argv"""		,			A  ):
								run_swag.main()
								lowerCamelCase								=     get_results(A  )
								self.assertGreaterEqual(result["""val_accuracy"""]		,			0.8  )







		@slow
		def 				__A      (   self  )     ->   List[Any]:







					'''simple docstring'''







					lowerCamelCase								=     self.get_auto_remove_tmp_dir()
					lowerCamelCase								=     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(A		,			"""argv"""		,			A  ):
								run_summarization.main()
								lowerCamelCase								=     get_results(A  )
								self.assertGreaterEqual(result["""rouge1"""]		,			10  )
								self.assertGreaterEqual(result["""rouge2"""]		,			2  )
								self.assertGreaterEqual(result["""rougeL"""]		,			7  )
								self.assertGreaterEqual(result["""rougeLsum"""]		,			7  )







		@slow
		def 				__A      (   self  )     ->   Any:







					'''simple docstring'''







					lowerCamelCase								=     self.get_auto_remove_tmp_dir()
					lowerCamelCase								=     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(A		,			"""argv"""		,			A  ):
								run_translation.main()
								lowerCamelCase								=     get_results(A  )
								self.assertGreaterEqual(result["""bleu"""]		,			30  )







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







					'''simple docstring'''







					lowerCamelCase								=     self.get_auto_remove_tmp_dir()
					lowerCamelCase								=     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(A		,			"""argv"""		,			A  ):
								run_image_classification.main()
								lowerCamelCase								=     get_results(A  )
								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,
)


UpperCAmelCase      :						List[str]    =		{
    "configuration_big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig", "BigBirdOnnxConfig"],
}

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

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase      :						str    =		["BigBirdTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase      :						Dict    =		[
		    "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:
		UpperCAmelCase      :						Dict    =		[
		    "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

		UpperCAmelCase      :						Dict    =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

