


'''simple docstring'''


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


lowerCamelCase__     						=  [
    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)

lowerCamelCase__     						=  logging.getLogger()






def 			__lowerCAmelCase			():
		_UpperCAmelCase		:							int             =   argparse.ArgumentParser()
		parser.add_argument("-f" )
		_UpperCAmelCase		:							Optional[Any]             =   parser.parse_args()
		return args.f






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






def 			__lowerCAmelCase			():
		return bool(tf.config.list_physical_devices("GPU" ) )


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






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




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



					'''simple docstring'''







					_UpperCAmelCase		:							List[str]             =   self.get_auto_remove_tmp_dir()
					_UpperCAmelCase		:							List[str]             =   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(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		:							List[str]             =   get_results(UpperCamelCase_			)
							self.assertGreaterEqual(result["eval_accuracy"]						,	0.7_5			)




			def 				lowerCAmelCase__     (		self	:  Optional[Any]			) ->int:



					'''simple docstring'''







					_UpperCAmelCase		:							Dict             =   self.get_auto_remove_tmp_dir()
					_UpperCAmelCase		:							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(UpperCamelCase_						,	"argv"						,	UpperCamelCase_			):
							run_clm.main()
							_UpperCAmelCase		:							str             =   get_results(UpperCamelCase_			)
							self.assertLess(result["eval_perplexity"]						,	1_00			)




			def 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   self.get_auto_remove_tmp_dir()
					_UpperCAmelCase		:							int             =   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(UpperCamelCase_						,	"argv"						,	UpperCamelCase_			):
							run_mlm.main()
							_UpperCAmelCase		:							str             =   get_results(UpperCamelCase_			)
							self.assertLess(result["eval_perplexity"]						,	42			)




			def 				lowerCAmelCase__     (		self	:  int			) ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:							List[Any]             =   7 if get_gpu_count() > 1 else 2

					_UpperCAmelCase		:							Dict             =   self.get_auto_remove_tmp_dir()
					_UpperCAmelCase		:							Optional[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(UpperCamelCase_						,	"argv"						,	UpperCamelCase_			):
							run_ner.main()
							_UpperCAmelCase		:							Optional[Any]             =   get_results(UpperCamelCase_			)
							self.assertGreaterEqual(result["accuracy"]						,	0.7_5			)




			def 				lowerCAmelCase__     (		self	:  Tuple			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:							List[Any]             =   self.get_auto_remove_tmp_dir()
					_UpperCAmelCase		:							int             =   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(UpperCamelCase_						,	"argv"						,	UpperCamelCase_			):
							run_squad.main()
							_UpperCAmelCase		:							Union[str, Any]             =   get_results(UpperCamelCase_			)
							self.assertGreaterEqual(result["f1"]						,	30			)
							self.assertGreaterEqual(result["exact"]						,	30			)




			def 				lowerCAmelCase__     (		self	:  List[str]			) ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:							str             =   self.get_auto_remove_tmp_dir()
					_UpperCAmelCase		:							Any             =   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(UpperCamelCase_						,	"argv"						,	UpperCamelCase_			):
							run_swag.main()
							_UpperCAmelCase		:							List[str]             =   get_results(UpperCamelCase_			)
							self.assertGreaterEqual(result["val_accuracy"]						,	0.8			)




			@slow
			def 				lowerCAmelCase__     (		self	:  Dict			) ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:							Dict             =   self.get_auto_remove_tmp_dir()
					_UpperCAmelCase		:							str             =   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(UpperCamelCase_						,	"argv"						,	UpperCamelCase_			):
							run_summarization.main()
							_UpperCAmelCase		:							List[str]             =   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 				lowerCAmelCase__     (		self	:  Tuple			) ->str:



					'''simple docstring'''







					_UpperCAmelCase		:							List[Any]             =   self.get_auto_remove_tmp_dir()
					_UpperCAmelCase		:							List[str]             =   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(UpperCamelCase_						,	"argv"						,	UpperCamelCase_			):
							run_translation.main()
							_UpperCAmelCase		:							Optional[int]             =   get_results(UpperCamelCase_			)
							self.assertGreaterEqual(result["bleu"]						,	30			)






			def 				lowerCAmelCase__     (		self	:  int			) ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							str             =   self.get_auto_remove_tmp_dir()
					_UpperCAmelCase		:							Dict             =   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(UpperCamelCase_						,	"argv"						,	UpperCamelCase_			):
							run_image_classification.main()
							_UpperCAmelCase		:							Union[str, Any]             =   get_results(UpperCamelCase_			)
							self.assertGreaterEqual(result["accuracy"]						,	0.7			)









from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


UpperCAmelCase__    :							List[str]       		=     {
    'configuration_ernie_m': ['ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieMConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							Any       		=     ['ErnieMTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							Dict       		=     [
       'ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ErnieMForMultipleChoice',
       'ErnieMForQuestionAnswering',
       'ErnieMForSequenceClassification',
       'ErnieMForTokenClassification',
       'ErnieMModel',
       'ErnieMPreTrainedModel',
       'ErnieMForInformationExtraction',
   ]


if TYPE_CHECKING:
   from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_ernie_m import ErnieMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ernie_m import (
          ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
          ErnieMForInformationExtraction,
          ErnieMForMultipleChoice,
          ErnieMForQuestionAnswering,
          ErnieMForSequenceClassification,
          ErnieMForTokenClassification,
          ErnieMModel,
          ErnieMPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase__    :							List[Any]       		=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
