


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__        =				[
    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__        =				logging.getLogger()
def 							UpperCAmelCase_   (				)	->     Dict:







 """simple docstring"""


 _lowercase				 =argparse.ArgumentParser()
 parser.add_argument('''-f'''		)
 _lowercase				 =parser.parse_args()
 return args.f
def 							UpperCAmelCase_   (				__snake_case		)	->     str:







 """simple docstring"""


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







 """simple docstring"""


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


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







class  lowerCamelCase__  (			_A):





     @skip('''Skipping until shape inference for to_tf_dataset PR is merged.'''			)
     def 	__A   (self			)	->  Optional[int]:
      _lowercase				 =self.get_auto_remove_tmp_dir()
      _lowercase				 =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(__lowerCamelCase						,	'''argv'''						,	__lowerCamelCase			):
       run_text_classification.main()
       # Reset the mixed precision policy so we don't break other tests
       tf.keras.mixed_precision.set_global_policy('''float32'''			)
       _lowercase				 =get_results(__lowerCamelCase			)
       self.assertGreaterEqual(result['''eval_accuracy''']						,	0.75			)





     def 	__A   (self			)	->  Any:
      _lowercase				 =self.get_auto_remove_tmp_dir()
      _lowercase				 =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(__lowerCamelCase						,	'''argv'''						,	__lowerCamelCase			):
       run_clm.main()
       _lowercase				 =get_results(__lowerCamelCase			)
       self.assertLess(result['''eval_perplexity''']						,	1_0_0			)





     def 	__A   (self			)	->  List[str]:
      _lowercase				 =self.get_auto_remove_tmp_dir()
      _lowercase				 =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(__lowerCamelCase						,	'''argv'''						,	__lowerCamelCase			):
       run_mlm.main()
       _lowercase				 =get_results(__lowerCamelCase			)
       self.assertLess(result['''eval_perplexity''']						,	4_2			)





     def 	__A   (self			)	->  int:
      # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
      _lowercase				 =7 if get_gpu_count() > 1 else 2

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





     def 	__A   (self			)	->  Dict:
      _lowercase				 =self.get_auto_remove_tmp_dir()
      _lowercase				 =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(__lowerCamelCase						,	'''argv'''						,	__lowerCamelCase			):
       run_squad.main()
       _lowercase				 =get_results(__lowerCamelCase			)
       self.assertGreaterEqual(result['''f1''']						,	3_0			)
       self.assertGreaterEqual(result['''exact''']						,	3_0			)





     def 	__A   (self			)	->  Optional[Any]:
      _lowercase				 =self.get_auto_remove_tmp_dir()
      _lowercase				 =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(__lowerCamelCase						,	'''argv'''						,	__lowerCamelCase			):
       run_swag.main()
       _lowercase				 =get_results(__lowerCamelCase			)
       self.assertGreaterEqual(result['''val_accuracy''']						,	0.8			)





     @slow
     def 	__A   (self			)	->  Union[str, Any]:
      _lowercase				 =self.get_auto_remove_tmp_dir()
      _lowercase				 =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(__lowerCamelCase						,	'''argv'''						,	__lowerCamelCase			):
       run_summarization.main()
       _lowercase				 =get_results(__lowerCamelCase			)
       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			)	->  Union[str, Any]:
      _lowercase				 =self.get_auto_remove_tmp_dir()
      _lowercase				 =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(__lowerCamelCase						,	'''argv'''						,	__lowerCamelCase			):
       run_translation.main()
       _lowercase				 =get_results(__lowerCamelCase			)
       self.assertGreaterEqual(result['''bleu''']						,	3_0			)





     def 	__A   (self			)	->  Any:
      _lowercase				 =self.get_auto_remove_tmp_dir()
      _lowercase				 =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(__lowerCamelCase						,	'''argv'''						,	__lowerCamelCase			):
       run_image_classification.main()
       _lowercase				 =get_results(__lowerCamelCase			)
       self.assertGreaterEqual(result['''accuracy''']						,	0.7			)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE     =	{"""configuration_yoso""": ["""YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YosoConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE     =	[
							    """YOSO_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """YosoForMaskedLM""",
							    """YosoForMultipleChoice""",
							    """YosoForQuestionAnswering""",
							    """YosoForSequenceClassification""",
							    """YosoForTokenClassification""",
							    """YosoLayer""",
							    """YosoModel""",
							    """YosoPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_yoso import (
														    YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    YosoForMaskedLM,
														    YosoForMultipleChoice,
														    YosoForQuestionAnswering,
														    YosoForSequenceClassification,
														    YosoForTokenClassification,
														    YosoLayer,
														    YosoModel,
														    YosoPreTrainedModel,
														)


else:
							import sys

							__SCREAMING_SNAKE_CASE     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)