




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__      :			Dict           =					[
    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__      :			Any           =					logging.getLogger()

def 					SCREAMING_SNAKE_CASE							(   )			->     Union[str, Any]:
     a      =      argparse.ArgumentParser()
     parser.add_argument("-f")
     a      =      parser.parse_args()
     return args.f

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     str:
     a      =      {}
     a      =      os.path.join(__UpperCamelCase		,					"all_results.json")
     if os.path.exists(__UpperCamelCase):
          with open(__UpperCamelCase		,					"r") as f:
               a      =      json.load(__UpperCamelCase)
     else:
          raise ValueError(f'''can\'t find {path}''')
     return results

def 					SCREAMING_SNAKE_CASE							(   )			->     Any:
     return bool(tf.config.list_physical_devices("GPU"))


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





class 			a__       (					UpperCamelCase__      ):







     @skip("Skipping until shape inference for to_tf_dataset PR is merged." )
     def 			lowerCAmelCase_				(	self )  ->    Optional[int]:



          '''simple docstring'''



          a      =      self.get_auto_remove_tmp_dir()
          a      =      F'''
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.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" )
               a      =      get_results(A )
               self.assertGreaterEqual(result["eval_accuracy"]  ,		0.7_5 )







     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self )  ->    int:



          '''simple docstring'''



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

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







     def 			lowerCAmelCase_				(	self )  ->    Union[str, Any]:



          '''simple docstring'''



          a      =      7 if get_gpu_count() > 1 else 2

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

          with patch.object(A  ,		"argv"  ,		A ):
               run_ner.main()
               a      =      get_results(A )
               self.assertGreaterEqual(result["accuracy"]  ,		0.7_5 )







     def 			lowerCAmelCase_				(	self )  ->    Union[str, Any]:



          '''simple docstring'''



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

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







     def 			lowerCAmelCase_				(	self )  ->    int:



          '''simple docstring'''



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

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







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



          '''simple docstring'''



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

          with patch.object(A  ,		"argv"  ,		A ):
               run_summarization.main()
               a      =      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 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



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

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







     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''simple docstring'''



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

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







import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


lowercase__      :			Union[str, Any]           =					logging.get_logger(__name__)




class 			a__       (					UpperCamelCase__      ):







     def __init__(	self  ,		*A  ,		**A )  ->    None:



          '''simple docstring'''



          warnings.warn(
              "The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use MobileNetV2ImageProcessor instead."  ,		A  ,		)
          super().__init__(*A  ,		**A )

