

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


SCREAMING_SNAKE_CASE		:Union[str, 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)

SCREAMING_SNAKE_CASE		:Tuple				  =    logging.getLogger()




def  lowerCAmelCase(			)->	Tuple:



    """simple docstring"""






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




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	List[Any]:



    """simple docstring"""






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




def  lowerCAmelCase(			)->	int:



    """simple docstring"""






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


SCREAMING_SNAKE_CASE		:Tuple				  =    logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 	__magic_name__				(       snake_case							):






     @skip("Skipping until shape inference for to_tf_dataset PR is merged."  )
     def   UpperCAmelCase_							(							self  )->    str:
         UpperCamelCase_      =	self.get_auto_remove_tmp_dir()
         UpperCamelCase_      =	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(_lowercase		,	"argv"		,	_lowercase  ):
             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_      =	get_results(_lowercase  )
             self.assertGreaterEqual(result["eval_accuracy"]		,	0.75  )






     def   UpperCAmelCase_							(							self  )->    Optional[int]:
         UpperCamelCase_      =	self.get_auto_remove_tmp_dir()
         UpperCamelCase_      =	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(_lowercase		,	"argv"		,	_lowercase  ):
             run_clm.main()
             UpperCamelCase_      =	get_results(_lowercase  )
             self.assertLess(result["eval_perplexity"]		,	100  )






     def   UpperCAmelCase_							(							self  )->    List[Any]:
         UpperCamelCase_      =	self.get_auto_remove_tmp_dir()
         UpperCamelCase_      =	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(_lowercase		,	"argv"		,	_lowercase  ):
             run_mlm.main()
             UpperCamelCase_      =	get_results(_lowercase  )
             self.assertLess(result["eval_perplexity"]		,	42  )






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

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






     def   UpperCAmelCase_							(							self  )->    str:
         UpperCamelCase_      =	self.get_auto_remove_tmp_dir()
         UpperCamelCase_      =	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(_lowercase		,	"argv"		,	_lowercase  ):
             run_squad.main()
             UpperCamelCase_      =	get_results(_lowercase  )
             self.assertGreaterEqual(result["f1"]		,	30  )
             self.assertGreaterEqual(result["exact"]		,	30  )






     def   UpperCAmelCase_							(							self  )->    int:
         UpperCamelCase_      =	self.get_auto_remove_tmp_dir()
         UpperCamelCase_      =	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(_lowercase		,	"argv"		,	_lowercase  ):
             run_swag.main()
             UpperCamelCase_      =	get_results(_lowercase  )
             self.assertGreaterEqual(result["val_accuracy"]		,	0.8  )






     @slow
     def   UpperCAmelCase_							(							self  )->    str:
         UpperCamelCase_      =	self.get_auto_remove_tmp_dir()
         UpperCamelCase_      =	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(_lowercase		,	"argv"		,	_lowercase  ):
             run_summarization.main()
             UpperCamelCase_      =	get_results(_lowercase  )
             self.assertGreaterEqual(result["rouge1"]		,	10  )
             self.assertGreaterEqual(result["rouge2"]		,	2  )
             self.assertGreaterEqual(result["rougeL"]		,	7  )
             self.assertGreaterEqual(result["rougeLsum"]		,	7  )






     @slow
     def   UpperCAmelCase_							(							self  )->    int:
         UpperCamelCase_      =	self.get_auto_remove_tmp_dir()
         UpperCamelCase_      =	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(_lowercase		,	"argv"		,	_lowercase  ):
             run_translation.main()
             UpperCamelCase_      =	get_results(_lowercase  )
             self.assertGreaterEqual(result["bleu"]		,	30  )






     def   UpperCAmelCase_							(							self  )->    Union[str, Any]:
         UpperCamelCase_      =	self.get_auto_remove_tmp_dir()
         UpperCamelCase_      =	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(_lowercase		,	"argv"		,	_lowercase  ):
             run_image_classification.main()
             UpperCamelCase_      =	get_results(_lowercase  )
             self.assertGreaterEqual(result["accuracy"]		,	0.7  )



import unittest

from transformers.image_processing_utils import get_size_dict






class 	__magic_name__				(       unittest.TestCase							):






     def   UpperCAmelCase_							(							self  )->    int:
         # Test a dict with the wrong keys raises an error
         UpperCamelCase_      =	{"wrong_key": 224}
         with self.assertRaises(_lowercase  ):
             get_size_dict(_lowercase  )

         UpperCamelCase_      =	{"height": 224}
         with self.assertRaises(_lowercase  ):
             get_size_dict(_lowercase  )

         UpperCamelCase_      =	{"width": 224, "shortest_edge": 224}
         with self.assertRaises(_lowercase  ):
             get_size_dict(_lowercase  )

         # Test a dict with the correct keys is returned as is
         UpperCamelCase_      =	{"height": 224, "width": 224}
         UpperCamelCase_      =	get_size_dict(_lowercase  )
         self.assertEqual(_lowercase		,	_lowercase  )

         UpperCamelCase_      =	{"shortest_edge": 224}
         UpperCamelCase_      =	get_size_dict(_lowercase  )
         self.assertEqual(_lowercase		,	{"shortest_edge": 224}  )

         UpperCamelCase_      =	{"longest_edge": 224, "shortest_edge": 224}
         UpperCamelCase_      =	get_size_dict(_lowercase  )
         self.assertEqual(_lowercase		,	{"longest_edge": 224, "shortest_edge": 224}  )

         # Test a single int value which  represents (size, size)
         UpperCamelCase_      =	get_size_dict(224  )
         self.assertEqual(_lowercase		,	{"height": 224, "width": 224}  )

         # Test a single int value which represents the shortest edge
         UpperCamelCase_      =	get_size_dict(224		,	default_to_square=_lowercase  )
         self.assertEqual(_lowercase		,	{"shortest_edge": 224}  )

         # Test a tuple of ints which represents (height, width)
         UpperCamelCase_      =	get_size_dict((150, 200)  )
         self.assertEqual(_lowercase		,	{"height": 150, "width": 200}  )

         # Test a tuple of ints which represents (width, height)
         UpperCamelCase_      =	get_size_dict((150, 200)		,	height_width_order=_lowercase  )
         self.assertEqual(_lowercase		,	{"height": 200, "width": 150}  )

         # Test an int representing the shortest edge and max_size which represents the longest edge
         UpperCamelCase_      =	get_size_dict(224		,	max_size=256		,	default_to_square=_lowercase  )
         self.assertEqual(_lowercase		,	{"shortest_edge": 224, "longest_edge": 256}  )

         # Test int with default_to_square=True and max_size fails
         with self.assertRaises(_lowercase  ):
             get_size_dict(224		,	max_size=256		,	default_to_square=_lowercase  )
