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


snake_case__						      =			[
    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)

snake_case__						      =			logging.getLogger()
def    __magic_name__(	)    ->     str:



      '''simple docstring'''

      _lowerCamelCase           =					argparse.ArgumentParser()
      parser.add_argument('''-f'''		)
      _lowerCamelCase           =					parser.parse_args()
      return args.f
def    __magic_name__(	__UpperCAmelCase		)    ->     List[str]:



      '''simple docstring'''

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



      '''simple docstring'''

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


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




class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

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






           """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 		UpperCamelCase_    (       self    )			->					str:






           """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 		UpperCamelCase_    (       self    )			->					str:






           """simple docstring"""
           # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
           _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 		UpperCamelCase_    (       self    )			->					Optional[int]:






           """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 		UpperCamelCase_    (       self    )			->					List[str]:






           """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 		UpperCamelCase_    (       self    )			->					List[str]:






           """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 		UpperCamelCase_    (       self    )			->					Optional[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 		UpperCamelCase_    (       self    )			->					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    )
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
snake_case__						      =			abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def    __magic_name__(	__UpperCAmelCase		)    ->     int:



      '''simple docstring'''

      from transformers.testing_utils import pytest_addoption_shared

      pytest_addoption_shared(__UpperCAmelCase		)




def    __magic_name__(	__UpperCAmelCase		)    ->     List[str]:



      '''simple docstring'''

      from transformers.testing_utils import pytest_terminal_summary_main

      _lowerCamelCase           =					terminalreporter.config.getoption('''--make-reports'''		)
      if make_reports:
            pytest_terminal_summary_main(__UpperCAmelCase			,    id=__UpperCAmelCase		)