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__(	)    ->     List[str]:



      '''simple docstring'''

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



      '''simple docstring'''

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



      '''simple docstring'''

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


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




class 			UpperCamelCase					(			snake_case__			):

     '''simple docstring'''

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






           """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    )			->					Tuple:






           """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    )			->					Tuple:






           """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    )			->					Optional[Any]:






           """simple docstring"""
           _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    )			->					Dict:






           """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    )			->					Dict:






           """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    )			->					Optional[int]:






           """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    )			->					List[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    )			->					str:






           """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    )







import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_a       =    '''src/transformers'''

# Matches is_xxx_available()
_a       =    re.compile(r'''is\_([a-z_]*)_available()''')
# Matches from xxx import bla
_a       =    re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
_a       =    re.compile(r'''^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)''')


_a       =    '''
{0} = None
'''

_a       =    '''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


_a       =    '''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''


def __A       ( __lowerCAmelCase      )->    str:





						"""simple docstring"""



						if _re_test_backend.search(__lowerCAmelCase      ) is None:
												return None
						_UpperCAmelCase							   =     [b[0] for b in _re_backend.findall(__lowerCAmelCase      )]
						backends.sort()
						return "_and_".join(__lowerCAmelCase      )


def __A       ( )->    int:





						"""simple docstring"""



						with open(os.path.join(__lowerCAmelCase					,						'__init__.py'      )					,						'r'					,						encoding='utf-8'					,						newline='\n'      ) as f:
												_UpperCAmelCase							   =     f.readlines()

						# Get to the point we do the actual imports for type checking
						_UpperCAmelCase							   =     0
						while not lines[line_index].startswith('if TYPE_CHECKING'      ):
												line_index += 1

						_UpperCAmelCase							   =     {}
						# Go through the end of the file
						while line_index < len(__lowerCAmelCase      ):
												# If the line is an if is_backend_available, we grab all objects associated.
												_UpperCAmelCase							   =     find_backend(lines[line_index]      )
												if backend is not None:
																		while not lines[line_index].startswith('    else:'      ):
																								line_index += 1
																		line_index += 1

																		_UpperCAmelCase							   =     []
																		# Until we unindent, add backend objects to the list
																		while len(lines[line_index]      ) <= 1 or lines[line_index].startswith(' ' * 8      ):
																								_UpperCAmelCase							   =     lines[line_index]
																								_UpperCAmelCase							   =     _re_single_line_import.search(__lowerCAmelCase      )
																								if single_line_import_search is not None:
																														objects.extend(single_line_import_search.groups()[0].split(', '      )      )
																								elif line.startswith(' ' * 12      ):
																														objects.append(line[12:-2]      )
																								line_index += 1

																		_UpperCAmelCase							   =     objects
												else:
																		line_index += 1

						return backend_specific_objects


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    Any:





						"""simple docstring"""



						if name.isupper():
												return DUMMY_CONSTANT.format(__lowerCAmelCase      )
						elif name.islower():
												return DUMMY_FUNCTION.format(__lowerCAmelCase					,						__lowerCAmelCase      )
						else:
												return DUMMY_CLASS.format(__lowerCAmelCase					,						__lowerCAmelCase      )


def __A       ( __lowerCAmelCase=None      )->    Union[str, Any]:





						"""simple docstring"""



						if backend_specific_objects is None:
												_UpperCAmelCase							   =     read_init()
						# For special correspondence backend to module name as used in the function requires_modulename
						_UpperCAmelCase							   =     {}

						for backend, objects in backend_specific_objects.items():
												_UpperCAmelCase							   =     '[' + ', '.join(F"""\"{b}\"""" for b in backend.split('_and_'      )      ) + ']'
												_UpperCAmelCase							   =     '# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
												dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
												dummy_file += "\n".join([create_dummy_object(__lowerCAmelCase					,						__lowerCAmelCase      ) for o in objects]      )
												_UpperCAmelCase							   =     dummy_file

						return dummy_files


def __A       ( __lowerCAmelCase=False      )->    int:





						"""simple docstring"""



						_UpperCAmelCase							   =     create_dummy_files()
						# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
						_UpperCAmelCase							   =     {'torch': 'pt'}

						# Locate actual dummy modules and read their content.
						_UpperCAmelCase							   =     os.path.join(__lowerCAmelCase					,						'utils'      )
						_UpperCAmelCase							   =     {
						    backend: os.path.join(__lowerCAmelCase					,						F"""dummy_{short_names.get(__lowerCAmelCase					,						__lowerCAmelCase      )}_objects.py"""      )
						    for backend in dummy_files.keys()
						}

						_UpperCAmelCase							   =     {}
						for backend, file_path in dummy_file_paths.items():
												if os.path.isfile(__lowerCAmelCase      ):
																		with open(__lowerCAmelCase					,						'r'					,						encoding='utf-8'					,						newline='\n'      ) as f:
																								_UpperCAmelCase							   =     f.read()
												else:
																		_UpperCAmelCase							   =     ''

						for backend in dummy_files.keys():
												if dummy_files[backend] != actual_dummies[backend]:
																		if overwrite:
																								print(
																								    F"""Updating transformers.utils.dummy_{short_names.get(__lowerCAmelCase					,						__lowerCAmelCase      )}_objects.py as the main """
																								    '__init__ has new objects.'      )
																								with open(dummy_file_paths[backend]					,						'w'					,						encoding='utf-8'					,						newline='\n'      ) as f:
																														f.write(dummy_files[backend]      )
																		else:
																								raise ValueError(
																								    'The main __init__ has objects that are not present in '
																								    F"""transformers.utils.dummy_{short_names.get(__lowerCAmelCase					,						__lowerCAmelCase      )}_objects.py. Run `make fix-copies` """
																								    'to fix this.'      )


if __name__ == "__main__":
		_a       =    argparse.ArgumentParser()
		parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
		_a       =    parser.parse_args()

		check_dummies(args.fix_and_overwrite)
