



"""simple docstring"""







import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

A    =			logging.getLogger()

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							Dict:
							A         =					{}
							A         =					os.path.join(lowerCamelCase__				,      'all_results.json'			)
							if os.path.exists(lowerCamelCase__			):
														with open(lowerCamelCase__				,      'r'			) as f:
																					A         =					json.load(lowerCamelCase__			)
							else:
														raise ValueError(f"""can't find {path}"""			)
							return results


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






@require_torch_tpu
class      UpperCAmelCase__		(      UpperCamelCase     ):



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






									'''simple docstring'''

									import xla_spawn

									A         =					self.get_auto_remove_tmp_dir()
									A         =					f"""
            ./examples/pytorch/text-classification/run_glue.py
            --num_cores=8
            ./examples/pytorch/text-classification/run_glue.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
            --debug tpu_metrics_debug
            --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()

									with patch.object(snake_case      ,							'argv'      ,							snake_case				):
																A         =					time()
																xla_spawn.main()
																A         =					time()

																A         =					get_results(snake_case				)
																self.assertGreaterEqual(result['eval_accuracy']      ,							0.75				)

																# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
																self.assertLess(end - start      ,							500				)



		def 					A_		( self  :    Dict				)			->						Tuple:






									'''simple docstring'''

									import xla_spawn

									A         =					'\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            '.split()
									with patch.object(snake_case      ,							'argv'      ,							snake_case				):
																xla_spawn.main()








"""simple docstring"""







import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY






@is_pipeline_test
class      UpperCAmelCase__		(      unittest.TestCase     ):
		lowerCAmelCase_       :	int 							=     MODEL_FOR_MASKED_LM_MAPPING
		lowerCAmelCase_       :	Tuple 							=     TF_MODEL_FOR_MASKED_LM_MAPPING



		def 					A_		( self  :    Tuple				)			->						int:






									'''simple docstring'''

									super().tearDown()
									# clean-up as much as possible GPU memory occupied by PyTorch
									gc.collect()
									if is_torch_available():
																import torch

																torch.cuda.empty_cache()



		@require_tf
		def 					A_		( self  :    int				)			->						Optional[int]:






									'''simple docstring'''

									A         =					pipeline(task='fill-mask'      ,							model='sshleifer/tiny-distilroberta-base'      ,							top_k=2      ,							framework='tf'				)
									A         =					unmasker('My name is <mask>'				)
									self.assertEqual(
									    nested_simplify(snake_case      ,							decimals=6				)      ,							[
									        {'sequence': 'My name is grouped', 'score': 2.1E-0_5, 'token': 38_015, 'token_str': ' grouped'},
									        {'sequence': 'My name is accuser', 'score': 2.1E-0_5, 'token': 25_506, 'token_str': ' accuser'},
									    ]      ,							)

									A         =					unmasker('The largest city in France is <mask>'				)
									self.assertEqual(
									    nested_simplify(snake_case      ,							decimals=6				)      ,							[
									        {
									            'sequence': 'The largest city in France is grouped',
									            'score': 2.1E-0_5,
									            'token': 38_015,
									            'token_str': ' grouped',
									        },
									        {
									            'sequence': 'The largest city in France is accuser',
									            'score': 2.1E-0_5,
									            'token': 25_506,
									            'token_str': ' accuser',
									        },
									    ]      ,							)

									A         =					unmasker('My name is <mask>'      ,							targets=[' Patrick', ' Clara', ' Teven']      ,							top_k=3				)
									self.assertEqual(
									    nested_simplify(snake_case      ,							decimals=6				)      ,							[
									        {'sequence': 'My name is Clara', 'score': 2E-0_5, 'token': 13_606, 'token_str': ' Clara'},
									        {'sequence': 'My name is Patrick', 'score': 2E-0_5, 'token': 3_499, 'token_str': ' Patrick'},
									        {'sequence': 'My name is Te', 'score': 1.9E-0_5, 'token': 2_941, 'token_str': ' Te'},
									    ]      ,							)



		@require_torch
		def 					A_		( self  :    str				)			->						int:






									'''simple docstring'''

									A         =					pipeline(task='fill-mask'      ,							model='sshleifer/tiny-distilroberta-base'      ,							top_k=2      ,							framework='pt'				)

									A         =					unmasker('My name is <mask>'				)
									self.assertEqual(
									    nested_simplify(snake_case      ,							decimals=6				)      ,							[
									        {'sequence': 'My name is Maul', 'score': 2.2E-0_5, 'token': 35_676, 'token_str': ' Maul'},
									        {'sequence': 'My name isELS', 'score': 2.2E-0_5, 'token': 16_416, 'token_str': 'ELS'},
									    ]      ,							)

									A         =					unmasker('The largest city in France is <mask>'				)
									self.assertEqual(
									    nested_simplify(snake_case      ,							decimals=6				)      ,							[
									        {
									            'sequence': 'The largest city in France is Maul',
									            'score': 2.2E-0_5,
									            'token': 35_676,
									            'token_str': ' Maul',
									        },
									        {'sequence': 'The largest city in France isELS', 'score': 2.2E-0_5, 'token': 16_416, 'token_str': 'ELS'},
									    ]      ,							)

									A         =					unmasker('My name is <mask>'      ,							targets=[' Patrick', ' Clara', ' Teven']      ,							top_k=3				)
									self.assertEqual(
									    nested_simplify(snake_case      ,							decimals=6				)      ,							[
									        {'sequence': 'My name is Patrick', 'score': 2.1E-0_5, 'token': 3_499, 'token_str': ' Patrick'},
									        {'sequence': 'My name is Te', 'score': 2E-0_5, 'token': 2_941, 'token_str': ' Te'},
									        {'sequence': 'My name is Clara', 'score': 2E-0_5, 'token': 13_606, 'token_str': ' Clara'},
									    ]      ,							)

									A         =					unmasker('My name is <mask> <mask>'      ,							top_k=2				)

									self.assertEqual(
									    nested_simplify(snake_case      ,							decimals=6				)      ,							[
									        [
									            {
									                'score': 2.2E-0_5,
									                'token': 35_676,
									                'token_str': ' Maul',
									                'sequence': '<s>My name is Maul<mask></s>',
									            },
									            {'score': 2.2E-0_5, 'token': 16_416, 'token_str': 'ELS', 'sequence': '<s>My name isELS<mask></s>'},
									        ],
									        [
									            {
									                'score': 2.2E-0_5,
									                'token': 35_676,
									                'token_str': ' Maul',
									                'sequence': '<s>My name is<mask> Maul</s>',
									            },
									            {'score': 2.2E-0_5, 'token': 16_416, 'token_str': 'ELS', 'sequence': '<s>My name is<mask>ELS</s>'},
									        ],
									    ]      ,							)



		@require_torch_gpu
		def 					A_		( self  :    Tuple				)			->						List[str]:






									'''simple docstring'''

									A         =					pipeline('fill-mask'      ,							model='hf-internal-testing/tiny-random-distilbert'      ,							device=0      ,							framework='pt'				)

									# convert model to fp16
									pipe.model.half()

									A         =					pipe('Paris is the [MASK] of France.'				)
									# We actually don't care about the result, we just want to make sure
									# it works, meaning the float16 tensor got casted back to float32
									# for postprocessing.
									self.assertIsInstance(snake_case      ,							snake_case				)



		@slow
		@require_torch
		def 					A_		( self  :    List[Any]				)			->						int:






									'''simple docstring'''

									A         =					pipeline(task='fill-mask'      ,							model='distilroberta-base'      ,							top_k=2      ,							framework='pt'				)
									self.run_large_test(snake_case				)



		@slow
		@require_tf
		def 					A_		( self  :    Tuple				)			->						Optional[int]:






									'''simple docstring'''

									A         =					pipeline(task='fill-mask'      ,							model='distilroberta-base'      ,							top_k=2      ,							framework='tf'				)
									self.run_large_test(snake_case				)



		def 					A_		( self  :    Dict      ,							snake_case  :    List[Any]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					unmasker('My name is <mask>'				)
									self.assertEqual(
									    nested_simplify(snake_case				)      ,							[
									        {'sequence': 'My name is John', 'score': 0.008, 'token': 610, 'token_str': ' John'},
									        {'sequence': 'My name is Chris', 'score': 0.007, 'token': 1_573, 'token_str': ' Chris'},
									    ]      ,							)
									A         =					unmasker('The largest city in France is <mask>'				)
									self.assertEqual(
									    nested_simplify(snake_case				)      ,							[
									        {
									            'sequence': 'The largest city in France is Paris',
									            'score': 0.251,
									            'token': 2_201,
									            'token_str': ' Paris',
									        },
									        {
									            'sequence': 'The largest city in France is Lyon',
									            'score': 0.214,
									            'token': 12_790,
									            'token_str': ' Lyon',
									        },
									    ]      ,							)

									A         =					unmasker('My name is <mask>'      ,							targets=[' Patrick', ' Clara', ' Teven']      ,							top_k=3				)
									self.assertEqual(
									    nested_simplify(snake_case				)      ,							[
									        {'sequence': 'My name is Patrick', 'score': 0.005, 'token': 3_499, 'token_str': ' Patrick'},
									        {'sequence': 'My name is Clara', 'score': 0.000, 'token': 13_606, 'token_str': ' Clara'},
									        {'sequence': 'My name is Te', 'score': 0.000, 'token': 2_941, 'token_str': ' Te'},
									    ]      ,							)



		@require_torch
		def 					A_		( self  :    List[str]				)			->						Any:






									'''simple docstring'''

									A         =					pipeline(task='fill-mask'      ,							model='sshleifer/tiny-distilroberta-base'      ,							framework='pt'				)
									A         =					None
									A         =					None
									self.run_pipeline_test(snake_case      ,							[]				)



		@require_tf
		def 					A_		( self  :    Tuple				)			->						Dict:






									'''simple docstring'''

									A         =					pipeline(task='fill-mask'      ,							model='sshleifer/tiny-distilroberta-base'      ,							framework='tf'				)
									A         =					None
									A         =					None
									self.run_pipeline_test(snake_case      ,							[]				)



		def 					A_		( self  :    str      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    Optional[int]      ,							snake_case  :    Optional[Any]				)			->						Union[str, Any]:






									'''simple docstring'''

									if tokenizer is None or tokenizer.mask_token_id is None:
																self.skipTest('The provided tokenizer has no mask token, (probably reformer or wav2vec2)'				)

									A         =					FillMaskPipeline(model=snake_case      ,							tokenizer=snake_case				)
									A         =					[
									    f"""This is another {tokenizer.mask_token} test""",
									]
									return fill_masker, examples



		def 					A_		( self  :    Any      ,							snake_case  :    Any      ,							snake_case  :    Optional[Any]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					fill_masker.tokenizer
									A         =					fill_masker.model

									A         =					fill_masker(
									    f"""This is a {tokenizer.mask_token}"""      ,							)
									self.assertEqual(
									    snake_case      ,							[
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									    ]      ,							)

									A         =					fill_masker([f"""This is a {tokenizer.mask_token}"""]				)
									self.assertEqual(
									    snake_case      ,							[
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									    ]      ,							)

									A         =					fill_masker([f"""This is a {tokenizer.mask_token}""", f"""Another {tokenizer.mask_token} great test."""]				)
									self.assertEqual(
									    snake_case      ,							[
									        [
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        ],
									        [
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        ],
									    ]      ,							)

									with self.assertRaises(snake_case				):
																fill_masker([None]				)
									# No mask_token is not supported
									with self.assertRaises(snake_case				):
																fill_masker('This is'				)

									self.run_test_top_k(snake_case      ,							snake_case				)
									self.run_test_targets(snake_case      ,							snake_case				)
									self.run_test_top_k_targets(snake_case      ,							snake_case				)
									self.fill_mask_with_duplicate_targets_and_top_k(snake_case      ,							snake_case				)
									self.fill_mask_with_multiple_masks(snake_case      ,							snake_case				)



		def 					A_		( self  :    str      ,							snake_case  :    Any      ,							snake_case  :    Optional[int]				)			->						str:






									'''simple docstring'''

									A         =					tokenizer.get_vocab()
									A         =					sorted(vocab.keys()				)[:2]
									# Pipeline argument
									A         =					FillMaskPipeline(model=snake_case      ,							tokenizer=snake_case      ,							targets=snake_case				)
									A         =					fill_masker(f"""This is a {tokenizer.mask_token}"""				)
									self.assertEqual(
									    snake_case      ,							[
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									    ]      ,							)
									A         =					{vocab[el] for el in targets}
									self.assertEqual({el['token'] for el in outputs}      ,							snake_case				)
									A         =					[tokenizer.decode([x]				) for x in target_ids]
									self.assertEqual({el['token_str'] for el in outputs}      ,							set(snake_case				)				)

									# Call argument
									A         =					FillMaskPipeline(model=snake_case      ,							tokenizer=snake_case				)
									A         =					fill_masker(f"""This is a {tokenizer.mask_token}"""      ,							targets=snake_case				)
									self.assertEqual(
									    snake_case      ,							[
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									    ]      ,							)
									A         =					{vocab[el] for el in targets}
									self.assertEqual({el['token'] for el in outputs}      ,							snake_case				)
									A         =					[tokenizer.decode([x]				) for x in target_ids]
									self.assertEqual({el['token_str'] for el in outputs}      ,							set(snake_case				)				)

									# Score equivalence
									A         =					fill_masker(f"""This is a {tokenizer.mask_token}"""      ,							targets=snake_case				)
									A         =					[top_mask['token_str'] for top_mask in outputs]
									A         =					[top_mask['score'] for top_mask in outputs]

									# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
									if set(snake_case				) == set(snake_case				):
																A         =					fill_masker(f"""This is a {tokenizer.mask_token}"""      ,							targets=snake_case				)
																A         =					[top_mask['score'] for top_mask in unmasked_targets]
																self.assertEqual(nested_simplify(snake_case				)      ,							nested_simplify(snake_case				)				)

									# Raises with invalid
									with self.assertRaises(snake_case				):
																A         =					fill_masker(f"""This is a {tokenizer.mask_token}"""      ,							targets=[]				)
									# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
									if "" not in tokenizer.get_vocab():
																with self.assertRaises(snake_case				):
																							A         =					fill_masker(f"""This is a {tokenizer.mask_token}"""      ,							targets=['']				)
																with self.assertRaises(snake_case				):
																							A         =					fill_masker(f"""This is a {tokenizer.mask_token}"""      ,							targets=''				)



		def 					A_		( self  :    Any      ,							snake_case  :    Optional[Any]      ,							snake_case  :    int				)			->						List[Any]:






									'''simple docstring'''

									A         =					FillMaskPipeline(model=snake_case      ,							tokenizer=snake_case      ,							top_k=2				)
									A         =					fill_masker(f"""This is a {tokenizer.mask_token}"""				)
									self.assertEqual(
									    snake_case      ,							[
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									    ]      ,							)

									A         =					FillMaskPipeline(model=snake_case      ,							tokenizer=snake_case				)
									A         =					fill_masker(f"""This is a {tokenizer.mask_token}"""      ,							top_k=2				)
									self.assertEqual(
									    snake_case      ,							[
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									    ]      ,							)
									self.assertEqual(nested_simplify(snake_case				)      ,							nested_simplify(snake_case				)				)



		def 					A_		( self  :    str      ,							snake_case  :    List[str]      ,							snake_case  :    Dict				)			->						Tuple:






									'''simple docstring'''

									A         =					tokenizer.get_vocab()
									A         =					FillMaskPipeline(model=snake_case      ,							tokenizer=snake_case				)

									# top_k=2, ntargets=3
									A         =					sorted(vocab.keys()				)[:3]
									A         =					fill_masker(f"""This is a {tokenizer.mask_token}"""      ,							top_k=2      ,							targets=snake_case				)

									# If we use the most probably targets, and filter differently, we should still
									# have the same results
									A         =					[el['token_str'] for el in sorted(snake_case      ,							key=lambda snake_case				: x["score"]      ,							reverse=snake_case				)]
									# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
									if set(snake_case				).issubset(snake_case				):
																A         =					fill_masker(f"""This is a {tokenizer.mask_token}"""      ,							top_k=3      ,							targets=snake_case				)
																# They should yield exactly the same result
																self.assertEqual(nested_simplify(snake_case				)      ,							nested_simplify(snake_case				)				)



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    Dict				)			->						int:






									'''simple docstring'''

									A         =					FillMaskPipeline(model=snake_case      ,							tokenizer=snake_case				)
									A         =					tokenizer.get_vocab()
									# String duplicates + id duplicates
									A         =					sorted(vocab.keys()				)[:3]
									A         =					[targets[0], targets[1], targets[0], targets[2], targets[1]]
									A         =					fill_masker(f"""My name is {tokenizer.mask_token}"""      ,							targets=snake_case      ,							top_k=10				)

									# The target list contains duplicates, so we can't output more
									# than them
									self.assertEqual(len(snake_case				)      ,							3				)



		def 					A_		( self  :    str      ,							snake_case  :    List[Any]      ,							snake_case  :    Union[str, Any]				)			->						Union[str, Any]:






									'''simple docstring'''

									A         =					FillMaskPipeline(model=snake_case      ,							tokenizer=snake_case				)

									A         =					fill_masker(
									    f"""This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}"""      ,							top_k=2				)
									self.assertEqual(
									    snake_case      ,							[
									        [
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        ],
									        [
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        ],
									        [
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        ],
									    ]      ,							)



