




"""simple docstring"""







import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
		import os

		# The slow tests are often failing with OOM error on GPU
		# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
		# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
		a  =				'''platform'''
		import jax
		import jax.numpy as jnp
		import numpy as np

		from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel






@require_flax
class    SCREAMING_SNAKE_CASE__       :
						_a         =    PegasusConfig
						_a         =    {}
						_a         =    """gelu"""


						def __init__(				self					:      Union[str, Any]    ,    lowerCAmelCase					:      List[str]    ,    lowerCAmelCase					:      List[Any]=13    ,    lowerCAmelCase					:      int=7    ,    lowerCAmelCase					:      Dict=True    ,    lowerCAmelCase					:      Optional[int]=False    ,    lowerCAmelCase					:      str=99    ,    lowerCAmelCase					:      Union[str, Any]=32    ,    lowerCAmelCase					:      str=5    ,    lowerCAmelCase					:      int=4    ,    lowerCAmelCase					:      Optional[Any]=37    ,    lowerCAmelCase					:      Optional[Any]=0.1    ,    lowerCAmelCase					:      List[str]=0.1    ,    lowerCAmelCase					:      List[str]=20    ,    lowerCAmelCase					:      Dict=2    ,    lowerCAmelCase					:      Optional[Any]=1    ,    lowerCAmelCase					:      int=0    ,    ):
										lowerCAmelCase						=					parent
										lowerCAmelCase						=					batch_size
										lowerCAmelCase						=					seq_length
										lowerCAmelCase						=					is_training
										lowerCAmelCase						=					use_labels
										lowerCAmelCase						=					vocab_size
										lowerCAmelCase						=					hidden_size
										lowerCAmelCase						=					num_hidden_layers
										lowerCAmelCase						=					num_attention_heads
										lowerCAmelCase						=					intermediate_size

										lowerCAmelCase						=					hidden_dropout_prob
										lowerCAmelCase						=					attention_probs_dropout_prob
										lowerCAmelCase						=					max_position_embeddings
										lowerCAmelCase						=					eos_token_id
										lowerCAmelCase						=					pad_token_id
										lowerCAmelCase						=					bos_token_id


						def 	__lowercase			(				self					:      List[str]       ):
										lowerCAmelCase						=					ids_tensor([self.batch_size, self.seq_length - 1]    ,    self.vocab_size       ).clip(3    ,    self.vocab_size       )
										lowerCAmelCase						=					np.expand_dims(np.array([self.eos_token_id] * self.batch_size       )    ,    1       )
										lowerCAmelCase						=					np.concatenate([input_ids, eos_tensor]    ,    axis=1       )

										lowerCAmelCase						=					ids_tensor([self.batch_size, self.seq_length]    ,    self.vocab_size       )

										lowerCAmelCase						=					self.config_cls(
										    vocab_size=self.vocab_size    ,    d_model=self.hidden_size    ,    encoder_layers=self.num_hidden_layers    ,    decoder_layers=self.num_hidden_layers    ,    encoder_attention_heads=self.num_attention_heads    ,    decoder_attention_heads=self.num_attention_heads    ,    encoder_ffn_dim=self.intermediate_size    ,    decoder_ffn_dim=self.intermediate_size    ,    dropout=self.hidden_dropout_prob    ,    attention_dropout=self.attention_probs_dropout_prob    ,    max_position_embeddings=self.max_position_embeddings    ,    eos_token_ids=[2]    ,    bos_token_id=self.bos_token_id    ,    pad_token_id=self.pad_token_id    ,    decoder_start_token_id=self.pad_token_id    ,    **self.config_updates    ,    )
										lowerCAmelCase						=					prepare_pegasus_inputs_dict(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__       )
										return config, inputs_dict


						def 	__lowercase			(				self					:      Union[str, Any]    ,    lowerCAmelCase					:      Tuple    ,    lowerCAmelCase					:      str    ,    lowerCAmelCase					:      Optional[int]       ):
										lowerCAmelCase						=					20
										lowerCAmelCase						=					model_class_name(lowerCAmelCase__       )

										lowerCAmelCase						=					model.encode(inputs_dict["""input_ids"""]       )

										lowerCAmelCase						=					(
										    inputs_dict["decoder_input_ids"],
										    inputs_dict["decoder_attention_mask"],
										)

										lowerCAmelCase						=					model.init_cache(decoder_input_ids.shape[0]    ,    lowerCAmelCase__    ,    lowerCAmelCase__       )
										lowerCAmelCase						=					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,    dtype="""i4"""       )

										lowerCAmelCase						=					jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]    ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,    )
										lowerCAmelCase						=					model.decode(
										    decoder_input_ids[:, :-1]    ,    lowerCAmelCase__    ,    decoder_attention_mask=lowerCAmelCase__    ,    past_key_values=lowerCAmelCase__    ,    decoder_position_ids=lowerCAmelCase__    ,    )

										lowerCAmelCase						=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,    dtype="""i4"""       )
										lowerCAmelCase						=					model.decode(
										    decoder_input_ids[:, -1:]    ,    lowerCAmelCase__    ,    decoder_attention_mask=lowerCAmelCase__    ,    past_key_values=outputs_cache.past_key_values    ,    decoder_position_ids=lowerCAmelCase__    ,    )

										lowerCAmelCase						=					model.decode(lowerCAmelCase__    ,    lowerCAmelCase__       )

										lowerCAmelCase						=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
										self.parent.assertTrue(diff < 1e-3    ,    msg=f'''Max diff is {diff}'''       )




						def 	__lowercase			(				self					:      Union[str, Any]    ,    lowerCAmelCase					:      Union[str, Any]    ,    lowerCAmelCase					:      Union[str, Any]    ,    lowerCAmelCase					:      Dict       ):
										lowerCAmelCase						=					20
										lowerCAmelCase						=					model_class_name(lowerCAmelCase__       )

										lowerCAmelCase						=					model.encode(inputs_dict["""input_ids"""]       )

										lowerCAmelCase						=					(
										    inputs_dict["decoder_input_ids"],
										    inputs_dict["decoder_attention_mask"],
										)

										lowerCAmelCase						=					jnp.concatenate(
										    [
										        decoder_attention_mask,
										        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])       ),
										    ]    ,    axis=-1    ,    )

										lowerCAmelCase						=					model.init_cache(decoder_input_ids.shape[0]    ,    lowerCAmelCase__    ,    lowerCAmelCase__       )
										lowerCAmelCase						=					jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]    ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,    )

										lowerCAmelCase						=					model.decode(
										    decoder_input_ids[:, :-1]    ,    lowerCAmelCase__    ,    decoder_attention_mask=lowerCAmelCase__    ,    past_key_values=lowerCAmelCase__    ,    decoder_position_ids=lowerCAmelCase__    ,    )
										lowerCAmelCase						=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,    dtype="""i4"""       )
										lowerCAmelCase						=					model.decode(
										    decoder_input_ids[:, -1:]    ,    lowerCAmelCase__    ,    past_key_values=outputs_cache.past_key_values    ,    decoder_attention_mask=lowerCAmelCase__    ,    decoder_position_ids=lowerCAmelCase__    ,    )

										lowerCAmelCase						=					model.decode(lowerCAmelCase__    ,    lowerCAmelCase__    ,    decoder_attention_mask=lowerCAmelCase__       )

										lowerCAmelCase						=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
										self.parent.assertTrue(diff < 1e-3    ,    msg=f'''Max diff is {diff}'''       )





def 				lowercase       (snake_case__ :			Dict   ,							snake_case__ :			int   ,							snake_case__ :			Optional[Any]   ,							snake_case__ :			Dict=None   ,							snake_case__ :			List[Any]=None   ,							)  ->	Union[str, Any]:


				'''simple docstring'''






				if attention_mask is None:
								lowerCAmelCase						=					np.not_equal(_lowercase   ,							config.pad_token_id			).astype(np.inta			)
				if decoder_attention_mask is None:
								lowerCAmelCase						=					np.concatenate(
								    [
								        np.ones(decoder_input_ids[:, :1].shape   ,							dtype=np.inta			),
								        np.not_equal(decoder_input_ids[:, 1:]   ,							config.pad_token_id			).astype(np.inta			),
								    ]   ,							axis=-1   ,							)
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": decoder_attention_mask,
				}






@require_flax
class    SCREAMING_SNAKE_CASE__       (       a__    ,					unittest.TestCase       ):
						_a         =    (
						    (
						        FlaxPegasusForConditionalGeneration,
						        FlaxPegasusModel,
						    )
						    if is_flax_available()
						    else ()
						)
						_a         =    (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
						_a         =    True
						_a         =    False
						_a         =    False
						_a         =    False


						def 	__lowercase			(				self					:      List[Any]       ):
										lowerCAmelCase						=					FlaxPegasusModelTester(self       )
										lowerCAmelCase						=					ConfigTester(self    ,    config_class=lowerCAmelCase__       )


						def 	__lowercase			(				self					:      str       ):
										self.config_tester.run_common_tests()


						def 	__lowercase			(				self					:      Union[str, Any]       ):
										lowerCAmelCase						=					self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
														self.model_tester.check_use_cache_forward(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__       )


						def 	__lowercase			(				self					:      List[Any]       ):
										lowerCAmelCase						=					self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
														self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__       )


						def 	__lowercase			(				self					:      List[str]       ):
										lowerCAmelCase						=					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__       ):
																		lowerCAmelCase						=					self._prepare_for_class(lowerCAmelCase__    ,    lowerCAmelCase__       )
																		lowerCAmelCase						=					model_class(lowerCAmelCase__       )

																		@jax.jit
																		def encode_jitted(lowerCAmelCase					:      Any    ,    lowerCAmelCase					:      Any=None    ,    **lowerCAmelCase					:      Tuple       ):
																						return model.encode(input_ids=lowerCAmelCase__    ,    attention_mask=lowerCAmelCase__       )

																		with self.subTest("""JIT Enabled"""       ):
																						lowerCAmelCase						=					encode_jitted(**lowerCAmelCase__       ).to_tuple()

																		with self.subTest("""JIT Disabled"""       ):
																						with jax.disable_jit():
																										lowerCAmelCase						=					encode_jitted(**lowerCAmelCase__       ).to_tuple()

																		self.assertEqual(len(lowerCAmelCase__       )    ,    len(lowerCAmelCase__       )       )
																		for jitted_output, output in zip(lowerCAmelCase__    ,    lowerCAmelCase__       ):
																						self.assertEqual(jitted_output.shape    ,    output.shape       )


						def 	__lowercase			(				self					:      List[str]       ):
										lowerCAmelCase						=					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__       ):
																		lowerCAmelCase						=					model_class(lowerCAmelCase__       )
																		lowerCAmelCase						=					model.encode(inputs_dict["""input_ids"""]    ,    inputs_dict["""attention_mask"""]       )

																		lowerCAmelCase						=					{
																		    "decoder_input_ids": inputs_dict["decoder_input_ids"],
																		    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
																		    "encoder_outputs": encoder_outputs,
																		}

																		@jax.jit
																		def decode_jitted(lowerCAmelCase					:      int    ,    lowerCAmelCase					:      Any    ,    lowerCAmelCase					:      Dict       ):
																						return model.decode(
																						    decoder_input_ids=lowerCAmelCase__    ,    decoder_attention_mask=lowerCAmelCase__    ,    encoder_outputs=lowerCAmelCase__    ,    )

																		with self.subTest("""JIT Enabled"""       ):
																						lowerCAmelCase						=					decode_jitted(**lowerCAmelCase__       ).to_tuple()

																		with self.subTest("""JIT Disabled"""       ):
																						with jax.disable_jit():
																										lowerCAmelCase						=					decode_jitted(**lowerCAmelCase__       ).to_tuple()

																		self.assertEqual(len(lowerCAmelCase__       )    ,    len(lowerCAmelCase__       )       )
																		for jitted_output, output in zip(lowerCAmelCase__    ,    lowerCAmelCase__       ):
																						self.assertEqual(jitted_output.shape    ,    output.shape       )


						@slow
						def 	__lowercase			(				self					:      List[str]       ):
										for model_class_name in self.all_model_classes:
														lowerCAmelCase						=					model_class_name.from_pretrained("""google/pegasus-large"""    ,    from_pt=lowerCAmelCase__       )
														lowerCAmelCase						=					np.ones((1, 1)       )
														lowerCAmelCase						=					model(lowerCAmelCase__       )
														self.assertIsNotNone(lowerCAmelCase__       )




						@slow
						def 	__lowercase			(				self					:      List[str]       ):
										lowerCAmelCase						=					FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum"""       )
										lowerCAmelCase						=					PegasusTokenizer.from_pretrained("""google/pegasus-xsum"""       )

										lowerCAmelCase						=					[
										    " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
										    " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
										]

										lowerCAmelCase						=					[
										    "California's largest electricity provider has turned off power to hundreds of thousands of customers.",
										    "Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.",
										]

										lowerCAmelCase						=					tokenizer(lowerCAmelCase__    ,    return_tensors="""np"""    ,    truncation=lowerCAmelCase__    ,    max_length=512    ,    padding=lowerCAmelCase__       )
										lowerCAmelCase						=					model.generate(**lowerCAmelCase__    ,    num_beams=2       ).sequences
										lowerCAmelCase						=					tokenizer.batch_decode(lowerCAmelCase__    ,    skip_special_tokens=lowerCAmelCase__       )
										assert tgt_text == decoded












"""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  :   Union[str, Any]    =MODEL_FOR_MASKED_LM_MAPPING
							lowerCamelCase  :   List[str]    =TF_MODEL_FOR_MASKED_LM_MAPPING






							def        __a  (  self )      ->					Tuple:
								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 )      ->					List[Any]:
								a :						Dict      =							pipeline(task="fill-mask" ,   model="sshleifer/tiny-distilroberta-base" ,   top_k=2 ,   framework="tf" )
								a :						Optional[Any]      =							unmasker("My name is <mask>" )
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__ ,   decimals=6 ) ,   [
								        {"sequence": "My name is grouped", "score": 2.1E-05, "token": 3_8015, "token_str": " grouped"},
								        {"sequence": "My name is accuser", "score": 2.1E-05, "token": 2_5506, "token_str": " accuser"},
								    ] ,   )

								a :						str      =							unmasker("The largest city in France is <mask>" )
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__ ,   decimals=6 ) ,   [
								        {
								            "sequence": "The largest city in France is grouped",
								            "score": 2.1E-05,
								            "token": 3_8015,
								            "token_str": " grouped",
								        },
								        {
								            "sequence": "The largest city in France is accuser",
								            "score": 2.1E-05,
								            "token": 2_5506,
								            "token_str": " accuser",
								        },
								    ] ,   )

								a :						Any      =							unmasker("My name is <mask>" ,   targets=[" Patrick", " Clara", " Teven"] ,   top_k=3 )
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__ ,   decimals=6 ) ,   [
								        {"sequence": "My name is Clara", "score": 2E-05, "token": 1_3606, "token_str": " Clara"},
								        {"sequence": "My name is Patrick", "score": 2E-05, "token": 3499, "token_str": " Patrick"},
								        {"sequence": "My name is Te", "score": 1.9E-05, "token": 2941, "token_str": " Te"},
								    ] ,   )






							@require_torch
							def        __a  (  self )      ->					Tuple:
								a :						Optional[int]      =							pipeline(task="fill-mask" ,   model="sshleifer/tiny-distilroberta-base" ,   top_k=2 ,   framework="pt" )

								a :						int      =							unmasker("My name is <mask>" )
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__ ,   decimals=6 ) ,   [
								        {"sequence": "My name is Maul", "score": 2.2E-05, "token": 3_5676, "token_str": " Maul"},
								        {"sequence": "My name isELS", "score": 2.2E-05, "token": 1_6416, "token_str": "ELS"},
								    ] ,   )

								a :						Optional[Any]      =							unmasker("The largest city in France is <mask>" )
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__ ,   decimals=6 ) ,   [
								        {
								            "sequence": "The largest city in France is Maul",
								            "score": 2.2E-05,
								            "token": 3_5676,
								            "token_str": " Maul",
								        },
								        {"sequence": "The largest city in France isELS", "score": 2.2E-05, "token": 1_6416, "token_str": "ELS"},
								    ] ,   )

								a :						List[str]      =							unmasker("My name is <mask>" ,   targets=[" Patrick", " Clara", " Teven"] ,   top_k=3 )
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__ ,   decimals=6 ) ,   [
								        {"sequence": "My name is Patrick", "score": 2.1E-05, "token": 3499, "token_str": " Patrick"},
								        {"sequence": "My name is Te", "score": 2E-05, "token": 2941, "token_str": " Te"},
								        {"sequence": "My name is Clara", "score": 2E-05, "token": 1_3606, "token_str": " Clara"},
								    ] ,   )

								a :						Optional[int]      =							unmasker("My name is <mask> <mask>" ,   top_k=2 )

								self.assertEqual(
								    nested_simplify(lowerCAmelCase__ ,   decimals=6 ) ,   [
								        [
								            {
								                "score": 2.2E-05,
								                "token": 3_5676,
								                "token_str": " Maul",
								                "sequence": "<s>My name is Maul<mask></s>",
								            },
								            {"score": 2.2E-05, "token": 1_6416, "token_str": "ELS", "sequence": "<s>My name isELS<mask></s>"},
								        ],
								        [
								            {
								                "score": 2.2E-05,
								                "token": 3_5676,
								                "token_str": " Maul",
								                "sequence": "<s>My name is<mask> Maul</s>",
								            },
								            {"score": 2.2E-05, "token": 1_6416, "token_str": "ELS", "sequence": "<s>My name is<mask>ELS</s>"},
								        ],
								    ] ,   )






							@require_torch_gpu
							def        __a  (  self )      ->					Optional[Any]:
								a :						List[Any]      =							pipeline("fill-mask" ,   model="hf-internal-testing/tiny-random-distilbert" ,   device=0 ,   framework="pt" )

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

								a :						Optional[Any]      =							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(lowerCAmelCase__ ,   lowerCAmelCase__ )






							@slow
							@require_torch
							def        __a  (  self )      ->					Any:
								a :						List[Any]      =							pipeline(task="fill-mask" ,   model="distilroberta-base" ,   top_k=2 ,   framework="pt" )
								self.run_large_test(lowerCAmelCase__ )






							@slow
							@require_tf
							def        __a  (  self )      ->					Any:
								a :						int      =							pipeline(task="fill-mask" ,   model="distilroberta-base" ,   top_k=2 ,   framework="tf" )
								self.run_large_test(lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ )      ->					List[Any]:
								a :						List[str]      =							unmasker("My name is <mask>" )
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__ ) ,   [
								        {"sequence": "My name is John", "score": 0.008, "token": 610, "token_str": " John"},
								        {"sequence": "My name is Chris", "score": 0.007, "token": 1573, "token_str": " Chris"},
								    ] ,   )
								a :						Union[str, Any]      =							unmasker("The largest city in France is <mask>" )
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__ ) ,   [
								        {
								            "sequence": "The largest city in France is Paris",
								            "score": 0.251,
								            "token": 2201,
								            "token_str": " Paris",
								        },
								        {
								            "sequence": "The largest city in France is Lyon",
								            "score": 0.214,
								            "token": 1_2790,
								            "token_str": " Lyon",
								        },
								    ] ,   )

								a :						Tuple      =							unmasker("My name is <mask>" ,   targets=[" Patrick", " Clara", " Teven"] ,   top_k=3 )
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__ ) ,   [
								        {"sequence": "My name is Patrick", "score": 0.005, "token": 3499, "token_str": " Patrick"},
								        {"sequence": "My name is Clara", "score": 0.000, "token": 1_3606, "token_str": " Clara"},
								        {"sequence": "My name is Te", "score": 0.000, "token": 2941, "token_str": " Te"},
								    ] ,   )






							@require_torch
							def        __a  (  self )      ->					str:
								a :						Any      =							pipeline(task="fill-mask" ,   model="sshleifer/tiny-distilroberta-base" ,   framework="pt" )
								a :						List[str]      =							None
								a :						Dict      =							None
								self.run_pipeline_test(lowerCAmelCase__ ,   [] )






							@require_tf
							def        __a  (  self )      ->					str:
								a :						Optional[Any]      =							pipeline(task="fill-mask" ,   model="sshleifer/tiny-distilroberta-base" ,   framework="tf" )
								a :						Any      =							None
								a :						Optional[int]      =							None
								self.run_pipeline_test(lowerCAmelCase__ ,   [] )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Tuple:
								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 :						str      =							FillMaskPipeline(model=lowerCAmelCase__ ,   tokenizer=lowerCAmelCase__ )
								a :						Optional[Any]      =							[
								    f"""This is another {tokenizer.mask_token} test""",
								]
								return fill_masker, examples






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					str:
								a :						Any      =							fill_masker.tokenizer
								a :						Optional[int]      =							fill_masker.model

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

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

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

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

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






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Dict:
								a :						Union[str, Any]      =							tokenizer.get_vocab()
								a :						List[Any]      =							sorted(vocab.keys() )[:2]
								# Pipeline argument
								a :						str      =							FillMaskPipeline(model=lowerCAmelCase__ ,   tokenizer=lowerCAmelCase__ ,   targets=lowerCAmelCase__ )
								a :						Dict      =							fill_masker(f"""This is a {tokenizer.mask_token}""" )
								self.assertEqual(
								    lowerCAmelCase__ ,   [
								        {"sequence": ANY(lowerCAmelCase__ ), "score": ANY(lowerCAmelCase__ ), "token": ANY(lowerCAmelCase__ ), "token_str": ANY(lowerCAmelCase__ )},
								        {"sequence": ANY(lowerCAmelCase__ ), "score": ANY(lowerCAmelCase__ ), "token": ANY(lowerCAmelCase__ ), "token_str": ANY(lowerCAmelCase__ )},
								    ] ,   )
								a :						List[str]      =							{vocab[el] for el in targets}
								self.assertEqual({el["token"] for el in outputs} ,   lowerCAmelCase__ )
								a :						str      =							[tokenizer.decode([x] ) for x in target_ids]
								self.assertEqual({el["token_str"] for el in outputs} ,   set(lowerCAmelCase__ ) )

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

								# Score equivalence
								a :						Tuple      =							fill_masker(f"""This is a {tokenizer.mask_token}""" ,   targets=lowerCAmelCase__ )
								a :						List[str]      =							[top_mask["token_str"] for top_mask in outputs]
								a :						Dict      =							[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(lowerCAmelCase__ ) == set(lowerCAmelCase__ ):
									a :						Union[str, Any]      =							fill_masker(f"""This is a {tokenizer.mask_token}""" ,   targets=lowerCAmelCase__ )
									a :						Tuple      =							[top_mask["score"] for top_mask in unmasked_targets]
									self.assertEqual(nested_simplify(lowerCAmelCase__ ) ,   nested_simplify(lowerCAmelCase__ ) )

								# Raises with invalid
								with self.assertRaises(lowerCAmelCase__ ):
									a :						Tuple      =							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(lowerCAmelCase__ ):
										a :						List[Any]      =							fill_masker(f"""This is a {tokenizer.mask_token}""" ,   targets=[""] )
									with self.assertRaises(lowerCAmelCase__ ):
										a :						Optional[int]      =							fill_masker(f"""This is a {tokenizer.mask_token}""" ,   targets="" )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Optional[Any]:
								a :						int      =							FillMaskPipeline(model=lowerCAmelCase__ ,   tokenizer=lowerCAmelCase__ ,   top_k=2 )
								a :						List[Any]      =							fill_masker(f"""This is a {tokenizer.mask_token}""" )
								self.assertEqual(
								    lowerCAmelCase__ ,   [
								        {"sequence": ANY(lowerCAmelCase__ ), "score": ANY(lowerCAmelCase__ ), "token": ANY(lowerCAmelCase__ ), "token_str": ANY(lowerCAmelCase__ )},
								        {"sequence": ANY(lowerCAmelCase__ ), "score": ANY(lowerCAmelCase__ ), "token": ANY(lowerCAmelCase__ ), "token_str": ANY(lowerCAmelCase__ )},
								    ] ,   )

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






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					str:
								a :						Tuple      =							tokenizer.get_vocab()
								a :						List[str]      =							FillMaskPipeline(model=lowerCAmelCase__ ,   tokenizer=lowerCAmelCase__ )

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

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






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Any:
								a :						Tuple      =							FillMaskPipeline(model=lowerCAmelCase__ ,   tokenizer=lowerCAmelCase__ )
								a :						Union[str, Any]      =							tokenizer.get_vocab()
								# String duplicates + id duplicates
								a :						Optional[Any]      =							sorted(vocab.keys() )[:3]
								a :						int      =							[targets[0], targets[1], targets[0], targets[2], targets[1]]
								a :						Tuple      =							fill_masker(f"""My name is {tokenizer.mask_token}""" ,   targets=lowerCAmelCase__ ,   top_k=10 )

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






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Optional[int]:
								a :						str      =							FillMaskPipeline(model=lowerCAmelCase__ ,   tokenizer=lowerCAmelCase__ )

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


