

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 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			__lowerCamelCase        =       MODEL_FOR_MASKED_LM_MAPPING
			__lowerCamelCase        =       TF_MODEL_FOR_MASKED_LM_MAPPING


			def      UpperCamelCase		(    self       )  ->						Any:
							'''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      UpperCamelCase		(    self       )  ->						List[str]:
							'''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(lowercase      ,      decimals=6       )      ,      [
							        {"sequence": "My name is grouped", "score": 2.1e-05, "token": 38015, "token_str": " grouped"},
							        {"sequence": "My name is accuser", "score": 2.1e-05, "token": 25506, "token_str": " accuser"},
							    ]      ,      )

							A__        =			unmasker("The largest city in France is <mask>"       )
							self.assertEqual(
							    nested_simplify(lowercase      ,      decimals=6       )      ,      [
							        {
							            "sequence": "The largest city in France is grouped",
							            "score": 2.1e-05,
							            "token": 38015,
							            "token_str": " grouped",
							        },
							        {
							            "sequence": "The largest city in France is accuser",
							            "score": 2.1e-05,
							            "token": 25506,
							            "token_str": " accuser",
							        },
							    ]      ,      )

							A__        =			unmasker("My name is <mask>"      ,      targets=[" Patrick", " Clara", " Teven"]      ,      top_k=3       )
							self.assertEqual(
							    nested_simplify(lowercase      ,      decimals=6       )      ,      [
							        {"sequence": "My name is Clara", "score": 2e-05, "token": 13606, "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      UpperCamelCase		(    self       )  ->						Optional[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(lowercase      ,      decimals=6       )      ,      [
							        {"sequence": "My name is Maul", "score": 2.2e-05, "token": 35676, "token_str": " Maul"},
							        {"sequence": "My name isELS", "score": 2.2e-05, "token": 16416, "token_str": "ELS"},
							    ]      ,      )

							A__        =			unmasker("The largest city in France is <mask>"       )
							self.assertEqual(
							    nested_simplify(lowercase      ,      decimals=6       )      ,      [
							        {
							            "sequence": "The largest city in France is Maul",
							            "score": 2.2e-05,
							            "token": 35676,
							            "token_str": " Maul",
							        },
							        {"sequence": "The largest city in France isELS", "score": 2.2e-05, "token": 16416, "token_str": "ELS"},
							    ]      ,      )

							A__        =			unmasker("My name is <mask>"      ,      targets=[" Patrick", " Clara", " Teven"]      ,      top_k=3       )
							self.assertEqual(
							    nested_simplify(lowercase      ,      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": 13606, "token_str": " Clara"},
							    ]      ,      )

							A__        =			unmasker("My name is <mask> <mask>"      ,      top_k=2       )

							self.assertEqual(
							    nested_simplify(lowercase      ,      decimals=6       )      ,      [
							        [
							            {
							                "score": 2.2e-05,
							                "token": 35676,
							                "token_str": " Maul",
							                "sequence": "<s>My name is Maul<mask></s>",
							            },
							            {"score": 2.2e-05, "token": 16416, "token_str": "ELS", "sequence": "<s>My name isELS<mask></s>"},
							        ],
							        [
							            {
							                "score": 2.2e-05,
							                "token": 35676,
							                "token_str": " Maul",
							                "sequence": "<s>My name is<mask> Maul</s>",
							            },
							            {"score": 2.2e-05, "token": 16416, "token_str": "ELS", "sequence": "<s>My name is<mask>ELS</s>"},
							        ],
							    ]      ,      )


			@require_torch_gpu
			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''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(lowercase      ,      lowercase       )


			@slow
			@require_torch
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			pipeline(task="fill-mask"      ,      model="distilroberta-base"      ,      top_k=2      ,      framework="pt"       )
							self.run_large_test(lowercase       )


			@slow
			@require_tf
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			pipeline(task="fill-mask"      ,      model="distilroberta-base"      ,      top_k=2      ,      framework="tf"       )
							self.run_large_test(lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						str:
							'''simple docstring'''






							A__        =			unmasker("My name is <mask>"       )
							self.assertEqual(
							    nested_simplify(lowercase       )      ,      [
							        {"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__        =			unmasker("The largest city in France is <mask>"       )
							self.assertEqual(
							    nested_simplify(lowercase       )      ,      [
							        {
							            "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": 12790,
							            "token_str": " Lyon",
							        },
							    ]      ,      )

							A__        =			unmasker("My name is <mask>"      ,      targets=[" Patrick", " Clara", " Teven"]      ,      top_k=3       )
							self.assertEqual(
							    nested_simplify(lowercase       )      ,      [
							        {"sequence": "My name is Patrick", "score": 0.005, "token": 3499, "token_str": " Patrick"},
							        {"sequence": "My name is Clara", "score": 0.000, "token": 13606, "token_str": " Clara"},
							        {"sequence": "My name is Te", "score": 0.000, "token": 2941, "token_str": " Te"},
							    ]      ,      )


			@require_torch
			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			pipeline(task="fill-mask"      ,      model="sshleifer/tiny-distilroberta-base"      ,      framework="pt"       )
							A__        =			None
							A__        =			None
							self.run_pipeline_test(lowercase      ,      []       )


			@require_tf
			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							A__        =			pipeline(task="fill-mask"      ,      model="sshleifer/tiny-distilroberta-base"      ,      framework="tf"       )
							A__        =			None
							A__        =			None
							self.run_pipeline_test(lowercase      ,      []       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Tuple:
							'''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=lowercase      ,      tokenizer=lowercase       )
							A__        =			[
							    F'This is another {tokenizer.mask_token} test',
							]
							return fill_masker, examples


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase       )  ->						Dict:
							'''simple docstring'''






							A__        =			fill_masker.tokenizer
							A__        =			fill_masker.model

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

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

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

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

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


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase       )  ->						List[Any]:
							'''simple docstring'''






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

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

							# Score equivalence
							A__        =			fill_masker(F'This is a {tokenizer.mask_token}'      ,      targets=lowercase       )
							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(lowercase       ) == set(lowercase       ):
											A__        =			fill_masker(F'This is a {tokenizer.mask_token}'      ,      targets=lowercase       )
											A__        =			[top_mask["score"] for top_mask in unmasked_targets]
											self.assertEqual(nested_simplify(lowercase       )      ,      nested_simplify(lowercase       )       )

							# Raises with invalid
							with self.assertRaises(lowercase       ):
											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(lowercase       ):
															A__        =			fill_masker(F'This is a {tokenizer.mask_token}'      ,      targets=[""]       )
											with self.assertRaises(lowercase       ):
															A__        =			fill_masker(F'This is a {tokenizer.mask_token}'      ,      targets=""       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase       )  ->						Optional[Any]:
							'''simple docstring'''






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

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


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase       )  ->						Optional[int]:
							'''simple docstring'''






							A__        =			tokenizer.get_vocab()
							A__        =			FillMaskPipeline(model=lowercase      ,      tokenizer=lowercase       )

							# top_k=2, ntargets=3
							A__        =			sorted(vocab.keys()       )[:3]
							A__        =			fill_masker(F'This is a {tokenizer.mask_token}'      ,      top_k=2      ,      targets=lowercase       )

							# 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(lowercase      ,      key=lambda lowercase       : x["score"]      ,      reverse=lowercase       )]
							# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
							if set(lowercase       ).issubset(lowercase       ):
											A__        =			fill_masker(F'This is a {tokenizer.mask_token}'      ,      top_k=3      ,      targets=lowercase       )
											# They should yield exactly the same result
											self.assertEqual(nested_simplify(lowercase       )      ,      nested_simplify(lowercase       )       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			FillMaskPipeline(model=lowercase      ,      tokenizer=lowercase       )
							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=lowercase      ,      top_k=10       )

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


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			FillMaskPipeline(model=lowercase      ,      tokenizer=lowercase       )

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




from __future__ import annotations



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							list[str] | None = None      )			->      list[list[str]]:




				'''simple docstring'''





				A__        =			word_bank or []
				# create a table
				A__        =			len(SCREAMING_SNAKE_CASE_      ) + 1

				A__        =			[]
				for _ in range(SCREAMING_SNAKE_CASE_      ):
								table.append([]      )
				# seed value
				A__        =			[[]]  # because empty string has empty combination

				# iterate through the indices
				for i in range(SCREAMING_SNAKE_CASE_      ):
								# condition
								if table[i] != []:
												for word in word_bank:
																# slice condition
																if target[i : i + len(SCREAMING_SNAKE_CASE_      )] == word:
																				A__        =			[
																				    [word, *way] for way in table[i]
																				]
																				# adds the word to every combination the current position holds
																				# now,push that combination to the table[i+len(word)]
																				table[i + len(SCREAMING_SNAKE_CASE_      )] += new_combinations

    # combinations are in reverse order so reverse for better output
				for combination in table[len(SCREAMING_SNAKE_CASE_      )]:
								combination.reverse()

				return table[len(SCREAMING_SNAKE_CASE_      )]


if __name__ == "__main__":
	print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
	print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
	print(
	    all_construct(
	        """hexagonosaurus""",
	        ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
	    )
	)

