
import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE							:Optional[Any]										=    get_tests_dir('fixtures/test_sentencepiece.model')





@require_sentencepiece
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					XLMProphetNetTokenizer
	snake_case_    =					False
	snake_case_    =					True






	def     UpperCamelCase_  (	self :			str			):
					super().setUp()

					# We have a SentencePiece fixture for testing
					__A             =			XLMProphetNetTokenizer(A							,keep_accents=A			)
					tokenizer.save_pretrained(self.tmpdirname			)






	def     UpperCamelCase_  (	self :			Optional[int]			):
					__A             =			"[PAD]"
					__A             =			0

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(A			)							,A			)
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(A			)							,A			)






	def     UpperCamelCase_  (	self :			List[str]			):
					__A             =			list(self.get_tokenizer().get_vocab().keys()			)

					self.assertEqual(vocab_keys[0]							,"[PAD]"			)
					self.assertEqual(vocab_keys[1]							,"[CLS]"			)
					self.assertEqual(vocab_keys[-1]							,"j"			)
					self.assertEqual(len(A			)							,10_12			)






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					self.assertEqual(self.get_tokenizer().vocab_size							,10_12			)






	def     UpperCamelCase_  (	self :			Tuple			):
					__A             =			XLMProphetNetTokenizer(A							,keep_accents=A			)

					__A             =			tokenizer.tokenize("This is a test"			)
					self.assertListEqual(A							,["▁This", "▁is", "▁a", "▁t", "est"]			)

					self.assertListEqual(
					    tokenizer.convert_tokens_to_ids(A			)							,[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]							,)

					__A             =			tokenizer.tokenize("I was born in 92000, and this is falsé."			)
					self.assertListEqual(
					    A							,[
					        SPIECE_UNDERLINE + "I",
					        SPIECE_UNDERLINE + "was",
					        SPIECE_UNDERLINE + "b",
					        "or",
					        "n",
					        SPIECE_UNDERLINE + "in",
					        SPIECE_UNDERLINE + "",
					        "9",
					        "2",
					        "0",
					        "0",
					        "0",
					        ",",
					        SPIECE_UNDERLINE + "and",
					        SPIECE_UNDERLINE + "this",
					        SPIECE_UNDERLINE + "is",
					        SPIECE_UNDERLINE + "f",
					        "al",
					        "s",
					        "é",
					        ".",
					    ]							,)
					__A             =			tokenizer.convert_tokens_to_ids(A			)
					self.assertListEqual(
					    A							,[
					        value + tokenizer.fairseq_offset
					        for value in [8, 21, 84, 55, 24, 19, 7, -9, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, -9, 4]
					    ]							,)

					__A             =			tokenizer.convert_ids_to_tokens(A			)
					self.assertListEqual(
					    A							,[
					        SPIECE_UNDERLINE + "I",
					        SPIECE_UNDERLINE + "was",
					        SPIECE_UNDERLINE + "b",
					        "or",
					        "n",
					        SPIECE_UNDERLINE + "in",
					        SPIECE_UNDERLINE + "",
					        "[UNK]",
					        "2",
					        "0",
					        "0",
					        "0",
					        ",",
					        SPIECE_UNDERLINE + "and",
					        SPIECE_UNDERLINE + "this",
					        SPIECE_UNDERLINE + "is",
					        SPIECE_UNDERLINE + "f",
					        "al",
					        "s",
					        "[UNK]",
					        ".",
					    ]							,)






	@cached_property
	def     UpperCamelCase_  (	self :			Optional[int]			):
					return XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"			)






	@slow
	def     UpperCamelCase_  (	self :			Any			):
					__A             =			"Hello World!"
					__A             =			[3_53_89, 66_72, 49, 2]
					self.assertListEqual(A							,self.big_tokenizer.encode(A			)			)







	@slow
	def     UpperCamelCase_  (	self :			Optional[int]			):
					# fmt: off
					__A             =			{"input_ids": [[1_10_73, 8_27_83, 18, 26, 8_27_83, 5_49, 5_15_40, 2_48, 1_72_09, 13_01, 2_17, 20, 21_51_86, 13_25, 1_47, 1_72_09, 13_01, 2_17, 20, 5_63_70, 53, 12_20_20, 20, 1_64_77, 27, 8_73_55, 45_48, 20, 47_28, 7_83_92, 17, 15_99_69, 18, 26, 2_44_91, 6_29, 15, 5_38, 2_27_04, 54_39, 15, 27_88, 2_44_91, 98_85, 15, 4_35_34, 6_05, 15, 8_14, 1_84_03, 3_32_00, 29, 15, 4_35_34, 2_44_58, 1_24_10, 1_11, 2_49_66, 8_36_69, 96_37, 14_40_68, 26, 8_50, 2_23_46, 27, 1_47, 2_49_66, 8_36_69, 8_34_90, 26, 3_91_13, 7_35, 27, 6_89, 6_56, 28_00, 13_39, 46_00, 53, 12_20_20, 11_57_85, 34, 8_16, 13_39, 4_68_87, 18, 1_47, 5_39_05, 19_51, 4_22_38, 4_11_70, 1_77_32, 8_34, 4_36, 15, 2_75_23, 9_87_33, 2_17, 1_47, 55_42, 49_81, 9_30, 1_73_47, 16, 2], [2_00_91, 6_29, 94, 8_27_86, 58, 4_90, 20, 15_28, 84, 5_39_05, 3_44, 8_05_92, 11_01_28, 1_88_22, 52_67, 13_06, 62, 15_25_37, 3_08, 79_97, 4_01, 12_44_27, 5_49, 3_54_42, 2_25, 1_09, 1_50_55, 2_57_48, 1_47, 71_19, 4_37_12, 34, 7_67, 13_53_66, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_92, 6_37_84, 11_94_66, 17, 14_78_08, 8_82_14, 18, 6_56, 81, 32, 32_96, 1_02_80, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
					# fmt: on

					self.tokenizer_integration_test_util(
					    expected_encoding=A							,model_name="microsoft/xprophetnet-large-wiki100-cased"							,revision="1acad1643ddd54a44df6a1b797ada8373685d90e"							,)


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"facebook/bart-large-mnli"
	snake_case_    =					(
	    "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
	    "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
	    "It returns the most likely label in the list of provided `labels` for the input text."
	)
	snake_case_    =					"text_classifier"
	snake_case_    =					AutoTokenizer
	snake_case_    =					AutoModelForSequenceClassification

	snake_case_    =					["text", ["text"]]
	snake_case_    =					["text"]






	def     UpperCamelCase_  (	self :			str			):
					super().setup()
					__A             =			self.model.config
					__A             =			-1
					for idx, label in config.idalabel.items():
									if label.lower().startswith("entail"			):
													__A             =			int(A			)
					if self.entailment_id == -1:
									raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."			)






	def     UpperCamelCase_  (	self :			int							,A :			Optional[Any]							,A :			Dict			):
					__A             =			labels
					return self.pre_processor(
					    [text] * len(A			)							,[f'''This example is {label}''' for label in labels]							,return_tensors="pt"							,padding="max_length"							,)







	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			Tuple			):
					__A             =			outputs.logits
					__A             =			torch.argmax(logits[:, 2]			).item()
					return self._labels[label_id]
