



"""simple docstring"""


import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class 				A_      (					SCREAMING_SNAKE_CASE_     ,       unittest.TestCase    ):



						"""simple docstring"""
						__UpperCamelCase		    =							MvpTokenizer
						__UpperCamelCase		    =							MvpTokenizerFast
						__UpperCamelCase		    =							True
						__UpperCamelCase		    =							filter_roberta_detectors






						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      Tuple:
												super().setUp()
												UpperCAmelCase					=			[
												    'l',
												    'o',
												    'w',
												    'e',
												    'r',
												    's',
												    't',
												    'i',
												    'd',
												    'n',
												    '\u0120',
												    '\u0120l',
												    '\u0120n',
												    '\u0120lo',
												    '\u0120low',
												    'er',
												    '\u0120lowest',
												    '\u0120newer',
												    '\u0120wider',
												    '<unk>',
												]
												UpperCAmelCase					=			dict(zip(lowercase_       ,  range(len(lowercase_ ) ) ) )
												UpperCAmelCase					=			['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
												UpperCAmelCase					=			{'unk_token': '<unk>'}

												UpperCAmelCase					=			os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES['vocab_file'] )
												UpperCAmelCase					=			os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES['merges_file'] )
												with open(self.vocab_file       ,  'w'       ,  encoding='utf-8' ) as fp:
																		fp.write(json.dumps(lowercase_ ) + '\n' )
												with open(self.merges_file       ,  'w'       ,  encoding='utf-8' ) as fp:
																		fp.write('\n'.join(lowercase_ ) )






						def      UpperCAmelCase__					( self			:Any       ,  **lowercase_			:List[Any] )    ->      Union[str, Any]:
												kwargs.update(self.special_tokens_map )
												return self.tokenizer_class.from_pretrained(self.tmpdirname       ,  **lowercase_ )






						def      UpperCAmelCase__					( self			:int       ,  **lowercase_			:Optional[Any] )    ->      Union[str, Any]:
												kwargs.update(self.special_tokens_map )
												return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,  **lowercase_ )






						def      UpperCAmelCase__					( self			:int       ,  lowercase_			:Optional[Any] )    ->      Dict:
												return "lower newer", "lower newer"






						@cached_property
						def      UpperCAmelCase__					( self			:int )    ->      Optional[Any]:
												return MvpTokenizer.from_pretrained('RUCAIBox/mvp' )






						@cached_property
						def      UpperCAmelCase__					( self			:Tuple )    ->      Tuple:
												return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp' )






						@require_torch
						def      UpperCAmelCase__					( self			:List[Any] )    ->      Optional[Any]:
												UpperCAmelCase					=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
												UpperCAmelCase					=			[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		UpperCAmelCase					=			tokenizer(lowercase_       ,  max_length=len(lowercase_ )       ,  padding=lowercase_       ,  return_tensors='pt' )
																		self.assertIsInstance(lowercase_       ,  lowercase_ )

																		self.assertEqual((2, 9)       ,  batch.input_ids.shape )
																		self.assertEqual((2, 9)       ,  batch.attention_mask.shape )
																		UpperCAmelCase					=			batch.input_ids.tolist()[0]
																		self.assertListEqual(lowercase_       ,  lowercase_ )
																		# Test that special tokens are reset






						@require_torch
						def      UpperCAmelCase__					( self			:Any )    ->      Optional[Any]:
												UpperCAmelCase					=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		UpperCAmelCase					=			tokenizer(lowercase_       ,  padding=lowercase_       ,  return_tensors='pt' )
																		# check if input_ids are returned and no labels
																		self.assertIn('input_ids'       ,  lowercase_ )
																		self.assertIn('attention_mask'       ,  lowercase_ )
																		self.assertNotIn('labels'       ,  lowercase_ )
																		self.assertNotIn('decoder_attention_mask'       ,  lowercase_ )






						@require_torch
						def      UpperCAmelCase__					( self			:Any )    ->      Optional[int]:
												UpperCAmelCase					=			[
												    'Summary of the text.',
												    'Another summary.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		UpperCAmelCase					=			tokenizer(text_target=lowercase_       ,  max_length=32       ,  padding='max_length'       ,  return_tensors='pt' )
																		self.assertEqual(32       ,  targets['input_ids'].shape[1] )






						@require_torch
						def      UpperCAmelCase__					( self			:Any )    ->      Optional[Any]:
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		UpperCAmelCase					=			tokenizer(
																		    ['I am a small frog' * 10_24, 'I am a small frog']       ,  padding=lowercase_       ,  truncation=lowercase_       ,  return_tensors='pt' )
																		self.assertIsInstance(lowercase_       ,  lowercase_ )
																		self.assertEqual(batch.input_ids.shape       ,  (2, 10_24) )






						@require_torch
						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Optional[int]:
												UpperCAmelCase					=			['A long paragraph for summarization.']
												UpperCAmelCase					=			[
												    'Summary of the text.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		UpperCAmelCase					=			tokenizer(lowercase_       ,  text_target=lowercase_       ,  return_tensors='pt' )
																		UpperCAmelCase					=			inputs['input_ids']
																		UpperCAmelCase					=			inputs['labels']
																		self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
																		self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
																		self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
																		self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )






						def      UpperCAmelCase__					( self			:List[Any] )    ->      Optional[Any]:
												pass






						def      UpperCAmelCase__					( self			:List[str] )    ->      Union[str, Any]:
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
																								UpperCAmelCase					=			self.rust_tokenizer_class.from_pretrained(lowercase_       ,  **lowercase_ )
																								UpperCAmelCase					=			self.tokenizer_class.from_pretrained(lowercase_       ,  **lowercase_ )
																								UpperCAmelCase					=			'A, <mask> AllenNLP sentence.'
																								UpperCAmelCase					=			tokenizer_r.encode_plus(lowercase_       ,  add_special_tokens=lowercase_       ,  return_token_type_ids=lowercase_ )
																								UpperCAmelCase					=			tokenizer_p.encode_plus(lowercase_       ,  add_special_tokens=lowercase_       ,  return_token_type_ids=lowercase_ )

																								# token_type_ids should put 0 everywhere
																								self.assertEqual(sum(tokens_r['token_type_ids'] )       ,  sum(tokens_p['token_type_ids'] ) )

																								# attention_mask should put 1 everywhere, so sum over length should be 1
																								self.assertEqual(
																								    sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] )       ,  sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] )       ,  )

																								UpperCAmelCase					=			tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
																								UpperCAmelCase					=			tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )

																								# Rust correctly handles the space before the mask while python doesnt
																								self.assertSequenceEqual(tokens_p['input_ids']       ,  [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
																								self.assertSequenceEqual(tokens_r['input_ids']       ,  [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )

																								self.assertSequenceEqual(
																								    lowercase_       ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
																								self.assertSequenceEqual(
																								    lowercase_       ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )










"""simple docstring"""


# 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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							"""philschmid/bart-large-cnn-samsum"""
						__UpperCamelCase		    =							(
						    """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
						    """and returns a summary of the text."""
						)
						__UpperCamelCase		    =							"""summarizer"""
						__UpperCamelCase		    =							AutoTokenizer
						__UpperCamelCase		    =							AutoModelForSeqaSeqLM

						__UpperCamelCase		    =							["""text"""]
						__UpperCamelCase		    =							["""text"""]






						def      UpperCAmelCase__					( self			:int       ,  lowercase_			:Tuple )    ->      int:
												return self.pre_processor(lowercase_       ,  return_tensors='pt'       ,  truncation=lowercase_ )






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:Dict )    ->      Optional[int]:
												return self.model.generate(**lowercase_ )[0]






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:Tuple )    ->      List[str]:
												return self.pre_processor.decode(lowercase_       ,  skip_special_tokens=lowercase_       ,  clean_up_tokenization_spaces=lowercase_ )





