



import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
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 						lowerCamelCase__			(		_a					,       unittest.TestCase ):
						_lowerCAmelCase             =			BartTokenizer
						_lowerCAmelCase             =			BartTokenizerFast
						_lowerCAmelCase             =			True
						_lowerCAmelCase             =			filter_roberta_detectors


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

													a__:							List[Any]									=os.path.join(self.tmpdirname   ,							VOCAB_FILES_NAMES["vocab_file"]      )
													a__:							List[Any]									=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(_A      ) + "\n"      )
													with open(self.merges_file   ,							"w"   ,							encoding="utf-8"      ) as fp:
																				fp.write("\n".join(_A      )      )


						def 	_lowerCamelCase      (			self    :	List[Any]   ,							**_a    :	int      ):
													kwargs.update(self.special_tokens_map      )
													return self.tokenizer_class.from_pretrained(self.tmpdirname   ,							**_A      )


						def 	_lowerCamelCase      (			self    :	Dict   ,							**_a    :	str      ):
													kwargs.update(self.special_tokens_map      )
													return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,							**_A      )


						def 	_lowerCamelCase      (			self    :	Optional[Any]   ,							_a    :	Any      ):
													return "lower newer", "lower newer"


						@cached_property
						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													return BartTokenizer.from_pretrained("facebook/bart-large"      )


						@cached_property
						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													return BartTokenizerFast.from_pretrained("facebook/bart-large"      )


						@require_torch
						def 	_lowerCamelCase      (			self    :	int      ):
													a__:							List[Any]									=['A long paragraph for summarization.', 'Another paragraph for summarization.']
													a__:							Dict									=[0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				a__:							List[str]									=tokenizer(_A   ,							max_length=len(_A      )   ,							padding=_A   ,							return_tensors="pt"      )
																				self.assertIsInstance(_A   ,							_A      )

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


						@require_torch
						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													a__:							Optional[int]									=['A long paragraph for summarization.', 'Another paragraph for summarization.']
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				a__:							List[str]									=tokenizer(_A   ,							padding=_A   ,							return_tensors="pt"      )
																				# check if input_ids are returned and no labels
																				self.assertIn("input_ids"   ,							_A      )
																				self.assertIn("attention_mask"   ,							_A      )
																				self.assertNotIn("labels"   ,							_A      )
																				self.assertNotIn("decoder_attention_mask"   ,							_A      )


						@require_torch
						def 	_lowerCamelCase      (			self    :	List[str]      ):
													a__:							Dict									=[
													    'Summary of the text.',
													    'Another summary.',
													]
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				a__:							int									=tokenizer(text_target=_A   ,							max_length=3_2   ,							padding="max_length"   ,							return_tensors="pt"      )
																				self.assertEqual(3_2   ,							targets["input_ids"].shape[1]      )


						@require_torch
						def 	_lowerCamelCase      (			self    :	Any      ):
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				a__:							Optional[Any]									=tokenizer(
																				    ["I am a small frog" * 1_0_2_4, "I am a small frog"]   ,							padding=_A   ,							truncation=_A   ,							return_tensors="pt"      )
																				self.assertIsInstance(_A   ,							_A      )
																				self.assertEqual(batch.input_ids.shape   ,							(2, 1_0_2_4)      )


						@require_torch
						def 	_lowerCamelCase      (			self    :	Tuple      ):
													a__:							Tuple									=['A long paragraph for summarization.']
													a__:							Dict									=[
													    'Summary of the text.',
													]
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				a__:							Optional[Any]									=tokenizer(_A   ,							return_tensors="pt"      )
																				a__:							Dict									=tokenizer(text_target=_A   ,							return_tensors="pt"      )
																				a__:							Optional[int]									=inputs['input_ids']
																				a__:							Optional[int]									=targets['input_ids']
																				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 	_lowerCamelCase      (			self    :	Optional[Any]      ):
													pass







						def 	_lowerCamelCase      (			self    :	Optional[Any]      ):
													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																				with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
																											a__:							Optional[int]									=self.rust_tokenizer_class.from_pretrained(_A   ,							**_A      )
																											a__:							Optional[Any]									=self.tokenizer_class.from_pretrained(_A   ,							**_A      )
																											a__:							List[str]									='A, <mask> AllenNLP sentence.'
																											a__:							Dict									=tokenizer_r.encode_plus(_A   ,							add_special_tokens=_A   ,							return_token_type_ids=_A      )
																											a__:							List[Any]									=tokenizer_p.encode_plus(_A   ,							add_special_tokens=_A   ,							return_token_type_ids=_A      )

																											# 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"]      )   ,							)

																											a__:							List[str]									=tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]      )
																											a__:							Tuple									=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_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]      )
																											self.assertSequenceEqual(tokens_r["input_ids"]   ,							[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]      )

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


_a	:	Union[str, Any]=					"Here is a text containing a lot of information: '''{text}'''.\n\nCan you answer this question about the text: '{question}'"

class 						UpperCamelCase     (       lowercase		):
				UpperCAmelCase							: str       =						"""google/flan-t5-base"""
				UpperCAmelCase							: int       =						(
				    """This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the """
				    """text where to find the answer, and `question`, which is the question, and returns the answer to the question."""
				)
				UpperCAmelCase							: Dict       =						"""text_qa"""
				UpperCAmelCase							: Dict       =						AutoTokenizer
				UpperCAmelCase							: Optional[Any]       =						AutoModelForSeqaSeqLM

				UpperCAmelCase							: List[str]       =						["""text""", """text"""]
				UpperCAmelCase							: Any       =						["""text"""]



				def    _lowercase	(self							: Tuple      ,    _A							: str      ,    _A							: str)       ->      Tuple:
					__snake_case     :				List[Any] 						=					QA_PROMPT.format(text=_A      ,    question=_A)
					return self.pre_processor(_A      ,    return_tensors='pt')



				def    _lowercase	(self							: Tuple      ,    _A							: Optional[Any])       ->      List[Any]:
					__snake_case     :				List[Any] 						=					self.model.generate(**_A)

					__snake_case						,							__snake_case     :				Any 						=					inputs['input_ids'].shape
					__snake_case     :				Any 						=					output_ids.shape[0]

					return output_ids.reshape(_A      ,    out_b // in_b      ,    *output_ids.shape[1:])[0][0]






				def    _lowercase	(self							: Optional[int]      ,    _A							: int)       ->      str:
					return self.pre_processor.decode(_A      ,    skip_special_tokens=_A      ,    clean_up_tokenization_spaces=_A)





