"""simple docstring"""




import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_a      :		Tuple           =   get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class 						__A							( SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			Optional[int]							=		ReformerTokenizer
     _UpperCamelCase							:			Tuple							=		ReformerTokenizerFast
     _UpperCamelCase							:			Tuple							=		True
     _UpperCamelCase							:			int							=		False
     _UpperCamelCase							:			Union[str, Any]							=		True






     def     __A  (		self  ):
       super().setUp()

       _lowerCAmelCase						:       Any  			=     ReformerTokenizer(a__				,     keep_accents=a__  )
       tokenizer.save_pretrained(self.tmpdirname  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     """<s>"""
       _lowerCAmelCase						:       Dict  			=     1

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






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     list(self.get_tokenizer().get_vocab().keys()  )

       self.assertEqual(vocab_keys[0]				,     """<unk>"""  )
       self.assertEqual(vocab_keys[1]				,     """<s>"""  )
       self.assertEqual(vocab_keys[-1]				,     """j"""  )
       self.assertEqual(len(a__  )				,     1000  )






     def     __A  (		self  ):
       self.assertEqual(self.get_tokenizer().vocab_size				,     1000  )






     def     __A  (		self  ):
       if not self.test_rust_tokenizer:
         return

       _lowerCAmelCase						:       Any  			=     self.get_tokenizer()
       _lowerCAmelCase						:       Dict  			=     self.get_rust_tokenizer()

       _lowerCAmelCase						:       Any  			=     """I was born in 92000, and this is falsé."""

       _lowerCAmelCase						:       Any  			=     tokenizer.tokenize(a__  )
       _lowerCAmelCase						:       Dict  			=     rust_tokenizer.tokenize(a__  )
       self.assertListEqual(a__				,     a__  )

       _lowerCAmelCase						:       int  			=     tokenizer.encode(a__				,     add_special_tokens=a__  )
       _lowerCAmelCase						:       List[Any]  			=     rust_tokenizer.encode(a__				,     add_special_tokens=a__  )
       self.assertListEqual(a__				,     a__  )

       _lowerCAmelCase						:       Optional[int]  			=     self.get_rust_tokenizer()
       _lowerCAmelCase						:       str  			=     tokenizer.encode(a__  )
       _lowerCAmelCase						:       Dict  			=     rust_tokenizer.encode(a__  )
       self.assertListEqual(a__				,     a__  )






     def     __A  (		self				,     a__=15  ):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"  ):
           _lowerCAmelCase						:       Any  			=     self.rust_tokenizer_class.from_pretrained(a__				,     **a__  )

           # Simple input
           _lowerCAmelCase						:       List[str]  			=     """This is a simple input"""
           _lowerCAmelCase						:       Optional[int]  			=     ["""This is a simple input 1""", """This is a simple input 2"""]
           _lowerCAmelCase						:       List[Any]  			=     ("""This is a simple input""", """This is a pair""")
           _lowerCAmelCase						:       Optional[Any]  			=     [
               ("""This is a simple input 1""", """This is a simple input 2"""),
               ("""This is a simple pair 1""", """This is a simple pair 2"""),
           ]

           # Simple input tests
           self.assertRaises(a__				,     tokenizer_r.encode				,     a__				,     max_length=a__				,     padding="""max_length"""  )

           # Simple input
           self.assertRaises(a__				,     tokenizer_r.encode_plus				,     a__				,     max_length=a__				,     padding="""max_length"""  )

           # Simple input
           self.assertRaises(
               a__				,     tokenizer_r.batch_encode_plus				,     a__				,     max_length=a__				,     padding="""max_length"""				,     )

           # Pair input
           self.assertRaises(a__				,     tokenizer_r.encode				,     a__				,     max_length=a__				,     padding="""max_length"""  )

           # Pair input
           self.assertRaises(a__				,     tokenizer_r.encode_plus				,     a__				,     max_length=a__				,     padding="""max_length"""  )

           # Pair input
           self.assertRaises(
               a__				,     tokenizer_r.batch_encode_plus				,     a__				,     max_length=a__				,     padding="""max_length"""				,     )






     def     __A  (		self  ):
       pass






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[Any]  			=     ReformerTokenizer(a__				,     keep_accents=a__  )

       _lowerCAmelCase						:       Any  			=     tokenizer.tokenize("""This is a test"""  )
       self.assertListEqual(a__				,     ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]  )

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(a__  )				,     [285, 46, 10, 170, 382]				,     )

       _lowerCAmelCase						:       List[str]  			=     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""",
               """.""",
           ]				,     )
       _lowerCAmelCase						:       Tuple  			=     tokenizer.convert_tokens_to_ids(a__  )
       self.assertListEqual(
           a__				,     [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]				,     )

       _lowerCAmelCase						:       List[str]  			=     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     __A  (		self  ):
       return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""  )






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     """Hello World!"""
       _lowerCAmelCase						:       Tuple  			=     [126, 32, 262, 152, 38, 72, 287]

       self.assertListEqual(a__				,     self.big_tokenizer.encode(a__  )  )






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     (
           """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
           """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
       )
       _lowerCAmelCase						:       Union[str, Any]  			=     [
           108,
           265,
           24,
           111,
           4,
           258,
           156,
           35,
           28,
           275,
           3,
           259,
           297,
           260,
           84,
           4,
           35,
           110,
           44,
           8,
           259,
           91,
           268,
           21,
           11,
           209,
           274,
           109,
           266,
           277,
           117,
           86,
           93,
           315,
           258,
           278,
           258,
           277,
           258,
           0,
           258,
           288,
           258,
           319,
           258,
           0,
           258,
           0,
           258,
           0,
           258,
           0,
           258,
           287,
           258,
           315,
           258,
           289,
           258,
           278,
           99,
           269,
           266,
           262,
           8,
           259,
           241,
           4,
           217,
           230,
           268,
           266,
           55,
           168,
           106,
           75,
           193,
           266,
           223,
           27,
           49,
           26,
           282,
           25,
           264,
           299,
           19,
           26,
           0,
           258,
           277,
           117,
           86,
           93,
           176,
           183,
           270,
           11,
           262,
           42,
           61,
           265,
       ]

       self.assertListEqual(a__				,     self.big_tokenizer.encode(a__  )  )






     @require_torch
     @slow
     def     __A  (		self  ):
       import torch

       from transformers import ReformerConfig, ReformerModel

       # Build sequence
       _lowerCAmelCase						:       Union[str, Any]  			=     list(self.big_tokenizer.get_vocab().keys()  )[:10]
       _lowerCAmelCase						:       Dict  			=     """ """.join(a__  )
       _lowerCAmelCase						:       List[str]  			=     self.big_tokenizer.encode_plus(a__				,     return_tensors="""pt"""  )
       _lowerCAmelCase						:       Optional[int]  			=     self.big_tokenizer.batch_encode_plus([sequence, sequence]				,     return_tensors="""pt"""  )

       _lowerCAmelCase						:       List[Any]  			=     ReformerConfig()
       # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
       _lowerCAmelCase						:       Optional[int]  			=     encoded_sequence["""input_ids"""].shape
       _lowerCAmelCase						:       Any  			=     ReformerModel(a__  )

       # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
       assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

       with torch.no_grad():
         model(**a__  )
         model(**a__  )






     @slow
     def     __A  (		self  ):
       # fmt: off
       _lowerCAmelCase						:       Dict  			=     {"""input_ids""": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], """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]]}  # noqa: E501
       # fmt: on

       # This tokenizer does not know some characters like ")".
       # That is the reason why we use very simple texts here.
       # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
       _lowerCAmelCase						:       str  			=     [
           """This is a very simple sentence.""",
           """The quick brown fox jumps over the lazy dog.""",
       ]

       self.tokenizer_integration_test_util(
           expected_encoding=a__				,     model_name="""google/reformer-crime-and-punishment"""				,     revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""				,     padding=a__				,     sequences=a__				,     )





"""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.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
     from PIL import Image
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Optional[Any]							=		"naver-clova-ix/donut-base-finetuned-docvqa"
     _UpperCamelCase							:			Dict							=		(
         "This is a tool that answers a question about an document (pdf). It takes an input named `document` which "
         "should be the document containing the information, as well as a `question` that is the question about the "
         "document. It returns a text that contains the answer to the question."
     )
     _UpperCamelCase							:			Optional[int]							=		"document_qa"
     _UpperCamelCase							:			Any							=		AutoProcessor
     _UpperCamelCase							:			Union[str, Any]							=		VisionEncoderDecoderModel

     _UpperCamelCase							:			Union[str, Any]							=		["image", "text"]
     _UpperCamelCase							:			List[str]							=		["text"]






     def __init__(		self				,     *a__				,     **a__  ):
       if not is_vision_available():
         raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool."""  )

       super().__init__(*a__				,     **a__  )






     def     __A  (		self				,     a__				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
       _lowerCAmelCase						:       Dict  			=     task_prompt.replace("""{user_input}"""				,     a__  )
       _lowerCAmelCase						:       str  			=     self.pre_processor.tokenizer(
           a__				,     add_special_tokens=a__				,     return_tensors="""pt"""  ).input_ids
       _lowerCAmelCase						:       Dict  			=     self.pre_processor(a__				,     return_tensors="""pt"""  ).pixel_values

       return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}






     def     __A  (		self				,     a__  ):
       return self.model.generate(
           inputs["""pixel_values"""].to(self.device  )				,     decoder_input_ids=inputs["""decoder_input_ids"""].to(self.device  )				,     max_length=self.model.decoder.config.max_position_embeddings				,     early_stopping=a__				,     pad_token_id=self.pre_processor.tokenizer.pad_token_id				,     eos_token_id=self.pre_processor.tokenizer.eos_token_id				,     use_cache=a__				,     num_beams=1				,     bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]				,     return_dict_in_generate=a__				,     ).sequences






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Tuple  			=     self.pre_processor.batch_decode(a__  )[0]
       _lowerCAmelCase						:       int  			=     sequence.replace(self.pre_processor.tokenizer.eos_token				,     """"""  )
       _lowerCAmelCase						:       List[str]  			=     sequence.replace(self.pre_processor.tokenizer.pad_token				,     """"""  )
       _lowerCAmelCase						:       List[str]  			=     re.sub(r"""<.*?>"""				,     """"""				,     a__				,     count=1  ).strip()  # remove first task start token
       _lowerCAmelCase						:       List[str]  			=     self.pre_processor.tokenajson(a__  )

       return sequence["answer"]




