






import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 				__UpperCAmelCase		(_UpperCAmelCase	,unittest.TestCase   ):
			__snake_case    :				Optional[Any]									=							OpenAIGPTTokenizer
			__snake_case    :				Dict									=							OpenAIGPTTokenizerFast
			__snake_case    :				Optional[Any]									=							True
			__snake_case    :				Dict									=							False







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									_SCREAMING_SNAKE_CASE     				=  [
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """w</w>""",
									    """r</w>""",
									    """t</w>""",
									    """lo""",
									    """low""",
									    """er</w>""",
									    """low</w>""",
									    """lowest</w>""",
									    """newer</w>""",
									    """wider</w>""",
									    """<unk>""",
									]
									_SCREAMING_SNAKE_CASE     				=  dict(zip(UpperCAmelCase_				,     range(len(UpperCAmelCase_					)					)					)					)
									_SCREAMING_SNAKE_CASE     				=  ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]

									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES["""vocab_file"""]					)
									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES["""merges_file"""]					)
									with open(self.vocab_file				,     """w"""					) as fp:
															fp.write(json.dumps(UpperCAmelCase_					)					)
									with open(self.merges_file				,     """w"""					) as fp:
															fp.write("""\n""".join(UpperCAmelCase_					)					)







			def 							UpperCamelCase				(     self:  List[Any]				,     UpperCAmelCase_:  Dict					):







									'''simple docstring'''




									return "lower newer", "lower newer"







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  OpenAIGPTTokenizer(self.vocab_file				,     self.merges_file					)

									_SCREAMING_SNAKE_CASE     				=  """lower"""
									_SCREAMING_SNAKE_CASE     				=  ["""low""", """er</w>"""]
									_SCREAMING_SNAKE_CASE     				=  tokenizer.tokenize(UpperCAmelCase_					)
									self.assertListEqual(UpperCAmelCase_				,     UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  tokens + ["""<unk>"""]
									_SCREAMING_SNAKE_CASE     				=  [14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_					)				,     UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[Any]				,     UpperCAmelCase_:  Union[str, Any]=15					):







									'''simple docstring'''




									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'					):
																					_SCREAMING_SNAKE_CASE     				=  self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_				,     **UpperCAmelCase_					)

																					# Simple input
																					_SCREAMING_SNAKE_CASE     				=  """This is a simple input"""
																					_SCREAMING_SNAKE_CASE     				=  ["""This is a simple input 1""", """This is a simple input 2"""]
																					_SCREAMING_SNAKE_CASE     				=  ("""This is a simple input""", """This is a pair""")
																					_SCREAMING_SNAKE_CASE     				=  [
																					    ("""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(UpperCAmelCase_				,     tokenizer_r.encode				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

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

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

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

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

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







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									pass



@require_ftfy
@require_spacy
@require_tokenizers
class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			pass









# 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 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Dict									=							"naver-clova-ix/donut-base-finetuned-docvqa"
			__snake_case    :				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."
			)
			__snake_case    :				Dict									=							"document_qa"
			__snake_case    :				Any									=							AutoProcessor
			__snake_case    :				int									=							VisionEncoderDecoderModel

			__snake_case    :				Union[str, Any]									=							["image", "text"]
			__snake_case    :				Optional[int]									=							["text"]







			def __init__(     self:  List[Any]				,     *UpperCAmelCase_:  Optional[int]				,     **UpperCAmelCase_:  List[str]					):







									'''simple docstring'''




									if not is_vision_available():
															raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool."""					)

									super().__init__(*UpperCAmelCase_				,     **UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[int]				,     UpperCAmelCase_:  "Image"				,     UpperCAmelCase_:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
									_SCREAMING_SNAKE_CASE     				=  task_prompt.replace("""{user_input}"""				,     UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  self.pre_processor.tokenizer(
									    UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_				,     return_tensors="""pt"""					).input_ids
									_SCREAMING_SNAKE_CASE     				=  self.pre_processor(UpperCAmelCase_				,     return_tensors="""pt"""					).pixel_values

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







			def 							UpperCamelCase				(     self:  Dict				,     UpperCAmelCase_:  str					):







									'''simple docstring'''




									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=UpperCAmelCase_				,     pad_token_id=self.pre_processor.tokenizer.pad_token_id				,     eos_token_id=self.pre_processor.tokenizer.eos_token_id				,     use_cache=UpperCAmelCase_				,     num_beams=1				,     bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]				,     return_dict_in_generate=UpperCAmelCase_				,     ).sequences







			def 							UpperCamelCase				(     self:  Dict				,     UpperCAmelCase_:  Tuple					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.pre_processor.batch_decode(UpperCAmelCase_					)[0]
									_SCREAMING_SNAKE_CASE     				=  sequence.replace(self.pre_processor.tokenizer.eos_token				,     """"""					)
									_SCREAMING_SNAKE_CASE     				=  sequence.replace(self.pre_processor.tokenizer.pad_token				,     """"""					)
									_SCREAMING_SNAKE_CASE     				=  re.sub(R"""<.*?>"""				,     """"""				,     UpperCAmelCase_				,     count=1					).strip()  # remove first task start token
									_SCREAMING_SNAKE_CASE     				=  self.pre_processor.tokenajson(UpperCAmelCase_					)

									return sequence["answer"]

