






import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


UpperCamelCase			    =     500_000

UpperCamelCase    ,   UpperCamelCase			    =     os.path.split(__file__)
UpperCamelCase			    =     os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))

@get_duration
def 	__lowerCamelCase      (		snake_case__	,**snake_case__		)							->  List[str]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  dataset.map(**snake_case__		)

@get_duration
def 	__lowerCamelCase      (		snake_case__	,**snake_case__		)							->  Optional[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  dataset.filter(**snake_case__		)

def 	__lowerCamelCase      (		)							->  Optional[int]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  {"""num examples""": SPEED_TEST_N_EXAMPLES}
						with tempfile.TemporaryDirectory() as tmp_dir:
												_SCREAMING_SNAKE_CASE     				=  datasets.Features({"""text""": datasets.Value("""string"""		), """numbers""": datasets.Value("""float32"""		)}		)
												_SCREAMING_SNAKE_CASE     				=  generate_example_dataset(
												    os.path.join(snake_case__	,"""dataset.arrow"""		)	,snake_case__	,num_examples=snake_case__		)

												_SCREAMING_SNAKE_CASE     				=  transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""	,use_fast=snake_case__		)

												def tokenize(snake_case__		):
																		return tokenizer(examples["""text"""]		)

												_SCREAMING_SNAKE_CASE     				=  map(snake_case__		)

												_SCREAMING_SNAKE_CASE     				=  map(snake_case__	,batched=snake_case__		)

												_SCREAMING_SNAKE_CASE     				=  map(snake_case__	,function=lambda snake_case__		: None	,batched=snake_case__		)

												with dataset.formatted_as(type="""numpy"""		):
																		_SCREAMING_SNAKE_CASE     				=  map(snake_case__	,function=lambda snake_case__		: None	,batched=snake_case__		)

												with dataset.formatted_as(type="""pandas"""		):
																		_SCREAMING_SNAKE_CASE     				=  map(snake_case__	,function=lambda snake_case__		: None	,batched=snake_case__		)

												with dataset.formatted_as(type="""torch"""	,columns="""numbers"""		):
																		_SCREAMING_SNAKE_CASE     				=  map(snake_case__	,function=lambda snake_case__		: None	,batched=snake_case__		)

												with dataset.formatted_as(type="""tensorflow"""	,columns="""numbers"""		):
																		_SCREAMING_SNAKE_CASE     				=  map(snake_case__	,function=lambda snake_case__		: None	,batched=snake_case__		)

												_SCREAMING_SNAKE_CASE     				=  map(snake_case__	,function=snake_case__	,batched=snake_case__		)

												_SCREAMING_SNAKE_CASE     				=  filter(snake_case__		)

												# Activate later when tokenizer support batched inputs
												# with dataset.formatted_as(type='numpy'):
												#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

						with open(snake_case__	,"""wb"""		) as f:
												f.write(json.dumps(snake_case__		).encode("""utf-8"""		)		)


if __name__ == "__main__":  # useful to run the profiler
		benchmark_map_filter()









# 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"]

