


from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)







def 						lowerCamelCase(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =[]
				for part_id in partition_order:
								_SCREAMING_SNAKE_CASE			  =df.where(f"SPARK_PARTITION_ID() = {part_id}").collect()
								for row_idx, row in enumerate(a__):
												expected_row_ids_and_row_dicts.append((f"{part_id}_{row_idx}", row.asDict()))
				return expected_row_ids_and_row_dicts







@require_not_windows
@require_dill_gt_0_3_2
def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  =pyspark.sql.SparkSession.builder.master('''local[*]''').appName('''pyspark''').getOrCreate()
				_SCREAMING_SNAKE_CASE			  =spark.range(100).repartition(1)
				_SCREAMING_SNAKE_CASE			  =Spark(a__)
				# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
				# that each partition can hold 2 rows.
				spark_builder._repartition_df_if_needed(max_shard_size=16)
				# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
				assert spark_builder.df.rdd.getNumPartitions() == 50







@require_not_windows
@require_dill_gt_0_3_2
def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  =pyspark.sql.SparkSession.builder.master('''local[*]''').appName('''pyspark''').getOrCreate()
				_SCREAMING_SNAKE_CASE			  =spark.range(10).repartition(2)
				_SCREAMING_SNAKE_CASE			  =[1, 0]
				_SCREAMING_SNAKE_CASE			  =_generate_iterable_examples(a__	,a__)  # Reverse the partitions.
				_SCREAMING_SNAKE_CASE			  =_get_expected_row_ids_and_row_dicts_for_partition_order(a__	,a__)

				for i, (row_id, row_dict) in enumerate(generate_fn()):
								_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =expected_row_ids_and_row_dicts[i]
								assert row_id == expected_row_id
								assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  =pyspark.sql.SparkSession.builder.master('''local[*]''').appName('''pyspark''').getOrCreate()
				_SCREAMING_SNAKE_CASE			  =spark.range(10).repartition(1)
				_SCREAMING_SNAKE_CASE			  =SparkExamplesIterable(a__)
				assert it.n_shards == 1
				for i, (row_id, row_dict) in enumerate(a__):
								assert row_id == f"0_{i}"
								assert row_dict == {"id": i}







@require_not_windows
@require_dill_gt_0_3_2
def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  =pyspark.sql.SparkSession.builder.master('''local[*]''').appName('''pyspark''').getOrCreate()
				_SCREAMING_SNAKE_CASE			  =spark.range(30).repartition(3)
				# Mock the generator so that shuffle reverses the partition indices.
				with patch('''numpy.random.Generator''') as generator_mock:
								_SCREAMING_SNAKE_CASE			  =lambda a__: x.reverse()
								_SCREAMING_SNAKE_CASE			  =_get_expected_row_ids_and_row_dicts_for_partition_order(a__	,[2, 1, 0])

								_SCREAMING_SNAKE_CASE			  =SparkExamplesIterable(a__).shuffle_data_sources(a__)
								assert shuffled_it.n_shards == 3
								for i, (row_id, row_dict) in enumerate(a__):
												_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =expected_row_ids_and_row_dicts[i]
												assert row_id == expected_row_id
												assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  =pyspark.sql.SparkSession.builder.master('''local[*]''').appName('''pyspark''').getOrCreate()
				_SCREAMING_SNAKE_CASE			  =spark.range(20).repartition(4)

				# Partitions 0 and 2
				_SCREAMING_SNAKE_CASE			  =SparkExamplesIterable(a__).shard_data_sources(worker_id=0	,num_workers=2)
				assert shard_it_a.n_shards == 2
				_SCREAMING_SNAKE_CASE			  =_get_expected_row_ids_and_row_dicts_for_partition_order(a__	,[0, 2])
				for i, (row_id, row_dict) in enumerate(a__):
								_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =expected_row_ids_and_row_dicts_a[i]
								assert row_id == expected_row_id
								assert row_dict == expected_row_dict

				# Partitions 1 and 3
				_SCREAMING_SNAKE_CASE			  =SparkExamplesIterable(a__).shard_data_sources(worker_id=1	,num_workers=2)
				assert shard_it_a.n_shards == 2
				_SCREAMING_SNAKE_CASE			  =_get_expected_row_ids_and_row_dicts_for_partition_order(a__	,[1, 3])
				for i, (row_id, row_dict) in enumerate(a__):
								_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =expected_row_ids_and_row_dicts_a[i]
								assert row_id == expected_row_id
								assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  =pyspark.sql.SparkSession.builder.master('''local[*]''').appName('''pyspark''').getOrCreate()
				_SCREAMING_SNAKE_CASE			  =spark.range(100).repartition(1)
				_SCREAMING_SNAKE_CASE			  =Spark(a__)
				# Choose a small max_shard_size for maximum partitioning.
				spark_builder._repartition_df_if_needed(max_shard_size=1)
				# The new number of partitions should not be greater than the number of rows.
				assert spark_builder.df.rdd.getNumPartitions() == 100



# 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__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"naver-clova-ix/donut-base-finetuned-docvqa"
						UpperCAmelCase             =						(
						    "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             =						"document_qa"
						UpperCAmelCase             =						AutoProcessor
						UpperCAmelCase             =						VisionEncoderDecoderModel

						UpperCAmelCase             =						["image", "text"]
						UpperCAmelCase             =						["text"]




						def __init__(       self					:      Any				,    *_a					:      int				,    **_a					:      Dict  )							->     int:


										"""simple docstring"""





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

										super().__init__(*_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Optional[Any]				,    _a					:      "Image"				,    _a					:      str  )							->     Tuple:


										"""simple docstring"""





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

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




						def 							__UpperCamelCase				(       self					:      List[Any]				,    _a					:      Optional[Any]  )							->     int:


										"""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=_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 							__UpperCamelCase				(       self					:      Any				,    _a					:      int  )							->     Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.pre_processor.batch_decode(_a  )[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'''<.*?>'''				,    ''''''				,    _a				,    count=1  ).strip()  # remove first task start token
										_SCREAMING_SNAKE_CASE			  =self.pre_processor.tokenajson(_a  )

										return sequence["answer"]