






import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


lowerCamelCase       :       Optional[int]        =				re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
lowerCamelCase       :       List[str]        =				10
lowerCamelCase       :       Dict        =				2_56
def 					snake_case_						(   lowerCAmelCase_				:  List[str]					):
							if len(lowerCAmelCase_					) < MIN_NUM_TOKENS:
														return None
							__lowercase					:							List[Any]			=   MinHash(num_perm=lowerCAmelCase_					)
							for token in set(lowerCAmelCase_					):
														min_hash.update(token.encode()					)
							return min_hash
def 					snake_case_						(   lowerCAmelCase_				:  str					):
							return {t for t in NON_ALPHA.split(lowerCAmelCase_					) if len(t.strip()					) > 0}

class 						lowerCAmelCase   :

					'''simple docstring'''

					def __init__(     self :		str						,   *,
					    __a :		float = 0.85						,   )	->     Dict:


												"""simple docstring"""

												__lowercase					:							int			=   duplication_jaccard_threshold
												__lowercase					:							List[str]			=   NUM_PERM
												__lowercase					:							Union[str, Any]			=   MinHashLSH(threshold=self._duplication_jaccard_threshold						,   num_perm=self._num_perm							)

												__lowercase					:							int			=   defaultdict(__a							)



					def 					lowerCAmelCase (     self :		List[str]						,   __a :		Tuple						,   __a :		MinHash							)	->     None:


												"""simple docstring"""

												__lowercase					:							Union[str, Any]			=   self._index.query(__a							)
												if code_key in self._index.keys:
																			print(F"Duplicate key {code_key}"							)
																			return

												self._index.insert(__a						,   __a							)
												if len(__a							) > 0:
																			for base_duplicate in close_duplicates:
																										if base_duplicate in self._duplicate_clusters:
																																	self._duplicate_clusters[base_duplicate].add(__a							)
																																	break
																			else:
																										self._duplicate_clusters[close_duplicates[0]].add(__a							)



					def 					lowerCAmelCase (     self :		Any							)	->     List[List[Dict]]:


												"""simple docstring"""

												__lowercase					:							Tuple			=   []
												for base, duplicates in self._duplicate_clusters.items():
																			__lowercase					:							Tuple			=   [base] + list(__a							)
																			# reformat the cluster to be a list of dict
																			__lowercase					:							Any			=   [{"""base_index""": el[0], """repo_name""": el[1], """path""": el[2]} for el in cluster]
																			duplicate_clusters.append(__a							)
												return duplicate_clusters



					def 					lowerCAmelCase (     self :		List[str]						,   __a :		Optional[int]							)	->     None:


												"""simple docstring"""

												__lowercase					:							Optional[int]			=   self.get_duplicate_clusters()
												with open(__a						,   """w"""							) as f:
																			json.dump(__a						,   __a							)







def 					snake_case_						(   lowerCAmelCase_				:  Union[str, Any]					):
							__lowercase	,	__lowercase					:							Any			=   element
							__lowercase					:							str			=   get_min_hash([t for t in NON_ALPHA.split(data["""content"""]					) if len(t.strip()					) > 0]					)
							if min_hash is not None:
														return (index, data["repo_name"], data["path"]), min_hash
def 					snake_case_						(   lowerCAmelCase_				:  Type[Dataset]					):
							with mp.Pool() as pool:
														for data in pool.imap_unordered(
														    _compute_min_hash				,				ThreadedIterator(lowerCAmelCase_				,				max_queue_size=10000					)				,				chunksize=100				,				):
																					if data is not None:
																												yield data
def 					snake_case_						(   lowerCAmelCase_				:  Type[Dataset]				,				lowerCAmelCase_				:  float					):
							__lowercase					:							Dict			=   DuplicationIndex(duplication_jaccard_threshold=lowerCAmelCase_					)

							for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(lowerCAmelCase_					)					)				,				max_queue_size=100					)					):
														di.add(lowerCAmelCase_				,				lowerCAmelCase_					)

							# Returns a List[Cluster] where Cluster is List[str] with the filenames.
							return di.get_duplicate_clusters()
def 					snake_case_						(   lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  str					):
							__lowercase					:							Optional[int]			=   get_tokens(lowerCAmelCase_					)
							__lowercase					:							str			=   get_tokens(lowerCAmelCase_					)
							return len(tokensa & tokensa					) / len(tokensa | tokensa					)


lowerCamelCase       :       Optional[int]        =				None
def 					snake_case_						(   lowerCAmelCase_				:  int				,				lowerCAmelCase_				:  List[str]					):
							__lowercase					:							Tuple			=   []
							for elementa in cluster:
														__lowercase					:							Optional[Any]			=   _shared_dataset[elementa["""base_index"""]]["""content"""]
														for elementa in extremes:
																					__lowercase					:							str			=   _shared_dataset[elementa["""base_index"""]]["""content"""]
																					if jaccard_similarity(lowerCAmelCase_				,				lowerCAmelCase_					) >= jaccard_threshold:
																												elementa["copies"] += 1
																												break
														else:
																					__lowercase					:							Tuple			=   1
																					extremes.append(lowerCAmelCase_					)
							return extremes
def 					snake_case_						(   lowerCAmelCase_				:  Union[str, Any]				,				lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  Union[str, Any]					):
							global _shared_dataset
							__lowercase					:							Optional[int]			=   dataset
							__lowercase					:							Union[str, Any]			=   []
							__lowercase					:							Any			=   partial(_find_cluster_extremes_shared				,				jaccard_threshold=lowerCAmelCase_					)
							with mp.Pool() as pool:
														for extremes in tqdm(
														    pool.imap_unordered(
														        lowerCAmelCase_				,				lowerCAmelCase_				,				)				,				total=len(lowerCAmelCase_					)				,				):
																					extremes_list.append(lowerCAmelCase_					)
							return extremes_list



def 					snake_case_						(   lowerCAmelCase_				:  Type[Dataset]				,				lowerCAmelCase_				:  float = 0.85					):
							__lowercase					:							Optional[Any]			=   make_duplicate_clusters(lowerCAmelCase_				,				lowerCAmelCase_					)
							__lowercase					:							List[str]			=   {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
							__lowercase					:							Union[str, Any]			=   {}
							__lowercase					:							List[Any]			=   find_extremes(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_					)
							for extremes in extremes_clusters:
														for element in extremes:
																					__lowercase					:							List[Any]			=   element
							__lowercase					:							List[Any]			=   duplicate_indices - set(extreme_dict.keys()					)
							__lowercase					:							Optional[int]			=   dataset.filter(lambda lowerCAmelCase_				,				lowerCAmelCase_					: idx not in remove_indices				,				with_indices=lowerCAmelCase_					)

							# update duplicate_clusters
							for cluster in duplicate_clusters:
														for element in cluster:
																					__lowercase					:							Optional[Any]			=   element["""base_index"""] in extreme_dict
																					if element["is_extreme"]:
																												__lowercase					:							List[Any]			=   extreme_dict[element["""base_index"""]]["""copies"""]

							print(F"Original dataset size: {len(lowerCAmelCase_					)}"					)
							print(F"Number of duplicate clusters: {len(lowerCAmelCase_					)}"					)
							print(F"Files in duplicate cluster: {len(lowerCAmelCase_					)}"					)
							print(F"Unique files in duplicate cluster: {len(lowerCAmelCase_					)}"					)
							print(F"Filtered dataset size: {len(lowerCAmelCase_					)}"					)

							return ds_filter, duplicate_clusters







# 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 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	List[str]     =					'''naver-clova-ix/donut-base-finetuned-docvqa'''
					_A  :	Any     =					(
					    '''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.'''
					)
					_A  :	Tuple     =					'''document_qa'''
					_A  :	Dict     =					AutoProcessor
					_A  :	Tuple     =					VisionEncoderDecoderModel

					_A  :	Optional[int]     =					['''image''', '''text''']
					_A  :	Optional[int]     =					['''text''']



					def __init__(     self :		Any						,   *__a :		List[str]						,   **__a :		Any							)	->     Optional[Any]:


												"""simple docstring"""

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

												super().__init__(*__a						,   **__a							)



					def 					lowerCAmelCase (     self :		List[Any]						,   __a :		"Image"						,   __a :		str							)	->     List[str]:


												"""simple docstring"""

												__lowercase					:							int			=   """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
												__lowercase					:							str			=   task_prompt.replace("""{user_input}"""						,   __a							)
												__lowercase					:							Union[str, Any]			=   self.pre_processor.tokenizer(
												    __a						,   add_special_tokens=__a						,   return_tensors="""pt"""							).input_ids
												__lowercase					:							int			=   self.pre_processor(__a						,   return_tensors="""pt"""							).pixel_values

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



					def 					lowerCAmelCase (     self :		Optional[int]						,   __a :		int							)	->     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 					lowerCAmelCase (     self :		Union[str, Any]						,   __a :		Union[str, Any]							)	->     Union[str, Any]:


												"""simple docstring"""

												__lowercase					:							Tuple			=   self.pre_processor.batch_decode(__a							)[0]
												__lowercase					:							int			=   sequence.replace(self.pre_processor.tokenizer.eos_token						,   """"""							)
												__lowercase					:							Union[str, Any]			=   sequence.replace(self.pre_processor.tokenizer.pad_token						,   """"""							)
												__lowercase					:							Optional[Any]			=   re.sub(r"""<.*?>"""						,   """"""						,   __a						,   count=1							).strip()  # remove first task start token
												__lowercase					:							Dict			=   self.pre_processor.tokenajson(__a							)

												return sequence["answer"]