






import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation

__lowerCamelCase      :    Optional[Any]   		=   logging.get_logger(__name__)


__lowerCamelCase      :    str   		=   {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__lowerCamelCase      :    List[Any]   		=   {
    """vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
    """merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
    """tokenizer_config_file""": {
        """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
    },
}

__lowerCamelCase      :    int   		=   {"""facebook/blenderbot-3B""": 128}







@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   int     =						(
	    list(range(ord("!"     )  ,						ord("~"     ) + 1     )     ) + list(range(ord("¡"     )  ,						ord("¬"     ) + 1     )     ) + list(range(ord("®"     )  ,						ord("ÿ"     ) + 1     )     )
	)
	snake_case__				:   Dict     =						bs[:]
	snake_case__				:   Tuple     =						0
	for b in range(2**8     ):
		if b not in bs:
			bs.append(snake_case_     )
			cs.append(2**8 + n     )
			n += 1
	snake_case__				:   Union[str, Any]     =						[chr(snake_case_     ) for n in cs]
	return dict(zip(snake_case_  ,						snake_case_     )     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Any     ):
	snake_case__				:   int     =						set()
	snake_case__				:   int     =						word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)     )
		snake_case__				:   Optional[int]     =						char
	return pairs





class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	VOCAB_FILES_NAMES
					a_			=	PRETRAINED_VOCAB_FILES_MAP
					a_			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a_			=	["input_ids", "attention_mask"]



					def __init__(      self  :						Dict							,	__A  :						int							,	__A  :						Dict							,	__A  :						int="replace"							,	__A  :						Tuple="<s>"							,	__A  :						Dict="</s>"							,	__A  :						Tuple="</s>"							,	__A  :						List[Any]="<s>"							,	__A  :						Any="<unk>"							,	__A  :						List[Any]="<pad>"							,	__A  :						List[Any]="<mask>"							,	__A  :						Optional[int]=False							,	**__A  :						Union[str, Any]							,	):
						snake_case__				:   Optional[int]     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else bos_token
						snake_case__				:   Optional[Any]     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else eos_token
						snake_case__				:   Optional[Any]     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else sep_token
						snake_case__				:   Optional[int]     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else cls_token
						snake_case__				:   Dict     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else unk_token
						snake_case__				:   str     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else pad_token

						# Mask token behave like a normal word, i.e. include the space before it
						snake_case__				:   List[Any]     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else mask_token

						super().__init__(
						    errors=__A							,	bos_token=__A							,	eos_token=__A							,	unk_token=__A							,	sep_token=__A							,	cls_token=__A							,	pad_token=__A							,	mask_token=__A							,	add_prefix_space=__A							,	**__A							,	)

						with open(__A							,	encoding="utf-8" ) as vocab_handle:
							snake_case__				:   Optional[int]     =						json.load(__A )
						snake_case__				:   Tuple     =						{v: k for k, v in self.encoder.items()}
						snake_case__				:   List[Any]     =						errors  # how to handle errors in decoding
						snake_case__				:   Any     =						bytes_to_unicode()
						snake_case__				:   Dict     =						{v: k for k, v in self.byte_encoder.items()}
						with open(__A							,	encoding="utf-8" ) as merges_handle:
							snake_case__				:   int     =						merges_handle.read().split("\n" )[1:-1]
						snake_case__				:   str     =						[tuple(merge.split() ) for merge in bpe_merges]
						snake_case__				:   str     =						dict(zip(__A							,	range(len(__A ) ) ) )
						snake_case__				:   Optional[Any]     =						{}
						snake_case__				:   Dict     =						add_prefix_space

						# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
						snake_case__				:   List[Any]     =						re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )



					@property
					# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
					def  _lowercase  (      self  :						List[str] ):
						return len(self.encoder )



					def  _lowercase  (      self  :						Any ):
						return dict(self.encoder							,	**self.added_tokens_encoder )



					def  _lowercase  (      self  :						Any							,	__A  :						Tuple ):
						if token in self.cache:
							return self.cache[token]
						snake_case__				:   List[Any]     =						tuple(__A )
						snake_case__				:   List[Any]     =						get_pairs(__A )

						if not pairs:
							return token

						while True:
							snake_case__				:   Any     =						min(__A							,	key=lambda __A : self.bpe_ranks.get(__A							,	float("inf" ) ) )
							if bigram not in self.bpe_ranks:
								break
							snake_case__,		snake_case__				:   str     =						bigram
							snake_case__				:   Dict     =						[]
							snake_case__				:   Optional[int]     =						0
							while i < len(__A ):
								try:
									snake_case__				:   List[str]     =						word.index(__A							,	__A )
								except ValueError:
									new_word.extend(word[i:] )
									break
								else:
									new_word.extend(word[i:j] )
									snake_case__				:   Tuple     =						j

								if word[i] == first and i < len(__A ) - 1 and word[i + 1] == second:
									new_word.append(first + second )
									i += 2
								else:
									new_word.append(word[i] )
									i += 1
							snake_case__				:   Tuple     =						tuple(__A )
							snake_case__				:   Optional[Any]     =						new_word
							if len(__A ) == 1:
								break
							else:
								snake_case__				:   Tuple     =						get_pairs(__A )
						snake_case__				:   int     =						" ".join(__A )
						snake_case__				:   List[str]     =						word
						return word



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						Any ):
						snake_case__				:   Any     =						[]
						for token in re.findall(self.pat							,	__A ):
							snake_case__				:   int     =						"".join(
							    self.byte_encoder[b] for b in token.encode("utf-8" ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
							bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__A ).split(" " ) )
						return bpe_tokens



					def  _lowercase  (      self  :						List[str]							,	__A  :						Tuple ):
						return self.encoder.get(__A							,	self.encoder.get(self.unk_token ) )



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						str ):
						return self.decoder.get(__A )



					def  _lowercase  (      self  :						List[Any]							,	__A  :						Dict ):
						snake_case__				:   Dict     =						"".join(__A )
						snake_case__				:   Tuple     =						bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8"							,	errors=self.errors )
						return text



					def  _lowercase  (      self  :						List[str]							,	__A  :						str							,	__A  :						Optional[str] = None ):
						if not os.path.isdir(__A ):
							logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
							return
						snake_case__				:   Union[str, Any]     =						os.path.join(
						    __A							,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
						snake_case__				:   Optional[int]     =						os.path.join(
						    __A							,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )

						with open(__A							,	"w"							,	encoding="utf-8" ) as f:
							f.write(json.dumps(self.encoder							,	indent=2							,	sort_keys=__A							,	ensure_ascii=__A ) + "\n" )

						snake_case__				:   Tuple     =						0
						with open(__A							,	"w"							,	encoding="utf-8" ) as writer:
							writer.write("#version: 0.2\n" )
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,	key=lambda __A : kv[1] ):
								if index != token_index:
									logger.warning(
									    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
									    " Please check that the tokenizer is not corrupted!" )
									snake_case__				:   int     =						token_index
								writer.write(" ".join(__A ) + "\n" )
								index += 1

						return vocab_file, merge_file



					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						List[int]							,	__A  :						Optional[List[int]] = None							,	__A  :						bool = False ):
						if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=__A							,	token_ids_a=__A							,	already_has_special_tokens=__A )

						if token_ids_a is None:
							return [1] + ([0] * len(__A )) + [1]
						return [1] + ([0] * len(__A )) + [1, 1] + ([0] * len(__A )) + [1]



					def  _lowercase  (      self  :						List[str]							,	__A  :						List[int]							,	__A  :						Optional[List[int]] = None ):
						snake_case__				:   List[str]     =						[self.sep_token_id]
						snake_case__				:   Any     =						[self.cls_token_id]

						if token_ids_a is None:
							return len(cls + token_ids_a + sep ) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]



					def  _lowercase  (      self  :						int							,	__A  :						Tuple							,	__A  :						Union[str, Any]=False							,	**__A  :						Any ):
						snake_case__				:   int     =						kwargs.pop("add_prefix_space"							,	self.add_prefix_space )
						if (is_split_into_words or add_prefix_space) and (len(__A ) > 0 and not text[0].isspace()):
							snake_case__				:   int     =						" " + text
						return (text, kwargs)



					def  _lowercase  (      self  :						Any							,	__A  :						List[int]							,	__A  :						Optional[List[int]] = None ):
						return token_ids_a + [self.eos_token_id]






					def  _lowercase  (      self  :						Any							,	__A  :						"Conversation" ):
						snake_case__				:   Tuple     =						[]
						for is_user, text in conversation.iter_texts():
							if is_user:
								# We need to space prefix as it's being done within blenderbot
								inputs.append(" " + text )
							else:
								# Generated responses should contain them already.
								inputs.append(__A )

						snake_case__				:   Optional[Any]     =						"  ".join(__A )
						snake_case__				:   str     =						self.encode(__A )
						if len(__A ) > self.model_max_length:
							snake_case__				:   Dict     =						input_ids[-self.model_max_length :]
							logger.warning(f'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.''' )
						return input_ids











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   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]  ,						snake_case_      :							Union[str, Any]     ):
	snake_case__				:   Optional[int]     =						[]
	for part_id in partition_order:
		snake_case__				:   List[Any]     =						df.where(F'''SPARK_PARTITION_ID() = {part_id}'''     ).collect()
		for row_idx, row in enumerate(snake_case_     ):
			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   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Tuple     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   Union[str, Any]     =						spark.range(100     ).repartition(1     )
	snake_case__				:   Any     =						Spark(snake_case_     )
	# 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   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Dict     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   Optional[Any]     =						spark.range(10     ).repartition(2     )
	snake_case__				:   Optional[Any]     =						[1, 0]
	snake_case__				:   Dict     =						_generate_iterable_examples(snake_case_  ,						snake_case_     )  # Reverse the partitions.
	snake_case__				:   Tuple     =						_get_expected_row_ids_and_row_dicts_for_partition_order(snake_case_  ,						snake_case_     )

	for i, (row_id, row_dict) in enumerate(generate_fn()     ):
		snake_case__,		snake_case__				:   Tuple     =						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   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Optional[int]     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   Optional[int]     =						spark.range(10     ).repartition(1     )
	snake_case__				:   Union[str, Any]     =						SparkExamplesIterable(snake_case_     )
	assert it.n_shards == 1
	for i, (row_id, row_dict) in enumerate(snake_case_     ):
		assert row_id == F'''0_{i}'''
		assert row_dict == {"id": i}







@require_not_windows
@require_dill_gt_0_3_2
def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Optional[int]     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   str     =						spark.range(30     ).repartition(3     )
	# Mock the generator so that shuffle reverses the partition indices.
	with patch("numpy.random.Generator"     ) as generator_mock:
		snake_case__				:   Union[str, Any]     =						lambda snake_case_     : x.reverse()
		snake_case__				:   Optional[int]     =						_get_expected_row_ids_and_row_dicts_for_partition_order(snake_case_  ,						[2, 1, 0]     )

		snake_case__				:   List[Any]     =						SparkExamplesIterable(snake_case_     ).shuffle_data_sources(snake_case_     )
		assert shuffled_it.n_shards == 3
		for i, (row_id, row_dict) in enumerate(snake_case_     ):
			snake_case__,		snake_case__				:   Optional[Any]     =						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   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Any     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   Tuple     =						spark.range(20     ).repartition(4     )

	# Partitions 0 and 2
	snake_case__				:   List[Any]     =						SparkExamplesIterable(snake_case_     ).shard_data_sources(worker_id=0  ,						num_workers=2     )
	assert shard_it_a.n_shards == 2
	snake_case__				:   List[str]     =						_get_expected_row_ids_and_row_dicts_for_partition_order(snake_case_  ,						[0, 2]     )
	for i, (row_id, row_dict) in enumerate(snake_case_     ):
		snake_case__,		snake_case__				:   Optional[int]     =						expected_row_ids_and_row_dicts_a[i]
		assert row_id == expected_row_id
		assert row_dict == expected_row_dict

	# Partitions 1 and 3
	snake_case__				:   Any     =						SparkExamplesIterable(snake_case_     ).shard_data_sources(worker_id=1  ,						num_workers=2     )
	assert shard_it_a.n_shards == 2
	snake_case__				:   List[Any]     =						_get_expected_row_ids_and_row_dicts_for_partition_order(snake_case_  ,						[1, 3]     )
	for i, (row_id, row_dict) in enumerate(snake_case_     ):
		snake_case__,		snake_case__				:   Optional[Any]     =						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   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Dict     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   Tuple     =						spark.range(100     ).repartition(1     )
	snake_case__				:   Union[str, Any]     =						Spark(snake_case_     )
	# 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



