






"""simple docstring"""

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation

lowercase__          = logging.get_logger(__name__)


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

lowercase__          = {
    """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"""
    },
}

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


class 		__lowerCamelCase				( A__			):





		'''simple docstring'''






		a_	:				Tuple            =							VOCAB_FILES_NAMES
		a_	:				Union[str, Any]            =							PRETRAINED_VOCAB_FILES_MAP
		a_	:				Optional[int]            =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a_	:				List[Any]            =							["input_ids", "attention_mask"]
		a_	:				Tuple            =							BlenderbotTokenizer





		def __init__(   self							:	int						,			a_							:	Dict=None						,			a_							:	Dict=None						,			a_							:	Dict=None						,			a_							:	Union[str, Any]="replace"						,			a_							:	Optional[Any]="<s>"						,			a_							:	Dict="</s>"						,			a_							:	Tuple="</s>"						,			a_							:	List[Any]="<s>"						,			a_							:	Tuple="<unk>"						,			a_							:	Dict="<pad>"						,			a_							:	Optional[int]="<mask>"						,			a_							:	Tuple=False						,			a_							:	str=True						,			**a_							:	Tuple						,			):
				super().__init__(
				    a_						,			a_						,			tokenizer_file=a_						,			errors=a_						,			bos_token=a_						,			eos_token=a_						,			sep_token=a_						,			cls_token=a_						,			unk_token=a_						,			pad_token=a_						,			mask_token=a_						,			add_prefix_space=a_						,			trim_offsets=a_						,			**a_						,			)

				lowerCAmelCase_   :							Tuple									=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
				if pre_tok_state.get("add_prefix_space"						,			a_	) != add_prefix_space:
						lowerCAmelCase_   :							Union[str, Any]									=			getattr(a_						,			pre_tok_state.pop("type"	)	)
						lowerCAmelCase_   :							str									=			add_prefix_space
						lowerCAmelCase_   :							Any									=			pre_tok_class(**a_	)

				lowerCAmelCase_   :							Tuple									=			add_prefix_space

				lowerCAmelCase_   :							int									=			"""post_processor"""
				lowerCAmelCase_   :							Dict									=			getattr(self.backend_tokenizer						,			a_						,			a_	)
				if tokenizer_component_instance:
						lowerCAmelCase_   :							int									=			json.loads(tokenizer_component_instance.__getstate__()	)

						# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
						if "sep" in state:
								lowerCAmelCase_   :							Any									=			tuple(state["sep"]	)
						if "cls" in state:
								lowerCAmelCase_   :							Union[str, Any]									=			tuple(state["cls"]	)

						lowerCAmelCase_   :							int									=			False

						if state.get("add_prefix_space"						,			a_	) != add_prefix_space:
								lowerCAmelCase_   :							List[str]									=			add_prefix_space
								lowerCAmelCase_   :							Union[str, Any]									=			True

						if state.get("trim_offsets"						,			a_	) != trim_offsets:
								lowerCAmelCase_   :							str									=			trim_offsets
								lowerCAmelCase_   :							str									=			True

						if changes_to_apply:
								lowerCAmelCase_   :							Union[str, Any]									=			getattr(a_						,			state.pop("type"	)	)
								lowerCAmelCase_   :							int									=			component_class(**a_	)
								setattr(self.backend_tokenizer						,			a_						,			a_	)





		@property
		# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
		def    lowerCamelCase       (   self							:	int	):







				if self._mask_token is None:
						if self.verbose:
								logger.error("Using mask_token, but it is not set yet."	)
						return None
				return str(self._mask_token	)





		@mask_token.setter
		def    lowerCamelCase       (   self							:	Tuple						,			a_							:	int	):
				lowerCAmelCase_   :							str									=			AddedToken(a_						,			lstrip=a_						,			rstrip=a_	) if isinstance(a_						,			a_	) else value
				lowerCAmelCase_   :							List[Any]									=			value





		def    lowerCamelCase       (   self							:	int						,			*a_							:	Any						,			**a_							:	List[str]	):
				lowerCAmelCase_   :							Optional[Any]									=			kwargs.get("is_split_into_words"						,			a_	)
				assert self.add_prefix_space or not is_split_into_words, (
				    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
				    "to use it with pretokenized inputs."
				)

				return super()._batch_encode_plus(*a_						,			**a_	)





		def    lowerCamelCase       (   self							:	Tuple						,			*a_							:	Dict						,			**a_							:	Tuple	):
				lowerCAmelCase_   :							List[str]									=			kwargs.get("is_split_into_words"						,			a_	)

				assert self.add_prefix_space or not is_split_into_words, (
				    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
				    "to use it with pretokenized inputs."
				)

				return super()._encode_plus(*a_						,			**a_	)





		def    lowerCamelCase       (   self							:	Dict						,			a_							:	Tuple						,			a_							:	List[str] = None	):
				lowerCAmelCase_   :							int									=			self._tokenizer.model.save(a_						,			name=a_	)
				return tuple(a_	)





		def    lowerCamelCase       (   self							:	List[str]						,			a_							:	Union[str, Any]						,			a_							:	Tuple = None	):
				lowerCAmelCase_   :							Optional[int]									=			[self.sep_token_id]
				lowerCAmelCase_   :							List[str]									=			[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    lowerCamelCase       (   self							:	Any						,			a_							:	str						,			a_							:	Any = None	):
				return token_ids_a + [self.eos_token_id]





		def    lowerCamelCase       (   self							:	Union[str, Any]						,			a_							:	Union[str, Any]	):
				lowerCAmelCase_   :							Dict									=			[]
				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_	)

				lowerCAmelCase_   :							List[Any]									=			"""  """.join(a_	)
				lowerCAmelCase_   :							Optional[Any]									=			self.encode(a_	)
				if len(a_	) > self.model_max_length:
						lowerCAmelCase_   :							Any									=			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


import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Any:
       assert isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       assert dataset.num_rows == 4
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_1", "col_2", "col_3"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype







@require_sqlalchemy
@pytest.mark.parametrize("""keep_in_memory"""					,	[False, True]				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       lowercase							:     Union[str, Any]       	=      tmp_path / """cache"""
       lowercase							:     Optional[Any]       	=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              lowercase							:     Any       	=      SqlDatasetReader(
                  """dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__					,	keep_in_memory=SCREAMING_SNAKE_CASE__				).read()
       _check_sql_dataset(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







@require_sqlalchemy
@pytest.mark.parametrize(
    """features"""					,	[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]					,	)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Tuple:
       lowercase							:     Union[str, Any]       	=      tmp_path / """cache"""
       lowercase							:     int       	=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       lowercase							:     str       	=      features.copy() if features else default_expected_features
       lowercase							:     Optional[Any]       	=      (
           Features({feature: Value(SCREAMING_SNAKE_CASE__				) for feature, dtype in features.items()}				) if features is not None else None
       )
       lowercase							:     Optional[int]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	features=SCREAMING_SNAKE_CASE__					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       _check_sql_dataset(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       with contextlib.closing(sqlitea.connect(SCREAMING_SNAKE_CASE__				)				) as con:
              lowercase							:     Optional[int]       	=      con.cursor()
              cur.execute("""SELECT * FROM dataset"""				)
              for row in cur:
                     yield row







@require_sqlalchemy
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    int:
       lowercase							:     Any       	=      tmp_path / """cache"""
       lowercase							:     int       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	"""tmp.sql"""				)
       lowercase							:     List[str]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       SqlDatasetWriter(SCREAMING_SNAKE_CASE__					,	"""dataset"""					,	"""sqlite:///""" + output_sqlite_path					,	num_proc=1				).write()

       lowercase							:     List[str]       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Tuple       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)

       for rowa, rowa in zip(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
              assert rowa == rowa







@require_sqlalchemy
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Dict:
       lowercase							:     Dict       	=      tmp_path / """cache"""
       lowercase							:     List[str]       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	"""tmp.sql"""				)
       lowercase							:     List[str]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       SqlDatasetWriter(SCREAMING_SNAKE_CASE__					,	"""dataset"""					,	"""sqlite:///""" + output_sqlite_path					,	num_proc=2				).write()

       lowercase							:     Optional[int]       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[Any]       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)

       for rowa, rowa in zip(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
              assert rowa == rowa







@require_sqlalchemy
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     str       	=      tmp_path / """cache"""
       lowercase							:     Optional[int]       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	"""tmp.sql"""				)
       lowercase							:     Optional[Any]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       with pytest.raises(SCREAMING_SNAKE_CASE__				):
              SqlDatasetWriter(SCREAMING_SNAKE_CASE__					,	"""dataset"""					,	"""sqlite:///""" + output_sqlite_path					,	num_proc=0				).write()
