
import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
	import torch

	from ..models.auto.modeling_auto import (
	    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
	    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
	)

if is_tf_available() and is_tensorflow_probability_available():
	import tensorflow as tf
	import tensorflow_probability as tfp

	from ..models.auto.modeling_tf_auto import (
	    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
	    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
	)

class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	def __call__(      self				:			Tuple							,			snake_case__				:			Dict=None							,			snake_case__				:			int=None							,			**snake_case__				:			List[Any] ):
			"""simple docstring"""





			requires_backends(self							,			"pandas" )
			import pandas as pd

			if table is None:
					raise ValueError("Keyword argument `table` cannot be None." )
			elif query is None:
					if isinstance(snake_case__							,			snake_case__ ) and table.get("query" ) is not None and table.get("table" ) is not None:
							A				=[table]
					elif isinstance(snake_case__							,			snake_case__ ) and len(snake_case__ ) > 0:
							if not all(isinstance(snake_case__							,			snake_case__ ) for d in table ):
									raise ValueError(
									    f'''Keyword argument `table` should be a list of dict, but is {(type(snake_case__ ) for d in table)}''' )

							if table[0].get("query" ) is not None and table[0].get("table" ) is not None:
									A				=table
							else:
									raise ValueError(
									    "If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`"
									    f''' and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys.''' )
					elif Dataset is not None and isinstance(snake_case__							,			snake_case__ ) or isinstance(snake_case__							,			types.GeneratorType ):
							return table
					else:
							raise ValueError(
							    "Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but "
							    f'''is {type(snake_case__ )})''' )
			else:
					A				=[{"table": table, "query": query}]

			for tqa_pipeline_input in tqa_pipeline_inputs:
					if not isinstance(tqa_pipeline_input["table"]							,			pd.DataFrame ):
							if tqa_pipeline_input["table"] is None:
									raise ValueError("Table cannot be None." )

							A				=pd.DataFrame(tqa_pipeline_input["table"] )

			return tqa_pipeline_inputs


@add_end_docstrings(lowerCAmelCase__	)
class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		"table,query"







	def __init__(      self				:			int							,			snake_case__				:			Optional[int]=TableQuestionAnsweringArgumentHandler()							,			*snake_case__				:			List[str]							,			**snake_case__				:			Dict ):
			"""simple docstring"""





			super().__init__(*snake_case__							,			**snake_case__ )
			A				=args_parser

			self.check_model_type(
			    dict(
			        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
			        + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items() )
			    if self.framework == "tf"
			    else dict(
			        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items() ) )

			A				=bool(getattr(self.model.config							,			"aggregation_labels"							,			snake_case__ ) ) and bool(
			    getattr(self.model.config							,			"num_aggregation_labels"							,			snake_case__ ) )
			A				="tapas" if hasattr(self.model.config							,			"aggregation_labels" ) else None







	def        _a					(      self				:			str							,			**snake_case__				:			str ):
			"""simple docstring"""





			return self.model(**snake_case__ )







	def        _a					(      self				:			Any							,			**snake_case__				:			int ):
			"""simple docstring"""





			if self.framework == "pt":
					A				=[]
					A				=[]
					A				=None
					A				=inputs["input_ids"].shape[0]

					A				=inputs["input_ids"].to(self.device )
					A				=inputs["attention_mask"].to(self.device )
					A				=inputs["token_type_ids"].to(self.device )
					A				=None

					for index in range(snake_case__ ):
							# If sequences have already been processed, the token type IDs will be created according to the previous
							# answer.
							if prev_answers is not None:
									A				=token_type_ids_example[:, 3]  # shape (seq_len,)
									A				=np.zeros_like(prev_labels_example.cpu().numpy() )  # shape (seq_len,)

									A				=token_type_ids[index]  # shape (seq_len, 7)
									for i in range(model_labels.shape[0] ):
											A				=token_type_ids_example[:, 0].tolist()[i]
											A				=token_type_ids_example[:, 1].tolist()[i] - 1
											A				=token_type_ids_example[:, 2].tolist()[i] - 1

											if row_id >= 0 and col_id >= 0 and segment_id == 1:
													A				=int(prev_answers[(col_id, row_id)] )

									A				=torch.from_numpy(snake_case__ ).type(torch.long ).to(self.device )

							A				=input_ids[index]
							A				=attention_mask[index]  # shape (seq_len,)
							A				=token_type_ids[index]  # shape (seq_len, 7)
							A				=self.model(
							    input_ids=input_ids_example.unsqueeze(0 )							,			attention_mask=attention_mask_example.unsqueeze(0 )							,			token_type_ids=token_type_ids_example.unsqueeze(0 )							,			)
							A				=outputs.logits

							if self.aggregate:
									all_aggregations.append(outputs.logits_aggregation )

							all_logits.append(snake_case__ )

							A				=torch.distributions.Bernoulli(logits=snake_case__ )
							A				=dist_per_token.probs * attention_mask_example.type(torch.floataa ).to(
							    dist_per_token.probs.device )

							A				=collections.defaultdict(snake_case__ )
							for i, p in enumerate(probabilities.squeeze().tolist() ):
									A				=token_type_ids_example[:, 0].tolist()[i]
									A				=token_type_ids_example[:, 1].tolist()[i] - 1
									A				=token_type_ids_example[:, 2].tolist()[i] - 1
									if col >= 0 and row >= 0 and segment_id == 1:
											coords_to_probs[(col, row)].append(snake_case__ )

							A				={key: np.array(coords_to_probs[key] ).mean() > 0.5 for key in coords_to_probs}

					A				=torch.cat(tuple(snake_case__ )							,			0 )

					return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(snake_case__ )							,			0 ))
			else:
					A				=[]
					A				=[]
					A				=None
					A				=inputs["input_ids"].shape[0]

					A				=inputs["input_ids"]
					A				=inputs["attention_mask"]
					A				=inputs["token_type_ids"].numpy()
					A				=None

					for index in range(snake_case__ ):
							# If sequences have already been processed, the token type IDs will be created according to the previous
							# answer.
							if prev_answers is not None:
									A				=token_type_ids_example[:, 3]  # shape (seq_len,)
									A				=np.zeros_like(snake_case__							,			dtype=np.intaa )  # shape (seq_len,)

									A				=token_type_ids[index]  # shape (seq_len, 7)
									for i in range(model_labels.shape[0] ):
											A				=token_type_ids_example[:, 0].tolist()[i]
											A				=token_type_ids_example[:, 1].tolist()[i] - 1
											A				=token_type_ids_example[:, 2].tolist()[i] - 1

											if row_id >= 0 and col_id >= 0 and segment_id == 1:
													A				=int(prev_answers[(col_id, row_id)] )

									A				=model_labels

							A				=input_ids[index]
							A				=attention_mask[index]  # shape (seq_len,)
							A				=token_type_ids[index]  # shape (seq_len, 7)
							A				=self.model(
							    input_ids=np.expand_dims(snake_case__							,			axis=0 )							,			attention_mask=np.expand_dims(snake_case__							,			axis=0 )							,			token_type_ids=np.expand_dims(snake_case__							,			axis=0 )							,			)
							A				=outputs.logits

							if self.aggregate:
									all_aggregations.append(outputs.logits_aggregation )

							all_logits.append(snake_case__ )

							A				=tfp.distributions.Bernoulli(logits=snake_case__ )
							A				=dist_per_token.probs_parameter() * tf.cast(snake_case__							,			tf.floataa )

							A				=collections.defaultdict(snake_case__ )
							A				=token_type_ids_example
							for i, p in enumerate(tf.squeeze(snake_case__ ).numpy().tolist() ):
									A				=token_type_ids_example[:, 0].tolist()[i]
									A				=token_type_ids_example[:, 1].tolist()[i] - 1
									A				=token_type_ids_example[:, 2].tolist()[i] - 1
									if col >= 0 and row >= 0 and segment_id == 1:
											coords_to_probs[(col, row)].append(snake_case__ )

							A				={key: np.array(coords_to_probs[key] ).mean() > 0.5 for key in coords_to_probs}

					A				=tf.concat(tuple(snake_case__ )							,			0 )

					return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(snake_case__ )							,			0 ))







	def __call__(      self				:			Optional[Any]							,			*snake_case__				:			List[str]							,			**snake_case__				:			Optional[Any] ):
			"""simple docstring"""





			A				=self._args_parser(*snake_case__							,			**snake_case__ )

			A				=super().__call__(snake_case__							,			**snake_case__ )
			if len(snake_case__ ) == 1:
					return results[0]
			return results







	def        _a					(      self				:			Dict							,			snake_case__				:			Union[str, Any]=None							,			snake_case__				:			Any=None							,			snake_case__				:			Any=None							,			**snake_case__				:			Union[str, Any] ):
			"""simple docstring"""





			A				={}
			if padding is not None:
					A				=padding
			if truncation is not None:
					A				=truncation

			A				={}
			if sequential is not None:
					A				=sequential
			return preprocess_params, forward_params, {}







	def        _a					(      self				:			Optional[int]							,			snake_case__				:			Optional[int]							,			snake_case__				:			List[Any]=None							,			snake_case__				:			int=True							,			snake_case__				:			str=None ):
			"""simple docstring"""





			if truncation is None:
					if self.type == "tapas":
							A				="drop_rows_to_fit"
					else:
							A				="do_not_truncate"

			A			,    A				=pipeline_input["table"], pipeline_input["query"]
			if table.empty:
					raise ValueError("table is empty" )
			if query is None or query == "":
					raise ValueError("query is empty" )
			A				=self.tokenizer(snake_case__							,			snake_case__							,			return_tensors=self.framework							,			truncation=snake_case__							,			padding=snake_case__ )
			A				=table
			return inputs







	def        _a					(      self				:			Optional[int]							,			snake_case__				:			Union[str, Any]							,			snake_case__				:			Any=False ):
			"""simple docstring"""





			A				=model_inputs.pop("table" )

			if self.type == "tapas":
					if sequential:
							A				=self.sequential_inference(**snake_case__ )
					else:
							A				=self.batch_inference(**snake_case__ )
			else:
					A				=self.model.generate(**snake_case__ )
			A				={"model_inputs": model_inputs, "table": table, "outputs": outputs}
			return model_outputs







	def        _a					(      self				:			Any							,			snake_case__				:			Tuple ):
			"""simple docstring"""





			A				=model_outputs["model_inputs"]
			A				=model_outputs["table"]
			A				=model_outputs["outputs"]
			if self.type == "tapas":
					if self.aggregate:
							A			,    A				=outputs[:2]
							A				=self.tokenizer.convert_logits_to_predictions(snake_case__							,			snake_case__							,			snake_case__ )
							A			,    A				=predictions
							A				={i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(snake_case__ )}

							A				=self.model.config.no_aggregation_label_index
							A				={
							    i: aggregators[i] + " > " for i, pred in enumerate(snake_case__ ) if pred != no_agg_label_index
							}
					else:
							A				=outputs[0]
							A				=self.tokenizer.convert_logits_to_predictions(snake_case__							,			snake_case__ )
							A				=predictions[0]
							A				={}
							A				={}
					A				=[]
					for index, coordinates in enumerate(snake_case__ ):
							A				=[table.iat[coordinate] for coordinate in coordinates]
							A				=aggregators.get(snake_case__							,			"" )
							A				=aggregators_prefix.get(snake_case__							,			"" )
							A				={
							    "answer": aggregator_prefix + ", ".join(snake_case__ ),
							    "coordinates": coordinates,
							    "cells": [table.iat[coordinate] for coordinate in coordinates],
							}
							if aggregator:
									A				=aggregator

							answers.append(snake_case__ )
					if len(snake_case__ ) == 0:
							raise PipelineException("Empty answer" )
			else:
					A				=[{"answer": answer} for answer in self.tokenizer.batch_decode(snake_case__							,			skip_special_tokens=snake_case__ )]

			return answers if len(snake_case__ ) > 1 else answers[0]






import json
import os
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

__a							  =						logging.get_logger(__name__)

__a							  =						{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

__a							  =						{
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

__a							  =						{
    """microsoft/deberta-base""": 5_1_2,
    """microsoft/deberta-large""": 5_1_2,
    """microsoft/deberta-xlarge""": 5_1_2,
    """microsoft/deberta-base-mnli""": 5_1_2,
    """microsoft/deberta-large-mnli""": 5_1_2,
    """microsoft/deberta-xlarge-mnli""": 5_1_2,
}

__a							  =						{
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}

def    UpperCamelCase_ (	)     ->Optional[int]:
		A				=(
		    list(range(ord("!"		)  ,				ord("~"		) + 1		)		) + list(range(ord("¡"		)  ,				ord("¬"		) + 1		)		) + list(range(ord("®"		)  ,				ord("ÿ"		) + 1		)		)
		)
		A				=bs[:]
		A				=0
		for b in range(2**8		):
				if b not in bs:
						bs.append(a_		)
						cs.append(2**8 + n		)
						n += 1
		A				=[chr(a_		) for n in cs]
		return dict(zip(a_  ,				a_		)		)

def    UpperCamelCase_ (	a_		)     ->Dict:
		A				=set()
		A				=word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)		)
				A				=char
		return pairs






class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		VOCAB_FILES_NAMES
	_A  =		PRETRAINED_VOCAB_FILES_MAP
	_A  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A  =		["input_ids", "attention_mask", "token_type_ids"]







	def __init__(      self				:			Union[str, Any]							,			snake_case__				:			Tuple							,			snake_case__				:			Union[str, Any]							,			snake_case__				:			Union[str, Any]="replace"							,			snake_case__				:			Any="[CLS]"							,			snake_case__				:			List[Any]="[SEP]"							,			snake_case__				:			str="[SEP]"							,			snake_case__				:			Tuple="[CLS]"							,			snake_case__				:			Optional[Any]="[UNK]"							,			snake_case__				:			Union[str, Any]="[PAD]"							,			snake_case__				:			List[Any]="[MASK]"							,			snake_case__				:			Any=False							,			snake_case__				:			List[str]=False							,			**snake_case__				:			str							,			):
			"""simple docstring"""





			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else bos_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else eos_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else sep_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else cls_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else unk_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else pad_token

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

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

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

			# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
			A				=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.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
	def        _a					(      self				:			Optional[int] ):
			"""simple docstring"""





			return len(self.encoder )







	def        _a					(      self				:			int ):
			"""simple docstring"""





			return dict(self.encoder							,			**self.added_tokens_encoder )







	def        _a					(      self				:			List[str]							,			snake_case__				:			List[str] ):
			"""simple docstring"""





			if token in self.cache:
					return self.cache[token]
			A				=tuple(snake_case__ )
			A				=get_pairs(snake_case__ )

			if not pairs:
					return token

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

							if word[i] == first and i < len(snake_case__ ) - 1 and word[i + 1] == second:
									new_word.append(first + second )
									i += 2
							else:
									new_word.append(word[i] )
									i += 1
					A				=tuple(snake_case__ )
					A				=new_word
					if len(snake_case__ ) == 1:
							break
					else:
							A				=get_pairs(snake_case__ )
			A				=" ".join(snake_case__ )
			A				=word
			return word







	def        _a					(      self				:			Any							,			snake_case__				:			List[int]							,			snake_case__				:			Optional[List[int]] = None ):
			"""simple docstring"""





			if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			A				=[self.cls_token_id]
			A				=[self.sep_token_id]
			return cls + token_ids_a + sep + token_ids_a + sep







	def        _a					(      self				:			List[Any]							,			snake_case__				:			List[int]							,			snake_case__				:			Optional[List[int]] = None							,			snake_case__				:			bool = False ):
			"""simple docstring"""





			if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=snake_case__							,			token_ids_a=snake_case__							,			already_has_special_tokens=snake_case__ )

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







	def        _a					(      self				:			str							,			snake_case__				:			List[int]							,			snake_case__				:			Optional[List[int]] = None ):
			"""simple docstring"""





			A				=[self.sep_token_id]
			A				=[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 ) * [0] + len(token_ids_a + sep ) * [1]







	def        _a					(      self				:			List[str]							,			snake_case__				:			List[Any] ):
			"""simple docstring"""





			A				=[]
			for token in re.findall(self.pat							,			snake_case__ ):
					A				="".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(snake_case__ ).split(" " ) )
			return bpe_tokens







	def        _a					(      self				:			List[Any]							,			snake_case__				:			List[str] ):
			"""simple docstring"""





			return self.encoder.get(snake_case__							,			self.encoder.get(self.unk_token ) )







	def        _a					(      self				:			int							,			snake_case__				:			List[str] ):
			"""simple docstring"""





			return self.decoder.get(snake_case__ )







	def        _a					(      self				:			Union[str, Any]							,			snake_case__				:			Optional[int] ):
			"""simple docstring"""





			A				="".join(snake_case__ )
			A				=bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8"							,			errors=self.errors )
			return text







	def        _a					(      self				:			int							,			snake_case__				:			str							,			snake_case__				:			Optional[str] = None ):
			"""simple docstring"""





			if not os.path.isdir(snake_case__ ):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
					return
			A				=os.path.join(
			    snake_case__							,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
			A				=os.path.join(
			    snake_case__							,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )

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

			A				=0
			with open(snake_case__							,			"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 snake_case__ : 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!" )
									A				=token_index
							writer.write(" ".join(snake_case__ ) + "\n" )
							index += 1

			return vocab_file, merge_file







	def        _a					(      self				:			List[Any]							,			snake_case__				:			Optional[Any]							,			snake_case__				:			Any=False							,			**snake_case__				:			Optional[int] ):
			"""simple docstring"""





			A				=kwargs.pop("add_prefix_space"							,			self.add_prefix_space )
			if (is_split_into_words or add_prefix_space) and (len(snake_case__ ) > 0 and not text[0].isspace()):
					A				=" " + text
			return (text, kwargs)







	def        _a					(      self				:			List[str]							,			snake_case__				:			"Conversation" ):
			"""simple docstring"""





			A				=[]
			for is_user, text in conversation.iter_texts():
					input_ids.extend(self.encode(snake_case__							,			add_special_tokens=snake_case__ ) + [self.eos_token_id] )
			if len(snake_case__ ) > self.model_max_length:
					A				=input_ids[-self.model_max_length :]
			return input_ids




