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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


lowercase        =  logging.get_logger(__name__)

lowercase        =  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase        =  {
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
    },
}

lowercase        =  {
    '''gpt2''': 1_0_2_4,
    '''gpt2-medium''': 1_0_2_4,
    '''gpt2-large''': 1_0_2_4,
    '''gpt2-xl''': 1_0_2_4,
    '''distilgpt2''': 1_0_2_4,
}





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE			    =    ['''input_ids''', '''attention_mask''']
					SCREAMING_SNAKE_CASE			    =    GPTaTokenizer

					def __init__(	self					:     Tuple   ,					__UpperCamelCase					:     Optional[int]=None   ,					__UpperCamelCase					:     Optional[int]=None   ,					__UpperCamelCase					:     int=None   ,					__UpperCamelCase					:     Dict="<|endoftext|>"   ,					__UpperCamelCase					:     Any="<|endoftext|>"   ,					__UpperCamelCase					:     Optional[int]="<|endoftext|>"   ,					__UpperCamelCase					:     List[str]=False   ,					**__UpperCamelCase					:     List[str]   ,					):
												super().__init__(
												    __UpperCamelCase   ,					__UpperCamelCase   ,					tokenizer_file=__UpperCamelCase   ,					unk_token=__UpperCamelCase   ,					bos_token=__UpperCamelCase   ,					eos_token=__UpperCamelCase   ,					add_prefix_space=__UpperCamelCase   ,					**__UpperCamelCase   ,					)

												lowerCamelCase_							  =					kwargs.pop("""add_bos_token"""   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
												if pre_tok_state.get("""add_prefix_space"""   ,					__UpperCamelCase   ) != add_prefix_space:
																			lowerCamelCase_							  =					getattr(__UpperCamelCase   ,					pre_tok_state.pop("""type"""   )   )
																			lowerCamelCase_							  =					add_prefix_space
																			lowerCamelCase_							  =					pre_tok_class(**__UpperCamelCase   )

												lowerCamelCase_							  =					add_prefix_space

					def    lowercase__      (	self					:     Union[str, Any]   ,					*__UpperCamelCase					:     List[Any]   ,					**__UpperCamelCase					:     List[Any]   ):
												lowerCamelCase_							  =					kwargs.get("""is_split_into_words"""   ,					__UpperCamelCase   )
												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(*__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     Any   ,					*__UpperCamelCase					:     Any   ,					**__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					kwargs.get("""is_split_into_words"""   ,					__UpperCamelCase   )

												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(*__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Optional[str] = None   ):
												lowerCamelCase_							  =					self._tokenizer.model.save(__UpperCamelCase   ,					name=__UpperCamelCase   )
												return tuple(__UpperCamelCase   )



					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     "Conversation"   ):
												lowerCamelCase_							  =					[]
												for is_user, text in conversation.iter_texts():
																			input_ids.extend(self.encode(__UpperCamelCase   ,					add_special_tokens=__UpperCamelCase   ) + [self.eos_token_id]   )

												if len(__UpperCamelCase   ) > self.model_max_length:
																			lowerCamelCase_							  =					input_ids[-self.model_max_length :]
												return input_ids







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
		import torch

		from transformers.generation import DisjunctiveConstraint





@require_torch
class       __A(				unittest.TestCase       ):

					def    lowercase__      (	self					:     Optional[Any]   ):
												# For consistency across different places the DisjunctiveConstraint is called,
												# dc.token_ids is a list of integers. It is also initialized only by integers.

												lowerCamelCase_							  =					[[1, 2, 4], [1, 2, 3, 4]]
												lowerCamelCase_							  =					DisjunctiveConstraint(__UpperCamelCase   )
												self.assertTrue(isinstance(dc.token_ids   ,					__UpperCamelCase   )   )

												with self.assertRaises(__UpperCamelCase   ):
																			DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]   )   )

												with self.assertRaises(__UpperCamelCase   ):
																			DisjunctiveConstraint([torch.LongTensor([1, 2, 4]   ), torch.LongTensor([1, 2, 3, 4, 5]   )]   )

					def    lowercase__      (	self					:     List[Any]   ):
												# We can't have constraints that are complete subsets of another. This leads to a preverse
												# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
												# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
												# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
												# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
												lowerCamelCase_							  =					[[1, 2], [1, 2, 3, 4]]

												with self.assertRaises(__UpperCamelCase   ):
																			DisjunctiveConstraint(__UpperCamelCase   )  # fails here

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					[[1, 2, 3], [1, 2, 4]]

												lowerCamelCase_							  =					DisjunctiveConstraint(__UpperCamelCase   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(1   )
												lowerCamelCase_							  =					stepped is True and completed is False and reset is False
												self.assertTrue(__UpperCamelCase   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.current_seq == [1]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(2   )
												lowerCamelCase_							  =					stepped is True and completed is False and reset is False
												self.assertTrue(__UpperCamelCase   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.current_seq == [1, 2]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(3   )
												lowerCamelCase_							  =					stepped is True and completed is True and reset is False
												self.assertTrue(__UpperCamelCase   )
												self.assertTrue(dc.completed   )  # Completed!
												self.assertTrue(dc.current_seq == [1, 2, 3]   )



					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

												lowerCamelCase_							  =					DisjunctiveConstraint(__UpperCamelCase   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(1   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.current_seq == [1]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(2   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.current_seq == [1, 2]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(4   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.current_seq == [1, 2, 4]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(5   )
												self.assertTrue(dc.completed   )  # Completed!
												self.assertTrue(dc.current_seq == [1, 2, 4, 5]   )

												dc.reset()

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(1   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.remaining() == 3   )
												self.assertTrue(dc.current_seq == [1]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(2   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.remaining() == 2   )
												self.assertTrue(dc.current_seq == [1, 2]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(5   )
												self.assertTrue(dc.completed   )  # Completed!
												self.assertTrue(dc.remaining() == 0   )
												self.assertTrue(dc.current_seq == [1, 2, 5]   )





