

import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 				SCREAMING_SNAKE_CASE__ (__snake_case		,	unittest.TestCase					):
    __lowerCamelCase						:	Optional[int]														=					None
    __lowerCamelCase						:	List[str]														=					BloomTokenizerFast
    __lowerCamelCase						:	str														=					BloomTokenizerFast
    __lowerCamelCase						:	List[Any]														=					True
    __lowerCamelCase						:	Optional[Any]														=					False
    __lowerCamelCase						:	int														=					"""tokenizer_file"""
    __lowerCamelCase						:	Tuple														=					{"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}





    def 				snake_case_    (				self):
         super().setUp()
         lowercase__   :       Tuple				=	BloomTokenizerFast.from_pretrained('bigscience/tokenizer')
         tokenizer.save_pretrained(self.tmpdirname)





    def 				snake_case_    (				self						,       **a):
         kwargs.update(self.special_tokens_map)
         return BloomTokenizerFast.from_pretrained(self.tmpdirname						,       **a)





    def 				snake_case_    (				self):
         lowercase__   :       Tuple				=	self.get_rust_tokenizer()

         lowercase__   :       Any				=	['The quick brown fox</s>', 'jumps over the lazy dog</s>']
         lowercase__   :       Optional[int]				=	[[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

         lowercase__   :       Tuple				=	tokenizer.batch_encode_plus(a)['input_ids']
         self.assertListEqual(a						,       a)

         lowercase__   :       List[str]				=	tokenizer.batch_decode(a)
         self.assertListEqual(a						,       a)





    def 				snake_case_    (				self						,       a=6):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                   lowercase__   :       List[Any]				=	self.rust_tokenizer_class.from_pretrained(a						,       **a)
                   # tokenizer_r.pad_token = None # Hotfixing padding = None
                   # Simple input
                   lowercase__   :       Optional[Any]				=	'This is a simple input'
                   lowercase__   :       Union[str, Any]				=	['This is a simple input 1', 'This is a simple input 2']
                   lowercase__   :       Dict				=	('This is a simple input', 'This is a pair')
                   lowercase__   :       Dict				=	[
                       ('This is a simple input 1', 'This is a simple input 2'),
                       ('This is a simple pair 1', 'This is a simple pair 2'),
                   ]

                   # Simple input tests
                   try:
                        tokenizer_r.encode(a						,       max_length=a)
                        tokenizer_r.encode_plus(a						,       max_length=a)

                        tokenizer_r.batch_encode_plus(a						,       max_length=a)
                        tokenizer_r.encode(a						,       max_length=a)
                        tokenizer_r.batch_encode_plus(a						,       max_length=a)
                   except ValueError:
                        self.fail('Bloom Tokenizer should be able to deal with padding')

                   lowercase__   :       Optional[int]				=	None  # Hotfixing padding = None
                   self.assertRaises(a						,       tokenizer_r.encode						,       a						,       max_length=a						,       padding='max_length')

                   # Simple input
                   self.assertRaises(a						,       tokenizer_r.encode_plus						,       a						,       max_length=a						,       padding='max_length')

                   # Simple input
                   self.assertRaises(
                       a						,       tokenizer_r.batch_encode_plus						,       a						,       max_length=a						,       padding='max_length'						,       )

                   # Pair input
                   self.assertRaises(a						,       tokenizer_r.encode						,       a						,       max_length=a						,       padding='max_length')

                   # Pair input
                   self.assertRaises(a						,       tokenizer_r.encode_plus						,       a						,       max_length=a						,       padding='max_length')

                   # Pair input
                   self.assertRaises(
                       a						,       tokenizer_r.batch_encode_plus						,       a						,       max_length=a						,       padding='max_length'						,       )





    def 				snake_case_    (				self):
         lowercase__   :       Union[str, Any]				=	self.get_rust_tokenizer()
         lowercase__   :       Optional[int]				=	load_dataset('xnli'						,       'all_languages'						,       split='test'						,       streaming=a)

         lowercase__   :       Any				=	next(iter(a))['premise']  # pick up one data
         lowercase__   :       Dict				=	list(sample_data.values())

         lowercase__   :       Tuple				=	list(map(tokenizer.encode						,       a))
         lowercase__   :       Any				=	[tokenizer.decode(a						,       clean_up_tokenization_spaces=a) for x in output_tokens]
         self.assertListEqual(a						,       a)





    def 				snake_case_    (				self):
         # The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
         # any sequence length constraints. This test of the parent class will fail since it relies on the
         # maximum sequence length of the positoonal embeddings.
         self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map)						,       1)
         self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0])						,       1)




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


snake_case_        =			logging.get_logger(__name__)

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

snake_case_        =			{
    '''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''',
    },
}

snake_case_        =			{
    '''gpt2''': 1_024,
    '''gpt2-medium''': 1_024,
    '''gpt2-large''': 1_024,
    '''gpt2-xl''': 1_024,
    '''distilgpt2''': 1_024,
}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	List[str]														=					VOCAB_FILES_NAMES
    __lowerCamelCase						:	Any														=					PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:	str														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:	int														=					["""input_ids""", """attention_mask"""]
    __lowerCamelCase						:	str														=					GPTaTokenizer





    def __init__(				self						,       a=None						,       a=None						,       a=None						,       a="<|endoftext|>"						,       a="<|endoftext|>"						,       a="<|endoftext|>"						,       a=False						,       **a						,       ):
         super().__init__(
             a						,       a						,       tokenizer_file=a						,       unk_token=a						,       bos_token=a						,       eos_token=a						,       add_prefix_space=a						,       **a						,       )

         lowercase__   :       int				=	kwargs.pop('add_bos_token'						,       a)

         lowercase__   :       List[str]				=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get('add_prefix_space'						,       a) != add_prefix_space:
              lowercase__   :       Optional[Any]				=	getattr(a						,       pre_tok_state.pop('type'))
              lowercase__   :       List[Any]				=	add_prefix_space
              lowercase__   :       str				=	pre_tok_class(**a)

         lowercase__   :       Tuple				=	add_prefix_space





    def 				snake_case_    (				self						,       *a						,       **a):
         lowercase__   :       Tuple				=	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 				snake_case_    (				self						,       *a						,       **a):
         lowercase__   :       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()._encode_plus(*a						,       **a)





    def 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Any				=	self._tokenizer.model.save(a						,       name=a)
         return tuple(a)





    def 				snake_case_    (				self						,       a):
         lowercase__   :       Union[str, Any]				=	[]
         for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(a						,       add_special_tokens=a) + [self.eos_token_id])

         if len(a) > self.model_max_length:
              lowercase__   :       Union[str, Any]				=	input_ids[-self.model_max_length :]
         return input_ids

