


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


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :   Optional[Any]							      =				{"tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE :   str							      =				{
    "tokenizer_file": {
        "bigscience/tokenizer": "https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json",
        "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json",
        "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json",
        "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json",
        "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json",
        "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json",
        "bigscience/bloom": "https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json",
    },
}






class _lowerCamelCase(		_a     ):
       lowercase_				:			Dict									=    VOCAB_FILES_NAMES
       lowercase_				:			int									=    PRETRAINED_VOCAB_FILES_MAP
       lowercase_				:			Any									=    ["""input_ids""", """attention_mask"""]
       lowercase_				:			List[str]									=    None
       def __init__(      self,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase="<unk>",   lowerCamelCase="<s>",   lowerCamelCase="</s>",   lowerCamelCase="<pad>",   lowerCamelCase=False,   lowerCamelCase=False,   **lowerCamelCase,   ) ->     List[Any]:




           """simple docstring"""

           super().__init__(
               __a,   __a,   tokenizer_file=__a,   unk_token=__a,   bos_token=__a,   eos_token=__a,   pad_token=__a,   add_prefix_space=__a,   clean_up_tokenization_spaces=__a,   **__a,   )
           _lowercase :							Optional[int]   	=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
           if pre_tok_state.get('add_prefix_space',   __a) != add_prefix_space:
               _lowercase :							Tuple   	=	getattr(__a,   pre_tok_state.pop('type'))
               _lowercase :							Tuple   	=	add_prefix_space
               _lowercase :							Union[str, Any]   	=	pre_tok_class(**__a)

           _lowercase :							str   	=	add_prefix_space
       def     UpperCamelCase  (      self,   *lowerCamelCase,   **lowerCamelCase) ->     BatchEncoding:




           """simple docstring"""

           _lowercase :							Tuple   	=	kwargs.get('is_split_into_words',   __a)
           if not (self.add_prefix_space or not is_split_into_words):
               raise Exception(
                   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     UpperCamelCase  (      self,   *lowerCamelCase,   **lowerCamelCase) ->     BatchEncoding:




           """simple docstring"""

           _lowercase :							List[str]   	=	kwargs.get('is_split_into_words',   __a)

           if not (self.add_prefix_space or not is_split_into_words):
               raise Exception(
                   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     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase = None) ->     Tuple[str]:




           """simple docstring"""

           _lowercase :							str   	=	self._tokenizer.model.save(__a,   name=__a)
           return tuple(__a)




       def     UpperCamelCase  (      self,   lowerCamelCase) ->     List[int]:




           """simple docstring"""

           _lowercase :							str   	=	[]
           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 :							List[str]   	=	input_ids[-self.model_max_length :]
           return input_ids




import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
 from transformers import MarkupLMFeatureExtractor






class _lowerCamelCase(		unittest.TestCase     ):
       def __init__(      self,   lowerCamelCase) ->     List[str]:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	parent




       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           return {}



def        UpperCamelCase_(   )					->		List[str]:
    _lowercase :							int   	=	'<HTML>\n\n    <HEAD>\n    <TITLE>sample document</TITLE>\n    </HEAD>\n\n    <BODY BGCOLOR="FFFFFF">\n    <HR>\n    <a href="http://google.com">Goog</a>\n    <H1>This is one header</H1>\n    <H2>This is a another Header</H2>\n    <P>Travel from\n        <P>\n        <B>SFO to JFK</B>\n        <BR>\n        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n        <HR>\n        <div style="color:#0000FF">\n            <h3>Traveler <b> name </b> is\n            <p> John Doe </p>\n        </div>'

    _lowercase :							List[Any]   	=	'\n    <!DOCTYPE html>\n    <html>\n    <body>\n\n    <h1>My First Heading</h1>\n    <p>My first paragraph.</p>\n\n    </body>\n    </html>\n    '

    return [html_string_a, html_string_a]






@require_bsa
class _lowerCamelCase(		_a,				unittest.TestCase     ):
       lowercase_				:			Tuple									=    MarkupLMFeatureExtractor if is_bsa_available() else None
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Tuple   	=	MarkupLMFeatureExtractionTester(self)
       @property
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           return self.feature_extract_tester.prepare_feat_extract_dict()




       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							int   	=	self.feature_extraction_class()

           # Test not batched input
           _lowercase :							List[Any]   	=	get_html_strings()[0]
           _lowercase :							Optional[Any]   	=	feature_extractor(lowerCamelCase)

           # fmt: off
           _lowercase :							str   	=	[['sample document', 'Goog', 'This is one header', 'This is a another Header', 'Travel from', 'SFO to JFK', 'on May 2, 2015 at 2:00 pm. For details go to confirm.com', 'Traveler', 'name', 'is', 'John Doe']]
           _lowercase :							Dict   	=	[['/html/head/title', '/html/body/a', '/html/body/h1', '/html/body/h2', '/html/body/p', '/html/body/p/p/b[1]', '/html/body/p/p/b[2]/i', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/b', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/p']]
           # fmt: on

           self.assertEqual(encoding.nodes,   lowerCamelCase)
           self.assertEqual(encoding.xpaths,   lowerCamelCase)

           # Test batched
           _lowercase :							Optional[int]   	=	get_html_strings()
           _lowercase :							Tuple   	=	feature_extractor(lowerCamelCase)

           # fmt: off
           _lowercase :							List[Any]   	=	expected_nodes + [['My First Heading', 'My first paragraph.']]
           _lowercase :							List[str]   	=	expected_xpaths + [['/html/body/h1', '/html/body/p']]

           self.assertEqual(len(encoding.nodes),   2)
           self.assertEqual(len(encoding.xpaths),   2)

           self.assertEqual(encoding.nodes,   lowerCamelCase)
           self.assertEqual(encoding.xpaths,   lowerCamelCase)


