





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

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


UpperCAmelCase     :     int							=	logging.get_logger(__name__)

UpperCAmelCase     :     Optional[Any]							=	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase     :     int							=	{
    """tokenizer_file""": {
        """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json""",
    },
}

UpperCAmelCase     :     Union[str, Any]							=	{
    """gpt-neox-20b""": 2048,
}







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : List[str]            =      VOCAB_FILES_NAMES
      _lowercase    : int            =      PRETRAINED_VOCAB_FILES_MAP
      _lowercase    : Tuple            =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase    : Optional[Any]            =      ["""input_ids""", """attention_mask"""]







      def __init__(   self		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__="<|endoftext|>"		,				lowerCAmelCase__="<|endoftext|>"		,				lowerCAmelCase__="<|endoftext|>"		,				lowerCAmelCase__=False		,				**lowerCAmelCase__		,				)       ->						Any:



        '''simple docstring'''




        super().__init__(
            lowerCAmelCase__		,				lowerCAmelCase__		,				tokenizer_file=lowerCAmelCase__		,				unk_token=lowerCAmelCase__		,				bos_token=lowerCAmelCase__		,				eos_token=lowerCAmelCase__		,				add_prefix_space=lowerCAmelCase__		,				**lowerCAmelCase__		,				)

        a__     : int  =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
        if pre_tok_state.get("add_prefix_space"		,				lowerCAmelCase__							) != add_prefix_space:
          a__     : Optional[int]  =getattr(lowerCAmelCase__		,				pre_tok_state.pop("type"							)							)
          a__     : Any  =add_prefix_space
          a__     : Tuple  =pre_tok_class(**lowerCAmelCase__							)

        a__     : List[str]  =add_prefix_space







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						Tuple[str]:



        '''simple docstring'''




        a__     : Union[str, Any]  =self._tokenizer.model.save(lowerCAmelCase__		,				name=lowerCAmelCase__							)
        return tuple(lowerCAmelCase__							)







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						List[int]:



        '''simple docstring'''




        a__     : str  =[]
        for is_user, text in conversation.iter_texts():
          input_ids.extend(self.encode(lowerCAmelCase__		,				add_special_tokens=lowerCAmelCase__							) + [self.eos_token_id]							)

        if len(lowerCAmelCase__							) > self.model_max_length:
          a__     : Optional[int]  =input_ids[-self.model_max_length :]
        return input_ids













import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
UpperCAmelCase     :     List[Any]							=	{"""LayoutLMv2Config""", """LayoutLMv3Config"""}







@is_pipeline_test
class     __lowerCAmelCase	(   unittest.TestCase):
      _lowercase    : str            =      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      _lowercase    : Tuple            =      TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

      if model_mapping is not None:
            _lowercase    : Dict            =      {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
      if tf_model_mapping is not None:
            _lowercase    : int            =      {
                config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
            }







      @require_torch
      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        a__     : str  =pipeline(
            task="text-classification"		,				model="hf-internal-testing/tiny-random-distilbert"		,				framework="pt"							)

        a__     : Any  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)

        a__     : Any  =text_classifier("This is great !"		,				top_k=2							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]							)

        a__     : Tuple  =text_classifier(["This is great !", "This is bad"]		,				top_k=2							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
            ]		,				)

        a__     : List[Any]  =text_classifier("This is great !"		,				top_k=1							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)

        # Legacy behavior
        a__     : Any  =text_classifier("This is great !"		,				return_all_scores=lowerCAmelCase__							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)

        a__     : List[str]  =text_classifier("This is great !"		,				return_all_scores=lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]]							)

        a__     : Optional[int]  =text_classifier(["This is great !", "Something else"]		,				return_all_scores=lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
            ]		,				)

        a__     : int  =text_classifier(["This is great !", "Something else"]		,				return_all_scores=lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[
                {"label": "LABEL_0", "score": 0.5_04},
                {"label": "LABEL_0", "score": 0.5_04},
            ]		,				)







      @require_torch
      def        _lowercase  (   self							)       ->						List[Any]:



        '''simple docstring'''




        import torch

        a__     : Dict  =pipeline(
            task="text-classification"		,				model="hf-internal-testing/tiny-random-distilbert"		,				framework="pt"		,				device=torch.device("cpu"							)		,				)

        a__     : Optional[Any]  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)







      @require_tf
      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : Optional[int]  =pipeline(
            task="text-classification"		,				model="hf-internal-testing/tiny-random-distilbert"		,				framework="tf"							)

        a__     : Optional[Any]  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)







      @slow
      @require_torch
      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__     : Union[str, Any]  =pipeline("text-classification"							)

        a__     : Union[str, Any]  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "POSITIVE", "score": 1.0}]							)
        a__     : Optional[Any]  =text_classifier("This is bad !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "NEGATIVE", "score": 1.0}]							)
        a__     : Dict  =text_classifier("Birds are a type of animal"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "POSITIVE", "score": 0.9_88}]							)







      @slow
      @require_tf
      def        _lowercase  (   self							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : Tuple  =pipeline("text-classification"		,				framework="tf"							)

        a__     : str  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "POSITIVE", "score": 1.0}]							)
        a__     : str  =text_classifier("This is bad !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "NEGATIVE", "score": 1.0}]							)
        a__     : Dict  =text_classifier("Birds are a type of animal"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "POSITIVE", "score": 0.9_88}]							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : List[Any]  =TextClassificationPipeline(model=lowerCAmelCase__		,				tokenizer=lowerCAmelCase__							)
        return text_classifier, ["HuggingFace is in", "This is another test"]







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						List[str]:



        '''simple docstring'''




        a__     : Tuple  =text_classifier.model
        # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
        a__     : List[Any]  ="HuggingFace is in"
        a__     : int  =text_classifier(lowerCAmelCase__							)

        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}]							)
        self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()							)

        a__     : Optional[int]  =["HuggingFace is in ", "Paris is in France"]
        a__     : Optional[Any]  =text_classifier(lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}, {"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}]		,				)
        self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()							)
        self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()							)

        # Forcing to get all results with `top_k=None`
        # This is NOT the legacy format
        a__     : Union[str, Any]  =text_classifier(lowerCAmelCase__		,				top_k=lowerCAmelCase__							)
        a__     : Optional[Any]  =len(model.config.idalabel.values()							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}] * N, [{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}] * N]		,				)

        a__     : List[str]  ={"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
        a__     : Optional[Any]  =text_classifier(lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}		,				)
        self.assertTrue(outputs["label"] in model.config.idalabel.values()							)

        # This might be used a text pair, but tokenizer + pipe interaction
        # makes it hard to understand that it's not using the pair properly
        # https://github.com/huggingface/transformers/issues/17305
        # We disabled this usage instead as it was outputting wrong outputs.
        a__     : Any  =[["HuggingFace is in ", "Paris is in France"]]
        with self.assertRaises(lowerCAmelCase__							):
          text_classifier(lowerCAmelCase__							)

        # This used to be valid for doing text pairs
        # We're keeping it working because of backward compatibility
        a__     : Optional[int]  =text_classifier([[["HuggingFace is in ", "Paris is in France"]]]							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}]		,				)
        self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()							)






