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.
_lowercase							:      Dict      							={'''LayoutLMv2Config''', '''LayoutLMv3Config'''}



@is_pipeline_test
class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     Dict            =				MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
  lowercase		:     Optional[Any]            =				TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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







  @require_torch
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				List[Any]:
         A						:     Dict         =pipeline(
             task='text-classification'      ,	model='hf-internal-testing/tiny-random-distilbert'      ,	framework='pt'     )

         A						:     Tuple         =text_classifier('This is great !'     )
         self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )      ,	[{'label': 'LABEL_0', 'score': 0.5_0_4}]     )

         A						:     Union[str, Any]         =text_classifier('This is great !'      ,	top_k=2     )
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__     )      ,	[{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}]     )

         A						:     Dict         =text_classifier(['This is great !', 'This is bad']      ,	top_k=2     )
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__     )      ,	[
                 [{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}],
                 [{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}],
             ]      ,	)

         A						:     Optional[Any]         =text_classifier('This is great !'      ,	top_k=1     )
         self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )      ,	[{'label': 'LABEL_0', 'score': 0.5_0_4}]     )

         # Legacy behavior
         A						:     Optional[Any]         =text_classifier('This is great !'      ,	return_all_scores=SCREAMING_SNAKE_CASE__     )
         self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )      ,	[{'label': 'LABEL_0', 'score': 0.5_0_4}]     )

         A						:     Optional[int]         =text_classifier('This is great !'      ,	return_all_scores=SCREAMING_SNAKE_CASE__     )
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__     )      ,	[[{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}]]     )

         A						:     str         =text_classifier(['This is great !', 'Something else']      ,	return_all_scores=SCREAMING_SNAKE_CASE__     )
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__     )      ,	[
                 [{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}],
                 [{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}],
             ]      ,	)

         A						:     int         =text_classifier(['This is great !', 'Something else']      ,	return_all_scores=SCREAMING_SNAKE_CASE__     )
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__     )      ,	[
                 {'label': 'LABEL_0', 'score': 0.5_0_4},
                 {'label': 'LABEL_0', 'score': 0.5_0_4},
             ]      ,	)







  @require_torch
  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				List[Any]:
         import torch

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

         A						:     str         =text_classifier('This is great !'     )
         self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )      ,	[{'label': 'LABEL_0', 'score': 0.5_0_4}]     )







  @require_tf
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				str:
         A						:     Any         =pipeline(
             task='text-classification'      ,	model='hf-internal-testing/tiny-random-distilbert'      ,	framework='tf'     )

         A						:     List[str]         =text_classifier('This is great !'     )
         self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )      ,	[{'label': 'LABEL_0', 'score': 0.5_0_4}]     )







  @slow
  @require_torch
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				str:
         A						:     str         =pipeline('text-classification'     )

         A						:     Union[str, Any]         =text_classifier('This is great !'     )
         self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )      ,	[{'label': 'POSITIVE', 'score': 1.0}]     )
         A						:     Any         =text_classifier('This is bad !'     )
         self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )      ,	[{'label': 'NEGATIVE', 'score': 1.0}]     )
         A						:     List[Any]         =text_classifier('Birds are a type of animal'     )
         self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )      ,	[{'label': 'POSITIVE', 'score': 0.9_8_8}]     )







  @slow
  @require_tf
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Union[str, Any]:
         A						:     str         =pipeline('text-classification'      ,	framework='tf'     )

         A						:     Tuple         =text_classifier('This is great !'     )
         self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )      ,	[{'label': 'POSITIVE', 'score': 1.0}]     )
         A						:     List[str]         =text_classifier('This is bad !'     )
         self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )      ,	[{'label': 'NEGATIVE', 'score': 1.0}]     )
         A						:     Optional[int]         =text_classifier('Birds are a type of animal'     )
         self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )      ,	[{'label': 'POSITIVE', 'score': 0.9_8_8}]     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					int     )    ->				List[Any]:
         A						:     Any         =TextClassificationPipeline(model=SCREAMING_SNAKE_CASE__      ,	tokenizer=SCREAMING_SNAKE_CASE__     )
         return text_classifier, ["HuggingFace is in", "This is another test"]







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]     )    ->				Optional[Any]:
         A						:     Tuple         =text_classifier.model
         # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
         A						:     Union[str, Any]         ='HuggingFace is in'
         A						:     List[Any]         =text_classifier(SCREAMING_SNAKE_CASE__     )

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

         A						:     Dict         =['HuggingFace is in ', 'Paris is in France']
         A						:     Optional[Any]         =text_classifier(SCREAMING_SNAKE_CASE__     )
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__     )      ,	[{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}, {'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}]      ,	)
         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						:     Tuple         =text_classifier(SCREAMING_SNAKE_CASE__      ,	top_k=SCREAMING_SNAKE_CASE__     )
         A						:     int         =len(model.config.idalabel.values()     )
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__     )      ,	[[{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}] * N, [{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}] * N]      ,	)

         A						:     str         ={'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
         A						:     Dict         =text_classifier(SCREAMING_SNAKE_CASE__     )
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__     )      ,	{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}      ,	)
         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						:     List[str]         =[['HuggingFace is in ', 'Paris is in France']]
         with self.assertRaises(SCREAMING_SNAKE_CASE__     ):
                text_classifier(SCREAMING_SNAKE_CASE__     )

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







from typing import List

from .keymap import KEYMAP, get_character



def A__  (			lowercase:     str					)				->		List[str]:

       def decorator(lowercase:     int					):
              A						:     Tuple         =getattr(lowercase,		'handle_key',		[]					)
              handle += [key]
              setattr(lowercase,		'handle_key',		lowercase					)
              return func

       return decorator



def A__  (			*lowercase:     List[str]					)				->		Dict:

       def decorator(lowercase:     Union[str, Any]					):
              A						:     Optional[int]         =getattr(lowercase,		'handle_key',		[]					)
              handle += keys
              setattr(lowercase,		'handle_key',		lowercase					)
              return func

       return decorator



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  def __new__(				cls    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[str]     )    ->				Any:
         A						:     Dict         =super().__new__(cls      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
         if not hasattr(SCREAMING_SNAKE_CASE__      ,	'key_handler'     ):
                setattr(SCREAMING_SNAKE_CASE__      ,	'key_handler'      ,	{}     )
         setattr(SCREAMING_SNAKE_CASE__      ,	'handle_input'      ,	KeyHandler.handle_input     )

         for value in attrs.values():
                A						:     Optional[Any]         =getattr(SCREAMING_SNAKE_CASE__      ,	'handle_key'      ,	[]     )
                for key in handled_keys:
                       A						:     str         =value
         return new_cls







  @staticmethod
  def 		SCREAMING_SNAKE_CASE_		(				cls    :					str     )    ->				Any:
         A						:     str         =get_character()
         if char != KEYMAP["undefined"]:
                A						:     List[str]         =ord(SCREAMING_SNAKE_CASE__     )
         A						:     List[str]         =cls.key_handler.get(SCREAMING_SNAKE_CASE__     )
         if handler:
                A						:     List[str]         =char
                return handler(cls     )
         else:
                return None







def A__  (			cls:     Optional[int]					)				->		str:
       return KeyHandler(cls.__name__,		cls.__bases__,		cls.__dict__.copy()					)






