

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.
SCREAMING_SNAKE_CASE		:Optional[Any]						=     {"""LayoutLMv2Config""", """LayoutLMv3Config"""}






@is_pipeline_test
class 						__magic_name__					(							unittest.TestCase				):
     UpperCamelCase_	:List[Any]										=      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
     UpperCamelCase_	:Union[str, Any]										=      TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

     if model_mapping is not None:
          UpperCamelCase_	:Optional[int]										=      {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
     if tf_model_mapping is not None:
          UpperCamelCase_	:List[str]										=      {
              config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
          }






     @require_torch
     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         UpperCamelCase_			  =						pipeline(
             task="text-classification"		,						model="hf-internal-testing/tiny-random-distilbert"		,						framework="pt"	)

         UpperCamelCase_			  =						text_classifier("This is great !"	)
         self.assertEqual(nested_simplify(_lowercase	)		,						[{"label": "LABEL_0", "score": 0.504}]	)

         UpperCamelCase_			  =						text_classifier("This is great !"		,						top_k=2	)
         self.assertEqual(
             nested_simplify(_lowercase	)		,						[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]	)

         UpperCamelCase_			  =						text_classifier(["This is great !", "This is bad"]		,						top_k=2	)
         self.assertEqual(
             nested_simplify(_lowercase	)		,						[
                 [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
                 [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
             ]		,						)

         UpperCamelCase_			  =						text_classifier("This is great !"		,						top_k=1	)
         self.assertEqual(nested_simplify(_lowercase	)		,						[{"label": "LABEL_0", "score": 0.504}]	)

         # Legacy behavior
         UpperCamelCase_			  =						text_classifier("This is great !"		,						return_all_scores=_lowercase	)
         self.assertEqual(nested_simplify(_lowercase	)		,						[{"label": "LABEL_0", "score": 0.504}]	)

         UpperCamelCase_			  =						text_classifier("This is great !"		,						return_all_scores=_lowercase	)
         self.assertEqual(
             nested_simplify(_lowercase	)		,						[[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]]	)

         UpperCamelCase_			  =						text_classifier(["This is great !", "Something else"]		,						return_all_scores=_lowercase	)
         self.assertEqual(
             nested_simplify(_lowercase	)		,						[
                 [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
                 [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
             ]		,						)

         UpperCamelCase_			  =						text_classifier(["This is great !", "Something else"]		,						return_all_scores=_lowercase	)
         self.assertEqual(
             nested_simplify(_lowercase	)		,						[
                 {"label": "LABEL_0", "score": 0.504},
                 {"label": "LABEL_0", "score": 0.504},
             ]		,						)






     @require_torch
     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         import torch

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

         UpperCamelCase_			  =						text_classifier("This is great !"	)
         self.assertEqual(nested_simplify(_lowercase	)		,						[{"label": "LABEL_0", "score": 0.504}]	)






     @require_tf
     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         UpperCamelCase_			  =						pipeline(
             task="text-classification"		,						model="hf-internal-testing/tiny-random-distilbert"		,						framework="tf"	)

         UpperCamelCase_			  =						text_classifier("This is great !"	)
         self.assertEqual(nested_simplify(_lowercase	)		,						[{"label": "LABEL_0", "score": 0.504}]	)






     @slow
     @require_torch
     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						pipeline("text-classification"	)

         UpperCamelCase_			  =						text_classifier("This is great !"	)
         self.assertEqual(nested_simplify(_lowercase	)		,						[{"label": "POSITIVE", "score": 1.0}]	)
         UpperCamelCase_			  =						text_classifier("This is bad !"	)
         self.assertEqual(nested_simplify(_lowercase	)		,						[{"label": "NEGATIVE", "score": 1.0}]	)
         UpperCamelCase_			  =						text_classifier("Birds are a type of animal"	)
         self.assertEqual(nested_simplify(_lowercase	)		,						[{"label": "POSITIVE", "score": 0.988}]	)






     @slow
     @require_tf
     def 	UpperCAmelCase_				(				self	)->     List[Any]:
         UpperCamelCase_			  =						pipeline("text-classification"		,						framework="tf"	)

         UpperCamelCase_			  =						text_classifier("This is great !"	)
         self.assertEqual(nested_simplify(_lowercase	)		,						[{"label": "POSITIVE", "score": 1.0}]	)
         UpperCamelCase_			  =						text_classifier("This is bad !"	)
         self.assertEqual(nested_simplify(_lowercase	)		,						[{"label": "NEGATIVE", "score": 1.0}]	)
         UpperCamelCase_			  =						text_classifier("Birds are a type of animal"	)
         self.assertEqual(nested_simplify(_lowercase	)		,						[{"label": "POSITIVE", "score": 0.988}]	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase	)->     Union[str, Any]:
         UpperCamelCase_			  =						TextClassificationPipeline(model=_lowercase		,						tokenizer=_lowercase	)
         return text_classifier, ["HuggingFace is in", "This is another test"]






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase	)->     Tuple:
         UpperCamelCase_			  =						text_classifier.model
         # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
         UpperCamelCase_			  =						"HuggingFace is in"
         UpperCamelCase_			  =						text_classifier(_lowercase	)

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

         UpperCamelCase_			  =						["HuggingFace is in ", "Paris is in France"]
         UpperCamelCase_			  =						text_classifier(_lowercase	)
         self.assertEqual(
             nested_simplify(_lowercase	)		,						[{"label": ANY(_lowercase	), "score": ANY(_lowercase	)}, {"label": ANY(_lowercase	), "score": ANY(_lowercase	)}]		,						)
         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
         UpperCamelCase_			  =						text_classifier(_lowercase		,						top_k=_lowercase	)
         UpperCamelCase_			  =						len(model.config.idalabel.values()	)
         self.assertEqual(
             nested_simplify(_lowercase	)		,						[[{"label": ANY(_lowercase	), "score": ANY(_lowercase	)}] * N, [{"label": ANY(_lowercase	), "score": ANY(_lowercase	)}] * N]		,						)

         UpperCamelCase_			  =						{"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
         UpperCamelCase_			  =						text_classifier(_lowercase	)
         self.assertEqual(
             nested_simplify(_lowercase	)		,						{"label": ANY(_lowercase	), "score": ANY(_lowercase	)}		,						)
         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.
         UpperCamelCase_			  =						[["HuggingFace is in ", "Paris is in France"]]
         with self.assertRaises(_lowercase	):
             text_classifier(_lowercase	)

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



def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_ = 4_0_0_0_0_0_0			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						[0, 1]
    UpperCamelCase_			  =						0
    while fib[i] <= n:
        fib.append(fib[i] + fib[i + 1]			)
        if fib[i + 2] > n:
            break
        i += 1
    UpperCamelCase_			  =						0
    for j in range(len(SCREAMING_SNAKE_CASE_			) - 1			):
        if fib[j] % 2 == 0:
            total += fib[j]

    return total


if __name__ == "__main__":
 print(F'''{solution() = }''')
