

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__		 =     {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}

@is_pipeline_test
class     __snake_case    (				unittest.TestCase  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
       __SCREAMING_SNAKE_CASE				    =							TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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


       @require_torch
       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


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

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

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

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

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

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

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

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

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


       @require_torch
       def 			a		(		self   )      ->							Optional[int]:






              """simple docstring"""


              import torch

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

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


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






              """simple docstring"""


              __snake_case       	=							pipeline(
                  task="""text-classification"""      , model="""hf-internal-testing/tiny-random-distilbert"""      , framework="""tf"""   )

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


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






              """simple docstring"""


              __snake_case       	=							pipeline("""text-classification"""   )

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


       @slow
       @require_tf
       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              __snake_case       	=							pipeline("""text-classification"""      , framework="""tf"""   )

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


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Tuple:






              """simple docstring"""


              __snake_case       	=							TextClassificationPipeline(model=_UpperCamelCase      , tokenizer=_UpperCamelCase   )
              return text_classifier, ["HuggingFace is in", "This is another test"]






       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase   )      ->							str:






              """simple docstring"""


              __snake_case       	=							text_classifier.model
              # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
              __snake_case       	=							"""HuggingFace is in"""
              __snake_case       	=							text_classifier(_UpperCamelCase   )

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

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

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

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




from __future__ import annotations







def 						lowerCamelCase__      ( __A    :list[float]	,__A    :Union[str, Any]		):


       """simple docstring"""




       print(F'Vertex\tShortest Distance from vertex {src}'		)
       for i, d in enumerate(__A		):
              print(F'{i}\t\t{d}'		)







def 						lowerCamelCase__      ( __A    :list[dict[str, int]]	,__A    :list[float]	,__A    :int		):


       """simple docstring"""




       for j in range(__A		):
              __snake_case	,    __snake_case	,    __snake_case       	=							(graph[j][k] for k in ["""src""", """dst""", """weight"""])
              if distance[u] != float("""inf"""		) and distance[u] + w < distance[v]:
                     return True
       return False







def 						lowerCamelCase__      ( __A    :list[dict[str, int]]	,__A    :int	,__A    :int	,__A    :int		):


       """simple docstring"""




       __snake_case       	=							[float("""inf"""		)] * vertex_count
       __snake_case       	=							0.0

       for _ in range(vertex_count - 1		):
              for j in range(__A		):
                     __snake_case	,    __snake_case	,    __snake_case       	=							(graph[j][k] for k in ["""src""", """dst""", """weight"""])

                     if distance[u] != float("""inf"""		) and distance[u] + w < distance[v]:
                            __snake_case       	=							distance[u] + w

       __snake_case       	=							check_negative_cycle(__A	,__A	,__A		)
       if negative_cycle_exists:
              raise Exception("""Negative cycle found"""		)

       return distance


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    UpperCamelCase__		 =     int(input('''Enter number of vertices: ''').strip())
    UpperCamelCase__		 =     int(input('''Enter number of edges: ''').strip())

    UpperCamelCase__		 =     [{} for _ in range(E)]

    for i in range(E):
        print('''Edge ''', i + 1)
        UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__		 =     (
            int(x)
            for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
        )
        UpperCamelCase__		 =     {'''src''': src, '''dst''': dest, '''weight''': weight}

    UpperCamelCase__		 =     int(input('''\nEnter shortest path source:''').strip())
    UpperCamelCase__		 =     bellman_ford(graph, V, E, source)
    print_distance(shortest_distance, 0)

