


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.
__lowerCamelCase      :Tuple	    =		{'LayoutLMv2Config', 'LayoutLMv3Config'}


@is_pipeline_test
class 			A__	(						unittest.TestCase):


     """simple docstring"""
     snake_case__	:					List[Any]													=MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
     snake_case__	:					Union[str, Any]													=TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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

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

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

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

           lowerCamelCase					:					List[str]        =	text_classifier(["""This is great !""", """This is bad"""]     ,		top_k=2	)
           self.assertEqual(
               nested_simplify(__a	)     ,		[
                   [{"""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}],
               ]     ,		)

           lowerCamelCase					:					int        =	text_classifier("""This is great !"""     ,		top_k=1	)
           self.assertEqual(nested_simplify(__a	)     ,		[{"""label""": """LABEL_0""", """score""": 0.5_04}]	)

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

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

           lowerCamelCase					:					Union[str, Any]        =	text_classifier(["""This is great !""", """Something else"""]     ,		return_all_scores=__a	)
           self.assertEqual(
               nested_simplify(__a	)     ,		[
                   [{"""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}],
               ]     ,		)

           lowerCamelCase					:					List[str]        =	text_classifier(["""This is great !""", """Something else"""]     ,		return_all_scores=__a	)
           self.assertEqual(
               nested_simplify(__a	)     ,		[
                   {"""label""": """LABEL_0""", """score""": 0.5_04},
                   {"""label""": """LABEL_0""", """score""": 0.5_04},
               ]     ,		)

     @require_torch
     def 		a__						(	self:						Dict	)->    List[str]:
           import torch

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

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

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

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

     @slow
     @require_torch
     def 		a__						(	self:						Dict	)->    Dict:
           lowerCamelCase					:					str        =	pipeline("""text-classification"""	)

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

     @slow
     @require_tf
     def 		a__						(	self:						Dict	)->    List[str]:
           lowerCamelCase					:					Optional[Any]        =	pipeline("""text-classification"""     ,		framework="""tf"""	)

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

     def 		a__						(	self:						str     ,		__a:						str     ,		__a:						int     ,		__a:						Dict	)->    Tuple:
           lowerCamelCase					:					List[str]        =	TextClassificationPipeline(model=__a     ,		tokenizer=__a	)
           return text_classifier, ["HuggingFace is in", "This is another test"]







     def 		a__						(	self:						Optional[int]     ,		__a:						List[Any]     ,		__a:						List[str]	)->    Optional[int]:
           lowerCamelCase					:					Dict        =	text_classifier.model
           # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
           lowerCamelCase					:					int        =	"""HuggingFace is in"""
           lowerCamelCase					:					Optional[Any]        =	text_classifier(__a	)

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

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

           lowerCamelCase					:					Tuple        =	{"""text""": """HuggingFace is in """, """text_pair""": """Paris is in France"""}
           lowerCamelCase					:					Dict        =	text_classifier(__a	)
           self.assertEqual(
               nested_simplify(__a	)     ,		{"""label""": ANY(__a	), """score""": ANY(__a	)}     ,		)
           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.
           lowerCamelCase					:					Union[str, Any]        =	[["""HuggingFace is in """, """Paris is in France"""]]
           with self.assertRaises(__a	):
                 text_classifier(__a	)

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








"""simple docstring"""


__lowerCamelCase      :List[Any]	    =		{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

__lowerCamelCase      :Union[str, Any]	    =		{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}




def   snake_case		(     UpperCamelCase__       :						dict[int, list[int]]			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						list[bool]    )   ->      list[int]:
      lowerCamelCase					:					Tuple        =	True
      lowerCamelCase					:					Any        =	[]

      for neighbour in graph[vert]:
            if not visited[neighbour]:
                  order += topology_sort(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )

      order.append(UpperCamelCase__    )

      return order




def   snake_case		(     UpperCamelCase__       :						dict[int, list[int]]			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						list[bool]    )   ->      list[int]:
      lowerCamelCase					:					List[Any]        =	True
      lowerCamelCase					:					int        =	[vert]

      for neighbour in reversed_graph[vert]:
            if not visited[neighbour]:
                  component += find_components(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )

      return component






def   snake_case		(     UpperCamelCase__       :						dict[int, list[int]]    )   ->      list[list[int]]:
      lowerCamelCase					:					int        =	len(UpperCamelCase__    ) * [False]
      lowerCamelCase					:					dict[int, list[int]]        =	{vert: [] for vert in range(len(UpperCamelCase__    )    )}

      for vert, neighbours in graph.items():
            for neighbour in neighbours:
                  reversed_graph[neighbour].append(UpperCamelCase__    )

      lowerCamelCase					:					int        =	[]
      for i, was_visited in enumerate(UpperCamelCase__    ):
            if not was_visited:
                  order += topology_sort(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )

      lowerCamelCase					:					Tuple        =	[]
      lowerCamelCase					:					str        =	len(UpperCamelCase__    ) * [False]

      for i in range(len(UpperCamelCase__    )    ):
            lowerCamelCase					:					Any        =	order[len(UpperCamelCase__    ) - i - 1]
            if not visited[vert]:
                  lowerCamelCase					:					List[str]        =	find_components(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )
                  components_list.append(UpperCamelCase__    )

      return components_list




