





import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
    import torch

@is_pipeline_test
class    lowerCAmelCase							(					unittest.TestCase			):
 UpperCAmelCase__													=		MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
 UpperCAmelCase__													=		TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
 def      A_	(      self  :      Tuple			,     UpperCAmelCase  :      Optional[int]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      List[str]	)			->	Any:
     lowerCamelCase__    :      List[Any]					 =		TextaTextGenerationPipeline(model=UpperCAmelCase			,     tokenizer=UpperCAmelCase	)
     return generator, ["Something to write", "Something else"]
 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      Any	)			->	str:
     lowerCamelCase__    :      int					 =		generator('Something there'	)
     self.assertEqual(UpperCAmelCase			,     [{'generated_text': ANY(UpperCAmelCase	)}]	)
     # These are encoder decoder, they don't just append to incoming string
     self.assertFalse(outputs[0]['generated_text'].startswith('Something there'	)	)

     lowerCamelCase__    :      Dict					 =		generator(['This is great !', 'Something else']			,     num_return_sequences=2			,     do_sample=UpperCAmelCase	)
     self.assertEqual(
         UpperCAmelCase			,     [
             [{'generated_text': ANY(UpperCAmelCase	)}, {'generated_text': ANY(UpperCAmelCase	)}],
             [{'generated_text': ANY(UpperCAmelCase	)}, {'generated_text': ANY(UpperCAmelCase	)}],
         ]			,     )

     lowerCamelCase__    :      Optional[int]					 =		generator(
         ['This is great !', 'Something else']			,     num_return_sequences=2			,     batch_size=2			,     do_sample=UpperCAmelCase	)
     self.assertEqual(
         UpperCAmelCase			,     [
             [{'generated_text': ANY(UpperCAmelCase	)}, {'generated_text': ANY(UpperCAmelCase	)}],
             [{'generated_text': ANY(UpperCAmelCase	)}, {'generated_text': ANY(UpperCAmelCase	)}],
         ]			,     )

     with self.assertRaises(UpperCAmelCase	):
         generator(4	)
 @require_torch
 def      A_	(      self  :      Tuple	)			->	Dict:
     lowerCamelCase__    :      Optional[int]					 =		pipeline('text2text-generation'			,     model='patrickvonplaten/t5-tiny-random'			,     framework='pt'	)
     # do_sample=False necessary for reproducibility
     lowerCamelCase__    :      Optional[Any]					 =		generator('Something there'			,     do_sample=UpperCAmelCase	)
     self.assertEqual(UpperCAmelCase			,     [{'generated_text': ''}]	)

     lowerCamelCase__    :      int					 =		3
     lowerCamelCase__    :      Optional[Any]					 =		generator(
         'Something there'			,     num_return_sequences=UpperCAmelCase			,     num_beams=UpperCAmelCase			,     )
     lowerCamelCase__    :      Optional[Any]					 =		[
         {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
         {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
         {'generated_text': ''},
     ]
     self.assertEqual(UpperCAmelCase			,     UpperCAmelCase	)

     lowerCamelCase__    :      Optional[Any]					 =		generator('This is a test'			,     do_sample=UpperCAmelCase			,     num_return_sequences=2			,     return_tensors=UpperCAmelCase	)
     self.assertEqual(
         UpperCAmelCase			,     [
             {'generated_token_ids': ANY(torch.Tensor	)},
             {'generated_token_ids': ANY(torch.Tensor	)},
         ]			,     )
     lowerCamelCase__    :      int					 =		generator.model.config.eos_token_id
     lowerCamelCase__    :      Union[str, Any]					 =		'<pad>'
     lowerCamelCase__    :      Union[str, Any]					 =		generator(
         ['This is a test', 'This is a second test']			,     do_sample=UpperCAmelCase			,     num_return_sequences=2			,     batch_size=2			,     return_tensors=UpperCAmelCase			,     )
     self.assertEqual(
         UpperCAmelCase			,     [
             [
                 {'generated_token_ids': ANY(torch.Tensor	)},
                 {'generated_token_ids': ANY(torch.Tensor	)},
             ],
             [
                 {'generated_token_ids': ANY(torch.Tensor	)},
                 {'generated_token_ids': ANY(torch.Tensor	)},
             ],
         ]			,     )







 @require_tf
 def      A_	(      self  :      List[Any]	)			->	Optional[Any]:
     lowerCamelCase__    :      Any					 =		pipeline('text2text-generation'			,     model='patrickvonplaten/t5-tiny-random'			,     framework='tf'	)
     # do_sample=False necessary for reproducibility
     lowerCamelCase__    :      str					 =		generator('Something there'			,     do_sample=UpperCAmelCase	)
     self.assertEqual(UpperCAmelCase			,     [{'generated_text': ''}]	)












def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     set:
    lowerCamelCase__    :      Optional[Any]					 =		set()
    # edges = list of graph's edges
    lowerCamelCase__    :      List[str]					 =		get_edges(_UpperCAmelCase )

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		edges.pop()
        chosen_vertices.add(_UpperCAmelCase )
        chosen_vertices.add(_UpperCAmelCase )
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(_UpperCAmelCase )
    return chosen_vertices


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     set:
    lowerCamelCase__    :      Union[str, Any]					 =		set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node) )
    return edges


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
    # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")





