



"""simple docstring"""







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 				UpperCamelCase__       (     unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      _SCREAMING_SNAKE_CASE      =					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]	):
        lowerCAmelCase_       :    List[str]								=			TextaTextGenerationPipeline(model=__UpperCamelCase   ,      tokenizer=__UpperCamelCase	)
        return generator, ["Something to write", "Something else"]



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				List[Any]	):
        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_       :    int								=			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_       :    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 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        lowerCAmelCase_       :    List[Any]								=			pipeline('text2text-generation'   ,      model='patrickvonplaten/t5-tiny-random'   ,      framework='pt'	)
        # do_sample=False necessary for reproducibility
        lowerCAmelCase_       :    int								=			generator('Something there'   ,      do_sample=__UpperCamelCase	)
        self.assertEqual(__UpperCamelCase   ,      [{'generated_text': ''}]	)

        lowerCAmelCase_       :    str								=			3
        lowerCAmelCase_       :    List[str]								=			generator(
            'Something there'   ,      num_return_sequences=__UpperCamelCase   ,      num_beams=__UpperCamelCase   ,      )
        lowerCAmelCase_       :    List[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_       :    int								=			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_       :    Tuple								=			generator.model.config.eos_token_id
        lowerCAmelCase_       :    str								=			'<pad>'
        lowerCAmelCase_       :    int								=			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 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        lowerCAmelCase_       :    Optional[int]								=			pipeline('text2text-generation'   ,      model='patrickvonplaten/t5-tiny-random'   ,      framework='tf'	)
        # do_sample=False necessary for reproducibility
        lowerCAmelCase_       :    List[Any]								=			generator('Something there'   ,      do_sample=__UpperCamelCase	)
        self.assertEqual(__UpperCamelCase   ,      [{'generated_text': ''}]	)





"""simple docstring"""







def     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[Any]   ,       lowerCAmelCase__				:    str						)						->       List[Any]:

  """simple docstring"""




  return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[Any]   ,       lowerCAmelCase__				:    str=0						)						->       Union[str, Any]:

  """simple docstring"""




  return sorted(lowerCAmelCase__   ,       key=lambda lowerCAmelCase__						: x[column]						)

def     UpperCamelCase_   (   lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    Optional[Any]=float('inf'						)						)						->       Optional[int]:

  """simple docstring"""




  for i in range(points_counts - 1						):
    for j in range(i + 1   ,       lowerCAmelCase__						):
      lowerCAmelCase_       :    Union[str, Any]								=			euclidean_distance_sqr(points[i]   ,       points[j]						)
      if current_dis < min_dis:
        lowerCAmelCase_       :    Optional[int]								=			current_dis
  return min_dis

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[Any]   ,       lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    Union[str, Any]=float('inf'						)						)						->       Dict:

  """simple docstring"""




  for i in range(min(6   ,       points_counts - 1						)   ,       lowerCAmelCase__						):
    for j in range(max(0   ,       i - 6						)   ,       lowerCAmelCase__						):
      lowerCAmelCase_       :    Any								=			euclidean_distance_sqr(points[i]   ,       points[j]						)
      if current_dis < min_dis:
        lowerCAmelCase_       :    Union[str, Any]								=			current_dis
  return min_dis

def     UpperCamelCase_   (   lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    Optional[int]						)						->       Dict:

  """simple docstring"""




  if points_counts <= 3:
    return dis_between_closest_pair(lowerCAmelCase__   ,       lowerCAmelCase__						)

  # recursion
  lowerCAmelCase_       :    int								=			points_counts // 2
  lowerCAmelCase_       :    Optional[Any]								=			closest_pair_of_points_sqr(
      lowerCAmelCase__   ,       points_sorted_on_y[:mid]   ,       lowerCAmelCase__						)
  lowerCAmelCase_       :    Optional[Any]								=			closest_pair_of_points_sqr(
      lowerCAmelCase__   ,       points_sorted_on_y[mid:]   ,       points_counts - mid						)
  lowerCAmelCase_       :    Any								=			min(lowerCAmelCase__   ,       lowerCAmelCase__						)
  lowerCAmelCase_       :    str								=			[]
  for point in points_sorted_on_x:
    if abs(point[0] - points_sorted_on_x[mid][0]						) < closest_pair_dis:
      cross_strip.append(lowerCAmelCase__						)

  lowerCAmelCase_       :    List[Any]								=			dis_between_closest_in_strip(
      lowerCAmelCase__   ,       len(lowerCAmelCase__						)   ,       lowerCAmelCase__						)
  return min(lowerCAmelCase__   ,       lowerCAmelCase__						)

def     UpperCamelCase_   (   lowerCAmelCase__				:    str   ,       lowerCAmelCase__				:    Tuple						)						->       List[Any]:

  """simple docstring"""




  lowerCAmelCase_       :    List[str]								=			column_based_sort(lowerCAmelCase__   ,       column=0						)
  lowerCAmelCase_       :    Dict								=			column_based_sort(lowerCAmelCase__   ,       column=1						)
  return (
      closest_pair_of_points_sqr(
          lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)
  ) ** 0.5


if __name__ == "__main__":
     lowercase__		:    List[str]				   =     [(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
     print("""Distance:""", closest_pair_of_points(points, len(points)))





