
"""simple docstring"""

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     :    Tuple       =			{"LayoutLMv2Config", "LayoutLMv3Config"}

@is_pipeline_test
class __lowerCAmelCase		(				unittest.TestCase      ):
     lowercase						       =						MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
     lowercase						       =						TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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





     @require_torch
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




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

      __UpperCamelCase      =	text_classifier('This is great !'				)
      self.assertEqual(nested_simplify(__UpperCAmelCase				)     ,					[{'label': 'LABEL_0', 'score': 0.5_0_4}]				)

      __UpperCamelCase      =	text_classifier('This is great !'     ,					top_k=2				)
      self.assertEqual(
          nested_simplify(__UpperCAmelCase				)     ,					[{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}]				)

      __UpperCamelCase      =	text_classifier(['This is great !', 'This is bad']     ,					top_k=2				)
      self.assertEqual(
          nested_simplify(__UpperCAmelCase				)     ,					[
              [{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}],
              [{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}],
          ]     ,					)

      __UpperCamelCase      =	text_classifier('This is great !'     ,					top_k=1				)
      self.assertEqual(nested_simplify(__UpperCAmelCase				)     ,					[{'label': 'LABEL_0', 'score': 0.5_0_4}]				)

      # Legacy behavior
      __UpperCamelCase      =	text_classifier('This is great !'     ,					return_all_scores=__UpperCAmelCase				)
      self.assertEqual(nested_simplify(__UpperCAmelCase				)     ,					[{'label': 'LABEL_0', 'score': 0.5_0_4}]				)

      __UpperCamelCase      =	text_classifier('This is great !'     ,					return_all_scores=__UpperCAmelCase				)
      self.assertEqual(
          nested_simplify(__UpperCAmelCase				)     ,					[[{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}]]				)

      __UpperCamelCase      =	text_classifier(['This is great !', 'Something else']     ,					return_all_scores=__UpperCAmelCase				)
      self.assertEqual(
          nested_simplify(__UpperCAmelCase				)     ,					[
              [{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}],
              [{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}],
          ]     ,					)

      __UpperCamelCase      =	text_classifier(['This is great !', 'Something else']     ,					return_all_scores=__UpperCAmelCase				)
      self.assertEqual(
          nested_simplify(__UpperCAmelCase				)     ,					[
              {'label': 'LABEL_0', 'score': 0.5_0_4},
              {'label': 'LABEL_0', 'score': 0.5_0_4},
          ]     ,					)





     @require_torch
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      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(__UpperCAmelCase				)     ,					[{'label': 'LABEL_0', 'score': 0.5_0_4}]				)





     @require_tf
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




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

      __UpperCamelCase      =	text_classifier('This is great !'				)
      self.assertEqual(nested_simplify(__UpperCAmelCase				)     ,					[{'label': 'LABEL_0', 'score': 0.5_0_4}]				)





     @slow
     @require_torch
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	pipeline('text-classification'				)

      __UpperCamelCase      =	text_classifier('This is great !'				)
      self.assertEqual(nested_simplify(__UpperCAmelCase				)     ,					[{'label': 'POSITIVE', 'score': 1.0}]				)
      __UpperCamelCase      =	text_classifier('This is bad !'				)
      self.assertEqual(nested_simplify(__UpperCAmelCase				)     ,					[{'label': 'NEGATIVE', 'score': 1.0}]				)
      __UpperCamelCase      =	text_classifier('Birds are a type of animal'				)
      self.assertEqual(nested_simplify(__UpperCAmelCase				)     ,					[{'label': 'POSITIVE', 'score': 0.9_8_8}]				)





     @slow
     @require_tf
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	pipeline('text-classification'     ,					framework='tf'				)

      __UpperCamelCase      =	text_classifier('This is great !'				)
      self.assertEqual(nested_simplify(__UpperCAmelCase				)     ,					[{'label': 'POSITIVE', 'score': 1.0}]				)
      __UpperCamelCase      =	text_classifier('This is bad !'				)
      self.assertEqual(nested_simplify(__UpperCAmelCase				)     ,					[{'label': 'NEGATIVE', 'score': 1.0}]				)
      __UpperCamelCase      =	text_classifier('Birds are a type of animal'				)
      self.assertEqual(nested_simplify(__UpperCAmelCase				)     ,					[{'label': 'POSITIVE', 'score': 0.9_8_8}]				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




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







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	text_classifier.model
      # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
      __UpperCamelCase      =	'HuggingFace is in'
      __UpperCamelCase      =	text_classifier(__UpperCAmelCase				)

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

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

      __UpperCamelCase      =	{'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
      __UpperCamelCase      =	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.
      __UpperCamelCase      =	[['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
      __UpperCamelCase      =	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()				)





"""simple docstring"""

import heapq as hq
import math
from collections.abc import Iterator

class __lowerCAmelCase		:





     def __init__(     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	str(id_				)
      __UpperCamelCase      =	None
      __UpperCamelCase      =	None
      __UpperCamelCase      =	[]
      __UpperCamelCase      =	{}  # {vertex:distance}





     def __lt__(     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      return self.key < other.key





     def __repr__(     self				):

      '''simple docstring'''




      return self.id





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      self.neighbors.append(__UpperCAmelCase				)







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	weight






def 							A						(    snake_case				:List[Any]    , snake_case				:Dict    , snake_case				:Any    , snake_case				:str		)				->     List[str]:
 # add the neighbors:
 graph[a - 1].add_neighbor(graph[b - 1]		)
 graph[b - 1].add_neighbor(graph[a - 1]		)
 # add the edges:
 graph[a - 1].add_edge(graph[b - 1]    , snake_case		)
 graph[b - 1].add_edge(graph[a - 1]    , snake_case		)






def 							A						(    snake_case				:list    , snake_case				:Vertex		)				->     list:
 __UpperCamelCase      =	[]
 for u in graph:
  __UpperCamelCase      =	math.inf
  __UpperCamelCase      =	None
 __UpperCamelCase      =	0
 __UpperCamelCase      =	graph[:]
 while q:
  __UpperCamelCase      =	min(snake_case		)
  q.remove(snake_case		)
  for v in u.neighbors:
   if (v in q) and (u.edges[v.id] < v.key):
    __UpperCamelCase      =	u
    __UpperCamelCase      =	u.edges[v.id]
 for i in range(1    , len(snake_case		)		):
  a.append((int(graph[i].id		) + 1, int(graph[i].pi.id		) + 1)		)
 return a






def 							A						(    snake_case				:list    , snake_case				:Vertex		)				->     Iterator[tuple]:
 for u in graph:
  __UpperCamelCase      =	math.inf
  __UpperCamelCase      =	None
 __UpperCamelCase      =	0

 __UpperCamelCase      =	list(snake_case		)
 hq.heapify(snake_case		)

 while h:
  __UpperCamelCase      =	hq.heappop(snake_case		)
  for v in u.neighbors:
   if (v in h) and (u.edges[v.id] < v.key):
    __UpperCamelCase      =	u
    __UpperCamelCase      =	u.edges[v.id]
    hq.heapify(snake_case		)

 for i in range(1    , len(snake_case		)		):
  yield (int(graph[i].id		) + 1, int(graph[i].pi.id		) + 1)






def 							A						(    )				->     None:
 pass


if __name__ == "__main__":
      import doctest

      doctest.testmod()



