




import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
						import torch


@require_torch
@require_optimum
@slow
class  A		(  unittest.TestCase     ):



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  'hf-internal-testing/tiny-random-t5'
								lowerCAmelCase_      			=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_   )
								lowerCAmelCase_      			=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_   )

								lowerCAmelCase_      			=  tokenizer('''This is me''',	return_tensors='''pt'''   )

								lowerCAmelCase_      			=  model.to_bettertransformer()

								self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()   )   )

								lowerCAmelCase_      			=  model.generate(**SCREAMING_SNAKE_CASE_   )

								lowerCAmelCase_      			=  model.reverse_bettertransformer()

								self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()   )   )

								with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_pretrained(SCREAMING_SNAKE_CASE_   )

														lowerCAmelCase_      			=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_   )

														self.assertFalse(
														    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()   )   )

														lowerCAmelCase_      			=  model_reloaded.generate(**SCREAMING_SNAKE_CASE_   )
														self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_   )   )







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  'hf-internal-testing/tiny-random-t5'
								lowerCAmelCase_      			=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_   )

								lowerCAmelCase_      			=  model.to_bettertransformer()

								with tempfile.TemporaryDirectory() as tmpdirname:
														with self.assertRaises(SCREAMING_SNAKE_CASE_   ):
																				model.save_pretrained(SCREAMING_SNAKE_CASE_   )

														lowerCAmelCase_      			=  model.reverse_bettertransformer()
														model.save_pretrained(SCREAMING_SNAKE_CASE_   )









"""simple docstring"""







import random

def 				UpperCamelCase_    (    lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				float    ,   lowerCAmelCase__      :				bool = False						)						->   dict:

  """simple docstring"""




  lowerCAmelCase_   :				dict								= {i: [] for i in range(lowerCAmelCase__						)}

  # if probability is greater or equal than 1, then generate a complete graph
  if probability >= 1:
    return complete_graph(lowerCAmelCase__						)
  # if probability is lower or equal than 0, then return a graph without edges
  if probability <= 0:
    return graph

  # for each couple of nodes, add an edge from u to v
  # if the number randomly generated is greater than probability probability
  for i in range(lowerCAmelCase__						):
    for j in range(i + 1    ,   lowerCAmelCase__						):
      if random.random() < probability:
        graph[i].append(lowerCAmelCase__						)
        if not directed:
          # if the graph is undirected, add an edge in from j to i, either
          graph[j].append(lowerCAmelCase__						)
  return graph

def 				UpperCamelCase_    (    lowerCAmelCase__      :				int						)						->   dict:

  """simple docstring"""




  return {
      i: [j for j in range(lowerCAmelCase__						) if i != j] for i in range(lowerCAmelCase__						)
  }


if __name__ == "__main__":
     import doctest

     doctest.testmod()





