

import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
  import torch

  from transformers.generation import (
      MaxLengthCriteria,
      MaxNewTokensCriteria,
      MaxTimeCriteria,
      StoppingCriteriaList,
      validate_stopping_criteria,
  )





@require_torch
class 					__lowerCAmelCase	(   unittest.TestCase				):




  def     A__	(							self				,  lowerCAmelCase   )    ->					int:
        '''simple docstring'''






        _lowercase      			=3
        _lowercase      			=250

        _lowercase      			=ids_tensor((batch_size, length)				,  lowerCAmelCase   )
        _lowercase      			=torch.ones((batch_size, length)				,  device=lowerCAmelCase				,  dtype=torch.float   ) / length
        return input_ids, scores




  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase			,			_lowercase      			=self._get_tensors(5   )

        _lowercase      			=StoppingCriteriaList(
            [
                MaxLengthCriteria(max_length=10   ),
                MaxTimeCriteria(max_time=0.1   ),
            ]   )

        self.assertFalse(criteria(lowerCAmelCase				,  lowerCAmelCase   )   )

        _lowercase			,			_lowercase      			=self._get_tensors(9   )
        self.assertFalse(criteria(lowerCAmelCase				,  lowerCAmelCase   )   )

        _lowercase			,			_lowercase      			=self._get_tensors(10   )
        self.assertTrue(criteria(lowerCAmelCase				,  lowerCAmelCase   )   )




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        _lowercase      			=MaxLengthCriteria(max_length=10   )

        _lowercase			,			_lowercase      			=self._get_tensors(5   )
        self.assertFalse(criteria(lowerCAmelCase				,  lowerCAmelCase   )   )

        _lowercase			,			_lowercase      			=self._get_tensors(9   )
        self.assertFalse(criteria(lowerCAmelCase				,  lowerCAmelCase   )   )

        _lowercase			,			_lowercase      			=self._get_tensors(10   )
        self.assertTrue(criteria(lowerCAmelCase				,  lowerCAmelCase   )   )




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase      			=MaxNewTokensCriteria(start_length=5				,  max_new_tokens=5   )

        _lowercase			,			_lowercase      			=self._get_tensors(5   )
        self.assertFalse(criteria(lowerCAmelCase				,  lowerCAmelCase   )   )

        _lowercase			,			_lowercase      			=self._get_tensors(9   )
        self.assertFalse(criteria(lowerCAmelCase				,  lowerCAmelCase   )   )

        _lowercase			,			_lowercase      			=self._get_tensors(10   )
        self.assertTrue(criteria(lowerCAmelCase				,  lowerCAmelCase   )   )

        _lowercase      			=StoppingCriteriaList([criteria]   )
        self.assertEqual(criteria_list.max_length				,  10   )




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase			,			_lowercase      			=self._get_tensors(5   )

        _lowercase      			=MaxTimeCriteria(max_time=0.1   )
        self.assertFalse(criteria(lowerCAmelCase				,  lowerCAmelCase   )   )

        _lowercase      			=MaxTimeCriteria(max_time=0.1				,  initial_timestamp=time.time() - 0.2   )
        self.assertTrue(criteria(lowerCAmelCase				,  lowerCAmelCase   )   )




  def     A__	(							self   )    ->					Optional[Any]:
        '''simple docstring'''






        validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )				,  10   )

        with self.assertWarns(lowerCAmelCase   ):
              validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )				,  11   )

        _lowercase      			=validate_stopping_criteria(StoppingCriteriaList()				,  11   )

        self.assertEqual(len(lowerCAmelCase   )				,  1   )





from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    42
  _a		     =    42


if is_transformers_available() and is_torch_available():
  from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


