import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ViTImageProcessor





class      lowerCAmelCase						(					unittest.TestCase			):



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__=13				,      lowerCAmelCase__=3				,      lowerCAmelCase__=224				,      lowerCAmelCase__=30				,      lowerCAmelCase__=400				,      lowerCAmelCase__=True				,      lowerCAmelCase__=None				,      lowerCAmelCase__=True				,      lowerCAmelCase__=[0.5, 0.5, 0.5]				,      lowerCAmelCase__=[0.5, 0.5, 0.5]				,      ):
            _A= size if size is not None else {'height': 18, 'width': 18}
            _A= parent
            _A= batch_size
            _A= num_channels
            _A= image_size
            _A= min_resolution
            _A= max_resolution
            _A= do_resize
            _A= size
            _A= do_normalize
            _A= image_mean
            _A= image_std





     def 			a__  (					self       ):
            return {
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_normalize": self.do_normalize,
                "do_resize": self.do_resize,
                "size": self.size,
            }





@require_torch
@require_vision
class      lowerCAmelCase						(					_a			,		unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     List[str]		=ViTImageProcessor if is_vision_available() else None



     def 			a__  (					self       ):
            _A= EfficientFormerImageProcessorTester(self       )



     @property
     def 			a__  (					self       ):
            return self.image_proc_tester.prepare_image_processor_dict()



     def 			a__  (					self       ):
            _A= self.image_processing_class(**self.image_processor_dict       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'image_mean'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'image_std'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'do_normalize'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'do_resize'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'size'       )       )



     def 			a__  (					self       ):
            pass



     def 			a__  (					self       ):
            # Initialize image_processor
            _A= self.image_processing_class(**self.image_processor_dict       )
            # create random PIL images
            _A= prepare_image_inputs(self.image_proc_tester				,      equal_resolution=lowerCAmelCase__       )
            for image in image_inputs:
                   self.assertIsInstance(lowerCAmelCase__				,      Image.Image       )

            # Test not batched input
            _A= image_processor(image_inputs[0]				,      return_tensors='pt'       ).pixel_values
            self.assertEqual(
                encoded_images.shape				,      (
                    1,
                    self.image_proc_tester.num_channels,
                    self.image_proc_tester.size['height'],
                    self.image_proc_tester.size['width'],
                )				,      )

            # Test batched
            _A= image_processor(lowerCAmelCase__				,      return_tensors='pt'       ).pixel_values
            self.assertEqual(
                encoded_images.shape				,      (
                    self.image_proc_tester.batch_size,
                    self.image_proc_tester.num_channels,
                    self.image_proc_tester.size['height'],
                    self.image_proc_tester.size['width'],
                )				,      )



     def 			a__  (					self       ):
            # Initialize image_processor
            _A= self.image_processing_class(**self.image_processor_dict       )
            # create random numpy tensors
            _A= prepare_image_inputs(self.image_proc_tester				,      equal_resolution=lowerCAmelCase__				,      numpify=lowerCAmelCase__       )
            for image in image_inputs:
                   self.assertIsInstance(lowerCAmelCase__				,      np.ndarray       )

            # Test not batched input
            _A= image_processor(image_inputs[0]				,      return_tensors='pt'       ).pixel_values
            self.assertEqual(
                encoded_images.shape				,      (
                    1,
                    self.image_proc_tester.num_channels,
                    self.image_proc_tester.size['height'],
                    self.image_proc_tester.size['width'],
                )				,      )

            # Test batched
            _A= image_processor(lowerCAmelCase__				,      return_tensors='pt'       ).pixel_values
            self.assertEqual(
                encoded_images.shape				,      (
                    self.image_proc_tester.batch_size,
                    self.image_proc_tester.num_channels,
                    self.image_proc_tester.size['height'],
                    self.image_proc_tester.size['width'],
                )				,      )





     def 			a__  (					self       ):
            # Initialize image_processor
            _A= self.image_processing_class(**self.image_processor_dict       )
            # create random PyTorch tensors
            _A= prepare_image_inputs(self.image_proc_tester				,      equal_resolution=lowerCAmelCase__				,      torchify=lowerCAmelCase__       )
            for image in image_inputs:
                   self.assertIsInstance(lowerCAmelCase__				,      torch.Tensor       )

            # Test not batched input
            _A= image_processor(image_inputs[0]				,      return_tensors='pt'       ).pixel_values
            self.assertEqual(
                encoded_images.shape				,      (
                    1,
                    self.image_proc_tester.num_channels,
                    self.image_proc_tester.size['height'],
                    self.image_proc_tester.size['width'],
                )				,      )

            # Test batched
            _A= image_processor(lowerCAmelCase__				,      return_tensors='pt'       ).pixel_values
            self.assertEqual(
                encoded_images.shape				,      (
                    self.image_proc_tester.batch_size,
                    self.image_proc_tester.num_channels,
                    self.image_proc_tester.size['height'],
                    self.image_proc_tester.size['width'],
                )				,      )
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__       ):
            _A=	3
            _A=	250

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



     def 			a__  (					self       ):
            _A,			_A=	self._get_tensors(5       )

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

            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(9       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(10       )
            self.assertTrue(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )



     def 			a__  (					self       ):
            _A=	MaxLengthCriteria(max_length=10       )

            _A,			_A=	self._get_tensors(5       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(9       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(10       )
            self.assertTrue(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )



     def 			a__  (					self       ):
            _A=	MaxNewTokensCriteria(start_length=5				,      max_new_tokens=5       )

            _A,			_A=	self._get_tensors(5       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(9       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(10       )
            self.assertTrue(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

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



     def 			a__  (					self       ):
            _A,			_A=	self._get_tensors(5       )

            _A=	MaxTimeCriteria(max_time=0.1       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A=	MaxTimeCriteria(max_time=0.1				,      initial_timestamp=time.time() - 0.2       )
            self.assertTrue(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )





     def 			a__  (					self       ):
            validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10       )]       )				,      10       )

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

            _A=	validate_stopping_criteria(StoppingCriteriaList()				,      11       )

            self.assertEqual(len(lowerCAmelCase__       )				,      1       )