




'''simple docstring'''



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_							:	Optional[Any]	     =		logging.get_logger(__name__)





class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   Union[str, Any]										=      ['''pixel_values''']


       def __init__( self     ,	__SCREAMING_SNAKE_CASE = True     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC     ,	__SCREAMING_SNAKE_CASE = True     ,	__SCREAMING_SNAKE_CASE = True     ,	__SCREAMING_SNAKE_CASE = 1 / 255     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = True     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	None:



            '''simple docstring'''




            super().__init__(**__SCREAMING_SNAKE_CASE	)
            __snake_case            =	size if size is not None else {'''height''': 224, '''width''': 224}
            __snake_case            =	get_size_dict(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
            __snake_case            =	get_size_dict(__SCREAMING_SNAKE_CASE     ,	default_to_square=__SCREAMING_SNAKE_CASE     ,	param_name='''crop_size'''	)

            __snake_case            =	do_resize
            __snake_case            =	do_rescale
            __snake_case            =	do_normalize
            __snake_case            =	do_center_crop
            __snake_case            =	crop_size
            __snake_case            =	size
            __snake_case            =	resample
            __snake_case            =	rescale_factor
            __snake_case            =	image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
            __snake_case            =	image_std if image_std is not None else IMAGENET_DEFAULT_STD


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR     ,	__SCREAMING_SNAKE_CASE = None     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	np.ndarray:



            '''simple docstring'''




            __snake_case            =	get_size_dict(__SCREAMING_SNAKE_CASE	)

            if "shortest_edge" in size:
                 __snake_case            =	get_resize_output_image_size(__SCREAMING_SNAKE_CASE     ,	size=size['''shortest_edge''']     ,	default_to_square=__SCREAMING_SNAKE_CASE	)
                 # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
            elif "height" in size and "width" in size:
                 __snake_case            =	(size['''height'''], size['''width'''])
            else:
                 raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''	)
            return resize(__SCREAMING_SNAKE_CASE     ,	size=__SCREAMING_SNAKE_CASE     ,	resample=__SCREAMING_SNAKE_CASE     ,	data_format=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	np.ndarray:



            '''simple docstring'''




            __snake_case            =	get_size_dict(__SCREAMING_SNAKE_CASE	)
            if "height" not in size or "width" not in size:
                 raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''	)
            return center_crop(__SCREAMING_SNAKE_CASE     ,	size=(size['''height'''], size['''width'''])     ,	data_format=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None     ,	**__SCREAMING_SNAKE_CASE	)   ->	np.ndarray:



            '''simple docstring'''




            return rescale(__SCREAMING_SNAKE_CASE     ,	scale=__SCREAMING_SNAKE_CASE     ,	data_format=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	np.ndarray:



            '''simple docstring'''




            return normalize(__SCREAMING_SNAKE_CASE     ,	mean=__SCREAMING_SNAKE_CASE     ,	std=__SCREAMING_SNAKE_CASE     ,	data_format=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	BatchFeature:



            '''simple docstring'''




            __snake_case            =	do_resize if do_resize is not None else self.do_resize
            __snake_case            =	do_rescale if do_rescale is not None else self.do_rescale
            __snake_case            =	do_normalize if do_normalize is not None else self.do_normalize
            __snake_case            =	do_center_crop if do_center_crop is not None else self.do_center_crop
            __snake_case            =	crop_size if crop_size is not None else self.crop_size
            __snake_case            =	get_size_dict(__SCREAMING_SNAKE_CASE     ,	param_name='''crop_size'''     ,	default_to_square=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	resample if resample is not None else self.resample
            __snake_case            =	rescale_factor if rescale_factor is not None else self.rescale_factor
            __snake_case            =	image_mean if image_mean is not None else self.image_mean
            __snake_case            =	image_std if image_std is not None else self.image_std

            __snake_case            =	size if size is not None else self.size
            __snake_case            =	get_size_dict(__SCREAMING_SNAKE_CASE	)

            if not is_batched(__SCREAMING_SNAKE_CASE	):
                 __snake_case            =	[images]

            if not valid_images(__SCREAMING_SNAKE_CASE	):
                 raise ValueError(
                     '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                     '''torch.Tensor, tf.Tensor or jax.ndarray.'''	)

            if do_resize and size is None:
                 raise ValueError('''Size must be specified if do_resize is True.'''	)

            if do_center_crop and crop_size is None:
                 raise ValueError('''Crop size must be specified if do_center_crop is True.'''	)

            if do_rescale and rescale_factor is None:
                 raise ValueError('''Rescale factor must be specified if do_rescale is True.'''	)

            # All transformations expect numpy arrays.
            __snake_case            =	[to_numpy_array(__SCREAMING_SNAKE_CASE	) for image in images]

            if do_resize:
                 __snake_case            =	[self.resize(image=__SCREAMING_SNAKE_CASE     ,	size=__SCREAMING_SNAKE_CASE     ,	resample=__SCREAMING_SNAKE_CASE	) for image in images]

            if do_center_crop:
                 __snake_case            =	[self.center_crop(image=__SCREAMING_SNAKE_CASE     ,	size=__SCREAMING_SNAKE_CASE	) for image in images]

            if do_rescale:
                 __snake_case            =	[self.rescale(image=__SCREAMING_SNAKE_CASE     ,	scale=__SCREAMING_SNAKE_CASE	) for image in images]

            if do_normalize:
                 __snake_case            =	[self.normalize(image=__SCREAMING_SNAKE_CASE     ,	mean=__SCREAMING_SNAKE_CASE     ,	std=__SCREAMING_SNAKE_CASE	) for image in images]

            __snake_case            =	[to_channel_dimension_format(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) for image in images]

            __snake_case            =	{'''pixel_values''': images}
            return BatchFeature(data=__SCREAMING_SNAKE_CASE     ,	tensor_type=__SCREAMING_SNAKE_CASE	)













'''simple docstring'''

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   UpperCAmelCase      (			unittest.TestCase							):
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :str				)->				Any:
     A__							=     3
     A__							=     2_50

     A__							=     ids_tensor((batch_size, length)		,			lowercase_				)
     A__							=     torch.ones((batch_size, length)		,			device=lowercase_		,			dtype=torch.float				) / length
     return input_ids, scores
 def 	UpperCAmelCase_				(							self :str				)->				Dict:
     A__,      A__							=     self._get_tensors(5				)

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

     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(9				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(10				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)
 def 	UpperCAmelCase_				(							self :List[Any]				)->				List[Any]:
     A__							=     MaxLengthCriteria(max_length=10				)

     A__,      A__							=     self._get_tensors(5				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(9				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(10				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)
 def 	UpperCAmelCase_				(							self :Tuple				)->				int:
     A__							=     MaxNewTokensCriteria(start_length=5		,			max_new_tokens=5				)

     A__,      A__							=     self._get_tensors(5				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(9				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(10				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)

     A__							=     StoppingCriteriaList([criteria]				)
     self.assertEqual(criteria_list.max_length		,			10				)
 def 	UpperCAmelCase_				(							self :Dict				)->				int:
     A__,      A__							=     self._get_tensors(5				)

     A__							=     MaxTimeCriteria(max_time=0.1				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__							=     MaxTimeCriteria(max_time=0.1		,			initial_timestamp=time.time() - 0.2				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)



 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				str:
     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)		,			10				)

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

     A__							=     validate_stopping_criteria(StoppingCriteriaList()		,			11				)

     self.assertEqual(len(lowercase_				)		,			1				)



