
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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


lowerCamelCase_							=     logging.get_logger(__name__)




class 	__A(				a__ ):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__			    =			["""pixel_values"""]







  def __init__(self       ,			SCREAMING_SNAKE_CASE_ = True       ,			SCREAMING_SNAKE_CASE_ = None       ,			SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC       ,			SCREAMING_SNAKE_CASE_ = True       ,			SCREAMING_SNAKE_CASE_ = None       ,			SCREAMING_SNAKE_CASE_ = 1 / 2_55       ,			SCREAMING_SNAKE_CASE_ = True       ,			SCREAMING_SNAKE_CASE_ = True       ,			SCREAMING_SNAKE_CASE_ = None       ,			SCREAMING_SNAKE_CASE_ = None       ,			**SCREAMING_SNAKE_CASE_       ,			):
       super().__init__(**__lowerCAmelCase						)
       UpperCamelCase__				=				size if size is not None else {'''height''': 2_56, '''width''': 2_56}
       UpperCamelCase__				=				get_size_dict(__lowerCAmelCase						)
       UpperCamelCase__				=				crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
       UpperCamelCase__				=				get_size_dict(__lowerCAmelCase       ,			param_name="""crop_size"""						)

       UpperCamelCase__				=				do_resize
       UpperCamelCase__				=				size
       UpperCamelCase__				=				resample
       UpperCamelCase__				=				do_center_crop
       UpperCamelCase__				=				crop_size
       UpperCamelCase__				=				do_rescale
       UpperCamelCase__				=				rescale_factor
       UpperCamelCase__				=				do_normalize
       UpperCamelCase__				=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       UpperCamelCase__				=				image_std if image_std is not None else IMAGENET_STANDARD_STD







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC       ,			SCREAMING_SNAKE_CASE_ = None       ,			**SCREAMING_SNAKE_CASE_       ,			):
       UpperCamelCase__				=				get_size_dict(__lowerCAmelCase						)
       if "height" not in size or "width" not in size:
            raise ValueError(F"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"						)
       return resize(
           __lowerCAmelCase       ,			size=(size["""height"""], size["""width"""])       ,			resample=__lowerCAmelCase       ,			data_format=__lowerCAmelCase       ,			**__lowerCAmelCase						)







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_ = None       ,			**SCREAMING_SNAKE_CASE_       ,			):
       UpperCamelCase__				=				get_size_dict(__lowerCAmelCase						)
       if "height" not in size or "width" not in size:
            raise ValueError(F"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"						)
       return center_crop(__lowerCAmelCase       ,			size=(size["""height"""], size["""width"""])       ,			data_format=__lowerCAmelCase       ,			**__lowerCAmelCase						)







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_ = None       ,			**SCREAMING_SNAKE_CASE_       ,			):
       return rescale(__lowerCAmelCase       ,			scale=__lowerCAmelCase       ,			data_format=__lowerCAmelCase       ,			**__lowerCAmelCase						)







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_ = None       ,			**SCREAMING_SNAKE_CASE_       ,			):
       return normalize(__lowerCAmelCase       ,			mean=__lowerCAmelCase       ,			std=__lowerCAmelCase       ,			data_format=__lowerCAmelCase       ,			**__lowerCAmelCase						)







  def        UpperCAmelCase_					(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_       ,			):
       UpperCamelCase__				=				do_resize if do_resize is not None else self.do_resize
       UpperCamelCase__				=				resample if resample is not None else self.resample
       UpperCamelCase__				=				do_center_crop if do_center_crop is not None else self.do_center_crop
       UpperCamelCase__				=				do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase__				=				rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCamelCase__				=				do_normalize if do_normalize is not None else self.do_normalize
       UpperCamelCase__				=				image_mean if image_mean is not None else self.image_mean
       UpperCamelCase__				=				image_std if image_std is not None else self.image_std

       UpperCamelCase__				=				size if size is not None else self.size
       UpperCamelCase__				=				get_size_dict(__lowerCAmelCase						)
       UpperCamelCase__				=				crop_size if crop_size is not None else self.crop_size
       UpperCamelCase__				=				get_size_dict(__lowerCAmelCase       ,			param_name="""crop_size"""						)

       UpperCamelCase__				=				make_list_of_images(__lowerCAmelCase						)

       if not valid_images(__lowerCAmelCase						):
            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 or resample is None:
            raise ValueError("""Size and resample 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."""						)

       if do_normalize and (image_mean is None or image_std is None):
            raise ValueError("""Image mean and std must be specified if do_normalize is True."""						)

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

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

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

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

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

       UpperCamelCase__				=				[to_channel_dimension_format(__lowerCAmelCase       ,			__lowerCAmelCase						) for image in images]

       UpperCamelCase__				=				{'''pixel_values''': images}
       return BatchFeature(data=__lowerCAmelCase       ,			tensor_type=__lowerCAmelCase						)






'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import tensorflow as tf

  from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       @slow
       def _lowerCAmelCase(						self )  ->  Dict:
        lowercase__				:    List[str]  =					TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' )
        lowercase__				:    Dict  =					AutoTokenizer.from_pretrained('''google/mt5-small''' )

        lowercase__				:    Any  =					tokenizer('''Hello there'''	,   return_tensors='''tf''' ).input_ids
        lowercase__				:    List[Any]  =					tokenizer('''Hi I am'''	,   return_tensors='''tf''' ).input_ids

        lowercase__				:    str  =					model(__lowerCAmelCase	,   labels=__lowerCAmelCase ).loss
        lowercase__				:    List[str]  =					-tf.math.reduce_mean(__lowerCAmelCase ).numpy()

        lowercase__				:    str  =					-2_1.2_2_8_1_6_8
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 2E-4 )

