






from typing import Dict, Iterable, 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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase__      			=						logging.get_logger(__name__)




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						snake_case__        =      ["""pixel_values"""]



						def __init__(self:					List[str] ,					__UpperCAmelCase:					bool = True ,					__UpperCAmelCase:					Dict[str, int] = None ,					__UpperCAmelCase:					PILImageResampling = PILImageResampling.BICUBIC ,					__UpperCAmelCase:					bool = True ,					__UpperCAmelCase:					Dict[str, int] = None ,					__UpperCAmelCase:					bool = True ,					__UpperCAmelCase:					Union[int, float] = 1 / 255 ,					__UpperCAmelCase:					bool = True ,					__UpperCAmelCase:					Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN ,					__UpperCAmelCase:					Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD ,					**__UpperCAmelCase:					Optional[Any] ,					)    ->     None:




											'''simple docstring'''



											super().__init__(**__UpperCAmelCase   )
											__a					:						Optional[int]      =					size if size is not None else {"shortest_edge": 224}
											__a					:						Any      =					get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase   )
											__a					:						Tuple      =					crop_size if crop_size is not None else {"height": 224, "width": 224}
											__a					:						Tuple      =					get_size_dict(__UpperCAmelCase ,					param_name="crop_size"   )

											__a					:						int      =					do_resize
											__a					:						Tuple      =					size
											__a					:						Any      =					resample
											__a					:						Optional[int]      =					do_center_crop
											__a					:						Any      =					crop_size
											__a					:						Union[str, Any]      =					do_rescale
											__a					:						Union[str, Any]      =					rescale_factor
											__a					:						int      =					do_normalize
											__a					:						Tuple      =					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
											__a					:						Union[str, Any]      =					image_std if image_std is not None else IMAGENET_DEFAULT_STD



						def        UpperCAmelCase__					(self:					Tuple ,					__UpperCAmelCase:					np.ndarray ,					__UpperCAmelCase:					Dict[str, int] ,					__UpperCAmelCase:					PILImageResampling = PILImageResampling.BICUBIC ,					__UpperCAmelCase:					Optional[Union[str, ChannelDimension]] = None ,					**__UpperCAmelCase:					List[Any] ,					)    ->     np.ndarray:




											'''simple docstring'''



											__a					:						int      =					get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase   )
											# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
											if "shortest_edge" in size:
																__a					:						Union[str, Any]      =					int((256 / 224) * size["shortest_edge"]   )
																__a					:						Optional[Any]      =					get_resize_output_image_size(__UpperCAmelCase ,					size=__UpperCAmelCase ,					default_to_square=__UpperCAmelCase   )
																__a					:						int      =					{"height": output_size[0], "width": output_size[1]}
											if "height" not in size_dict or "width" not in size_dict:
																raise ValueError(
																    f'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'   )
											return resize(
											    __UpperCAmelCase ,					size=(size_dict["height"], size_dict["width"]) ,					resample=__UpperCAmelCase ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Any ,					__UpperCAmelCase:					np.ndarray ,					__UpperCAmelCase:					Dict[str, int] ,					__UpperCAmelCase:					Optional[Union[str, ChannelDimension]] = None ,					**__UpperCAmelCase:					Dict ,					)    ->     np.ndarray:




											'''simple docstring'''



											__a					:						Any      =					get_size_dict(__UpperCAmelCase   )
											if "height" not in size or "width" not in size:
																raise ValueError(f'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'   )
											return center_crop(__UpperCAmelCase ,					size=(size["height"], size["width"]) ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					int ,					__UpperCAmelCase:					np.ndarray ,					__UpperCAmelCase:					Union[int, float] ,					__UpperCAmelCase:					Optional[Union[str, ChannelDimension]] = None ,					**__UpperCAmelCase:					Optional[Any] ,					)    ->     np.ndarray:




											'''simple docstring'''



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



						def        UpperCAmelCase__					(self:					Any ,					__UpperCAmelCase:					np.ndarray ,					__UpperCAmelCase:					Union[float, List[float]] ,					__UpperCAmelCase:					Union[float, List[float]] ,					__UpperCAmelCase:					Optional[Union[str, ChannelDimension]] = None ,					**__UpperCAmelCase:					Dict ,					)    ->     np.ndarray:




											'''simple docstring'''



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



						def        UpperCAmelCase__					(self:					Tuple ,					__UpperCAmelCase:					ImageInput ,					__UpperCAmelCase:					Optional[bool] = None ,					__UpperCAmelCase:					Optional[Dict[str, int]] = None ,					__UpperCAmelCase:					PILImageResampling = None ,					__UpperCAmelCase:					Optional[bool] = None ,					__UpperCAmelCase:					Optional[Dict[str, int]] = None ,					__UpperCAmelCase:					Optional[bool] = None ,					__UpperCAmelCase:					Optional[float] = None ,					__UpperCAmelCase:					Optional[bool] = None ,					__UpperCAmelCase:					Optional[Union[float, Iterable[float]]] = None ,					__UpperCAmelCase:					Optional[Union[float, Iterable[float]]] = None ,					__UpperCAmelCase:					Optional[TensorType] = None ,					__UpperCAmelCase:					ChannelDimension = ChannelDimension.FIRST ,					**__UpperCAmelCase:					Optional[int] ,					)    ->     BatchFeature:




											'''simple docstring'''



											__a					:						str      =					do_resize if do_resize is not None else self.do_resize
											__a					:						int      =					resample if resample is not None else self.resample
											__a					:						Optional[Any]      =					do_center_crop if do_center_crop is not None else self.do_center_crop
											__a					:						Dict      =					do_rescale if do_rescale is not None else self.do_rescale
											__a					:						str      =					rescale_factor if rescale_factor is not None else self.rescale_factor
											__a					:						Any      =					do_normalize if do_normalize is not None else self.do_normalize
											__a					:						Optional[Any]      =					image_mean if image_mean is not None else self.image_mean
											__a					:						List[Any]      =					image_std if image_std is not None else self.image_std

											__a					:						Dict      =					size if size is not None else self.size
											__a					:						Optional[int]      =					get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase   )
											__a					:						List[str]      =					crop_size if crop_size is not None else self.crop_size
											__a					:						str      =					get_size_dict(__UpperCAmelCase ,					param_name="crop_size"   )

											__a					:						int      =					make_list_of_images(__UpperCAmelCase   )

											if not valid_images(__UpperCAmelCase   ):
																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."   )

											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.
											__a					:						List[str]      =					[to_numpy_array(__UpperCAmelCase   ) for image in images]

											if do_resize:
																__a					:						int      =					[self.resize(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   ) for image in images]

											if do_center_crop:
																__a					:						Union[str, Any]      =					[self.center_crop(__UpperCAmelCase ,					__UpperCAmelCase   ) for image in images]

											if do_rescale:
																__a					:						Union[str, Any]      =					[self.rescale(__UpperCAmelCase ,					__UpperCAmelCase   ) for image in images]

											if do_normalize:
																__a					:						int      =					[self.normalize(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   ) for image in images]

											__a					:						Union[str, Any]      =					[to_channel_dimension_format(__UpperCAmelCase ,					__UpperCAmelCase   ) for image in images]

											__a					:						Optional[int]      =					{"pixel_values": images}
											return BatchFeature(data=__UpperCAmelCase ,					tensor_type=__UpperCAmelCase   )













import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
			import optax
			from flax.training.common_utils import onehot

			from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
			from transformers.models.ta.modeling_flax_ta import shift_tokens_right




@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 							snake_case_       (    unittest.TestCase      ):

						"""simple docstring"""



						@slow
						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     List[str]:




											'''simple docstring'''



											__a					:						Optional[Any]      =					FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"   )
											__a					:						Dict      =					AutoTokenizer.from_pretrained("google/mt5-small"   )

											__a					:						Any      =					tokenizer("Hello there" ,					return_tensors="np"   ).input_ids
											__a					:						Union[str, Any]      =					tokenizer("Hi I am" ,					return_tensors="np"   ).input_ids

											__a					:						Union[str, Any]      =					shift_tokens_right(__UpperCAmelCase ,					model.config.pad_token_id ,					model.config.decoder_start_token_id   )

											__a					:						Tuple      =					model(__UpperCAmelCase ,					decoder_input_ids=__UpperCAmelCase   ).logits
											__a					:						str      =					optax.softmax_cross_entropy(__UpperCAmelCase ,					onehot(__UpperCAmelCase ,					logits.shape[-1]   )   ).mean()

											__a					:						List[str]      =					-(labels.shape[-1] * loss.item())

											__a					:						Optional[int]      =					-84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1E-4   )





