



from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


lowerCAmelCase__					  =  logging.get_logger(__name__)

class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					lowercase_								=		["""pixel_values"""]







					def __init__(							self :     str				,       SCREAMING_SNAKE_CASE :     bool = True				,       SCREAMING_SNAKE_CASE :     Union[int, float] = 1 / 255				,       SCREAMING_SNAKE_CASE :     bool = True				,       SCREAMING_SNAKE_CASE :     int = 8				,       **SCREAMING_SNAKE_CASE :     Optional[Any]				,       ):
									super().__init__(**SCREAMING_SNAKE_CASE					)

									lowercase__					:   Union[str, Any]													=					do_rescale
									lowercase__					:   Union[str, Any]													=					rescale_factor
									lowercase__					:   Union[str, Any]													=					do_pad
									lowercase__					:   Tuple													=					pad_size







					def 			snake_case					(							self :     int				,       SCREAMING_SNAKE_CASE :     np.ndarray				,       SCREAMING_SNAKE_CASE :     float				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, ChannelDimension]] = None				,       **SCREAMING_SNAKE_CASE :     Union[str, Any]					):
									return rescale(SCREAMING_SNAKE_CASE				,       scale=SCREAMING_SNAKE_CASE				,       data_format=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Dict				,       SCREAMING_SNAKE_CASE :     np.ndarray				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, ChannelDimension]] = None					):
									lowercase__							,							lowercase__					:   Optional[int]													=					get_image_size(SCREAMING_SNAKE_CASE					)
									lowercase__					:   Any													=					(old_height // size + 1) * size - old_height
									lowercase__					:   List[str]													=					(old_width // size + 1) * size - old_width

									return pad(SCREAMING_SNAKE_CASE				,       ((0, pad_height), (0, pad_width))				,       mode="symmetric"				,       data_format=SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     ImageInput				,       SCREAMING_SNAKE_CASE :     Optional[bool] = None				,       SCREAMING_SNAKE_CASE :     Optional[float] = None				,       SCREAMING_SNAKE_CASE :     Optional[bool] = None				,       SCREAMING_SNAKE_CASE :     Optional[int] = None				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, TensorType]] = None				,       SCREAMING_SNAKE_CASE :     Union[str, ChannelDimension] = ChannelDimension.FIRST				,       **SCREAMING_SNAKE_CASE :     int				,       ):
									lowercase__					:   int													=					do_rescale if do_rescale is not None else self.do_rescale
									lowercase__					:   str													=					rescale_factor if rescale_factor is not None else self.rescale_factor
									lowercase__					:   Dict													=					do_pad if do_pad is not None else self.do_pad
									lowercase__					:   List[Any]													=					pad_size if pad_size is not None else self.pad_size

									lowercase__					:   List[str]													=					make_list_of_images(SCREAMING_SNAKE_CASE					)

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

									# All transformations expect numpy arrays.
									lowercase__					:   int													=					[to_numpy_array(SCREAMING_SNAKE_CASE					) for image in images]

									if do_rescale:
													lowercase__					:   int													=					[self.rescale(image=SCREAMING_SNAKE_CASE				,       scale=SCREAMING_SNAKE_CASE					) for image in images]

									if do_pad:
													lowercase__					:   str													=					[self.pad(SCREAMING_SNAKE_CASE				,       size=SCREAMING_SNAKE_CASE					) for image in images]

									lowercase__					:   Any													=					[to_channel_dimension_format(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					) for image in images]

									lowercase__					:   Optional[Any]													=					{"pixel_values": images}
									return BatchFeature(data=SCREAMING_SNAKE_CASE				,       tensor_type=SCREAMING_SNAKE_CASE					)









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 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   Dict													=					FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"					)
									lowercase__					:   Dict													=					AutoTokenizer.from_pretrained("google/mt5-small"					)

									lowercase__					:   Optional[Any]													=					tokenizer("Hello there"				,       return_tensors="np"					).input_ids
									lowercase__					:   Optional[Any]													=					tokenizer("Hi I am"				,       return_tensors="np"					).input_ids

									lowercase__					:   int													=					shift_tokens_right(SCREAMING_SNAKE_CASE				,       model.config.pad_token_id				,       model.config.decoder_start_token_id					)

									lowercase__					:   Tuple													=					model(SCREAMING_SNAKE_CASE				,       decoder_input_ids=SCREAMING_SNAKE_CASE					).logits
									lowercase__					:   Dict													=					optax.softmax_cross_entropy(SCREAMING_SNAKE_CASE				,       onehot(SCREAMING_SNAKE_CASE				,       logits.shape[-1]					)					).mean()

									lowercase__					:   Union[str, Any]													=					-(labels.shape[-1] * loss.item())

									lowercase__					:   Union[str, Any]													=					-84.9_127
									self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 1E-4					)




