



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_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 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					lowercase_								=		["""pixel_values"""]







					def __init__(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     bool = True				,       SCREAMING_SNAKE_CASE :     Dict[str, int] = None				,       SCREAMING_SNAKE_CASE :     float = None				,       SCREAMING_SNAKE_CASE :     PILImageResampling = PILImageResampling.BILINEAR				,       SCREAMING_SNAKE_CASE :     bool = True				,       SCREAMING_SNAKE_CASE :     Union[int, float] = 1 / 255				,       SCREAMING_SNAKE_CASE :     bool = True				,       SCREAMING_SNAKE_CASE :     Optional[Union[float, List[float]]] = None				,       SCREAMING_SNAKE_CASE :     Optional[Union[float, List[float]]] = None				,       **SCREAMING_SNAKE_CASE :     Tuple				,       ):
									super().__init__(**SCREAMING_SNAKE_CASE					)
									lowercase__					:   int													=					size if size is not None else {"shortest_edge": 384}
									lowercase__					:   Optional[Any]													=					get_size_dict(SCREAMING_SNAKE_CASE				,       default_to_square=SCREAMING_SNAKE_CASE					)

									lowercase__					:   List[str]													=					do_resize
									lowercase__					:   Optional[Any]													=					size
									# Default value set here for backwards compatibility where the value in config is None
									lowercase__					:   str													=					crop_pct if crop_pct is not None else 224 / 256
									lowercase__					:   int													=					resample
									lowercase__					:   Tuple													=					do_rescale
									lowercase__					:   Any													=					rescale_factor
									lowercase__					:   List[Any]													=					do_normalize
									lowercase__					:   Dict													=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									lowercase__					:   Optional[int]													=					image_std if image_std is not None else IMAGENET_STANDARD_STD







					def 			snake_case					(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     np.ndarray				,       SCREAMING_SNAKE_CASE :     Dict[str, int]				,       SCREAMING_SNAKE_CASE :     float				,       SCREAMING_SNAKE_CASE :     PILImageResampling = PILImageResampling.BICUBIC				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, ChannelDimension]] = None				,       **SCREAMING_SNAKE_CASE :     List[str]				,       ):
									lowercase__					:   Union[str, Any]													=					get_size_dict(SCREAMING_SNAKE_CASE				,       default_to_square=SCREAMING_SNAKE_CASE					)
									if "shortest_edge" not in size:
													raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""					)
									lowercase__					:   str													=					size["shortest_edge"]

									if shortest_edge < 384:
													# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
													lowercase__					:   Any													=					int(shortest_edge / crop_pct					)
													lowercase__					:   Any													=					get_resize_output_image_size(SCREAMING_SNAKE_CASE				,       size=SCREAMING_SNAKE_CASE				,       default_to_square=SCREAMING_SNAKE_CASE					)
													lowercase__					:   Optional[Any]													=					resize(image=SCREAMING_SNAKE_CASE				,       size=SCREAMING_SNAKE_CASE				,       resample=SCREAMING_SNAKE_CASE				,       data_format=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)
													# then crop to (shortest_edge, shortest_edge)
													return center_crop(image=SCREAMING_SNAKE_CASE				,       size=(shortest_edge, shortest_edge)				,       data_format=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)
									else:
													# warping (no cropping) when evaluated at 384 or larger
													return resize(
													    SCREAMING_SNAKE_CASE				,       size=(shortest_edge, shortest_edge)				,       resample=SCREAMING_SNAKE_CASE				,       data_format=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)







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







					def 			snake_case					(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     np.ndarray				,       SCREAMING_SNAKE_CASE :     Union[float, List[float]]				,       SCREAMING_SNAKE_CASE :     Union[float, List[float]]				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, ChannelDimension]] = None				,       **SCREAMING_SNAKE_CASE :     Optional[Any]				,       ):
									return normalize(SCREAMING_SNAKE_CASE				,       mean=SCREAMING_SNAKE_CASE				,       std=SCREAMING_SNAKE_CASE				,       data_format=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     ImageInput				,       SCREAMING_SNAKE_CASE :     bool = None				,       SCREAMING_SNAKE_CASE :     Dict[str, int] = None				,       SCREAMING_SNAKE_CASE :     float = None				,       SCREAMING_SNAKE_CASE :     PILImageResampling = None				,       SCREAMING_SNAKE_CASE :     bool = None				,       SCREAMING_SNAKE_CASE :     float = None				,       SCREAMING_SNAKE_CASE :     bool = None				,       SCREAMING_SNAKE_CASE :     Optional[Union[float, List[float]]] = None				,       SCREAMING_SNAKE_CASE :     Optional[Union[float, List[float]]] = None				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, TensorType]] = None				,       SCREAMING_SNAKE_CASE :     ChannelDimension = ChannelDimension.FIRST				,       **SCREAMING_SNAKE_CASE :     Any				,       ):
									lowercase__					:   Optional[int]													=					do_resize if do_resize is not None else self.do_resize
									lowercase__					:   Optional[Any]													=					crop_pct if crop_pct is not None else self.crop_pct
									lowercase__					:   Optional[int]													=					resample if resample is not None else self.resample
									lowercase__					:   List[Any]													=					do_rescale if do_rescale is not None else self.do_rescale
									lowercase__					:   Union[str, Any]													=					rescale_factor if rescale_factor is not None else self.rescale_factor
									lowercase__					:   List[str]													=					do_normalize if do_normalize is not None else self.do_normalize
									lowercase__					:   Any													=					image_mean if image_mean is not None else self.image_mean
									lowercase__					:   List[Any]													=					image_std if image_std is not None else self.image_std

									lowercase__					:   Any													=					size if size is not None else self.size
									lowercase__					:   str													=					get_size_dict(SCREAMING_SNAKE_CASE				,       default_to_square=SCREAMING_SNAKE_CASE					)

									lowercase__					:   Dict													=					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_resize and size is None or resample is None:
													raise ValueError("Size and resample must be specified if do_resize is True."					)

									if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
													raise ValueError("crop_pct must be specified if size < 384."					)

									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.
									lowercase__					:   Union[str, Any]													=					[to_numpy_array(SCREAMING_SNAKE_CASE					) for image in images]

									if do_resize:
													lowercase__					:   Dict													=					[self.resize(image=SCREAMING_SNAKE_CASE				,       size=SCREAMING_SNAKE_CASE				,       crop_pct=SCREAMING_SNAKE_CASE				,       resample=SCREAMING_SNAKE_CASE					) for image in images]

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

									if do_normalize:
													lowercase__					:   Any													=					[self.normalize(image=SCREAMING_SNAKE_CASE				,       mean=SCREAMING_SNAKE_CASE				,       std=SCREAMING_SNAKE_CASE					) for image in images]

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

									lowercase__					:   Optional[int]													=					{"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					)




