
"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


A_    							=					logging.get_logger(__name__)


if is_vision_available():
						import PIL




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      int          =    ['pixel_values']




				def __init__(				self							,						UpperCAmelCase = True							,						UpperCAmelCase = None							,						UpperCAmelCase = PILImageResampling.BICUBIC							,						UpperCAmelCase = True							,						UpperCAmelCase = None							,						UpperCAmelCase = True							,						UpperCAmelCase = 1 / 255							,						UpperCAmelCase = True							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = True							,						**UpperCAmelCase							,						):
							super().__init__(**UpperCAmelCase   )
							lowerCamelCase_					=  size if size is not None else {'''shortest_edge''': 224}
							lowerCamelCase_					=  get_size_dict(UpperCAmelCase							,						default_to_square=UpperCAmelCase   )
							lowerCamelCase_					=  crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
							lowerCamelCase_					=  get_size_dict(UpperCAmelCase							,						default_to_square=UpperCAmelCase							,						param_name='''crop_size'''   )

							lowerCamelCase_					=  do_resize
							lowerCamelCase_					=  size
							lowerCamelCase_					=  resample
							lowerCamelCase_					=  do_center_crop
							lowerCamelCase_					=  crop_size
							lowerCamelCase_					=  do_rescale
							lowerCamelCase_					=  rescale_factor
							lowerCamelCase_					=  do_normalize
							lowerCamelCase_					=  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							lowerCamelCase_					=  image_std if image_std is not None else OPENAI_CLIP_STD
							lowerCamelCase_					=  do_convert_rgb




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase = PILImageResampling.BICUBIC							,						UpperCAmelCase = None							,						**UpperCAmelCase							,						):
							lowerCamelCase_					=  get_size_dict(UpperCAmelCase							,						default_to_square=UpperCAmelCase   )
							if "shortest_edge" not in size:
										raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"   )
							lowerCamelCase_					=  get_resize_output_image_size(UpperCAmelCase							,						size=size['''shortest_edge''']							,						default_to_square=UpperCAmelCase   )
							return resize(UpperCAmelCase							,						size=UpperCAmelCase							,						resample=UpperCAmelCase							,						data_format=UpperCAmelCase							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase = None							,						**UpperCAmelCase							,						):
							lowerCamelCase_					=  get_size_dict(UpperCAmelCase   )
							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(UpperCAmelCase							,						size=(size['''height'''], size['''width'''])							,						data_format=UpperCAmelCase							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase = None							,						**UpperCAmelCase							,						):
							return rescale(UpperCAmelCase							,						scale=UpperCAmelCase							,						data_format=UpperCAmelCase							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase = None							,						**UpperCAmelCase							,						):
							return normalize(UpperCAmelCase							,						mean=UpperCAmelCase							,						std=UpperCAmelCase							,						data_format=UpperCAmelCase							,						**UpperCAmelCase   )





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = ChannelDimension.FIRST							,						**UpperCAmelCase							,						):
							lowerCamelCase_					=  do_resize if do_resize is not None else self.do_resize
							lowerCamelCase_					=  size if size is not None else self.size
							lowerCamelCase_					=  get_size_dict(UpperCAmelCase							,						param_name='''size'''							,						default_to_square=UpperCAmelCase   )
							lowerCamelCase_					=  resample if resample is not None else self.resample
							lowerCamelCase_					=  do_center_crop if do_center_crop is not None else self.do_center_crop
							lowerCamelCase_					=  crop_size if crop_size is not None else self.crop_size
							lowerCamelCase_					=  get_size_dict(UpperCAmelCase							,						param_name='''crop_size'''							,						default_to_square=UpperCAmelCase   )
							lowerCamelCase_					=  do_rescale if do_rescale is not None else self.do_rescale
							lowerCamelCase_					=  rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCamelCase_					=  do_normalize if do_normalize is not None else self.do_normalize
							lowerCamelCase_					=  image_mean if image_mean is not None else self.image_mean
							lowerCamelCase_					=  image_std if image_std is not None else self.image_std
							lowerCamelCase_					=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							lowerCamelCase_					=  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.'''   )

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
										lowerCamelCase_					=  [convert_to_rgb(UpperCAmelCase   ) for image in images]

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

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

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

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

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

							lowerCamelCase_					=  [to_channel_dimension_format(UpperCAmelCase							,						UpperCAmelCase   ) for image in images]

							lowerCamelCase_					=  {'''pixel_values''': images}
							return BatchFeature(data=UpperCAmelCase							,						tensor_type=UpperCAmelCase   )





"""simple docstring"""


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  __lowerCamelCase  ( unittest.TestCase			):




				@slow
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''   )
							lowerCamelCase_					=  AutoTokenizer.from_pretrained('''google/mt5-small'''   )

							lowerCamelCase_					=  tokenizer('''Hello there'''							,						return_tensors='''np'''   ).input_ids
							lowerCamelCase_					=  tokenizer('''Hi I am'''							,						return_tensors='''np'''   ).input_ids

							lowerCamelCase_					=  shift_tokens_right(UpperCAmelCase							,						model.config.pad_token_id							,						model.config.decoder_start_token_id   )

							lowerCamelCase_					=  model(UpperCAmelCase							,						decoder_input_ids=UpperCAmelCase   ).logits
							lowerCamelCase_					=  optax.softmax_cross_entropy(UpperCAmelCase							,						onehot(UpperCAmelCase							,						logits.shape[-1]   )   ).mean()

							lowerCamelCase_					=  -(labels.shape[-1] * loss.item())

							lowerCamelCase_					=  -8_4.9_1_2_7
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1e-4   )



