





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 						UpperCamelCase_  (  unittest.TestCase							):



					@slow
					def   __a     (		self     :     Optional[Any]						):
							lowerCamelCase_	:     int 						=      FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small'						)
							lowerCamelCase_	:     List[str] 						=      AutoTokenizer.from_pretrained('google/mt5-small'						)

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

							lowerCamelCase_	:     Any 						=      shift_tokens_right(lowerCamelCase       ,      model.config.pad_token_id       ,      model.config.decoder_start_token_id						)

							lowerCamelCase_	:     List[Any] 						=      model(lowerCamelCase       ,      decoder_input_ids=lowerCamelCase						).logits
							lowerCamelCase_	:     List[Any] 						=      optax.softmax_cross_entropy(lowerCamelCase       ,      onehot(lowerCamelCase       ,      logits.shape[-1]						)						).mean()

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

							lowerCamelCase_	:     List[str] 						=      -84.9_127
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 1E-4						)







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 convert_to_rgb, 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


if is_vision_available():
		import PIL


_lowercase		:  Tuple			       =logging.get_logger(__name__)






class 						UpperCamelCase_  (  snake_case__							):
					_a      :						List[str]						    =					['pixel_values']



					def __init__(		self     :     List[Any]       ,      lowerCamelCase     :     bool = True       ,      lowerCamelCase     :     Dict[str, int] = None       ,      lowerCamelCase     :     PILImageResampling = PILImageResampling.BICUBIC       ,      lowerCamelCase     :     bool = True       ,      lowerCamelCase     :     Union[int, float] = 1 / 2_55       ,      lowerCamelCase     :     bool = True       ,      lowerCamelCase     :     Optional[Union[float, List[float]]] = None       ,      lowerCamelCase     :     Optional[Union[float, List[float]]] = None       ,      lowerCamelCase     :     bool = True       ,      **lowerCamelCase     :     Optional[Any]       ,      ):
							super().__init__(**lowerCamelCase						)
							lowerCamelCase_	:     List[Any] 						=      size if size is not None else {'height': 3_84, 'width': 3_84}
							lowerCamelCase_	:     List[str] 						=      get_size_dict(lowerCamelCase       ,      default_to_square=lowerCamelCase						)

							lowerCamelCase_	:     Dict 						=      do_resize
							lowerCamelCase_	:     int 						=      size
							lowerCamelCase_	:     Union[str, Any] 						=      resample
							lowerCamelCase_	:     Tuple 						=      do_rescale
							lowerCamelCase_	:     Dict 						=      rescale_factor
							lowerCamelCase_	:     int 						=      do_normalize
							lowerCamelCase_	:     Any 						=      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							lowerCamelCase_	:     str 						=      image_std if image_std is not None else OPENAI_CLIP_STD
							lowerCamelCase_	:     int 						=      do_convert_rgb



					def   __a     (		self     :     Tuple       ,      lowerCamelCase     :     np.ndarray       ,      lowerCamelCase     :     Dict[str, int]       ,      lowerCamelCase     :     PILImageResampling = PILImageResampling.BICUBIC       ,      lowerCamelCase     :     Optional[Union[str, ChannelDimension]] = None       ,      **lowerCamelCase     :     List[Any]       ,      ):
							lowerCamelCase_	:     List[Any] 						=      get_size_dict(lowerCamelCase       ,      default_to_square=lowerCamelCase						)
							if "height" not in size or "width" not in size:
									raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"						)
							lowerCamelCase_	:     Optional[Any] 						=      (size['height'], size['width'])
							return resize(lowerCamelCase       ,      size=lowerCamelCase       ,      resample=lowerCamelCase       ,      data_format=lowerCamelCase       ,      **lowerCamelCase						)



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



					def   __a     (		self     :     str       ,      lowerCamelCase     :     np.ndarray       ,      lowerCamelCase     :     Union[float, List[float]]       ,      lowerCamelCase     :     Union[float, List[float]]       ,      lowerCamelCase     :     Optional[Union[str, ChannelDimension]] = None       ,      **lowerCamelCase     :     Optional[int]       ,      ):
							return normalize(lowerCamelCase       ,      mean=lowerCamelCase       ,      std=lowerCamelCase       ,      data_format=lowerCamelCase       ,      **lowerCamelCase						)





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

							lowerCamelCase_	:     Union[str, Any] 						=      size if size is not None else self.size
							lowerCamelCase_	:     Optional[int] 						=      get_size_dict(lowerCamelCase       ,      default_to_square=lowerCamelCase						)

							lowerCamelCase_	:     Dict 						=      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_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_	:     int 						=      [convert_to_rgb(lowerCamelCase						) for image in images]

							# All transformations expect numpy arrays.
							lowerCamelCase_	:     List[Any] 						=      [to_numpy_array(lowerCamelCase						) for image in images]

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

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

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

							lowerCamelCase_	:     Optional[int] 						=      [to_channel_dimension_format(lowerCamelCase       ,      lowerCamelCase						) for image in images]

							lowerCamelCase_	:     Optional[int] 						=      BatchFeature(data={'pixel_values': images}       ,      tensor_type=lowerCamelCase						)

							return encoded_outputs
