





'''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, 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


_SCREAMING_SNAKE_CASE			=							logging.get_logger(__name__)

class 				lowerCAmelCase_						(						__magic_name__						):
 __lowerCamelCase  :							Optional[int]    					=   ["pixel_values"]




 def __init__(		self     ,       _lowerCAmelCase = True     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = PIL.Image.NEAREST     ,       _lowerCAmelCase = False     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = 1 / 255     ,       _lowerCAmelCase = False     ,       _lowerCAmelCase = True     ,       _lowerCAmelCase = True     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = True     ,       **_lowerCAmelCase     ,       )      ->							None:
     super().__init__(**_lowerCAmelCase						)
     _lowerCAmelCase		     = size if size is not None else {"height": 346, "width": 346}
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase						)
     _lowerCAmelCase		     = crop_size if crop_size is not None else {"height": 289, "width": 289}
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase     ,       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		     = rescale_offset
     _lowerCAmelCase		     = do_normalize
     _lowerCAmelCase		     = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     _lowerCAmelCase		     = image_std if image_std is not None else IMAGENET_STANDARD_STD
     _lowerCAmelCase		     = include_top




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = PIL.Image.NEAREST     ,       _lowerCAmelCase = None     ,       **_lowerCAmelCase     ,       )      ->							np.ndarray:
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase						)
     if "height" not in size or "width" not in size:
         raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
     return resize(
         _lowerCAmelCase     ,       size=(size["height"], size["width"])     ,       resample=_lowerCAmelCase     ,       data_format=_lowerCAmelCase     ,       **_lowerCAmelCase						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = None     ,       **_lowerCAmelCase     ,       )      ->							np.ndarray:
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase						)
     if "height" not in size or "width" not in size:
         raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
     return center_crop(_lowerCAmelCase     ,       size=(size["height"], size["width"])     ,       data_format=_lowerCAmelCase     ,       **_lowerCAmelCase						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = True     ,       _lowerCAmelCase = None     ,       **_lowerCAmelCase     ,       )      ->							List[str]:
     if offset:
         _lowerCAmelCase		     = (image - 127.5) * scale
         if data_format is not None:
             _lowerCAmelCase		     = to_channel_dimension_format(_lowerCAmelCase     ,       _lowerCAmelCase						)
         _lowerCAmelCase		     = rescaled_image.astype(np.floataa						)
     else:
         _lowerCAmelCase		     = rescale(_lowerCAmelCase     ,       scale=_lowerCAmelCase     ,       data_format=_lowerCAmelCase     ,       **_lowerCAmelCase						)
     return rescaled_image




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = None     ,       **_lowerCAmelCase     ,       )      ->							np.ndarray:
     return normalize(_lowerCAmelCase     ,       mean=_lowerCAmelCase     ,       std=_lowerCAmelCase     ,       data_format=_lowerCAmelCase     ,       **_lowerCAmelCase						)





 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = ChannelDimension.FIRST     ,       **_lowerCAmelCase     ,       )      ->							PIL.Image.Image:
     _lowerCAmelCase		     = do_resize if do_resize is not None else self.do_resize
     _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		     = 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		     = rescale_offset if rescale_offset is not None else self.rescale_offset
     _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		     = include_top if include_top is not None else self.include_top

     _lowerCAmelCase		     = size if size is not None else self.size
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase						)
     _lowerCAmelCase		     = crop_size if crop_size is not None else self.crop_size
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase     ,       param_name="crop_size"						)

     _lowerCAmelCase		     = 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_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.
     _lowerCAmelCase		     = [to_numpy_array(_lowerCAmelCase						) for image in images]

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

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

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

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

     if include_top:
         _lowerCAmelCase		     = [self.normalize(image=_lowerCAmelCase     ,       mean=[0, 0, 0]     ,       std=_lowerCAmelCase						) for image in images]

     _lowerCAmelCase		     = [to_channel_dimension_format(_lowerCAmelCase     ,       _lowerCAmelCase						) for image in images]

     _lowerCAmelCase		     = {"pixel_values": images}
     return BatchFeature(data=_lowerCAmelCase     ,       tensor_type=_lowerCAmelCase						)









'''simple docstring'''


from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_SCREAMING_SNAKE_CASE			=							logging.get_logger(__name__)

class 				lowerCAmelCase_						(						__magic_name__						):
 __lowerCamelCase  :							str    					=   "mt5"
 __lowerCamelCase  :							Dict    					=   ["past_key_values"]




 def __init__(		self     ,       _lowerCAmelCase=250112     ,       _lowerCAmelCase=512     ,       _lowerCAmelCase=64     ,       _lowerCAmelCase=1024     ,       _lowerCAmelCase=8     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=6     ,       _lowerCAmelCase=32     ,       _lowerCAmelCase=128     ,       _lowerCAmelCase=0.1     ,       _lowerCAmelCase=1E-6     ,       _lowerCAmelCase=1.0     ,       _lowerCAmelCase="gated-gelu"     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase="T5Tokenizer"     ,       _lowerCAmelCase=False     ,       _lowerCAmelCase=0     ,       _lowerCAmelCase=1     ,       _lowerCAmelCase=0     ,       **_lowerCAmelCase     ,       )      ->							Optional[Any]:
     super().__init__(
         is_encoder_decoder=_lowerCAmelCase     ,       tokenizer_class=_lowerCAmelCase     ,       tie_word_embeddings=_lowerCAmelCase     ,       pad_token_id=_lowerCAmelCase     ,       eos_token_id=_lowerCAmelCase     ,       decoder_start_token_id=_lowerCAmelCase     ,       **_lowerCAmelCase     ,       )
     _lowerCAmelCase		     = vocab_size
     _lowerCAmelCase		     = d_model
     _lowerCAmelCase		     = d_kv
     _lowerCAmelCase		     = d_ff
     _lowerCAmelCase		     = num_layers
     _lowerCAmelCase		     = (
         num_decoder_layers if num_decoder_layers is not None else self.num_layers
     )  # default = symmetry
     _lowerCAmelCase		     = num_heads
     _lowerCAmelCase		     = relative_attention_num_buckets
     _lowerCAmelCase		     = relative_attention_max_distance
     _lowerCAmelCase		     = dropout_rate
     _lowerCAmelCase		     = layer_norm_epsilon
     _lowerCAmelCase		     = initializer_factor
     _lowerCAmelCase		     = feed_forward_proj
     _lowerCAmelCase		     = use_cache

     _lowerCAmelCase		     = self.feed_forward_proj.split("-"						)
     _lowerCAmelCase		     = act_info[-1]
     _lowerCAmelCase		     = act_info[0] == "gated"

     if len(_lowerCAmelCase						) > 1 and act_info[0] != "gated" or len(_lowerCAmelCase						) > 2:
         raise ValueError(
             f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
             "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
             "'gated-gelu' or 'relu'"						)

     # for backwards compatibility
     if feed_forward_proj == "gated-gelu":
         _lowerCAmelCase		     = "gelu_new"




 @property
 def 							_snake_case				(		self						)      ->							Any:
     return self.d_model




 @property
 def 							_snake_case				(		self						)      ->							Optional[Any]:
     return self.num_heads





 @property
 def 							_snake_case				(		self						)      ->							List[str]:
     return self.num_layers







class 				lowerCAmelCase_						(						__magic_name__						):




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def 							_snake_case				(		self						)      ->							Mapping[str, Mapping[int, str]]:
     _lowerCAmelCase		     = {
         "input_ids": {0: "batch", 1: "encoder_sequence"},
         "attention_mask": {0: "batch", 1: "encoder_sequence"},
     }
     if self.use_past:
         _lowerCAmelCase		     = "past_encoder_sequence + sequence"
         _lowerCAmelCase		     = {0: "batch"}
         _lowerCAmelCase		     = {0: "batch", 1: "past_decoder_sequence + sequence"}
     else:
         _lowerCAmelCase		     = {0: "batch", 1: "decoder_sequence"}
         _lowerCAmelCase		     = {0: "batch", 1: "decoder_sequence"}

     if self.use_past:
         self.fill_with_past_key_values_(_lowerCAmelCase     ,       direction="inputs"						)

     return common_inputs




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def 							_snake_case				(		self						)      ->							int:
     return 13





 @property
 def 							_snake_case				(		self						)      ->							float:
     return 5E-4


