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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    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 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					['pixel_values']


	def __init__(   self       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = PILImageResampling.BICUBIC       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = 1 / 255       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  **lowerCAmelCase_       ,  ):
		super().__init__(**lowerCAmelCase_       )
		_lowercase   =crop_size if crop_size is not None else {"height": 256, "width": 256}
		_lowercase   =get_size_dict(lowerCAmelCase_       ,  param_name="crop_size"       )
		_lowercase   =size if size is not None else {"height": 224, "width": 224}
		_lowercase   =get_size_dict(lowerCAmelCase_       )

		_lowercase   =do_center_crop
		_lowercase   =crop_size
		_lowercase   =do_resize
		_lowercase   =size
		_lowercase   =resample
		_lowercase   =do_rescale
		_lowercase   =rescale_factor
		_lowercase   =do_normalize
		_lowercase   =image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
		_lowercase   =image_std if image_std is not None else IMAGENET_DEFAULT_STD


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  **lowerCAmelCase_       ,  ):
		_lowercase   =self.size if size is None else size
		_lowercase   =get_size_dict(lowerCAmelCase_       )
		_lowercase   =get_size_dict(lowerCAmelCase_       ,  param_name="crop_size"       )

		_lowercase   ,  _lowercase   =get_image_size(lowerCAmelCase_       )
		_lowercase   =min(lowerCAmelCase_       ,  lowerCAmelCase_       )
		_lowercase   =(size["height"] / crop_size["height"]) * min_dim
		_lowercase   =(size["width"] / crop_size["width"]) * min_dim
		return center_crop(lowerCAmelCase_       ,  size=(cropped_height, cropped_width)       ,  data_format=lowerCAmelCase_       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = PIL.Image.BICUBIC       ,  lowerCAmelCase_ = None       ,  **lowerCAmelCase_       ,  ):
		_lowercase   =get_size_dict(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()}'''       )
		return resize(
		    lowerCAmelCase_       ,  size=(size["height"], size["width"])       ,  resample=lowerCAmelCase_       ,  data_format=lowerCAmelCase_       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ,  **lowerCAmelCase_       ,  ):
		return rescale(lowerCAmelCase_       ,  scale=lowerCAmelCase_       ,  data_format=lowerCAmelCase_       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ,  **lowerCAmelCase_       ,  ):
		return normalize(lowerCAmelCase_       ,  mean=lowerCAmelCase_       ,  std=lowerCAmelCase_       ,  data_format=lowerCAmelCase_       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   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_ = ChannelDimension.FIRST       ,  **lowerCAmelCase_       ,  ):
		_lowercase   =do_center_crop if do_center_crop is not None else self.do_center_crop
		_lowercase   =crop_size if crop_size is not None else self.crop_size
		_lowercase   =get_size_dict(lowerCAmelCase_       ,  param_name="crop_size"       )
		_lowercase   =do_resize if do_resize is not None else self.do_resize
		_lowercase   =size if size is not None else self.size
		_lowercase   =get_size_dict(lowerCAmelCase_       )
		_lowercase   =resample if resample is not None else self.resample
		_lowercase   =do_rescale if do_rescale is not None else self.do_rescale
		_lowercase   =rescale_factor if rescale_factor is not None else self.rescale_factor
		_lowercase   =do_normalize if do_normalize is not None else self.do_normalize
		_lowercase   =image_mean if image_mean is not None else self.image_mean
		_lowercase   =image_std if image_std is not None else self.image_std

		_lowercase   =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_center_crop and crop_size is None:
			raise ValueError("If `do_center_crop` is set to `True`, `crop_size` must be provided."       )

		if do_resize and size is None:
			raise ValueError("Size 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 image standard deviation must be specified if do_normalize is True."       )

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

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

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

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

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

		_lowercase   =[to_channel_dimension_format(lowerCAmelCase_       ,  lowerCAmelCase_       ) for image in images]

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





from typing import Mapping

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


lowerCAmelCase__  	=      logging.get_logger(__name__)

lowerCAmelCase__  	=      {
    "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
    "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
    "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
    "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
}
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					'longt5'
	__SCREAMING_SNAKE_CASE              =					['past_key_values']
	__SCREAMING_SNAKE_CASE              =					{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


	def __init__(   self       ,  lowerCAmelCase_=32128       ,  lowerCAmelCase_=512       ,  lowerCAmelCase_=64       ,  lowerCAmelCase_=2048       ,  lowerCAmelCase_=6       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=8       ,  lowerCAmelCase_=127       ,  lowerCAmelCase_=16       ,  lowerCAmelCase_=32       ,  lowerCAmelCase_=128       ,  lowerCAmelCase_=0.1       ,  lowerCAmelCase_=1e-6       ,  lowerCAmelCase_=1.0       ,  lowerCAmelCase_="relu"       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_="local"       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=0       ,  lowerCAmelCase_=1       ,  **lowerCAmelCase_       ,  ):
		_lowercase   =vocab_size
		_lowercase   =d_model
		_lowercase   =d_kv
		_lowercase   =d_ff
		_lowercase   =num_layers
		# default = symmetry
		_lowercase   =num_decoder_layers if num_decoder_layers is not None else self.num_layers
		_lowercase   =num_heads
		_lowercase   =local_radius
		_lowercase   =global_block_size
		_lowercase   =relative_attention_num_buckets
		_lowercase   =relative_attention_max_distance
		_lowercase   =dropout_rate
		_lowercase   =layer_norm_epsilon
		_lowercase   =initializer_factor
		_lowercase   =feed_forward_proj
		_lowercase   =encoder_attention_type
		_lowercase   =use_cache

		_lowercase   =self.feed_forward_proj.split("-"       )
		_lowercase   =act_info[-1]
		_lowercase   =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":
			_lowercase   ="gelu_new"

		super().__init__(
		    pad_token_id=lowerCAmelCase_       ,  eos_token_id=lowerCAmelCase_       ,  is_encoder_decoder=lowerCAmelCase_       ,  **lowerCAmelCase_       ,  )

class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	@property
	def __lowerCAmelCase      (   self       ):
		_lowercase   ={
		    "input_ids": {0: "batch", 1: "encoder_sequence"},
		    "attention_mask": {0: "batch", 1: "encoder_sequence"},
		}
		if self.use_past:
			_lowercase   ="past_encoder_sequence + sequence"
			_lowercase   ={0: "batch"}
			_lowercase   ={0: "batch", 1: "past_decoder_sequence + sequence"}
		else:
			_lowercase   ={0: "batch", 1: "decoder_sequence"}
			_lowercase   ={0: "batch", 1: "decoder_sequence"}

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

		return common_inputs


	@property
	def __lowerCAmelCase      (   self       ):
		return 13




