
from typing import Dict, List, Optional, Tuple, 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_torch_available, is_torch_tensor, logging


if is_torch_available():
			import torch


_A   =     logging.get_logger(__name__)





class 	_lowerCAmelCase					(	__a       ):
				_lowercase					=['''pixel_values''']




				def __init__(					self				,				_UpperCamelCase = True				,				_UpperCamelCase = None				,				_UpperCamelCase = PILImageResampling.BILINEAR				,				_UpperCamelCase = True				,				_UpperCamelCase = None				,				_UpperCamelCase = True				,				_UpperCamelCase = 1 / 255				,				_UpperCamelCase = True				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				**_UpperCamelCase				,				)				->      None:
							super().__init__(**_UpperCamelCase     )
							lowerCAmelCase_						=	size if size is not None else {"shortest_edge": 256}
							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				,				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 IMAGENET_STANDARD_MEAN
							lowerCAmelCase_						=	image_std if image_std is not None else IMAGENET_STANDARD_STD




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase = PILImageResampling.BICUBIC				,				_UpperCamelCase = None				,				**_UpperCamelCase				,				)				->      np.ndarray:
							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      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase = None				,				**_UpperCamelCase				,				)				->      np.ndarray:
							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` and `width`. Got {size.keys()}"""     )
							return center_crop(_UpperCamelCase				,				size=(size["height"], size["width"])				,				data_format=_UpperCamelCase				,				**_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase = None				,				**_UpperCamelCase     )				->      np.ndarray:
							return rescale(_UpperCamelCase				,				scale=_UpperCamelCase				,				data_format=_UpperCamelCase				,				**_UpperCamelCase     )




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




				def      __a						(					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 = ChannelDimension.FIRST				,				**_UpperCamelCase				,				)				->      Dict:
							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				,				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"     )
							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_						=	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."     )

							# 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     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase = None     )				->      Union[str, Any]:
							lowerCAmelCase_						=	outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
										if len(_UpperCamelCase     ) != len(_UpperCamelCase     ):
													raise ValueError(
													    "Make sure that you pass in as many target sizes as the batch dimension of the logits"     )

										if is_torch_tensor(_UpperCamelCase     ):
													lowerCAmelCase_						=	target_sizes.numpy()

										lowerCAmelCase_						=	[]

										for idx in range(len(_UpperCamelCase     )     ):
													lowerCAmelCase_						=	torch.nn.functional.interpolate(
													    logits[idx].unsqueeze(dim=0     )				,				size=target_sizes[idx]				,				mode="bilinear"				,				align_corners=_UpperCamelCase     )
													lowerCAmelCase_						=	resized_logits[0].argmax(dim=0     )
													semantic_segmentation.append(_UpperCamelCase     )
							else:
										lowerCAmelCase_						=	logits.argmax(dim=1     )
										lowerCAmelCase_						=	[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]     )]

							return semantic_segmentation





import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType





class 	_lowerCAmelCase					:




				def __init__(					self				,				_UpperCamelCase=2				,				_UpperCamelCase=3				,				_UpperCamelCase=64				,				_UpperCamelCase=None     )				->      Optional[Any]:
							lowerCAmelCase_						=	np.random.default_rng(_UpperCamelCase     )
							lowerCAmelCase_						=	length
							lowerCAmelCase_						=	rng.normal(size=(length,)     ).astype(np.floataa     )
							lowerCAmelCase_						=	a * self.x + b + rng.normal(scale=0.1				,				size=(length,)     ).astype(np.floataa     )




				def __len__(					self     )				->      int:
							return self.length




				def __getitem__(					self				,				_UpperCamelCase     )				->      Dict:
							return {"x": self.x[i], "y": self.y[i]}





class 	_lowerCAmelCase					(	torch.nn.Module       ):




				def __init__(					self				,				_UpperCamelCase=0				,				_UpperCamelCase=0				,				_UpperCamelCase=False     )				->      List[Any]:
							super().__init__()
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase_						=	True




				def      __a						(					self				,				_UpperCamelCase=None     )				->      Any:
							if self.first_batch:
										print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
										lowerCAmelCase_						=	False
							return x * self.a[0] + self.b[0]





class 	_lowerCAmelCase					(	torch.nn.Module       ):




				def __init__(					self				,				_UpperCamelCase=0				,				_UpperCamelCase=0				,				_UpperCamelCase=False     )				->      Optional[int]:
							super().__init__()
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor(_UpperCamelCase     ).float()     )
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor(_UpperCamelCase     ).float()     )
							lowerCAmelCase_						=	True




				def      __a						(					self				,				_UpperCamelCase=None     )				->      int:
							if self.first_batch:
										print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
										lowerCAmelCase_						=	False
							return x * self.a + self.b






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[Any]    ,							__lowerCAmelCase  :  int = 16				):




			"""simple docstring"""


			from datasets import load_dataset
			from transformers import AutoTokenizer

			lowerCAmelCase_						=	AutoTokenizer.from_pretrained("bert-base-cased"				)
			lowerCAmelCase_						=	{"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
			lowerCAmelCase_						=	load_dataset("csv"    ,							data_files=__lowerCAmelCase				)
			lowerCAmelCase_						=	datasets["train"].unique("label"				)

			lowerCAmelCase_						=	{v: i for i, v in enumerate(__lowerCAmelCase				)}

			def tokenize_function(__lowerCAmelCase  :  List[Any]				):
						# max_length=None => use the model max length (it's actually the default)
						lowerCAmelCase_						=	tokenizer(
						    examples["sentence1"]    ,							examples["sentence2"]    ,							truncation=__lowerCAmelCase    ,							max_length=__lowerCAmelCase    ,							padding="max_length"				)
						if "label" in examples:
									lowerCAmelCase_						=	[label_to_id[l] for l in examples["label"]]
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			lowerCAmelCase_						=	datasets.map(
			    __lowerCAmelCase    ,							batched=__lowerCAmelCase    ,							remove_columns=["sentence1", "sentence2", "label"]    ,							)

			def collate_fn(__lowerCAmelCase  :  Optional[int]				):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(__lowerCAmelCase    ,							padding="max_length"    ,							max_length=128    ,							return_tensors="pt"				)
						return tokenizer.pad(__lowerCAmelCase    ,							padding="longest"    ,							return_tensors="pt"				)

			# Instantiate dataloaders.
			lowerCAmelCase_						=	DataLoader(tokenized_datasets["train"]    ,							shuffle=__lowerCAmelCase    ,							collate_fn=__lowerCAmelCase    ,							batch_size=2				)
			lowerCAmelCase_						=	DataLoader(tokenized_datasets["validation"]    ,							shuffle=__lowerCAmelCase    ,							collate_fn=__lowerCAmelCase    ,							batch_size=1				)

			return train_dataloader, eval_dataloader



