


import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput



class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     int   ,  UpperCAmelCase				:     Dict   ,  UpperCAmelCase				:     List[Any]   ,  UpperCAmelCase				:     int):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						dataset
				SCREAMING_SNAKE_CASE_				:List[str]				     =						process
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						params






		def __len__(						self				:     Dict):
				return len(self.dataset)







		def __getitem__(						self				:     Optional[int]   ,  UpperCAmelCase				:     Any):
				SCREAMING_SNAKE_CASE_				:Any				     =						self.dataset[i]
				SCREAMING_SNAKE_CASE_				:List[str]				     =						self.process(UpperCAmelCase   ,  **self.params)
				return processed




class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     List[Any]   ,  UpperCAmelCase				:     str   ,  UpperCAmelCase				:     List[str]   ,  UpperCAmelCase				:     Any   ,  UpperCAmelCase				:     Optional[int]=None):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						loader
				SCREAMING_SNAKE_CASE_				:Any				     =						infer
				SCREAMING_SNAKE_CASE_				:Tuple				     =						params
				if loader_batch_size == 1:
						# Let's spare some time by deactivating altogether
						SCREAMING_SNAKE_CASE_				:int				     =						None
				SCREAMING_SNAKE_CASE_				:Tuple				     =						loader_batch_size

				# Internal bookkeeping
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						None
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						None






		def __len__(						self				:     List[Any]):
				return len(self.loader)






		def __iter__(						self				:     int):
				SCREAMING_SNAKE_CASE_				:str				     =						iter(self.loader)
				return self






		def _snake_case		(						self				:     int):
				if isinstance(self._loader_batch_data   ,  torch.Tensor):
						# Batch data is simple tensor, just fetch the slice
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self._loader_batch_data[self._loader_batch_index]
				else:
						# Batch data is assumed to be BaseModelOutput (or dict)
						SCREAMING_SNAKE_CASE_				:List[str]				     =						{}
						for k, element in self._loader_batch_data.items():
								if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
										# Convert ModelOutput to tuple first
										SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						element.to_tuple()
										if isinstance(element[0]   ,  torch.Tensor):
												SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
										elif isinstance(element[0]   ,  np.ndarray):
												SCREAMING_SNAKE_CASE_				:Tuple				     =						tuple(np.expand_dims(el[self._loader_batch_index]   ,  0) for el in element)
										continue
								if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(UpperCAmelCase   ,  UpperCAmelCase):
										# Those are stored as lists of tensors so need specific unbatching.
										if isinstance(element[0]   ,  torch.Tensor):
												SCREAMING_SNAKE_CASE_				:Optional[int]				     =						tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
										elif isinstance(element[0]   ,  np.ndarray):
												SCREAMING_SNAKE_CASE_				:List[str]				     =						tuple(np.expand_dims(el[self._loader_batch_index]   ,  0) for el in element)
										continue
								if element is None:
										# This can happen for optional data that get passed around
										SCREAMING_SNAKE_CASE_				:Tuple				     =						None
								elif isinstance(element[self._loader_batch_index]   ,  torch.Tensor):
										# Take correct batch data, but make it looked like batch_size=1
										# For compatibility with other methods within transformers

										SCREAMING_SNAKE_CASE_				:Any				     =						element[self._loader_batch_index].unsqueeze(0)
								elif isinstance(element[self._loader_batch_index]   ,  np.ndarray):
										# Take correct batch data, but make it looked like batch_size=1
										# For compatibility with other methods within transformers
										SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						np.expand_dims(element[self._loader_batch_index]   ,  0)
								else:
										# This is typically a list, so no need to `unsqueeze`.
										SCREAMING_SNAKE_CASE_				:Any				     =						element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self._loader_batch_data.__class__(UpperCAmelCase)
				self._loader_batch_index += 1
				return result







		def _snake_case		(						self				:     Optional[Any]):
				if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
						# We are currently unrolling a batch so we just need to return
						# the current item within a batch
						return self.loader_batch_item()

				# We're out of items within a batch
				SCREAMING_SNAKE_CASE_				:int				     =						next(self.iterator)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.infer(UpperCAmelCase   ,  **self.params)
				# We now have a batch of "inferred things".
				if self.loader_batch_size is not None:
						# Try to infer the size of the batch
						if isinstance(UpperCAmelCase   ,  torch.Tensor):
								SCREAMING_SNAKE_CASE_				:List[Any]				     =						processed
						else:
								SCREAMING_SNAKE_CASE_				:Dict				     =						list(processed.keys())[0]
								SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						processed[key]
						if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
								SCREAMING_SNAKE_CASE_				:Tuple				     =						len(UpperCAmelCase)
						else:
								SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						first_tensor.shape[0]
						if 0 < observed_batch_size < self.loader_batch_size:
								# could be last batch so we can't unroll as many
								# elements.
								SCREAMING_SNAKE_CASE_				:Tuple				     =						observed_batch_size
						# Setting internal index to unwrap the batch
						SCREAMING_SNAKE_CASE_				:Any				     =						processed
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						0
						return self.loader_batch_item()
				else:
						# We're not unrolling batches
						return processed




class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     List[str]   ,  UpperCAmelCase				:     int   ,  UpperCAmelCase				:     str   ,  UpperCAmelCase				:     Tuple   ,  UpperCAmelCase				:     int=None):
				super().__init__(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase)






		def __iter__(						self				:     Any):
				SCREAMING_SNAKE_CASE_				:str				     =						iter(self.loader)
				SCREAMING_SNAKE_CASE_				:str				     =						None
				return self







		def _snake_case		(						self				:     Union[str, Any]):
				if self.subiterator is None:
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.infer(next(self.iterator)   ,  **self.params)
				try:
						# Try to return next item
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						next(self.subiterator)
				except StopIteration:
						# When a preprocess iterator ends, we can start lookig at the next item
						# ChunkIterator will keep feeding until ALL elements of iterator
						# all have created their subiterator and have been iterating against.
						#
						# Another way to look at it, is we're basically flattening lists of lists
						# into a single list, but with generators
						SCREAMING_SNAKE_CASE_				:Dict				     =						self.infer(next(self.iterator)   ,  **self.params)
						SCREAMING_SNAKE_CASE_				:List[str]				     =						next(self.subiterator)
				return processed




class 						_UpperCAmelCase      (     lowercase							):






		def __iter__(						self				:     str):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						iter(self.loader)
				return self







		def _snake_case		(						self				:     Optional[int]):
				# Extremely similar to PipelineIterator in its unpacking mechanism
				# BUT, we have an extra required item which is the presence of `is_last`
				# That is because everything is flattened by `PipelineChunkIterator` we
				# need to keep track of how to regroup here in the original `process`
				# boundaries so that `process` and `postprocess` see the same data.

				# This iterator accumulates items (possibly while unbatching) until it
				# its a `is_last` and then just passes it on to the caller.
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						False
				SCREAMING_SNAKE_CASE_				:Any				     =						[]
				if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
						while self._loader_batch_index < self.loader_batch_size:
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.loader_batch_item()
								SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						item.pop("is_last")
								accumulator.append(UpperCAmelCase)
								if is_last:
										return accumulator

				while not is_last:
						SCREAMING_SNAKE_CASE_				:int				     =						self.infer(next(self.iterator)   ,  **self.params)
						if self.loader_batch_size is not None:
								if isinstance(UpperCAmelCase   ,  torch.Tensor):
										SCREAMING_SNAKE_CASE_				:int				     =						processed
								else:
										SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						list(processed.keys())[0]
										SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						processed[key]
								if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
										SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						len(UpperCAmelCase)
								else:
										SCREAMING_SNAKE_CASE_				:int				     =						first_tensor.shape[0]
								if 0 < observed_batch_size < self.loader_batch_size:
										# could be last batch so we can't unroll as many
										# elements.
										SCREAMING_SNAKE_CASE_				:List[Any]				     =						observed_batch_size
								SCREAMING_SNAKE_CASE_				:Any				     =						processed
								SCREAMING_SNAKE_CASE_				:Any				     =						0
								while self._loader_batch_index < self.loader_batch_size:
										SCREAMING_SNAKE_CASE_				:int				     =						self.loader_batch_item()
										SCREAMING_SNAKE_CASE_				:Tuple				     =						item.pop("is_last")
										accumulator.append(UpperCAmelCase)
										if is_last:
												return accumulator
						else:
								SCREAMING_SNAKE_CASE_				:Dict				     =						processed
								SCREAMING_SNAKE_CASE_				:Dict				     =						item.pop("is_last")
								accumulator.append(UpperCAmelCase)
				return accumulator




class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     List[str]   ,  UpperCAmelCase				:     Dataset   ,  UpperCAmelCase				:     str):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						dataset
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						key






		def __len__(						self				:     int):
				return len(self.dataset)







		def __getitem__(						self				:     List[str]   ,  UpperCAmelCase				:     str):
				return self.dataset[i][self.key]




class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     str   ,  UpperCAmelCase				:     Dataset   ,  UpperCAmelCase				:     str   ,  UpperCAmelCase				:     str):
				SCREAMING_SNAKE_CASE_				:Dict				     =						dataset
				SCREAMING_SNAKE_CASE_				:Dict				     =						keya
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						keya






		def __len__(						self				:     List[Any]):
				return len(self.dataset)







		def __getitem__(						self				:     Any   ,  UpperCAmelCase				:     Tuple):
				return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}










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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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__)


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Dict				     =						b.T
		SCREAMING_SNAKE_CASE_				:str				     =						np.sum(np.square(a )     , axis=1 )
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						np.sum(np.square(a )     , axis=0 )
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						np.matmul(a     , a )
		SCREAMING_SNAKE_CASE_				:Any				     =						aa[:, None] - 2 * ab + ba[None, :]
		return d


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:str				     =						x.reshape(-1     , 3 )
		SCREAMING_SNAKE_CASE_				:str				     =						squared_euclidean_distance(a     , a )
		return np.argmin(a     , axis=1 )



class 						_UpperCAmelCase      (     lowercase							):
		lowerCamelCase_	:			List[str]       			=  ["""pixel_values"""]






		def __init__(						self				:     Dict   ,  UpperCAmelCase				:     Optional[Union[List[List[int]], np.ndarray]] = None   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     Dict[str, int] = None   ,  UpperCAmelCase				:     PILImageResampling = PILImageResampling.BILINEAR   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     bool = True   ,  **UpperCAmelCase				:     Union[str, Any]   ,  ):
				super().__init__(**UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						size if size is not None else {"height": 2_56, "width": 2_56}
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						get_size_dict(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:int				     =						np.array(UpperCAmelCase) if clusters is not None else None
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						do_resize
				SCREAMING_SNAKE_CASE_				:Dict				     =						size
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						resample
				SCREAMING_SNAKE_CASE_				:Dict				     =						do_normalize
				SCREAMING_SNAKE_CASE_				:Tuple				     =						do_color_quantize






		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     np.ndarray   ,  UpperCAmelCase				:     Dict[str, int]   ,  UpperCAmelCase				:     PILImageResampling = PILImageResampling.BILINEAR   ,  UpperCAmelCase				:     Optional[Union[str, ChannelDimension]] = None   ,  **UpperCAmelCase				:     List[Any]   ,  ):
				SCREAMING_SNAKE_CASE_				:str				     =						get_size_dict(UpperCAmelCase)
				if "height" not in size or "width" not in size:
						raise ValueError(F"Size dictionary must contain both height and width keys. Got {size.keys()}")
				return resize(
				    UpperCAmelCase   ,  size=(size["height"], size["width"])   ,  resample=UpperCAmelCase   ,  data_format=UpperCAmelCase   ,  **UpperCAmelCase)






		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     np.ndarray   ,  UpperCAmelCase				:     Optional[Union[str, ChannelDimension]] = None   ,  ):
				SCREAMING_SNAKE_CASE_				:int				     =						rescale(image=UpperCAmelCase   ,  scale=1 / 127.5   ,  data_format=UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						image - 1
				return image







		def _snake_case		(						self				:     List[str]   ,  UpperCAmelCase				:     ImageInput   ,  UpperCAmelCase				:     bool = None   ,  UpperCAmelCase				:     Dict[str, int] = None   ,  UpperCAmelCase				:     PILImageResampling = None   ,  UpperCAmelCase				:     bool = None   ,  UpperCAmelCase				:     Optional[bool] = None   ,  UpperCAmelCase				:     Optional[Union[List[List[int]], np.ndarray]] = None   ,  UpperCAmelCase				:     Optional[Union[str, TensorType]] = None   ,  UpperCAmelCase				:     Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST   ,  **UpperCAmelCase				:     Optional[Any]   ,  ):
				SCREAMING_SNAKE_CASE_				:Any				     =						do_resize if do_resize is not None else self.do_resize
				SCREAMING_SNAKE_CASE_				:Dict				     =						size if size is not None else self.size
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						get_size_dict(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						resample if resample is not None else self.resample
				SCREAMING_SNAKE_CASE_				:Dict				     =						do_normalize if do_normalize is not None else self.do_normalize
				SCREAMING_SNAKE_CASE_				:List[str]				     =						do_color_quantize if do_color_quantize is not None else self.do_color_quantize
				SCREAMING_SNAKE_CASE_				:Tuple				     =						clusters if clusters is not None else self.clusters
				SCREAMING_SNAKE_CASE_				:Tuple				     =						np.array(UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Dict				     =						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 or resample is None:
						raise ValueError("Size and resample must be specified if do_resize is True.")

				if do_color_quantize and clusters is None:
						raise ValueError("Clusters must be specified if do_color_quantize is True.")

				# All transformations expect numpy arrays.
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[to_numpy_array(UpperCAmelCase) for image in images]

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

				if do_normalize:
						SCREAMING_SNAKE_CASE_				:int				     =						[self.normalize(image=UpperCAmelCase) for image in images]

				if do_color_quantize:
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						[to_channel_dimension_format(UpperCAmelCase   ,  ChannelDimension.LAST) for image in images]
						# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						np.array(UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:Tuple				     =						color_quantize(UpperCAmelCase   ,  UpperCAmelCase).reshape(images.shape[:-1])

						# flatten to (batch_size, height*width)
						SCREAMING_SNAKE_CASE_				:Any				     =						images.shape[0]
						SCREAMING_SNAKE_CASE_				:Dict				     =						images.reshape(UpperCAmelCase   ,  -1)

						# We need to convert back to a list of images to keep consistent behaviour across processors.
						SCREAMING_SNAKE_CASE_				:Any				     =						list(UpperCAmelCase)
				else:
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						[to_channel_dimension_format(UpperCAmelCase   ,  UpperCAmelCase) for image in images]

				SCREAMING_SNAKE_CASE_				:str				     =						{"input_ids": images}
				return BatchFeature(data=UpperCAmelCase   ,  tensor_type=UpperCAmelCase)






