



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

from ..utils.generic import ModelOutput



class   UpperCAmelCase_    (       a):







							def __init__(   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[str]    =    dataset
														_lowerCAmelCase     :							str    =    process
														_lowerCAmelCase     :							Optional[Any]    =    params







							def __len__(   self):

														'''simple docstring'''

														return len(self.dataset)







							def __getitem__(   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    self.dataset[i]
														_lowerCAmelCase     :							Tuple    =    self.process(__a,			**self.params)
														return processed



class   UpperCAmelCase_    (       a):







							def __init__(   self,			__a,			__a,			__a,			__a=None):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    loader
														_lowerCAmelCase     :							List[Any]    =    infer
														_lowerCAmelCase     :							Optional[int]    =    params
														if loader_batch_size == 1:
																					# Let's spare some time by deactivating altogether
																					_lowerCAmelCase     :							Optional[int]    =    None
														_lowerCAmelCase     :							List[Any]    =    loader_batch_size

														# Internal bookkeeping
														_lowerCAmelCase     :							Tuple    =    None
														_lowerCAmelCase     :							List[str]    =    None







							def __len__(   self):

														'''simple docstring'''

														return len(self.loader)







							def __iter__(   self):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    iter(self.loader)
														return self







							def    snake_case__      (   self):

														'''simple docstring'''

														if isinstance(self._loader_batch_data,			torch.Tensor):
																					# Batch data is simple tensor, just fetch the slice
																					_lowerCAmelCase     :							List[Any]    =    self._loader_batch_data[self._loader_batch_index]
														else:
																					# Batch data is assumed to be BaseModelOutput (or dict)
																					_lowerCAmelCase     :							Union[str, Any]    =    {}
																					for k, element in self._loader_batch_data.items():
																												if isinstance(__a,			__a):
																																			# Convert ModelOutput to tuple first
																																			_lowerCAmelCase     :							Optional[int]    =    element.to_tuple()
																																			if isinstance(element[0],			torch.Tensor):
																																										_lowerCAmelCase     :							Dict    =    tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
																																			elif isinstance(element[0],			np.ndarray):
																																										_lowerCAmelCase     :							int    =    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(__a,			__a):
																																			# Those are stored as lists of tensors so need specific unbatching.
																																			if isinstance(element[0],			torch.Tensor):
																																										_lowerCAmelCase     :							Optional[Any]    =    tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
																																			elif isinstance(element[0],			np.ndarray):
																																										_lowerCAmelCase     :							Optional[Any]    =    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
																																			_lowerCAmelCase     :							str    =    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

																																			_lowerCAmelCase     :							Tuple    =    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
																																			_lowerCAmelCase     :							Tuple    =    np.expand_dims(element[self._loader_batch_index],			0)
																												else:
																																			# This is typically a list, so no need to `unsqueeze`.
																																			_lowerCAmelCase     :							Optional[int]    =    element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
																					_lowerCAmelCase     :							str    =    self._loader_batch_data.__class__(__a)
														self._loader_batch_index += 1
														return result







							def    snake_case__      (   self):

														'''simple docstring'''

														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
														_lowerCAmelCase     :							Union[str, Any]    =    next(self.iterator)
														_lowerCAmelCase     :							Optional[int]    =    self.infer(__a,			**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(__a,			torch.Tensor):
																												_lowerCAmelCase     :							Any    =    processed
																					else:
																												_lowerCAmelCase     :							List[Any]    =    list(processed.keys())[0]
																												_lowerCAmelCase     :							List[Any]    =    processed[key]
																					if isinstance(__a,			__a):
																												_lowerCAmelCase     :							Optional[int]    =    len(__a)
																					else:
																												_lowerCAmelCase     :							List[str]    =    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.
																												_lowerCAmelCase     :							Any    =    observed_batch_size
																					# Setting internal index to unwrap the batch
																					_lowerCAmelCase     :							Tuple    =    processed
																					_lowerCAmelCase     :							Tuple    =    0
																					return self.loader_batch_item()
														else:
																					# We're not unrolling batches
																					return processed



class   UpperCAmelCase_    (       a):







							def __init__(   self,			__a,			__a,			__a,			__a=None):

														'''simple docstring'''

														super().__init__(__a,			__a,			__a)







							def __iter__(   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    iter(self.loader)
														_lowerCAmelCase     :							Dict    =    None
														return self







							def    snake_case__      (   self):

														'''simple docstring'''

														if self.subiterator is None:
																					_lowerCAmelCase     :							int    =    self.infer(next(self.iterator),			**self.params)
														try:
																					# Try to return next item
																					_lowerCAmelCase     :							Union[str, 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
																					_lowerCAmelCase     :							List[str]    =    self.infer(next(self.iterator),			**self.params)
																					_lowerCAmelCase     :							Optional[Any]    =    next(self.subiterator)
														return processed



class   UpperCAmelCase_    (       a):







							def __iter__(   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    iter(self.loader)
														return self







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    False
														_lowerCAmelCase     :							Dict    =    []
														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:
																												_lowerCAmelCase     :							Union[str, Any]    =    self.loader_batch_item()
																												_lowerCAmelCase     :							int    =    item.pop("is_last")
																												accumulator.append(__a)
																												if is_last:
																																			return accumulator

														while not is_last:
																					_lowerCAmelCase     :							Dict    =    self.infer(next(self.iterator),			**self.params)
																					if self.loader_batch_size is not None:
																												if isinstance(__a,			torch.Tensor):
																																			_lowerCAmelCase     :							List[str]    =    processed
																												else:
																																			_lowerCAmelCase     :							Any    =    list(processed.keys())[0]
																																			_lowerCAmelCase     :							Dict    =    processed[key]
																												if isinstance(__a,			__a):
																																			_lowerCAmelCase     :							Tuple    =    len(__a)
																												else:
																																			_lowerCAmelCase     :							List[str]    =    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.
																																			_lowerCAmelCase     :							List[Any]    =    observed_batch_size
																												_lowerCAmelCase     :							Optional[int]    =    processed
																												_lowerCAmelCase     :							List[str]    =    0
																												while self._loader_batch_index < self.loader_batch_size:
																																			_lowerCAmelCase     :							Union[str, Any]    =    self.loader_batch_item()
																																			_lowerCAmelCase     :							Optional[int]    =    item.pop("is_last")
																																			accumulator.append(__a)
																																			if is_last:
																																										return accumulator
																					else:
																												_lowerCAmelCase     :							Optional[int]    =    processed
																												_lowerCAmelCase     :							List[str]    =    item.pop("is_last")
																												accumulator.append(__a)
														return accumulator



class   UpperCAmelCase_    (       a):







							def __init__(   self,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    dataset
														_lowerCAmelCase     :							Optional[int]    =    key







							def __len__(   self):

														'''simple docstring'''

														return len(self.dataset)







							def __getitem__(   self,			__a):

														'''simple docstring'''

														return self.dataset[i][self.key]



class   UpperCAmelCase_    (       a):







							def __init__(   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    dataset
														_lowerCAmelCase     :							Any    =    keya
														_lowerCAmelCase     :							List[Any]    =    keya







							def __len__(   self):

														'''simple docstring'''

														return len(self.dataset)







							def __getitem__(   self,			__a):

														'''simple docstring'''

														return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_snake_case		=  logging.get_logger(__name__)

_snake_case		=  {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}



class   UpperCAmelCase_    (       a   ,					a):
							lowerCamelCase__   			=		'swin'

							lowerCamelCase__   			=		{
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}







							def __init__(   self,			__a=224,			__a=4,			__a=3,			__a=96,			__a=[2, 2, 6, 2],			__a=[3, 6, 12, 24],			__a=7,			__a=4.0,			__a=True,			__a=0.0,			__a=0.0,			__a=0.1,			__a="gelu",			__a=False,			__a=0.02,			__a=1E-5,			__a=32,			__a=None,			__a=None,			**__a,			):

														'''simple docstring'''

														super().__init__(**__a)

														_lowerCAmelCase     :							Any    =    image_size
														_lowerCAmelCase     :							Union[str, Any]    =    patch_size
														_lowerCAmelCase     :							Tuple    =    num_channels
														_lowerCAmelCase     :							List[Any]    =    embed_dim
														_lowerCAmelCase     :							Tuple    =    depths
														_lowerCAmelCase     :							Optional[Any]    =    len(__a)
														_lowerCAmelCase     :							int    =    num_heads
														_lowerCAmelCase     :							int    =    window_size
														_lowerCAmelCase     :							int    =    mlp_ratio
														_lowerCAmelCase     :							List[Any]    =    qkv_bias
														_lowerCAmelCase     :							str    =    hidden_dropout_prob
														_lowerCAmelCase     :							Union[str, Any]    =    attention_probs_dropout_prob
														_lowerCAmelCase     :							Any    =    drop_path_rate
														_lowerCAmelCase     :							int    =    hidden_act
														_lowerCAmelCase     :							Tuple    =    use_absolute_embeddings
														_lowerCAmelCase     :							Optional[int]    =    layer_norm_eps
														_lowerCAmelCase     :							Tuple    =    initializer_range
														_lowerCAmelCase     :							Tuple    =    encoder_stride
														# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
														# this indicates the channel dimension after the last stage of the model
														_lowerCAmelCase     :							List[str]    =    int(embed_dim * 2 ** (len(__a) - 1))
														_lowerCAmelCase     :							List[Any]    =    ["stem"] + [f"stage{idx}" for idx in range(1,			len(__a) + 1)]
														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[int]    =    get_aligned_output_features_output_indices(
														    out_features=__a,			out_indices=__a,			stage_names=self.stage_names)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		version.parse('1.11')







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return OrderedDict(
														    [
														        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
														    ])







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return 1E-4
