






"""simple docstring"""





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

from ..utils.generic import ModelOutput
class    __lowerCamelCase    (  _a  ):

   def __init__(					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       Union[str, Any]:
       UpperCamelCase__         =    dataset
       UpperCamelCase__         =    process
       UpperCamelCase__         =    params

   def __len__(					self      )		->       Any:
       return len(self.dataset      )







   def __getitem__(					self ,				snake_case_      )		->       List[Any]:
       UpperCamelCase__         =    self.dataset[i]
       UpperCamelCase__         =    self.process(snake_case_ ,				**self.params      )
       return processed







class    __lowerCamelCase    (  _a  ):

   def __init__(					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_=None      )		->       Dict:
       UpperCamelCase__         =    loader
       UpperCamelCase__         =    infer
       UpperCamelCase__         =    params
       if loader_batch_size == 1:
           # Let's spare some time by deactivating altogether
           UpperCamelCase__         =    None
       UpperCamelCase__         =    loader_batch_size

       # Internal bookkeeping
       UpperCamelCase__         =    None
       UpperCamelCase__         =    None

   def __len__(					self      )		->       Tuple:
       return len(self.loader      )

   def __iter__(					self      )		->       Optional[Any]:
       UpperCamelCase__         =    iter(self.loader      )
       return self

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

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







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[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
       UpperCamelCase__         =    next(self.iterator      )
       UpperCamelCase__         =    self.infer(snake_case_ ,				**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(snake_case_ ,				torch.Tensor      ):
               UpperCamelCase__         =    processed
           else:
               UpperCamelCase__         =    list(processed.keys()      )[0]
               UpperCamelCase__         =    processed[key]
           if isinstance(snake_case_ ,				snake_case_      ):
               UpperCamelCase__         =    len(snake_case_      )
           else:
               UpperCamelCase__         =    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.
               UpperCamelCase__         =    observed_batch_size
           # Setting internal index to unwrap the batch
           UpperCamelCase__         =    processed
           UpperCamelCase__         =    0
           return self.loader_batch_item()
       else:
           # We're not unrolling batches
           return processed







class    __lowerCamelCase    (  _a  ):

   def __init__(					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_=None      )		->       int:
       super().__init__(snake_case_ ,				snake_case_ ,				snake_case_      )

   def __iter__(					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    iter(self.loader      )
       UpperCamelCase__         =    None
       return self







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       if self.subiterator is None:
           UpperCamelCase__         =    self.infer(next(self.iterator      ) ,				**self.params      )
       try:
           # Try to return next item
           UpperCamelCase__         =    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
           UpperCamelCase__         =    self.infer(next(self.iterator      ) ,				**self.params      )
           UpperCamelCase__         =    next(self.subiterator      )
       return processed







class    __lowerCamelCase    (  _a  ):

   def __iter__(					self      )		->       str:
       UpperCamelCase__         =    iter(self.loader      )
       return self







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       # 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.
       UpperCamelCase__         =    False
       UpperCamelCase__         =    []
       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:
               UpperCamelCase__         =    self.loader_batch_item()
               UpperCamelCase__         =    item.pop('is_last'      )
               accumulator.append(snake_case_      )
               if is_last:
                   return accumulator

       while not is_last:
           UpperCamelCase__         =    self.infer(next(self.iterator      ) ,				**self.params      )
           if self.loader_batch_size is not None:
               if isinstance(snake_case_ ,				torch.Tensor      ):
                   UpperCamelCase__         =    processed
               else:
                   UpperCamelCase__         =    list(processed.keys()      )[0]
                   UpperCamelCase__         =    processed[key]
               if isinstance(snake_case_ ,				snake_case_      ):
                   UpperCamelCase__         =    len(snake_case_      )
               else:
                   UpperCamelCase__         =    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.
                   UpperCamelCase__         =    observed_batch_size
               UpperCamelCase__         =    processed
               UpperCamelCase__         =    0
               while self._loader_batch_index < self.loader_batch_size:
                   UpperCamelCase__         =    self.loader_batch_item()
                   UpperCamelCase__         =    item.pop('is_last'      )
                   accumulator.append(snake_case_      )
                   if is_last:
                       return accumulator
           else:
               UpperCamelCase__         =    processed
               UpperCamelCase__         =    item.pop('is_last'      )
               accumulator.append(snake_case_      )
       return accumulator







class    __lowerCamelCase    (  _a  ):

   def __init__(					self ,				snake_case_ ,				snake_case_      )		->       Any:
       UpperCamelCase__         =    dataset
       UpperCamelCase__         =    key

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







   def __getitem__(					self ,				snake_case_      )		->       Dict:
       return self.dataset[i][self.key]







class    __lowerCamelCase    (  _a  ):

   def __init__(					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       Optional[int]:
       UpperCamelCase__         =    dataset
       UpperCamelCase__         =    keya
       UpperCamelCase__         =    keya

   def __len__(					self      )		->       List[str]:
       return len(self.dataset      )







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












"""simple docstring"""





import warnings
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


A__					: str=	logging.get_logger(__name__)

A__					: List[Any]=	{
    """nvidia/segformer-b0-finetuned-ade-512-512""": (
        """https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class    __lowerCamelCase    (  _a  ):
   a      :				Any 						="""segformer"""

   def __init__(					self ,				snake_case_=3 ,				snake_case_=4 ,				snake_case_=[2, 2, 2, 2] ,				snake_case_=[8, 4, 2, 1] ,				snake_case_=[32, 64, 160, 256] ,				snake_case_=[7, 3, 3, 3] ,				snake_case_=[4, 2, 2, 2] ,				snake_case_=[1, 2, 5, 8] ,				snake_case_=[4, 4, 4, 4] ,				snake_case_="gelu" ,				snake_case_=0.0 ,				snake_case_=0.0 ,				snake_case_=0.1 ,				snake_case_=0.02 ,				snake_case_=0.1 ,				snake_case_=1E-6 ,				snake_case_=256 ,				snake_case_=255 ,				**snake_case_ ,				)		->       Tuple:
       super().__init__(**snake_case_      )

       if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
           warnings.warn(
               'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
               ' removed, as the behaviour will default to that of reshape_last_stage = True.' ,				snake_case_ ,				)

       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    num_encoder_blocks
       UpperCamelCase__         =    depths
       UpperCamelCase__         =    sr_ratios
       UpperCamelCase__         =    hidden_sizes
       UpperCamelCase__         =    patch_sizes
       UpperCamelCase__         =    strides
       UpperCamelCase__         =    mlp_ratios
       UpperCamelCase__         =    num_attention_heads
       UpperCamelCase__         =    hidden_act
       UpperCamelCase__         =    hidden_dropout_prob
       UpperCamelCase__         =    attention_probs_dropout_prob
       UpperCamelCase__         =    classifier_dropout_prob
       UpperCamelCase__         =    initializer_range
       UpperCamelCase__         =    drop_path_rate
       UpperCamelCase__         =    layer_norm_eps
       UpperCamelCase__         =    decoder_hidden_size
       UpperCamelCase__         =    kwargs.get('reshape_last_stage' ,				snake_case_      )
       UpperCamelCase__         =    semantic_loss_ignore_index







class    __lowerCamelCase    (  _a  ):
   a      :				Any 						=version.parse("""1.11"""  )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Mapping[str, Mapping[int, str]]:
       return OrderedDict(
           [
               ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
           ]      )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       float:
       return 1E-4







   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       return 12




