

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

from ..utils.generic import ModelOutput







class 							A__   (	A__  ):





  def __init__(		self				:     str							, _a				:     Dict							, _a				:     int							, _a				:     str			)     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =dataset
       _SCREAMING_SNAKE_CASE							   =process
       _SCREAMING_SNAKE_CASE							   =params





  def __len__(		self				:     int			)     ->   Tuple:





       '''simple docstring'''






       return len(self.dataset			)





  def __getitem__(		self				:     Optional[Any]							, _a				:     int			)     ->   List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.dataset[i]
       _SCREAMING_SNAKE_CASE							   =self.process(_a							, **self.params			)
       return processed







class 							A__   (	A__  ):





  def __init__(		self				:     str							, _a				:     Optional[Any]							, _a				:     Tuple							, _a				:     int							, _a				:     Any=None			)     ->   Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =loader
       _SCREAMING_SNAKE_CASE							   =infer
       _SCREAMING_SNAKE_CASE							   =params
       if loader_batch_size == 1:
            # Let's spare some time by deactivating altogether
            _SCREAMING_SNAKE_CASE							   =None
       _SCREAMING_SNAKE_CASE							   =loader_batch_size

       # Internal bookkeeping
       _SCREAMING_SNAKE_CASE							   =None
       _SCREAMING_SNAKE_CASE							   =None





  def __len__(		self				:     Union[str, Any]			)     ->   List[Any]:





       '''simple docstring'''






       return len(self.loader			)





  def __iter__(		self				:     Any			)     ->   List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =iter(self.loader			)
       return self





  def 						A  (		self				:     Union[str, Any]			)     ->   Any:





       '''simple docstring'''






       if isinstance(self._loader_batch_data							, torch.Tensor			):
            # Batch data is simple tensor, just fetch the slice
            _SCREAMING_SNAKE_CASE							   =self._loader_batch_data[self._loader_batch_index]
       else:
            # Batch data is assumed to be BaseModelOutput (or dict)
            _SCREAMING_SNAKE_CASE							   ={}
            for k, element in self._loader_batch_data.items():
                 if isinstance(_a							, _a			):
                      # Convert ModelOutput to tuple first
                      _SCREAMING_SNAKE_CASE							   =element.to_tuple()
                      if isinstance(element[0]							, torch.Tensor			):
                           _SCREAMING_SNAKE_CASE							   =tuple(el[self._loader_batch_index].unsqueeze(0			) for el in element			)
                      elif isinstance(element[0]							, np.ndarray			):
                           _SCREAMING_SNAKE_CASE							   =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			):
                           _SCREAMING_SNAKE_CASE							   =tuple(el[self._loader_batch_index].unsqueeze(0			) for el in element			)
                      elif isinstance(element[0]							, np.ndarray			):
                           _SCREAMING_SNAKE_CASE							   =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							   =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							   =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							   =np.expand_dims(element[self._loader_batch_index]							, 0			)
                 else:
                      # This is typically a list, so no need to `unsqueeze`.
                      _SCREAMING_SNAKE_CASE							   =element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
            _SCREAMING_SNAKE_CASE							   =self._loader_batch_data.__class__(_a			)
       self._loader_batch_index += 1
       return result





  def 						A  (		self				:     Tuple			)     ->   Optional[int]:





       '''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
       _SCREAMING_SNAKE_CASE							   =next(self.iterator			)
       _SCREAMING_SNAKE_CASE							   =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			):
                 _SCREAMING_SNAKE_CASE							   =processed
            else:
                 _SCREAMING_SNAKE_CASE							   =list(processed.keys()			)[0]
                 _SCREAMING_SNAKE_CASE							   =processed[key]
            if isinstance(_a							, _a			):
                 _SCREAMING_SNAKE_CASE							   =len(_a			)
            else:
                 _SCREAMING_SNAKE_CASE							   =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							   =observed_batch_size
            # Setting internal index to unwrap the batch
            _SCREAMING_SNAKE_CASE							   =processed
            _SCREAMING_SNAKE_CASE							   =0
            return self.loader_batch_item()
       else:
            # We're not unrolling batches
            return processed







class 							A__   (	A__  ):





  def __init__(		self				:     List[str]							, _a				:     Any							, _a				:     Any							, _a				:     int							, _a				:     Optional[Any]=None			)     ->   str:





       '''simple docstring'''






       super().__init__(_a							, _a							, _a			)





  def __iter__(		self				:     List[str]			)     ->   Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =iter(self.loader			)
       _SCREAMING_SNAKE_CASE							   =None
       return self





  def 						A  (		self				:     Any			)     ->   str:





       '''simple docstring'''






       if self.subiterator is None:
            _SCREAMING_SNAKE_CASE							   =self.infer(next(self.iterator			)							, **self.params			)
       try:
            # Try to return next item
            _SCREAMING_SNAKE_CASE							   =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							   =self.infer(next(self.iterator			)							, **self.params			)
            _SCREAMING_SNAKE_CASE							   =next(self.subiterator			)
       return processed







class 							A__   (	A__  ):





  def __iter__(		self				:     Optional[int]			)     ->   Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =iter(self.loader			)
       return self





  def 						A  (		self				:     List[Any]			)     ->   int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =False
       _SCREAMING_SNAKE_CASE							   =[]
       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							   =self.loader_batch_item()
                 _SCREAMING_SNAKE_CASE							   =item.pop('is_last'			)
                 accumulator.append(_a			)
                 if is_last:
                      return accumulator

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







class 							A__   (	A__  ):





  def __init__(		self				:     List[Any]							, _a				:     Dataset							, _a				:     str			)     ->   str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =dataset
       _SCREAMING_SNAKE_CASE							   =key





  def __len__(		self				:     Optional[int]			)     ->   str:





       '''simple docstring'''






       return len(self.dataset			)





  def __getitem__(		self				:     Optional[Any]							, _a				:     Dict			)     ->   Optional[Any]:





       '''simple docstring'''






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







class 							A__   (	A__  ):





  def __init__(		self				:     Any							, _a				:     Dataset							, _a				:     str							, _a				:     str			)     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =dataset
       _SCREAMING_SNAKE_CASE							   =keya
       _SCREAMING_SNAKE_CASE							   =keya





  def __len__(		self				:     List[Any]			)     ->   Union[str, Any]:





       '''simple docstring'''






       return len(self.dataset			)





  def __getitem__(		self				:     Optional[int]							, _a				:     Dict			)     ->   List[str]:





       '''simple docstring'''






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



'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase			:				Optional[int] 			=					{
    "configuration_efficientnet": [
        "EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientNetConfig",
        "EfficientNetOnnxConfig",
    ]
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				str 			=					["EfficientNetImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Tuple 			=					[
         "EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "EfficientNetForImageClassification",
         "EfficientNetModel",
         "EfficientNetPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_efficientnet import (
         EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
         EfficientNetConfig,
         EfficientNetOnnxConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .image_processing_efficientnet import EfficientNetImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_efficientnet import (
              EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              EfficientNetForImageClassification,
              EfficientNetModel,
              EfficientNetPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase			:				int 			=					_LazyModule(__name__, globals()["__file__"], _import_structure)
