


'''simple docstring'''



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

from ..utils.generic import ModelOutput


class 		_snake_case					(     lowercase__	):




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					Optional[Any]        =     dataset
								UpperCAmelCase__  :					int        =     process
								UpperCAmelCase__  :					Optional[int]        =     params




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




					def __getitem__(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					int        =     self.dataset[i]
								UpperCAmelCase__  :					Any        =     self.process(_lowerCamelCase							,							**self.params)
								return processed


class 		_snake_case					(     lowercase__	):




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase=None):
								UpperCAmelCase__  :					List[Any]        =     loader
								UpperCAmelCase__  :					Union[str, Any]        =     infer
								UpperCAmelCase__  :					str        =     params
								if loader_batch_size == 1:
											# Let's spare some time by deactivating altogether
											UpperCAmelCase__  :					List[str]        =     None
								UpperCAmelCase__  :					Dict        =     loader_batch_size

								# Internal bookkeeping
								UpperCAmelCase__  :					List[Any]        =     None
								UpperCAmelCase__  :					List[Any]        =     None




					def __len__(      self):
								return len(self.loader)




					def __iter__(      self):
								UpperCAmelCase__  :					List[str]        =     iter(self.loader)
								return self




					def 				snake_case__						(      self):
								if isinstance(self._loader_batch_data							,							torch.Tensor):
											# Batch data is simple tensor, just fetch the slice
											UpperCAmelCase__  :					Optional[int]        =     self._loader_batch_data[self._loader_batch_index]
								else:
											# Batch data is assumed to be BaseModelOutput (or dict)
											UpperCAmelCase__  :					Tuple        =     {}
											for k, element in self._loader_batch_data.items():
														if isinstance(_lowerCamelCase							,							_lowerCamelCase):
																	# Convert ModelOutput to tuple first
																	UpperCAmelCase__  :					Optional[int]        =     element.to_tuple()
																	if isinstance(element[0]							,							torch.Tensor):
																				UpperCAmelCase__  :					Tuple        =     tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
																	elif isinstance(element[0]							,							np.ndarray):
																				UpperCAmelCase__  :					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(_lowerCamelCase							,							_lowerCamelCase):
																	# Those are stored as lists of tensors so need specific unbatching.
																	if isinstance(element[0]							,							torch.Tensor):
																				UpperCAmelCase__  :					List[str]        =     tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
																	elif isinstance(element[0]							,							np.ndarray):
																				UpperCAmelCase__  :					int        =     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__  :					Dict        =     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__  :					List[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
																	UpperCAmelCase__  :					Tuple        =     np.expand_dims(element[self._loader_batch_index]							,							0)
														else:
																	# This is typically a list, so no need to `unsqueeze`.
																	UpperCAmelCase__  :					Optional[int]        =     element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
											UpperCAmelCase__  :					Dict        =     self._loader_batch_data.__class__(_lowerCamelCase)
								self._loader_batch_index += 1
								return result




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


class 		_snake_case					(     lowercase__	):




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase=None):
								super().__init__(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)




					def __iter__(      self):
								UpperCAmelCase__  :					Optional[Any]        =     iter(self.loader)
								UpperCAmelCase__  :					str        =     None
								return self




					def 				snake_case__						(      self):
								if self.subiterator is None:
											UpperCAmelCase__  :					Any        =     self.infer(next(self.iterator)							,							**self.params)
								try:
											# Try to return next item
											UpperCAmelCase__  :					Tuple        =     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__  :					Optional[Any]        =     self.infer(next(self.iterator)							,							**self.params)
											UpperCAmelCase__  :					Dict        =     next(self.subiterator)
								return processed


class 		_snake_case					(     lowercase__	):




					def __iter__(      self):
								UpperCAmelCase__  :					Optional[Any]        =     iter(self.loader)
								return self




					def 				snake_case__						(      self):
								# 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__  :					List[str]        =     False
								UpperCAmelCase__  :					Optional[int]        =     []
								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__  :					List[str]        =     self.loader_batch_item()
														UpperCAmelCase__  :					Dict        =     item.pop("""is_last""")
														accumulator.append(_lowerCamelCase)
														if is_last:
																	return accumulator

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


class 		_snake_case					(     lowercase__	):




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					Optional[Any]        =     dataset
								UpperCAmelCase__  :					Optional[int]        =     key




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




					def __getitem__(      self							,							_lowerCamelCase):
								return self.dataset[i][self.key]


class 		_snake_case					(     lowercase__	):




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					Tuple        =     dataset
								UpperCAmelCase__  :					Any        =     keya
								UpperCAmelCase__  :					Optional[int]        =     keya




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




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



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__A	={
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'EncodecModel',
		    'EncodecPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_encodec import (
		    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    EncodecConfig,
		)
		from .feature_extraction_encodec import EncodecFeatureExtractor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_encodec import (
				    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
				    EncodecModel,
				    EncodecPreTrainedModel,
				)

else:
		import sys

		__A	=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)