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

from ..utils.generic import ModelOutput




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	def __init__(      self	:List[str]  ,     a	:Any  ,     a	:Dict  ,     a	:str		)	->				Union[str, Any]:
								__UpperCamelCase			:     Union[str, Any]         =      dataset
								__UpperCamelCase			:     List[str]         =      process
								__UpperCamelCase			:     List[Any]         =      params







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







	def __getitem__(      self	:Tuple  ,     a	:Union[str, Any]		)	->				Union[str, Any]:
								__UpperCamelCase			:     List[str]         =      self.dataset[i]
								__UpperCamelCase			:     Union[str, Any]         =      self.process(a  ,     **self.params		)
								return processed




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




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

								# Internal bookkeeping
								__UpperCamelCase			:     Optional[Any]         =      None
								__UpperCamelCase			:     Union[str, Any]         =      None







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







	def __iter__(      self	:List[Any]		)	->				List[Any]:
								__UpperCamelCase			:     str         =      iter(self.loader		)
								return self







	def      _lowerCamelCase       (      self	:str		)	->				List[str]:
								if isinstance(self._loader_batch_data  ,     torch.Tensor		):
															# Batch data is simple tensor, just fetch the slice
															__UpperCamelCase			:     List[Any]         =      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(a  ,     a		):
																													# Convert ModelOutput to tuple first
																													__UpperCamelCase			:     List[str]         =      element.to_tuple()
																													if isinstance(element[0]  ,     torch.Tensor		):
																																				__UpperCamelCase			:     Optional[int]         =      tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
																													elif isinstance(element[0]  ,     np.ndarray		):
																																				__UpperCamelCase			:     str         =      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		):
																																				__UpperCamelCase			:     Optional[Any]         =      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			:     Optional[Any]         =      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			:     int         =      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			:     str         =      element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
															__UpperCamelCase			:     Tuple         =      self._loader_batch_data.__class__(a		)
								self._loader_batch_index += 1
								return result







	def      _lowerCamelCase       (      self	:Any		)	->				str:
								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			:     Any         =      next(self.iterator		)
								__UpperCamelCase			:     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		):
																						__UpperCamelCase			:     Optional[Any]         =      processed
															else:
																						__UpperCamelCase			:     Tuple         =      list(processed.keys()		)[0]
																						__UpperCamelCase			:     List[str]         =      processed[key]
															if isinstance(a  ,     a		):
																						__UpperCamelCase			:     str         =      len(a		)
															else:
																						__UpperCamelCase			:     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
															# Setting internal index to unwrap the batch
															__UpperCamelCase			:     Tuple         =      processed
															__UpperCamelCase			:     List[Any]         =      0
															return self.loader_batch_item()
								else:
															# We're not unrolling batches
															return processed




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	def __init__(      self	:Union[str, Any]  ,     a	:Tuple  ,     a	:Union[str, Any]  ,     a	:Any  ,     a	:Dict=None		)	->				Union[str, Any]:
								super().__init__(a  ,     a  ,     a		)







	def __iter__(      self	:Dict		)	->				Optional[Any]:
								__UpperCamelCase			:     Optional[Any]         =      iter(self.loader		)
								__UpperCamelCase			:     Optional[int]         =      None
								return self







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




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	def __iter__(      self	:Tuple		)	->				Optional[Any]:
								__UpperCamelCase			:     List[str]         =      iter(self.loader		)
								return self







	def      _lowerCamelCase       (      self	:str		)	->				Union[str, Any]:
								# 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			:     str         =      False
								__UpperCamelCase			:     List[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:
																						__UpperCamelCase			:     Optional[Any]         =      self.loader_batch_item()
																						__UpperCamelCase			:     Union[str, Any]         =      item.pop("is_last"		)
																						accumulator.append(a		)
																						if is_last:
																													return accumulator

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




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	def __init__(      self	:Any  ,     a	:Dataset  ,     a	:str		)	->				Optional[int]:
								__UpperCamelCase			:     Optional[int]         =      dataset
								__UpperCamelCase			:     Union[str, Any]         =      key







	def __len__(      self	:Optional[int]		)	->				Dict:
								return len(self.dataset		)







	def __getitem__(      self	:Dict  ,     a	:int		)	->				Union[str, Any]:
								return self.dataset[i][self.key]




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	def __init__(      self	:int  ,     a	:Dataset  ,     a	:str  ,     a	:str		)	->				List[Any]:
								__UpperCamelCase			:     int         =      dataset
								__UpperCamelCase			:     Optional[int]         =      keya
								__UpperCamelCase			:     Dict         =      keya







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







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

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase						:      Dict									= logging.get_logger(__name__)

lowercase						:      Optional[Any]									= {
    'facebook/s2t-wav2vec2-large-en-de': (
        'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  'speech_to_text_2'
	_A			=  ['past_key_values']
	_A			=  {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}







	def __init__(      self	:Dict  ,     a	:Tuple=1_0_0_0_0  ,     a	:Optional[int]=6  ,     a	:List[str]=2_0_4_8  ,     a	:Tuple=4  ,     a	:List[Any]=0.0  ,     a	:str=True  ,     a	:Any="relu"  ,     a	:Any=2_5_6  ,     a	:Optional[int]=0.1  ,     a	:Any=0.0  ,     a	:int=0.0  ,     a	:int=0.02  ,     a	:List[Any]=2  ,     a	:Tuple=True  ,     a	:str=1  ,     a	:Optional[int]=0  ,     a	:List[Any]=2  ,     a	:Any=1_0_2_4  ,     **a	:str  ,     )	->				int:
								__UpperCamelCase			:     List[str]         =      vocab_size
								__UpperCamelCase			:     int         =      d_model
								__UpperCamelCase			:     Optional[int]         =      decoder_ffn_dim
								__UpperCamelCase			:     Any         =      decoder_layers
								__UpperCamelCase			:     Any         =      decoder_attention_heads
								__UpperCamelCase			:     Tuple         =      dropout
								__UpperCamelCase			:     Any         =      attention_dropout
								__UpperCamelCase			:     Any         =      activation_dropout
								__UpperCamelCase			:     Dict         =      activation_function
								__UpperCamelCase			:     int         =      init_std
								__UpperCamelCase			:     List[str]         =      decoder_layerdrop
								__UpperCamelCase			:     Optional[Any]         =      use_cache
								__UpperCamelCase			:     int         =      decoder_layers
								__UpperCamelCase			:     Union[str, Any]         =      scale_embedding  # scale factor will be sqrt(d_model) if True
								__UpperCamelCase			:     Dict         =      max_target_positions

								super().__init__(
								    pad_token_id=a  ,     bos_token_id=a  ,     eos_token_id=a  ,     decoder_start_token_id=a  ,     **a  ,     )