'''simple docstring'''





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

from ..utils.generic import ModelOutput




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def __init__(						self					:						str       ,		lowercase__					:						Optional[Any]       ,		lowercase__					:						int       ,		lowercase__					:						Dict					)							->List[str]:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[int]   			=						dataset
										_UpperCamelCase			:			List[str]   			=						process
										_UpperCamelCase			:			Optional[Any]   			=						params





				def __len__(						self					:						int					)							->Tuple:


										'''simple docstring'''




										return len(self.dataset					)







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


										'''simple docstring'''




										_UpperCamelCase			:			Optional[Any]   			=						self.dataset[i]
										_UpperCamelCase			:			List[str]   			=						self.process(lowercase__       ,		**self.params					)
										return processed





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





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


										'''simple docstring'''




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

										# Internal bookkeeping
										_UpperCamelCase			:			Dict   			=						None
										_UpperCamelCase			:			List[str]   			=						None





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


										'''simple docstring'''




										return len(self.loader					)





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


										'''simple docstring'''




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





				def  snake_case__				(						self					:						Any					)							->List[str]:


										'''simple docstring'''




										if isinstance(self._loader_batch_data       ,		torch.Tensor					):
																# Batch data is simple tensor, just fetch the slice
																_UpperCamelCase			:			Any   			=						self._loader_batch_data[self._loader_batch_index]
										else:
																# Batch data is assumed to be BaseModelOutput (or dict)
																_UpperCamelCase			:			int   			=						{}
																for k, element in self._loader_batch_data.items():
																						if isinstance(lowercase__       ,		lowercase__					):
																												# Convert ModelOutput to tuple first
																												_UpperCamelCase			:			List[Any]   			=						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			:			List[Any]   			=						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(lowercase__       ,		lowercase__					):
																												# Those are stored as lists of tensors so need specific unbatching.
																												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			:			List[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
																												_UpperCamelCase			:			List[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

																												_UpperCamelCase			:			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			:			Dict   			=						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			:			Union[str, Any]   			=						self._loader_batch_data.__class__(lowercase__					)
										self._loader_batch_index += 1
										return result







				def  snake_case__				(						self					:						List[str]					)							->str:


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





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





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


										'''simple docstring'''




										super().__init__(lowercase__       ,		lowercase__       ,		lowercase__					)





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


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						iter(self.loader					)
										_UpperCamelCase			:			Tuple   			=						None
										return self







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


										'''simple docstring'''




										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			:			int   			=						self.infer(next(self.iterator					)       ,		**self.params					)
																_UpperCamelCase			:			Optional[Any]   			=						next(self.subiterator					)
										return processed





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





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


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						iter(self.loader					)
										return self







				def  snake_case__				(						self					:						List[str]					)							->List[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			str   			=						False
										_UpperCamelCase			:			Optional[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			:			Any   			=						self.loader_batch_item()
																						_UpperCamelCase			:			List[str]   			=						item.pop("is_last"					)
																						accumulator.append(lowercase__					)
																						if is_last:
																												return accumulator

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





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





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


										'''simple docstring'''




										_UpperCamelCase			:			Union[str, Any]   			=						dataset
										_UpperCamelCase			:			Optional[Any]   			=						key





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


										'''simple docstring'''




										return len(self.dataset					)







				def __getitem__(						self					:						Dict       ,		lowercase__					:						Dict					)							->Union[str, Any]:


										'''simple docstring'''




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





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





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


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						dataset
										_UpperCamelCase			:			Any   			=						keya
										_UpperCamelCase			:			List[str]   			=						keya





				def __len__(						self					:						Dict					)							->str:


										'''simple docstring'''




										return len(self.dataset					)







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


										'''simple docstring'''




										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_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase_	:		Any			=     {
    """configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2TextConfig"""],
    """processing_speech_to_text""": ["""Speech2TextProcessor"""],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		int			=     ["""Speech2TextTokenizer"""]

try:
			if not is_speech_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		Optional[Any]			=     ["""Speech2TextFeatureExtractor"""]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		Optional[int]			=     [
			    """TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFSpeech2TextForConditionalGeneration""",
			    """TFSpeech2TextModel""",
			    """TFSpeech2TextPreTrainedModel""",
			]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		str			=     [
			    """SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """Speech2TextForConditionalGeneration""",
			    """Speech2TextModel""",
			    """Speech2TextPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
			from .processing_speech_to_text import SpeechaTextProcessor

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_speech_to_text import SpeechaTextTokenizer

			try:
						if not is_speech_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_speech_to_text import (
						    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFSpeechaTextForConditionalGeneration,
						    TFSpeechaTextModel,
						    TFSpeechaTextPreTrainedModel,
						)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_speech_to_text import (
						    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SpeechaTextForConditionalGeneration,
						    SpeechaTextModel,
						    SpeechaTextPreTrainedModel,
						)

else:
			import sys

			lowerCAmelCase_	:		Union[str, Any]			=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


