
'''simple docstring'''






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

from ..utils.generic import ModelOutput




class     __snake_case		(   a__):
					def __init__(     self,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= dataset
									lowerCamelCase					:			Tuple									= process
									lowerCamelCase					:			List[str]									= params
					def __len__(     self      ):

									"""simple docstring"""


									return len(self.dataset      )




					def __getitem__(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.dataset[i]
									lowerCamelCase					:			Any									= self.process(A,    **self.params      )
									return processed





class     __snake_case		(   a__):
					def __init__(     self,    A,    A,    A,    A=None      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= loader
									lowerCamelCase					:			Dict									= infer
									lowerCamelCase					:			List[Any]									= params
									if loader_batch_size == 1:
													# Let's spare some time by deactivating altogether
													lowerCamelCase					:			Any									= None
									lowerCamelCase					:			Any									= loader_batch_size

									# Internal bookkeeping
									lowerCamelCase					:			Optional[int]									= None
									lowerCamelCase					:			Optional[Any]									= None
					def __len__(     self      ):

									"""simple docstring"""


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

									"""simple docstring"""


									lowerCamelCase					:			str									= iter(self.loader      )
									return self
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									if isinstance(self._loader_batch_data,    torch.Tensor      ):
													# Batch data is simple tensor, just fetch the slice
													lowerCamelCase					:			Any									= self._loader_batch_data[self._loader_batch_index]
									else:
													# Batch data is assumed to be BaseModelOutput (or dict)
													lowerCamelCase					:			str									= {}
													for k, element in self._loader_batch_data.items():
																	if isinstance(A,    A      ):
																					# Convert ModelOutput to tuple first
																					lowerCamelCase					:			Tuple									= element.to_tuple()
																					if isinstance(element[0],    torch.Tensor      ):
																									lowerCamelCase					:			Tuple									= tuple(el[self._loader_batch_index].unsqueeze(0      ) for el in element      )
																					elif isinstance(element[0],    np.ndarray      ):
																									lowerCamelCase					:			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      ):
																									lowerCamelCase					:			Optional[int]									= tuple(el[self._loader_batch_index].unsqueeze(0      ) for el in element      )
																					elif isinstance(element[0],    np.ndarray      ):
																									lowerCamelCase					:			Dict									= 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
																					lowerCamelCase					:			Optional[int]									= 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

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




					def     UpperCAmelCase_							(     self      ):

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





class     __snake_case		(   a__):
					def __init__(     self,    A,    A,    A,    A=None      ):

									"""simple docstring"""


									super().__init__(A,    A,    A      )
					def __iter__(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= iter(self.loader      )
									lowerCamelCase					:			List[Any]									= None
									return self




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									if self.subiterator is None:
													lowerCamelCase					:			int									= self.infer(next(self.iterator      ),    **self.params      )
									try:
													# Try to return next item
													lowerCamelCase					:			int									= 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
													lowerCamelCase					:			Union[str, Any]									= self.infer(next(self.iterator      ),    **self.params      )
													lowerCamelCase					:			Tuple									= next(self.subiterator      )
									return processed





class     __snake_case		(   a__):
					def __iter__(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= iter(self.loader      )
									return self




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= False
									lowerCamelCase					:			Union[str, 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:
																	lowerCamelCase					:			Optional[int]									= self.loader_batch_item()
																	lowerCamelCase					:			Optional[int]									= item.pop('is_last'      )
																	accumulator.append(A      )
																	if is_last:
																					return accumulator

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





class     __snake_case		(   a__):
					def __init__(     self,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= dataset
									lowerCamelCase					:			Optional[int]									= key
					def __len__(     self      ):

									"""simple docstring"""


									return len(self.dataset      )




					def __getitem__(     self,    A      ):

									"""simple docstring"""


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





class     __snake_case		(   a__):
					def __init__(     self,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= dataset
									lowerCamelCase					:			List[str]									= keya
									lowerCamelCase					:			Tuple									= keya
					def __len__(     self      ):

									"""simple docstring"""


									return len(self.dataset      )




					def __getitem__(     self,    A      ):

									"""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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


A			      =      {
    'configuration_clip': [
        'CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPConfig',
        'CLIPOnnxConfig',
        'CLIPTextConfig',
        'CLIPVisionConfig',
    ],
    'processing_clip': ['CLIPProcessor'],
    'tokenization_clip': ['CLIPTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      ['CLIPTokenizerFast']

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      ['CLIPFeatureExtractor']
					A			      =      ['CLIPImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'CLIPModel',
					    'CLIPPreTrainedModel',
					    'CLIPTextModel',
					    'CLIPTextModelWithProjection',
					    'CLIPVisionModel',
					    'CLIPVisionModelWithProjection',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFCLIPModel',
					    'TFCLIPPreTrainedModel',
					    'TFCLIPTextModel',
					    'TFCLIPVisionModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'FlaxCLIPModel',
					    'FlaxCLIPPreTrainedModel',
					    'FlaxCLIPTextModel',
					    'FlaxCLIPTextPreTrainedModel',
					    'FlaxCLIPVisionModel',
					    'FlaxCLIPVisionPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_clip import (
					    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    CLIPConfig,
					    CLIPOnnxConfig,
					    CLIPTextConfig,
					    CLIPVisionConfig,
					)
					from .processing_clip import CLIPProcessor
					from .tokenization_clip import CLIPTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_clip_fast import CLIPTokenizerFast

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_clip import CLIPFeatureExtractor
										from .image_processing_clip import CLIPImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_clip import (
										    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CLIPModel,
										    CLIPPreTrainedModel,
										    CLIPTextModel,
										    CLIPTextModelWithProjection,
										    CLIPVisionModel,
										    CLIPVisionModelWithProjection,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_clip import (
										    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCLIPModel,
										    TFCLIPPreTrainedModel,
										    TFCLIPTextModel,
										    TFCLIPVisionModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_clip import (
										    FlaxCLIPModel,
										    FlaxCLIPPreTrainedModel,
										    FlaxCLIPTextModel,
										    FlaxCLIPTextPreTrainedModel,
										    FlaxCLIPVisionModel,
										    FlaxCLIPVisionPreTrainedModel,
										)


else:
					import sys

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