






'''simple docstring'''

from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


lowerCAmelCase_ : int						  =      logging.get_logger(__name__)

# General docstring
lowerCAmelCase_ : List[Any]						  =      "RegNetConfig"

# Base docstring
lowerCAmelCase_ : List[str]						  =      "facebook/regnet-y-040"
lowerCAmelCase_ : List[Any]						  =      [1, 1_088, 7, 7]

# Image classification docstring
lowerCAmelCase_ : Tuple						  =      "facebook/regnet-y-040"
lowerCAmelCase_ : Optional[Any]						  =      "tabby, tabby cat"

lowerCAmelCase_ : int						  =      [
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]





class 							lowercase						(						tf.keras.layers.Layer ):


   def __init__(   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	int = 3   ,		__lowerCAmelCase				:	int = 1   ,		__lowerCAmelCase				:	int = 1   ,		__lowerCAmelCase				:	Optional[str] = "relu"   ,		**__lowerCAmelCase				:	List[str]   ,		)      ->					Tuple:
         super().__init__(**__lowerCAmelCase)
         # The padding and conv has been verified in
         # https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
         lowercase_     =      tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2)
         lowercase_     =      tf.keras.layers.ConvaD(
             filters=__lowerCAmelCase   ,		kernel_size=__lowerCAmelCase   ,		strides=__lowerCAmelCase   ,		padding="VALID"   ,		groups=__lowerCAmelCase   ,		use_bias=__lowerCAmelCase   ,		name="convolution"   ,		)
         lowercase_     =      tf.keras.layers.BatchNormalization(epsilon=1e-5   ,		momentum=0.9   ,		name="normalization")
         lowercase_     =      ACTaFN[activation] if activation is not None else tf.identity


   def __UpperCAmelCase       (   self				:	Dict   ,		__lowerCAmelCase				:	List[Any])      ->					Optional[int]:
         lowercase_     =      self.convolution(self.padding(__lowerCAmelCase))
         lowercase_     =      self.normalization(__lowerCAmelCase)
         lowercase_     =      self.activation(__lowerCAmelCase)
         return hidden_state





class 							lowercase						(						tf.keras.layers.Layer ):


   def __init__(   self				:	Any   ,		__lowerCAmelCase				:	RegNetConfig   ,		**__lowerCAmelCase				:	str)      ->					Tuple:
         super().__init__(**__lowerCAmelCase)
         lowercase_     =      config.num_channels
         lowercase_     =      TFRegNetConvLayer(
             out_channels=config.embedding_size   ,		kernel_size=3   ,		stride=2   ,		activation=config.hidden_act   ,		name="embedder"   ,		)


   def __UpperCAmelCase       (   self				:	Optional[Any]   ,		__lowerCAmelCase				:	List[str])      ->					Tuple:
         lowercase_     =      shape_list(__lowerCAmelCase)[1]
         if tf.executing_eagerly() and num_channels != self.num_channels:
               raise ValueError(
                   "Make sure that the channel dimension of the pixel values match with the one set in the configuration.")

         # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
         # So change the input format from `NCHW` to `NHWC`.
         # shape = (batch_size, in_height, in_width, in_channels=num_channels)
         lowercase_     =      tf.transpose(__lowerCAmelCase   ,		perm=(0, 2, 3, 1))
         lowercase_     =      self.embedder(__lowerCAmelCase)
         return hidden_state





class 							lowercase						(						tf.keras.layers.Layer ):


   def __init__(   self				:	Optional[Any]   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	int = 2   ,		**__lowerCAmelCase				:	Union[str, Any])      ->					List[str]:
         super().__init__(**__lowerCAmelCase)
         lowercase_     =      tf.keras.layers.ConvaD(
             filters=__lowerCAmelCase   ,		kernel_size=1   ,		strides=__lowerCAmelCase   ,		use_bias=__lowerCAmelCase   ,		name="convolution")
         lowercase_     =      tf.keras.layers.BatchNormalization(epsilon=1e-5   ,		momentum=0.9   ,		name="normalization")


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	tf.Tensor   ,		__lowerCAmelCase				:	bool = False)      ->					tf.Tensor:
         return self.normalization(self.convolution(__lowerCAmelCase)   ,		training=__lowerCAmelCase)





class 							lowercase						(						tf.keras.layers.Layer ):


   def __init__(   self				:	Optional[int]   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	int   ,		**__lowerCAmelCase				:	List[str])      ->					str:
         super().__init__(**__lowerCAmelCase)
         lowercase_     =      tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowerCAmelCase   ,		name="pooler")
         lowercase_     =      [
             tf.keras.layers.ConvaD(filters=__lowerCAmelCase   ,		kernel_size=1   ,		activation="relu"   ,		name="attention.0"),
             tf.keras.layers.ConvaD(filters=__lowerCAmelCase   ,		kernel_size=1   ,		activation="sigmoid"   ,		name="attention.2"),
         ]


   def __UpperCAmelCase       (   self				:	Optional[Any]   ,		__lowerCAmelCase				:	str)      ->					Dict:
         # [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
         lowercase_     =      self.pooler(__lowerCAmelCase)
         for layer_module in self.attention:
               lowercase_     =      layer_module(__lowerCAmelCase)
         lowercase_     =      hidden_state * pooled
         return hidden_state





class 							lowercase						(						tf.keras.layers.Layer ):


   def __init__(   self				:	Tuple   ,		__lowerCAmelCase				:	RegNetConfig   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	int = 1   ,		**__lowerCAmelCase				:	Optional[Any])      ->					Dict:
         super().__init__(**__lowerCAmelCase)
         lowercase_     =      in_channels != out_channels or stride != 1
         lowercase_     =      max(1   ,		out_channels // config.groups_width)
         lowercase_     =      (
             TFRegNetShortCut(__lowerCAmelCase   ,		stride=__lowerCAmelCase   ,		name="shortcut")
             if should_apply_shortcut
             else tf.keras.layers.Activation("linear"   ,		name="shortcut")
         )
         # `self.layers` instead of `self.layer` because that is a reserved argument.
         lowercase_     =      [
             TFRegNetConvLayer(__lowerCAmelCase   ,		kernel_size=1   ,		activation=config.hidden_act   ,		name="layer.0"),
             TFRegNetConvLayer(
                 __lowerCAmelCase   ,		stride=__lowerCAmelCase   ,		groups=__lowerCAmelCase   ,		activation=config.hidden_act   ,		name="layer.1"),
             TFRegNetConvLayer(__lowerCAmelCase   ,		kernel_size=1   ,		activation=__lowerCAmelCase   ,		name="layer.2"),
         ]
         lowercase_     =      ACTaFN[config.hidden_act]


   def __UpperCAmelCase       (   self				:	str   ,		__lowerCAmelCase				:	int)      ->					Tuple:
         lowercase_     =      hidden_state
         for layer_module in self.layers:
               lowercase_     =      layer_module(__lowerCAmelCase)
         lowercase_     =      self.shortcut(__lowerCAmelCase)
         hidden_state += residual
         lowercase_     =      self.activation(__lowerCAmelCase)
         return hidden_state





class 							lowercase						(						tf.keras.layers.Layer ):


   def __init__(   self				:	Optional[int]   ,		__lowerCAmelCase				:	RegNetConfig   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	int = 1   ,		**__lowerCAmelCase				:	int)      ->					Tuple:
         super().__init__(**__lowerCAmelCase)
         lowercase_     =      in_channels != out_channels or stride != 1
         lowercase_     =      max(1   ,		out_channels // config.groups_width)
         lowercase_     =      (
             TFRegNetShortCut(__lowerCAmelCase   ,		stride=__lowerCAmelCase   ,		name="shortcut")
             if should_apply_shortcut
             else tf.keras.layers.Activation("linear"   ,		name="shortcut")
         )
         lowercase_     =      [
             TFRegNetConvLayer(__lowerCAmelCase   ,		kernel_size=1   ,		activation=config.hidden_act   ,		name="layer.0"),
             TFRegNetConvLayer(
                 __lowerCAmelCase   ,		stride=__lowerCAmelCase   ,		groups=__lowerCAmelCase   ,		activation=config.hidden_act   ,		name="layer.1"),
             TFRegNetSELayer(__lowerCAmelCase   ,		reduced_channels=int(round(in_channels / 4))   ,		name="layer.2"),
             TFRegNetConvLayer(__lowerCAmelCase   ,		kernel_size=1   ,		activation=__lowerCAmelCase   ,		name="layer.3"),
         ]
         lowercase_     =      ACTaFN[config.hidden_act]


   def __UpperCAmelCase       (   self				:	Dict   ,		__lowerCAmelCase				:	List[Any])      ->					str:
         lowercase_     =      hidden_state
         for layer_module in self.layers:
               lowercase_     =      layer_module(__lowerCAmelCase)
         lowercase_     =      self.shortcut(__lowerCAmelCase)
         hidden_state += residual
         lowercase_     =      self.activation(__lowerCAmelCase)
         return hidden_state





class 							lowercase						(						tf.keras.layers.Layer ):


   def __init__(   self				:	List[str]   ,		__lowerCAmelCase				:	RegNetConfig   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	int = 2   ,		__lowerCAmelCase				:	int = 2   ,		**__lowerCAmelCase				:	str)      ->					int:
         super().__init__(**__lowerCAmelCase)

         lowercase_     =      TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
         lowercase_     =      [
             # downsampling is done in the first layer with stride of 2
             layer(__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		stride=__lowerCAmelCase   ,		name="layers.0"),
             *[layer(__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		name=F'layers.{i+1}') for i in range(depth - 1)],
         ]


   def __UpperCAmelCase       (   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	Tuple)      ->					List[Any]:
         for layer_module in self.layers:
               lowercase_     =      layer_module(__lowerCAmelCase)
         return hidden_state





class 							lowercase						(						tf.keras.layers.Layer ):


   def __init__(   self				:	Optional[int]   ,		__lowerCAmelCase				:	RegNetConfig   ,		**__lowerCAmelCase				:	Union[str, Any])      ->					Optional[int]:
         super().__init__(**__lowerCAmelCase)
         lowercase_     =      []
         # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
         self.stages.append(
             TFRegNetStage(
                 __lowerCAmelCase   ,		config.embedding_size   ,		config.hidden_sizes[0]   ,		stride=2 if config.downsample_in_first_stage else 1   ,		depth=config.depths[0]   ,		name="stages.0"   ,		))
         lowercase_     =      zip(config.hidden_sizes   ,		config.hidden_sizes[1:])
         for i, ((in_channels, out_channels), depth) in enumerate(zip(__lowerCAmelCase   ,		config.depths[1:])):
               self.stages.append(TFRegNetStage(__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		depth=__lowerCAmelCase   ,		name=F'stages.{i+1}'))


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	tf.Tensor   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	bool = True)      ->					TFBaseModelOutputWithNoAttention:
         lowercase_     =      () if output_hidden_states else None

         for stage_module in self.stages:
               if output_hidden_states:
                     lowercase_     =      hidden_states + (hidden_state,)

               lowercase_     =      stage_module(__lowerCAmelCase)

         if output_hidden_states:
               lowercase_     =      hidden_states + (hidden_state,)

         if not return_dict:
               return tuple(v for v in [hidden_state, hidden_states] if v is not None)

         return TFBaseModelOutputWithNoAttention(last_hidden_state=__lowerCAmelCase   ,		hidden_states=__lowerCAmelCase)





@keras_serializable
class 							lowercase						(						tf.keras.layers.Layer ):
   lowerCamelCase_    					=RegNetConfig


   def __init__(   self				:	Any   ,		__lowerCAmelCase				:	Tuple   ,		**__lowerCAmelCase				:	Optional[int])      ->					Any:
         super().__init__(**__lowerCAmelCase)
         lowercase_     =      config
         lowercase_     =      TFRegNetEmbeddings(__lowerCAmelCase   ,		name="embedder")
         lowercase_     =      TFRegNetEncoder(__lowerCAmelCase   ,		name="encoder")
         lowercase_     =      tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowerCAmelCase   ,		name="pooler")


   @unpack_inputs
   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	tf.Tensor   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	bool = False   ,		)      ->					TFBaseModelOutputWithPoolingAndNoAttention:
         lowercase_     =      (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         lowercase_     =      return_dict if return_dict is not None else self.config.use_return_dict

         lowercase_     =      self.embedder(__lowerCAmelCase   ,		training=__lowerCAmelCase)

         lowercase_     =      self.encoder(
             __lowerCAmelCase   ,		output_hidden_states=__lowerCAmelCase   ,		return_dict=__lowerCAmelCase   ,		training=__lowerCAmelCase)

         lowercase_     =      encoder_outputs[0]
         lowercase_     =      self.pooler(__lowerCAmelCase)

         # Change to NCHW output format have uniformity in the modules
         lowercase_     =      tf.transpose(__lowerCAmelCase   ,		perm=(0, 3, 1, 2))
         lowercase_     =      tf.transpose(__lowerCAmelCase   ,		perm=(0, 3, 1, 2))

         # Change the other hidden state outputs to NCHW as well
         if output_hidden_states:
               lowercase_     =      tuple([tf.transpose(__lowerCAmelCase   ,		perm=(0, 3, 1, 2)) for h in encoder_outputs[1]])

         if not return_dict:
               return (last_hidden_state, pooled_output) + encoder_outputs[1:]

         return TFBaseModelOutputWithPoolingAndNoAttention(
             last_hidden_state=__lowerCAmelCase   ,		pooler_output=__lowerCAmelCase   ,		hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states   ,		)





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=RegNetConfig
   lowerCamelCase_    					='regnet'
   lowerCamelCase_    					='pixel_values'


   @property
   def __UpperCAmelCase       (   self				:	Dict)      ->					Union[str, Any]:
         return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)   ,		dtype=tf.floataa)}




lowerCAmelCase_ : Union[str, Any]						  =      r"\n    Parameters:\n    This model is a Tensorflow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n    behavior.\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n"

lowerCAmelCase_ : Optional[Any]						  =      r"\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConveNextImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"





@add_start_docstrings(
    'The bare RegNet model outputting raw features without any specific head on top.' ,				__lowerCamelCase ,				)
class 							lowercase						(						__lowerCamelCase ):


   def __init__(   self				:	str   ,		__lowerCAmelCase				:	RegNetConfig   ,		*__lowerCAmelCase				:	int   ,		**__lowerCAmelCase				:	Union[str, Any])      ->					Optional[int]:
         super().__init__(__lowerCAmelCase   ,		*__lowerCAmelCase   ,		**__lowerCAmelCase)
         lowercase_     =      TFRegNetMainLayer(__lowerCAmelCase   ,		name="regnet")


   @unpack_inputs
   @add_start_docstrings_to_model_forward(__lowerCAmelCase)
   @add_code_sample_docstrings(
       checkpoint=_CHECKPOINT_FOR_DOC   ,		output_type=__lowerCAmelCase   ,		config_class=_CONFIG_FOR_DOC   ,		modality="vision"   ,		expected_output=_EXPECTED_OUTPUT_SHAPE   ,		)
   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	tf.Tensor   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Optional[int]=False   ,		)      ->					Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
         lowercase_     =      (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         lowercase_     =      return_dict if return_dict is not None else self.config.use_return_dict

         lowercase_     =      self.regnet(
             pixel_values=__lowerCAmelCase   ,		output_hidden_states=__lowerCAmelCase   ,		return_dict=__lowerCAmelCase   ,		training=__lowerCAmelCase   ,		)
         if not return_dict:
               return (outputs[0],) + outputs[1:]

         return TFBaseModelOutputWithPoolingAndNoAttention(
             last_hidden_state=outputs.last_hidden_state   ,		pooler_output=outputs.pooler_output   ,		hidden_states=outputs.hidden_states   ,		)





@add_start_docstrings(
    '\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    ' ,				__lowerCamelCase ,				)
class 							lowercase						(						__lowerCamelCase ,				__lowerCamelCase ):


   def __init__(   self				:	Any   ,		__lowerCAmelCase				:	RegNetConfig   ,		*__lowerCAmelCase				:	Any   ,		**__lowerCAmelCase				:	List[Any])      ->					Optional[Any]:
         super().__init__(__lowerCAmelCase   ,		*__lowerCAmelCase   ,		**__lowerCAmelCase)
         lowercase_     =      config.num_labels
         lowercase_     =      TFRegNetMainLayer(__lowerCAmelCase   ,		name="regnet")
         # classification head
         lowercase_     =      [
             tf.keras.layers.Flatten(),
             tf.keras.layers.Dense(config.num_labels   ,		name="classifier.1") if config.num_labels > 0 else tf.identity,
         ]


   @unpack_inputs
   @add_start_docstrings_to_model_forward(__lowerCAmelCase)
   @add_code_sample_docstrings(
       checkpoint=_IMAGE_CLASS_CHECKPOINT   ,		output_type=__lowerCAmelCase   ,		config_class=_CONFIG_FOR_DOC   ,		expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT   ,		)
   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	tf.Tensor = None   ,		__lowerCAmelCase				:	tf.Tensor = None   ,		__lowerCAmelCase				:	bool = None   ,		__lowerCAmelCase				:	bool = None   ,		__lowerCAmelCase				:	Optional[int]=False   ,		)      ->					Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
         lowercase_     =      (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         lowercase_     =      return_dict if return_dict is not None else self.config.use_return_dict

         lowercase_     =      self.regnet(
             __lowerCAmelCase   ,		output_hidden_states=__lowerCAmelCase   ,		return_dict=__lowerCAmelCase   ,		training=__lowerCAmelCase)

         lowercase_     =      outputs.pooler_output if return_dict else outputs[1]

         lowercase_     =      self.classifier[0](__lowerCAmelCase)
         lowercase_     =      self.classifier[1](__lowerCAmelCase)

         lowercase_     =      None if labels is None else self.hf_compute_loss(labels=__lowerCAmelCase   ,		logits=__lowerCAmelCase)

         if not return_dict:
               lowercase_     =      (logits,) + outputs[2:]
               return ((loss,) + output) if loss is not None else output

         return TFSequenceClassifierOutput(loss=__lowerCAmelCase   ,		logits=__lowerCAmelCase   ,		hidden_states=outputs.hidden_states)







'''simple docstring'''

from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream





class 							lowercase						(						__lowerCamelCase ):


   def __init__(   self				:	str   ,		__lowerCAmelCase				:	Callable   ,		__lowerCAmelCase				:	Optional[Features] = None   ,		__lowerCAmelCase				:	str = None   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	Optional[dict] = None   ,		__lowerCAmelCase				:	Optional[int] = None   ,		**__lowerCAmelCase				:	Union[str, Any]   ,		)      ->					List[Any]:
         super().__init__(
             features=__lowerCAmelCase   ,		cache_dir=__lowerCAmelCase   ,		keep_in_memory=__lowerCAmelCase   ,		streaming=__lowerCAmelCase   ,		num_proc=__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)
         lowercase_     =      Generator(
             cache_dir=__lowerCAmelCase   ,		features=__lowerCAmelCase   ,		generator=__lowerCAmelCase   ,		gen_kwargs=__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					List[str]:
         # Build iterable dataset
         if self.streaming:
               lowercase_     =      self.builder.as_streaming_dataset(split="train")
         # Build regular (map-style) dataset
         else:
               lowercase_     =      None
               lowercase_     =      None
               lowercase_     =      None
               lowercase_     =      None

               self.builder.download_and_prepare(
                   download_config=__lowerCAmelCase   ,		download_mode=__lowerCAmelCase   ,		verification_mode=__lowerCAmelCase   ,		base_path=__lowerCAmelCase   ,		num_proc=self.num_proc   ,		)
               lowercase_     =      self.builder.as_dataset(
                   split="train"   ,		verification_mode=__lowerCAmelCase   ,		in_memory=self.keep_in_memory)
         return dataset






