



'''simple docstring'''





import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


__a  			=						logging.get_logger(__name__)  # pylint: disable=invalid-name







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    def __init__(       self   :	int					,	snake_case_   :	AutoencoderKL					,	snake_case_   :	CLIPTextModel					,	snake_case_   :	CLIPTokenizer					,	snake_case_   :	UNetaDConditionModel					,	snake_case_   :	Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					,	snake_case_   :	StableDiffusionSafetyChecker					,	snake_case_   :	CLIPImageProcessor					,	):
          super().__init__()
          self.register_modules(
              vae=snake_case_					,	text_encoder=snake_case_					,	tokenizer=snake_case_					,	unet=snake_case_					,	scheduler=snake_case_					,	safety_checker=snake_case_					,	feature_extractor=snake_case_					,	)




    def lowerCamelCase			(       self   :	Any					,	snake_case_   :	Optional[Union[str, int]] = "auto"		):
          if slice_size == "auto":
                # half the attention head size is usually a good trade-off between
                # speed and memory
                snake_case__    :       List[str]       		= self.unet.config.attention_head_dim // 2
          self.unet.set_attention_slice(snake_case_		)




    def lowerCamelCase			(       self   :	Tuple		):
          self.enable_attention_slicing(snake_case_		)




    @torch.no_grad()
    def __call__(       self   :	Union[str, Any]					,	snake_case_   :	Union[str, List[str]]					,	snake_case_   :	int = 512					,	snake_case_   :	int = 512					,	snake_case_   :	int = 50					,	snake_case_   :	float = 7.5					,	snake_case_   :	Optional[Union[str, List[str]]] = None					,	snake_case_   :	Optional[int] = 1					,	snake_case_   :	float = 0.0					,	snake_case_   :	Optional[torch.Generator] = None					,	snake_case_   :	Optional[torch.FloatTensor] = None					,	snake_case_   :	Optional[str] = "pil"					,	snake_case_   :	bool = True					,	snake_case_   :	Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,	snake_case_   :	int = 1					,	snake_case_   :	Optional[torch.FloatTensor] = None					,	**snake_case_   :	Tuple					,	):

          if isinstance(snake_case_					,	snake_case_		):
                snake_case__    :       Any       		= 1
          elif isinstance(snake_case_					,	snake_case_		):
                snake_case__    :       str       		= len(snake_case_		)
          else:
                raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(snake_case_		)}"		)

          if height % 8 != 0 or width % 8 != 0:
                raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}."		)

          if (callback_steps is None) or (
              callback_steps is not None and (not isinstance(snake_case_					,	snake_case_		) or callback_steps <= 0)
          ):
                raise ValueError(
                    f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                    f" {type(snake_case_		)}."		)

          # get prompt text embeddings
          snake_case__    :       Optional[Any]       		= self.tokenizer(
              snake_case_					,	padding="""max_length"""					,	max_length=self.tokenizer.model_max_length					,	return_tensors="""pt"""					,	)
          snake_case__    :       List[Any]       		= text_inputs.input_ids

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                snake_case__    :       Union[str, Any]       		= self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]		)
                logger.warning(
                    """The following part of your input was truncated because CLIP can only handle sequences up to"""
                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"		)
                snake_case__    :       List[Any]       		= text_input_ids[:, : self.tokenizer.model_max_length]

          if text_embeddings is None:
                snake_case__    :       Optional[Any]       		= self.text_encoder(text_input_ids.to(self.device		)		)[0]

          # duplicate text embeddings for each generation per prompt, using mps friendly method
          snake_case__		,							snake_case__		,							snake_case__    :       Optional[int]       		= text_embeddings.shape
          snake_case__    :       List[str]       		= text_embeddings.repeat(1					,	snake_case_					,	1		)
          snake_case__    :       Optional[int]       		= text_embeddings.view(bs_embed * num_images_per_prompt					,	snake_case_					,	-1		)

          # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
          # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
          # corresponds to doing no classifier free guidance.
          snake_case__    :       Optional[int]       		= guidance_scale > 1.0
          # get unconditional embeddings for classifier free guidance
          if do_classifier_free_guidance:
                snake_case__    :       List[str]
                if negative_prompt is None:
                      snake_case__    :       Tuple       		= [""""""]
                elif type(snake_case_		) is not type(snake_case_		):
                      raise TypeError(
                          f"`negative_prompt` should be the same type to `prompt`, but got {type(snake_case_		)} !="
                          f" {type(snake_case_		)}."		)
                elif isinstance(snake_case_					,	snake_case_		):
                      snake_case__    :       Any       		= [negative_prompt]
                elif batch_size != len(snake_case_		):
                      raise ValueError(
                          f"`negative_prompt`: {negative_prompt} has batch size {len(snake_case_		)}, but `prompt`:"
                          f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                          """ the batch size of `prompt`."""		)
                else:
                      snake_case__    :       int       		= negative_prompt

                snake_case__    :       List[str]       		= text_input_ids.shape[-1]
                snake_case__    :       Tuple       		= self.tokenizer(
                    snake_case_					,	padding="""max_length"""					,	max_length=snake_case_					,	truncation=snake_case_					,	return_tensors="""pt"""					,	)
                snake_case__    :       Union[str, Any]       		= self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

                # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                snake_case__    :       Any       		= uncond_embeddings.shape[1]
                snake_case__    :       Optional[int]       		= uncond_embeddings.repeat(snake_case_					,	snake_case_					,	1		)
                snake_case__    :       List[str]       		= uncond_embeddings.view(batch_size * num_images_per_prompt					,	snake_case_					,	-1		)

                # For classifier free guidance, we need to do two forward passes.
                # Here we concatenate the unconditional and text embeddings into a single batch
                # to avoid doing two forward passes
                snake_case__    :       Dict       		= torch.cat([uncond_embeddings, text_embeddings]		)

          # get the initial random noise unless the user supplied it

          # Unlike in other pipelines, latents need to be generated in the target device
          # for 1-to-1 results reproducibility with the CompVis implementation.
          # However this currently doesn't work in `mps`.
          snake_case__    :       int       		= (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
          snake_case__    :       Dict       		= (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
          snake_case__    :       Union[str, Any]       		= text_embeddings.dtype
          if latents is None:
                if self.device.type == "mps":
                      # randn does not exist on mps
                      snake_case__    :       Any       		= torch.randn(
                          snake_case_					,	generator=snake_case_					,	device="""cpu"""					,	dtype=snake_case_		).to(self.device		)
                      snake_case__    :       Tuple       		= torch.randn(snake_case_					,	generator=snake_case_					,	device="""cpu"""					,	dtype=snake_case_		).to(
                          self.device		)
                else:
                      snake_case__    :       Optional[int]       		= torch.randn(
                          snake_case_					,	generator=snake_case_					,	device=self.device					,	dtype=snake_case_		)
                      snake_case__    :       str       		= torch.randn(snake_case_					,	generator=snake_case_					,	device=self.device					,	dtype=snake_case_		)
          else:
                if latents_reference.shape != latents_shape:
                      raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"		)
                snake_case__    :       Dict       		= latents_reference.to(self.device		)
                snake_case__    :       List[Any]       		= latents.to(self.device		)

          # This is the key part of the pipeline where we
          # try to ensure that the generated images w/ the same seed
          # but different sizes actually result in similar images
          snake_case__    :       List[str]       		= (latents_shape[3] - latents_shape_reference[3]) // 2
          snake_case__    :       int       		= (latents_shape[2] - latents_shape_reference[2]) // 2
          snake_case__    :       Optional[int]       		= latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
          snake_case__    :       Optional[int]       		= latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
          snake_case__    :       Optional[Any]       		= 0 if dx < 0 else dx
          snake_case__    :       Union[str, Any]       		= 0 if dy < 0 else dy
          snake_case__    :       Any       		= max(-dx					,	0		)
          snake_case__    :       Tuple       		= max(-dy					,	0		)
          # import pdb
          # pdb.set_trace()
          snake_case__    :       List[str]       		= latents_reference[:, :, dy : dy + h, dx : dx + w]

          # set timesteps
          self.scheduler.set_timesteps(snake_case_		)

          # Some schedulers like PNDM have timesteps as arrays
          # It's more optimized to move all timesteps to correct device beforehand
          snake_case__    :       int       		= self.scheduler.timesteps.to(self.device		)

          # scale the initial noise by the standard deviation required by the scheduler
          snake_case__    :       str       		= latents * self.scheduler.init_noise_sigma

          # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
          # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
          # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
          # and should be between [0, 1]
          snake_case__    :       List[str]       		= """eta""" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
          snake_case__    :       Optional[int]       		= {}
          if accepts_eta:
                snake_case__    :       Optional[int]       		= eta

          for i, t in enumerate(self.progress_bar(snake_case_		)		):
                # expand the latents if we are doing classifier free guidance
                snake_case__    :       str       		= torch.cat([latents] * 2		) if do_classifier_free_guidance else latents
                snake_case__    :       Union[str, Any]       		= self.scheduler.scale_model_input(snake_case_					,	snake_case_		)

                # predict the noise residual
                snake_case__    :       Dict       		= self.unet(snake_case_					,	snake_case_					,	encoder_hidden_states=snake_case_		).sample

                # perform guidance
                if do_classifier_free_guidance:
                      snake_case__		,							snake_case__    :       Optional[int]       		= noise_pred.chunk(2		)
                      snake_case__    :       Any       		= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                # compute the previous noisy sample x_t -> x_t-1
                snake_case__    :       List[str]       		= self.scheduler.step(snake_case_					,	snake_case_					,	snake_case_					,	**snake_case_		).prev_sample

                # call the callback, if provided
                if callback is not None and i % callback_steps == 0:
                      callback(snake_case_					,	snake_case_					,	snake_case_		)

          snake_case__    :       Optional[int]       		= 1 / 0.18215 * latents
          snake_case__    :       Union[str, Any]       		= self.vae.decode(snake_case_		).sample

          snake_case__    :       Any       		= (image / 2 + 0.5).clamp(0					,	1		)

          # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
          snake_case__    :       List[str]       		= image.cpu().permute(0					,	2					,	3					,	1		).float().numpy()

          if self.safety_checker is not None:
                snake_case__    :       Union[str, Any]       		= self.feature_extractor(self.numpy_to_pil(snake_case_		)					,	return_tensors="""pt"""		).to(
                    self.device		)
                snake_case__		,							snake_case__    :       Optional[Any]       		= self.safety_checker(
                    images=snake_case_					,	clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype		)		)
          else:
                snake_case__    :       List[Any]       		= None

          if output_type == "pil":
                snake_case__    :       List[str]       		= self.numpy_to_pil(snake_case_		)

          if not return_dict:
                return (image, has_nsfw_concept)

          return StableDiffusionPipelineOutput(images=snake_case_					,	nsfw_content_detected=snake_case_		)





'''simple docstring'''





import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__a  			=						logging.getLogger(__name__)







@dataclass
class        UpperCAmelCase_			:
    """simple docstring"""







    lowercase   =						42
    lowercase   =						42
    lowercase   =						42







@dataclass
class        UpperCAmelCase_			:
    """simple docstring"""







    lowercase   =						42
    lowercase   =						42
    lowercase   =						None
    lowercase   =						None







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						"train"
    lowercase   =						"dev"
    lowercase   =						"test"







class        UpperCAmelCase_			:
    """simple docstring"""







    @staticmethod
    def lowerCamelCase			(       snake_case_   :	Optional[Any]					,	snake_case_   :	Union[Split, str]		):
          raise NotImplementedError




    @staticmethod
    def lowerCamelCase			(       snake_case_   :	str		):
          raise NotImplementedError




    @staticmethod
    def lowerCamelCase			(       snake_case_   :	List[InputExample]					,	snake_case_   :	List[str]					,	snake_case_   :	int					,	snake_case_   :	PreTrainedTokenizer					,	snake_case_   :	Optional[int]=False					,	snake_case_   :	Dict="[CLS]"					,	snake_case_   :	str=1					,	snake_case_   :	Dict="[SEP]"					,	snake_case_   :	List[str]=False					,	snake_case_   :	int=False					,	snake_case_   :	Tuple=0					,	snake_case_   :	Union[str, Any]=0					,	snake_case_   :	List[Any]=-100					,	snake_case_   :	Any=0					,	snake_case_   :	Union[str, Any]=True					,	):
          snake_case__    :       int       		= {label: i for i, label in enumerate(snake_case_		)}

          snake_case__    :       List[Any]       		= []
          for ex_index, example in enumerate(snake_case_		):
                if ex_index % 10_000 == 0:
                      logger.info("""Writing example %d of %d"""					,	snake_case_					,	len(snake_case_		)		)

                snake_case__    :       Tuple       		= []
                snake_case__    :       Dict       		= []
                for word, label in zip(example.words					,	example.labels		):
                      snake_case__    :       Any       		= tokenizer.tokenize(snake_case_		)

                      # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                      if len(snake_case_		) > 0:
                            tokens.extend(snake_case_		)
                            # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                            label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(snake_case_		) - 1)		)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
                snake_case__    :       Dict       		= tokenizer.num_special_tokens_to_add()
                if len(snake_case_		) > max_seq_length - special_tokens_count:
                      snake_case__    :       Tuple       		= tokens[: (max_seq_length - special_tokens_count)]
                      snake_case__    :       Tuple       		= label_ids[: (max_seq_length - special_tokens_count)]

                # The convention in BERT is:
                # (a) For sequence pairs:
                #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
                #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
                # (b) For single sequences:
                #  tokens:   [CLS] the dog is hairy . [SEP]
                #  type_ids:   0   0   0   0  0     0   0
                #
                # Where "type_ids" are used to indicate whether this is the first
                # sequence or the second sequence. The embedding vectors for `type=0` and
                # `type=1` were learned during pre-training and are added to the wordpiece
                # embedding vector (and position vector). This is not *strictly* necessary
                # since the [SEP] token unambiguously separates the sequences, but it makes
                # it easier for the model to learn the concept of sequences.
                #
                # For classification tasks, the first vector (corresponding to [CLS]) is
                # used as the "sentence vector". Note that this only makes sense because
                # the entire model is fine-tuned.
                tokens += [sep_token]
                label_ids += [pad_token_label_id]
                if sep_token_extra:
                      # roberta uses an extra separator b/w pairs of sentences
                      tokens += [sep_token]
                      label_ids += [pad_token_label_id]
                snake_case__    :       Dict       		= [sequence_a_segment_id] * len(snake_case_		)

                if cls_token_at_end:
                      tokens += [cls_token]
                      label_ids += [pad_token_label_id]
                      segment_ids += [cls_token_segment_id]
                else:
                      snake_case__    :       str       		= [cls_token] + tokens
                      snake_case__    :       Union[str, Any]       		= [pad_token_label_id] + label_ids
                      snake_case__    :       Any       		= [cls_token_segment_id] + segment_ids

                snake_case__    :       Union[str, Any]       		= tokenizer.convert_tokens_to_ids(snake_case_		)

                # The mask has 1 for real tokens and 0 for padding tokens. Only real
                # tokens are attended to.
                snake_case__    :       Tuple       		= [1 if mask_padding_with_zero else 0] * len(snake_case_		)

                # Zero-pad up to the sequence length.
                snake_case__    :       Dict       		= max_seq_length - len(snake_case_		)
                if pad_on_left:
                      snake_case__    :       Optional[Any]       		= ([pad_token] * padding_length) + input_ids
                      snake_case__    :       Union[str, Any]       		= ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                      snake_case__    :       Union[str, Any]       		= ([pad_token_segment_id] * padding_length) + segment_ids
                      snake_case__    :       Union[str, Any]       		= ([pad_token_label_id] * padding_length) + label_ids
                else:
                      input_ids += [pad_token] * padding_length
                      input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                      segment_ids += [pad_token_segment_id] * padding_length
                      label_ids += [pad_token_label_id] * padding_length

                assert len(snake_case_		) == max_seq_length
                assert len(snake_case_		) == max_seq_length
                assert len(snake_case_		) == max_seq_length
                assert len(snake_case_		) == max_seq_length

                if ex_index < 5:
                      logger.info("""*** Example ***"""		)
                      logger.info("""guid: %s"""					,	example.guid		)
                      logger.info("""tokens: %s"""					,	""" """.join([str(snake_case_		) for x in tokens]		)		)
                      logger.info("""input_ids: %s"""					,	""" """.join([str(snake_case_		) for x in input_ids]		)		)
                      logger.info("""input_mask: %s"""					,	""" """.join([str(snake_case_		) for x in input_mask]		)		)
                      logger.info("""segment_ids: %s"""					,	""" """.join([str(snake_case_		) for x in segment_ids]		)		)
                      logger.info("""label_ids: %s"""					,	""" """.join([str(snake_case_		) for x in label_ids]		)		)

                if "token_type_ids" not in tokenizer.model_input_names:
                      snake_case__    :       Tuple       		= None

                features.append(
                    InputFeatures(
                        input_ids=snake_case_					,	attention_mask=snake_case_					,	token_type_ids=snake_case_					,	label_ids=snake_case_		)		)
          return features


if is_torch_available():
       import torch
       from torch import nn
       from torch.utils.data import Dataset







       class        UpperCAmelCase_			(			_a      ):
           """simple docstring"""







           lowercase   =						42
           lowercase   =						nn.CrossEntropyLoss().ignore_index




           def __init__(       self   :	int					,	snake_case_   :	TokenClassificationTask					,	snake_case_   :	str					,	snake_case_   :	PreTrainedTokenizer					,	snake_case_   :	List[str]					,	snake_case_   :	str					,	snake_case_   :	Optional[int] = None					,	snake_case_   :	Tuple=False					,	snake_case_   :	Split = Split.train					,	):
                 # Load data features from cache or dataset file
                 snake_case__    :       Tuple       		= os.path.join(
                     snake_case_					,	"""cached_{}_{}_{}""".format(mode.value					,	tokenizer.__class__.__name__					,	str(snake_case_		)		)					,	)

                 # Make sure only the first process in distributed training processes the dataset,
                 # and the others will use the cache.
                 snake_case__    :       Dict       		= cached_features_file + """.lock"""
                 with FileLock(snake_case_		):
                       if os.path.exists(snake_case_		) and not overwrite_cache:
                             logger.info(f"Loading features from cached file {cached_features_file}"		)
                             snake_case__    :       Tuple       		= torch.load(snake_case_		)
                       else:
                             logger.info(f"Creating features from dataset file at {data_dir}"		)
                             snake_case__    :       Any       		= token_classification_task.read_examples_from_file(snake_case_					,	snake_case_		)
                             # TODO clean up all this to leverage built-in features of tokenizers
                             snake_case__    :       Any       		= token_classification_task.convert_examples_to_features(
                                 snake_case_					,	snake_case_					,	snake_case_					,	snake_case_					,	cls_token_at_end=bool(model_type in ["""xlnet"""]		)					,	cls_token=tokenizer.cls_token					,	cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0					,	sep_token=tokenizer.sep_token					,	sep_token_extra=snake_case_					,	pad_on_left=bool(tokenizer.padding_side == """left"""		)					,	pad_token=tokenizer.pad_token_id					,	pad_token_segment_id=tokenizer.pad_token_type_id					,	pad_token_label_id=self.pad_token_label_id					,	)
                             logger.info(f"Saving features into cached file {cached_features_file}"		)
                             torch.save(self.features					,	snake_case_		)




           def __len__(       self   :	str		):
                 return len(self.features		)




           def __getitem__(       self   :	int					,	snake_case_   :	Dict		):
                 return self.features[i]


if is_tf_available():
       import tensorflow as tf







       class        UpperCAmelCase_			:
           """simple docstring"""







           lowercase   =						42
           lowercase   =						-1_00




           def __init__(       self   :	List[str]					,	snake_case_   :	TokenClassificationTask					,	snake_case_   :	str					,	snake_case_   :	PreTrainedTokenizer					,	snake_case_   :	List[str]					,	snake_case_   :	str					,	snake_case_   :	Optional[int] = None					,	snake_case_   :	Any=False					,	snake_case_   :	Split = Split.train					,	):
                 snake_case__    :       int       		= token_classification_task.read_examples_from_file(snake_case_					,	snake_case_		)
                 # TODO clean up all this to leverage built-in features of tokenizers
                 snake_case__    :       Optional[Any]       		= token_classification_task.convert_examples_to_features(
                     snake_case_					,	snake_case_					,	snake_case_					,	snake_case_					,	cls_token_at_end=bool(model_type in ["""xlnet"""]		)					,	cls_token=tokenizer.cls_token					,	cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0					,	sep_token=tokenizer.sep_token					,	sep_token_extra=snake_case_					,	pad_on_left=bool(tokenizer.padding_side == """left"""		)					,	pad_token=tokenizer.pad_token_id					,	pad_token_segment_id=tokenizer.pad_token_type_id					,	pad_token_label_id=self.pad_token_label_id					,	)

                 def gen():
                       for ex in self.features:
                             if ex.token_type_ids is None:
                                   yield (
                                       {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                       ex.label_ids,
                                   )
                             else:
                                   yield (
                                       {
                                           "input_ids": ex.input_ids,
                                           "attention_mask": ex.attention_mask,
                                           "token_type_ids": ex.token_type_ids,
                                       },
                                       ex.label_ids,
                                   )

                 if "token_type_ids" not in tokenizer.model_input_names:
                       snake_case__    :       str       		= tf.data.Dataset.from_generator(
                           snake_case_					,	({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)					,	(
                               {"""input_ids""": tf.TensorShape([None]		), """attention_mask""": tf.TensorShape([None]		)},
                               tf.TensorShape([None]		),
                           )					,	)
                 else:
                       snake_case__    :       str       		= tf.data.Dataset.from_generator(
                           snake_case_					,	({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)					,	(
                               {
                                   """input_ids""": tf.TensorShape([None]		),
                                   """attention_mask""": tf.TensorShape([None]		),
                                   """token_type_ids""": tf.TensorShape([None]		),
                               },
                               tf.TensorShape([None]		),
                           )					,	)




           def lowerCamelCase			(       self   :	Optional[Any]		):
                 snake_case__    :       Optional[Any]       		= self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features		)		)		)

                 return self.dataset




           def __len__(       self   :	Optional[Any]		):
                 return len(self.features		)




           def __getitem__(       self   :	str					,	snake_case_   :	List[str]		):
                 return self.features[i]
