





"""simple docstring"""



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

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_snake_case          =						logging.get_logger(__name__)  # pylint: disable=invalid-name
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				UpperCAmelCase							):






				'''simple docstring'''



				@register_to_config
				def __init__(					self	:			List[str]				,     UpperCAmelCase_	:			bool				,     UpperCAmelCase_	:			Optional[int] = None				,     UpperCAmelCase_	:			Optional[int] = None  )		->  List[str]:







											"""simple docstring"""




											super().__init__()

											_lowerCAmelCase  	=			learnable

											if self.learnable:
																		assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
																		assert length is not None, "learnable=True requires `length` to be set"

																		_lowerCAmelCase  	=			torch.zeros(UpperCAmelCase_				,     UpperCAmelCase_  )
											else:
																		_lowerCAmelCase  	=			None

											_lowerCAmelCase  	=			torch.nn.Parameter(UpperCAmelCase_  )






class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							VQModel
				SCREAMING_SNAKE_CASE_:							CLIPTextModel
				SCREAMING_SNAKE_CASE_:							CLIPTokenizer
				SCREAMING_SNAKE_CASE_:							TransformeraDModel
				SCREAMING_SNAKE_CASE_:							LearnedClassifierFreeSamplingEmbeddings
				SCREAMING_SNAKE_CASE_:							VQDiffusionScheduler


				def __init__(					self	:			Dict				,     UpperCAmelCase_	:			VQModel				,     UpperCAmelCase_	:			CLIPTextModel				,     UpperCAmelCase_	:			CLIPTokenizer				,     UpperCAmelCase_	:			TransformeraDModel				,     UpperCAmelCase_	:			VQDiffusionScheduler				,     UpperCAmelCase_	:			LearnedClassifierFreeSamplingEmbeddings				,     )		->  Union[str, Any]:







											"""simple docstring"""




											super().__init__()

											self.register_modules(
											    vqvae=UpperCAmelCase_				,     transformer=UpperCAmelCase_				,     text_encoder=UpperCAmelCase_				,     tokenizer=UpperCAmelCase_				,     scheduler=UpperCAmelCase_				,     learned_classifier_free_sampling_embeddings=UpperCAmelCase_				,     )


				def 				__lowerCamelCase	(					self	:			List[Any]				,     UpperCAmelCase_	:			Optional[Any]				,     UpperCAmelCase_	:			Dict				,     UpperCAmelCase_	:			List[str]  )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			len(UpperCAmelCase_  ) if isinstance(UpperCAmelCase_				,     UpperCAmelCase_  ) else 1

											# get prompt text embeddings
											_lowerCAmelCase  	=			self.tokenizer(
											    UpperCAmelCase_				,     padding='max_length'				,     max_length=self.tokenizer.model_max_length				,     return_tensors='pt'				,     )
											_lowerCAmelCase  	=			text_inputs.input_ids

											if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																		_lowerCAmelCase  	=			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}"""  )
																		_lowerCAmelCase  	=			text_input_ids[:, : self.tokenizer.model_max_length]
											_lowerCAmelCase  	=			self.text_encoder(text_input_ids.to(self.device  )  )[0]

											# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
											# While CLIP does normalize the pooled output of the text transformer when combining
											# the image and text embeddings, CLIP does not directly normalize the last hidden state.
											#
											# CLIP normalizing the pooled output.
											# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
											_lowerCAmelCase  	=			prompt_embeds / prompt_embeds.norm(dim=-1				,     keepdim=UpperCAmelCase_  )

											# duplicate text embeddings for each generation per prompt
											_lowerCAmelCase  	=			prompt_embeds.repeat_interleave(UpperCAmelCase_				,     dim=0  )

											if do_classifier_free_guidance:
																		if self.learned_classifier_free_sampling_embeddings.learnable:
																									_lowerCAmelCase  	=			self.learned_classifier_free_sampling_embeddings.embeddings
																									_lowerCAmelCase  	=			negative_prompt_embeds.unsqueeze(0  ).repeat(UpperCAmelCase_				,     1				,     1  )
																		else:
																									_lowerCAmelCase  	=			[''] * batch_size

																									_lowerCAmelCase  	=			text_input_ids.shape[-1]
																									_lowerCAmelCase  	=			self.tokenizer(
																									    UpperCAmelCase_				,     padding='max_length'				,     max_length=UpperCAmelCase_				,     truncation=UpperCAmelCase_				,     return_tensors='pt'				,     )
																									_lowerCAmelCase  	=			self.text_encoder(uncond_input.input_ids.to(self.device  )  )[0]
																									# See comment for normalizing text embeddings
																									_lowerCAmelCase  	=			negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1				,     keepdim=UpperCAmelCase_  )

																		# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																		_lowerCAmelCase  	=			negative_prompt_embeds.shape[1]
																		_lowerCAmelCase  	=			negative_prompt_embeds.repeat(1				,     UpperCAmelCase_				,     1  )
																		_lowerCAmelCase  	=			negative_prompt_embeds.view(batch_size * num_images_per_prompt				,     UpperCAmelCase_				,     -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
																		_lowerCAmelCase  	=			torch.cat([negative_prompt_embeds, prompt_embeds]  )

											return prompt_embeds


				@torch.no_grad()
				def __call__(					self	:			Optional[int]				,     UpperCAmelCase_	:			Union[str, List[str]]				,     UpperCAmelCase_	:			int = 100				,     UpperCAmelCase_	:			float = 5.0				,     UpperCAmelCase_	:			float = 1.0				,     UpperCAmelCase_	:			int = 1				,     UpperCAmelCase_	:			Optional[Union[torch.Generator, List[torch.Generator]]] = None				,     UpperCAmelCase_	:			Optional[torch.FloatTensor] = None				,     UpperCAmelCase_	:			Optional[str] = "pil"				,     UpperCAmelCase_	:			bool = True				,     UpperCAmelCase_	:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,     UpperCAmelCase_	:			int = 1				,     )		->  Union[ImagePipelineOutput, Tuple]:







											"""simple docstring"""




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

											_lowerCAmelCase  	=			batch_size * num_images_per_prompt

											_lowerCAmelCase  	=			guidance_scale > 1.0

											_lowerCAmelCase  	=			self._encode_prompt(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_  )

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

											# get the initial completely masked latents unless the user supplied it

											_lowerCAmelCase  	=			(batch_size, self.transformer.num_latent_pixels)
											if latents is None:
																		_lowerCAmelCase  	=			self.transformer.num_vector_embeds - 1
																		_lowerCAmelCase  	=			torch.full(UpperCAmelCase_				,     UpperCAmelCase_  ).to(self.device  )
											else:
																		if latents.shape != latents_shape:
																									raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""  )
																		if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
																									raise ValueError(
																									    'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
																									    F""" {self.transformer.num_vector_embeds - 1} (inclusive)."""  )
																		_lowerCAmelCase  	=			latents.to(self.device  )

											# set timesteps
											self.scheduler.set_timesteps(UpperCAmelCase_				,     device=self.device  )

											_lowerCAmelCase  	=			self.scheduler.timesteps.to(self.device  )

											_lowerCAmelCase  	=			latents

											for i, t in enumerate(self.progress_bar(UpperCAmelCase_  )  ):
																		# expand the sample if we are doing classifier free guidance
																		_lowerCAmelCase  	=			torch.cat([sample] * 2  ) if do_classifier_free_guidance else sample

																		# predict the un-noised image
																		# model_output == `log_p_x_0`
																		_lowerCAmelCase  	=			self.transformer(UpperCAmelCase_				,     encoder_hidden_states=UpperCAmelCase_				,     timestep=UpperCAmelCase_  ).sample

																		if do_classifier_free_guidance:
																									_lowerCAmelCase	,		_lowerCAmelCase  	=			model_output.chunk(2  )
																									_lowerCAmelCase  	=			model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																									model_output -= torch.logsumexp(UpperCAmelCase_				,     dim=1				,     keepdim=UpperCAmelCase_  )

																		_lowerCAmelCase  	=			self.truncate(UpperCAmelCase_				,     UpperCAmelCase_  )

																		# remove `log(0)`'s (`-inf`s)
																		_lowerCAmelCase  	=			model_output.clamp(-70  )

																		# compute the previous noisy sample x_t -> x_t-1
																		_lowerCAmelCase  	=			self.scheduler.step(UpperCAmelCase_				,     timestep=UpperCAmelCase_				,     sample=UpperCAmelCase_				,     generator=UpperCAmelCase_  ).prev_sample

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

											_lowerCAmelCase  	=			self.vqvae.config.vq_embed_dim
											_lowerCAmelCase  	=			(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
											_lowerCAmelCase  	=			self.vqvae.quantize.get_codebook_entry(UpperCAmelCase_				,     shape=UpperCAmelCase_  )
											_lowerCAmelCase  	=			self.vqvae.decode(UpperCAmelCase_				,     force_not_quantize=UpperCAmelCase_  ).sample

											_lowerCAmelCase  	=			(image / 2 + 0.5).clamp(0				,     1  )
											_lowerCAmelCase  	=			image.cpu().permute(0				,     2				,     3				,     1  ).numpy()

											if output_type == "pil":
																		_lowerCAmelCase  	=			self.numpy_to_pil(UpperCAmelCase_  )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=UpperCAmelCase_  )







				def 				__lowerCamelCase	(					self	:			Tuple				,     UpperCAmelCase_	:			torch.FloatTensor				,     UpperCAmelCase_	:			float  )		->  torch.FloatTensor:







											"""simple docstring"""




											_lowerCAmelCase	,		_lowerCAmelCase  	=			torch.sort(UpperCAmelCase_				,     1				,     descending=UpperCAmelCase_  )
											_lowerCAmelCase  	=			torch.exp(UpperCAmelCase_  )
											_lowerCAmelCase  	=			sorted_p_x_0.cumsum(dim=1  ) < truncation_rate

											# Ensure that at least the largest probability is not zeroed out
											_lowerCAmelCase  	=			torch.full_like(keep_mask[:, 0:1, :]				,     UpperCAmelCase_  )
											_lowerCAmelCase  	=			torch.cat((all_true, keep_mask)				,     dim=1  )
											_lowerCAmelCase  	=			keep_mask[:, :-1, :]

											_lowerCAmelCase  	=			keep_mask.gather(1				,     indices.argsort(1  )  )

											_lowerCAmelCase  	=			log_p_x_0.clone()

											_lowerCAmelCase  	=			-torch.inf  # -inf = log(0)

											return rv








"""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


_snake_case          =						logging.getLogger(__name__)
@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							str
				SCREAMING_SNAKE_CASE_:							List[str]
				SCREAMING_SNAKE_CASE_:							Optional[List[str]]






@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							List[int]
				SCREAMING_SNAKE_CASE_:							List[int]
				SCREAMING_SNAKE_CASE_:							Optional[List[int]]							=					None
				SCREAMING_SNAKE_CASE_:							Optional[List[int]]							=					None






class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Union[str, Any]							=					"train"
				SCREAMING_SNAKE_CASE_:							Tuple							=					"dev"
				SCREAMING_SNAKE_CASE_:							List[str]							=					"test"






class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				@staticmethod
				def 				__lowerCamelCase	(					UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Union[Split, str]  )		->  List[InputExample]:







											"""simple docstring"""




											raise NotImplementedError


				@staticmethod
				def 				__lowerCamelCase	(					UpperCAmelCase_	:			str  )		->  List[str]:







											"""simple docstring"""




											raise NotImplementedError







				@staticmethod
				def 				__lowerCamelCase	(					UpperCAmelCase_	:			List[InputExample]				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			PreTrainedTokenizer				,     UpperCAmelCase_	:			Union[str, Any]=False				,     UpperCAmelCase_	:			Optional[int]="[CLS]"				,     UpperCAmelCase_	:			Tuple=1				,     UpperCAmelCase_	:			str="[SEP]"				,     UpperCAmelCase_	:			Optional[Any]=False				,     UpperCAmelCase_	:			Union[str, Any]=False				,     UpperCAmelCase_	:			Union[str, Any]=0				,     UpperCAmelCase_	:			Dict=0				,     UpperCAmelCase_	:			List[Any]=-100				,     UpperCAmelCase_	:			Dict=0				,     UpperCAmelCase_	:			List[Any]=True				,     )		->  List[InputFeatures]:







											"""simple docstring"""




											_lowerCAmelCase  	=			{label: i for i, label in enumerate(UpperCAmelCase_  )}

											_lowerCAmelCase  	=			[]
											for ex_index, example in enumerate(UpperCAmelCase_  ):
																		if ex_index % 10_000 == 0:
																									logger.info('Writing example %d of %d'				,     UpperCAmelCase_				,     len(UpperCAmelCase_  )  )

																		_lowerCAmelCase  	=			[]
																		_lowerCAmelCase  	=			[]
																		for word, label in zip(example.words				,     example.labels  ):
																									_lowerCAmelCase  	=			tokenizer.tokenize(UpperCAmelCase_  )

																									# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																									if len(UpperCAmelCase_  ) > 0:
																																tokens.extend(UpperCAmelCase_  )
																																# 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(UpperCAmelCase_  ) - 1)  )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
																		_lowerCAmelCase  	=			tokenizer.num_special_tokens_to_add()
																		if len(UpperCAmelCase_  ) > max_seq_length - special_tokens_count:
																									_lowerCAmelCase  	=			tokens[: (max_seq_length - special_tokens_count)]
																									_lowerCAmelCase  	=			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]
																		_lowerCAmelCase  	=			[sequence_a_segment_id] * len(UpperCAmelCase_  )

																		if cls_token_at_end:
																									tokens += [cls_token]
																									label_ids += [pad_token_label_id]
																									segment_ids += [cls_token_segment_id]
																		else:
																									_lowerCAmelCase  	=			[cls_token] + tokens
																									_lowerCAmelCase  	=			[pad_token_label_id] + label_ids
																									_lowerCAmelCase  	=			[cls_token_segment_id] + segment_ids

																		_lowerCAmelCase  	=			tokenizer.convert_tokens_to_ids(UpperCAmelCase_  )

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

																		# Zero-pad up to the sequence length.
																		_lowerCAmelCase  	=			max_seq_length - len(UpperCAmelCase_  )
																		if pad_on_left:
																									_lowerCAmelCase  	=			([pad_token] * padding_length) + input_ids
																									_lowerCAmelCase  	=			([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																									_lowerCAmelCase  	=			([pad_token_segment_id] * padding_length) + segment_ids
																									_lowerCAmelCase  	=			([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(UpperCAmelCase_  ) == max_seq_length
																		assert len(UpperCAmelCase_  ) == max_seq_length
																		assert len(UpperCAmelCase_  ) == max_seq_length
																		assert len(UpperCAmelCase_  ) == max_seq_length

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

																		if "token_type_ids" not in tokenizer.model_input_names:
																									_lowerCAmelCase  	=			None

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


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






					'''simple docstring'''



					SCREAMING_SNAKE_CASE_:							List[InputFeatures]
					SCREAMING_SNAKE_CASE_:							int							=					nn.CrossEntropyLoss().ignore_index


					def __init__(					self	:			List[Any]				,     UpperCAmelCase_	:			TokenClassificationTask				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			PreTrainedTokenizer				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Optional[int] = None				,     UpperCAmelCase_	:			List[str]=False				,     UpperCAmelCase_	:			Split = Split.train				,     )		->  List[str]:







												"""simple docstring"""




												_lowerCAmelCase  	=			os.path.join(
												    UpperCAmelCase_				,     'cached_{}_{}_{}'.format(mode.value				,     tokenizer.__class__.__name__				,     str(UpperCAmelCase_  )  )				,     )

												# Make sure only the first process in distributed training processes the dataset,
												# and the others will use the cache.
												_lowerCAmelCase  	=			cached_features_file + '.lock'
												with FileLock(UpperCAmelCase_  ):
																			if os.path.exists(UpperCAmelCase_  ) and not overwrite_cache:
																										logger.info(F"""Loading features from cached file {cached_features_file}"""  )
																										_lowerCAmelCase  	=			torch.load(UpperCAmelCase_  )
																			else:
																										logger.info(F"""Creating features from dataset file at {data_dir}"""  )
																										_lowerCAmelCase  	=			token_classification_task.read_examples_from_file(UpperCAmelCase_				,     UpperCAmelCase_  )
																										# TODO clean up all this to leverage built-in features of tokenizers
																										_lowerCAmelCase  	=			token_classification_task.convert_examples_to_features(
																										    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     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=UpperCAmelCase_				,     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				,     UpperCAmelCase_  )


					def __len__(					self	:			Dict  )		->  int:







												"""simple docstring"""




												return len(self.features  )







					def __getitem__(					self	:			List[str]				,     UpperCAmelCase_	:			List[Any]  )		->  InputFeatures:







												"""simple docstring"""




												return self.features[i]


if is_tf_available():
	import tensorflow as tf
	class        _SCREAMING_SNAKE_CASE  :






					'''simple docstring'''



					SCREAMING_SNAKE_CASE_:							List[InputFeatures]
					SCREAMING_SNAKE_CASE_:							int							=					-1_0_0


					def __init__(					self	:			Tuple				,     UpperCAmelCase_	:			TokenClassificationTask				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			PreTrainedTokenizer				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Optional[int] = None				,     UpperCAmelCase_	:			Optional[Any]=False				,     UpperCAmelCase_	:			Split = Split.train				,     )		->  Dict:







												"""simple docstring"""




												_lowerCAmelCase  	=			token_classification_task.read_examples_from_file(UpperCAmelCase_				,     UpperCAmelCase_  )
												# TODO clean up all this to leverage built-in features of tokenizers
												_lowerCAmelCase  	=			token_classification_task.convert_examples_to_features(
												    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     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=UpperCAmelCase_				,     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:
																			_lowerCAmelCase  	=			tf.data.Dataset.from_generator(
																			    UpperCAmelCase_				,     ({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)				,     (
																			        {'input_ids': tf.TensorShape([None]  ), 'attention_mask': tf.TensorShape([None]  )},
																			        tf.TensorShape([None]  ),
																			    )				,     )
												else:
																			_lowerCAmelCase  	=			tf.data.Dataset.from_generator(
																			    UpperCAmelCase_				,     ({'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	:			str  )		->  Tuple:







												"""simple docstring"""




												_lowerCAmelCase  	=			self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features  )  )  )

												return self.dataset


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







												"""simple docstring"""




												return len(self.features  )







					def __getitem__(					self	:			int				,     UpperCAmelCase_	:			List[Any]  )		->  InputFeatures:







												"""simple docstring"""




												return self.features[i]






