

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


_UpperCAmelCase	:     Dict		       = logging.getLogger(__name__)







@dataclass
class 				lowercase       :
    __SCREAMING_SNAKE_CASE      :				str
    __SCREAMING_SNAKE_CASE      :				List[str]
    __SCREAMING_SNAKE_CASE      :				Optional[List[str]]







@dataclass
class 				lowercase       :
    __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 				lowercase       ( A_    ):
    __SCREAMING_SNAKE_CASE      :				List[str]     =    '''train'''
    __SCREAMING_SNAKE_CASE      :				Dict     =    '''dev'''
    __SCREAMING_SNAKE_CASE      :				List[str]     =    '''test'''







class 				lowercase       :




    @staticmethod
    def 					a							(      snake_case   ,     snake_case ):
           raise NotImplementedError




    @staticmethod
    def 					a							(      snake_case ):
           raise NotImplementedError




    @staticmethod
    def 					a							(      snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case=False   ,     snake_case="[CLS]"   ,     snake_case=1   ,     snake_case="[SEP]"   ,     snake_case=False   ,     snake_case=False   ,     snake_case=0   ,     snake_case=0   ,     snake_case=-100   ,     snake_case=0   ,     snake_case=True   ,     ):
           snake_case_           =					{label: i for i, label in enumerate(snake_case )}

           snake_case_           =					[]
           for ex_index, example in enumerate(snake_case ):
                  if ex_index % 1_0000 == 0:
                         logger.info('Writing example %d of %d'   ,     snake_case   ,     len(snake_case ) )

                  snake_case_           =					[]
                  snake_case_           =					[]
                  for word, label in zip(example.words   ,     example.labels ):
                         snake_case_           =					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_           =					tokenizer.num_special_tokens_to_add()
                  if len(snake_case ) > max_seq_length - special_tokens_count:
                         snake_case_           =					tokens[: (max_seq_length - special_tokens_count)]
                         snake_case_           =					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_           =					[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_           =					[cls_token] + tokens
                         snake_case_           =					[pad_token_label_id] + label_ids
                         snake_case_           =					[cls_token_segment_id] + segment_ids

                  snake_case_           =					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_           =					[1 if mask_padding_with_zero else 0] * len(snake_case )

                  # Zero-pad up to the sequence length.
                  snake_case_           =					max_seq_length - len(snake_case )
                  if pad_on_left:
                         snake_case_           =					([pad_token] * padding_length) + input_ids
                         snake_case_           =					([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                         snake_case_           =					([pad_token_segment_id] * padding_length) + segment_ids
                         snake_case_           =					([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_           =					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 				lowercase       ( A_    ):
         __SCREAMING_SNAKE_CASE      :				List[InputFeatures]
         __SCREAMING_SNAKE_CASE      :				int     =    nn.CrossEntropyLoss().ignore_index




         def __init__(      self   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case = None   ,     snake_case=False   ,     snake_case = Split.train   ,     ):
                # Load data features from cache or dataset file
                snake_case_           =					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_           =					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_           =					torch.load(snake_case )
                       else:
                              logger.info(F'''Creating features from dataset file at {data_dir}''' )
                              snake_case_           =					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_           =					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 ):
                return len(self.features )




         def __getitem__(      self   ,     snake_case ):
                return self.features[i]


if is_tf_available():
     import tensorflow as tf







     class 				lowercase       :
         __SCREAMING_SNAKE_CASE      :				List[InputFeatures]
         __SCREAMING_SNAKE_CASE      :				int     =    -100




         def __init__(      self   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case = None   ,     snake_case=False   ,     snake_case = Split.train   ,     ):
                snake_case_           =					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_           =					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_           =					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_           =					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 					a							(      self ):
                snake_case_           =					self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features ) ) )

                return self.dataset




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




         def __getitem__(      self   ,     snake_case ):
                return self.features[i]







# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    """stable diffusion controlnet""",
    """0.22.0""",
    """Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.""",
    standard_warn=False,
    stacklevel=3,
)




