





"""simple docstring"""





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

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


lowercase__				:Union[str, Any]				   =       logging.getLogger(__name__)




@dataclass(frozen=__UpperCAmelCase )
class 							snake_case							:

      '''simple docstring'''



      _A     :       str
      _A     :       str
      _A     :       Optional[str]         =       None
      _A     :       Optional[str]         =       None
      _A     :       Optional[str]         =       None




@dataclass(frozen=__UpperCAmelCase )
class 							snake_case							:

      '''simple docstring'''



      _A     :       List[int]
      _A     :       Optional[List[int]]         =       None
      _A     :       Optional[List[int]]         =       None
      _A     :       Optional[Union[int, float]]         =       None
      _A     :       Optional[int]         =       None


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




  class 							snake_case							(			__UpperCAmelCase ):

        '''simple docstring'''



        _A     :       List[InputFeatures]

        def __init__(							self       :      List[Any]						,				__lowercase       :      str						,				__lowercase       :      PreTrainedTokenizer						,				__lowercase       :      str						,				__lowercase       :      Optional[int] = None						,				__lowercase       :      Any=False						,				__lowercase       :      bool = False						,				):


          '''simple docstring'''




          __UpperCAmelCase    :						Optional[int]       =						hans_processors[task]()

          __UpperCAmelCase    :						Any       =						os.path.join(
              __lowercase						,				'''cached_{}_{}_{}_{}'''.format(
                  '''dev''' if evaluate else '''train'''						,				tokenizer.__class__.__name__						,				str(__lowercase )						,				__lowercase						,				)						,				)
          __UpperCAmelCase    :						Any       =						processor.get_labels()
          if tokenizer.__class__ in (
              RobertaTokenizer,
              RobertaTokenizerFast,
              XLMRobertaTokenizer,
              BartTokenizer,
              BartTokenizerFast,
          ):
            # HACK(label indices are swapped in RoBERTa pretrained model)
            __UpperCAmelCase       ,     __UpperCAmelCase    :						Any       =						label_list[2], label_list[1]
          __UpperCAmelCase    :						int       =						label_list

          # Make sure only the first process in distributed training processes the dataset,
          # and the others will use the cache.
          __UpperCAmelCase    :						Union[str, Any]       =						cached_features_file + '''.lock'''
          with FileLock(__lowercase ):
            if os.path.exists(__lowercase ) and not overwrite_cache:
              logger.info(f'''Loading features from cached file {cached_features_file}''' )
              __UpperCAmelCase    :						str       =						torch.load(__lowercase )
            else:
              logger.info(f'''Creating features from dataset file at {data_dir}''' )

              __UpperCAmelCase    :						str       =						(
                  processor.get_dev_examples(__lowercase ) if evaluate else processor.get_train_examples(__lowercase )
              )

              logger.info('''Training examples: %s'''						,				len(__lowercase ) )
              __UpperCAmelCase    :						Optional[Any]       =						hans_convert_examples_to_features(__lowercase						,				__lowercase						,				__lowercase						,				__lowercase )
              logger.info('''Saving features into cached file %s'''						,				__lowercase )
              torch.save(self.features						,				__lowercase )

        def __len__(							self       :      List[str] ):


          '''simple docstring'''




          return len(self.features )

        def __getitem__(							self       :      List[str]						,				__lowercase       :      Tuple ):


          '''simple docstring'''




          return self.features[i]







        def       A_					(							self       :      Any ):


          '''simple docstring'''




          return self.label_list


if is_tf_available():
  import tensorflow as tf




  class 							snake_case							:

        '''simple docstring'''



        _A     :       List[InputFeatures]

        def __init__(							self       :      Tuple						,				__lowercase       :      str						,				__lowercase       :      PreTrainedTokenizer						,				__lowercase       :      str						,				__lowercase       :      Optional[int] = 128						,				__lowercase       :      int=False						,				__lowercase       :      bool = False						,				):


          '''simple docstring'''




          __UpperCAmelCase    :						List[str]       =						hans_processors[task]()
          __UpperCAmelCase    :						int       =						processor.get_labels()
          if tokenizer.__class__ in (
              RobertaTokenizer,
              RobertaTokenizerFast,
              XLMRobertaTokenizer,
              BartTokenizer,
              BartTokenizerFast,
          ):
            # HACK(label indices are swapped in RoBERTa pretrained model)
            __UpperCAmelCase       ,     __UpperCAmelCase    :						Any       =						label_list[2], label_list[1]
          __UpperCAmelCase    :						Union[str, Any]       =						label_list

          __UpperCAmelCase    :						Any       =						processor.get_dev_examples(__lowercase ) if evaluate else processor.get_train_examples(__lowercase )
          __UpperCAmelCase    :						Tuple       =						hans_convert_examples_to_features(__lowercase						,				__lowercase						,				__lowercase						,				__lowercase )

          def gen():
            for ex_index, ex in tqdm.tqdm(enumerate(self.features )						,				desc='''convert examples to features''' ):
              if ex_index % 10_000 == 0:
                logger.info('''Writing example %d of %d''' % (ex_index, len(__lowercase )) )

              yield (
                  {
                      "example_id": 0,
                      "input_ids": ex.input_ids,
                      "attention_mask": ex.attention_mask,
                      "token_type_ids": ex.token_type_ids,
                  },
                  ex.label,
              )

          __UpperCAmelCase    :						Any       =						tf.data.Dataset.from_generator(
              __lowercase						,				(
                  {
                      '''example_id''': tf.intaa,
                      '''input_ids''': tf.intaa,
                      '''attention_mask''': tf.intaa,
                      '''token_type_ids''': tf.intaa,
                  },
                  tf.intaa,
              )						,				(
                  {
                      '''example_id''': tf.TensorShape([] ),
                      '''input_ids''': tf.TensorShape([None, None] ),
                      '''attention_mask''': tf.TensorShape([None, None] ),
                      '''token_type_ids''': tf.TensorShape([None, None] ),
                  },
                  tf.TensorShape([] ),
              )						,				)

        def       A_					(							self       :      Any ):


          '''simple docstring'''




          return self.dataset

        def __len__(							self       :      Optional[int] ):


          '''simple docstring'''




          return len(self.features )

        def __getitem__(							self       :      Optional[int]						,				__lowercase       :      Union[str, Any] ):


          '''simple docstring'''




          return self.features[i]







        def       A_					(							self       :      Dict ):


          '''simple docstring'''




          return self.label_list




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      def       A_					(							self       :      List[str]						,				__lowercase       :      Union[str, Any] ):


        '''simple docstring'''




        return self._create_examples(self._read_tsv(os.path.join(__lowercase						,				'''heuristics_train_set.txt''' ) )						,				'''train''' )

      def       A_					(							self       :      Dict						,				__lowercase       :      Optional[int] ):


        '''simple docstring'''




        return self._create_examples(self._read_tsv(os.path.join(__lowercase						,				'''heuristics_evaluation_set.txt''' ) )						,				'''dev''' )

      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        return ["contradiction", "entailment", "neutral"]







      def       A_					(							self       :      Optional[int]						,				__lowercase       :      Optional[int]						,				__lowercase       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[str]       =						[]
        for i, line in enumerate(__lowercase ):
          if i == 0:
            continue
          __UpperCAmelCase    :						Dict       =						'''%s-%s''' % (set_type, line[0])
          __UpperCAmelCase    :						Optional[Any]       =						line[5]
          __UpperCAmelCase    :						Union[str, Any]       =						line[6]
          __UpperCAmelCase    :						Dict       =						line[7][2:] if line[7].startswith('''ex''' ) else line[7]
          __UpperCAmelCase    :						str       =						line[0]
          examples.append(InputExample(guid=__lowercase						,				text_a=__lowercase						,				text_b=__lowercase						,				label=__lowercase						,				pairID=__lowercase ) )
        return examples


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			)						->List[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						Union[str, Any]       =						{label: i for i, label in enumerate(UpperCAmelCase_  )}

  __UpperCAmelCase    :						Union[str, Any]       =						[]
  for ex_index, example in tqdm.tqdm(enumerate(UpperCAmelCase_  )  ,			desc='''convert examples to features'''  ):
    if ex_index % 1_00_00 == 0:
      logger.info('''Writing example %d''' % (ex_index)  )

    __UpperCAmelCase    :						Tuple       =						tokenizer(
        example.text_a  ,			example.text_b  ,			add_special_tokens=UpperCAmelCase_  ,			max_length=UpperCAmelCase_  ,			padding='''max_length'''  ,			truncation=UpperCAmelCase_  ,			return_overflowing_tokens=UpperCAmelCase_  ,			)

    __UpperCAmelCase    :						Optional[int]       =						label_map[example.label] if example.label in label_map else 0

    __UpperCAmelCase    :						List[str]       =						int(example.pairID  )

    features.append(InputFeatures(**UpperCAmelCase_  ,			label=UpperCAmelCase_  ,			pairID=UpperCAmelCase_  )  )

  for i, example in enumerate(examples[:5]  ):
    logger.info('''*** Example ***'''  )
    logger.info(f'''guid: {example}'''  )
    logger.info(f'''features: {features[i]}'''  )

  return features


lowercase__				:Optional[int]				   =       {
    'hans': 3,
}







lowercase__				:Tuple				   =       {
    'hans': HansProcessor,
}






"""simple docstring"""





from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin




@dataclass
class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       torch.FloatTensor




class 							snake_case							(			__UpperCAmelCase      ,  __UpperCAmelCase ):

      '''simple docstring'''



      @register_to_config
      def __init__(							self       :      Union[str, Any]						,				__lowercase       :      int = 32						,				__lowercase       :      int = 64						,				__lowercase       :      int = 20						,				__lowercase       :      int = 768						,				__lowercase       :      Optional[int]=77						,				__lowercase       :      Union[str, Any]=4						,				__lowercase       :      float = 0.0						,				__lowercase       :      str = "silu"						,				__lowercase       :      Optional[str] = None						,				__lowercase       :      Optional[str] = None						,				__lowercase       :      Optional[str] = "linear"						,				__lowercase       :      Optional[str] = "prd"						,				__lowercase       :      Optional[int] = None						,				__lowercase       :      Optional[int] = None						,				__lowercase       :      Optional[int] = None						,				):


        '''simple docstring'''




        super().__init__()
        __UpperCAmelCase    :						int       =						num_attention_heads
        __UpperCAmelCase    :						List[Any]       =						attention_head_dim
        __UpperCAmelCase    :						int       =						num_attention_heads * attention_head_dim
        __UpperCAmelCase    :						List[str]       =						additional_embeddings

        __UpperCAmelCase    :						Optional[int]       =						time_embed_dim or inner_dim
        __UpperCAmelCase    :						Tuple       =						embedding_proj_dim or embedding_dim
        __UpperCAmelCase    :						Dict       =						clip_embed_dim or embedding_dim

        __UpperCAmelCase    :						Dict       =						Timesteps(__lowercase						,				__lowercase						,				0 )
        __UpperCAmelCase    :						List[str]       =						TimestepEmbedding(__lowercase						,				__lowercase						,				out_dim=__lowercase						,				act_fn=__lowercase )

        __UpperCAmelCase    :						Any       =						nn.Linear(__lowercase						,				__lowercase )

        if embedding_proj_norm_type is None:
          __UpperCAmelCase    :						Dict       =						None
        elif embedding_proj_norm_type == "layer":
          __UpperCAmelCase    :						Any       =						nn.LayerNorm(__lowercase )
        else:
          raise ValueError(f'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}''' )

        __UpperCAmelCase    :						List[Any]       =						nn.Linear(__lowercase						,				__lowercase )

        if encoder_hid_proj_type is None:
          __UpperCAmelCase    :						int       =						None
        elif encoder_hid_proj_type == "linear":
          __UpperCAmelCase    :						int       =						nn.Linear(__lowercase						,				__lowercase )
        else:
          raise ValueError(f'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}''' )

        __UpperCAmelCase    :						str       =						nn.Parameter(torch.zeros(1						,				num_embeddings + additional_embeddings						,				__lowercase ) )

        if added_emb_type == "prd":
          __UpperCAmelCase    :						Optional[int]       =						nn.Parameter(torch.zeros(1						,				1						,				__lowercase ) )
        elif added_emb_type is None:
          __UpperCAmelCase    :						str       =						None
        else:
          raise ValueError(
              f'''`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.''' )

        __UpperCAmelCase    :						Union[str, Any]       =						nn.ModuleList(
            [
                BasicTransformerBlock(
                    __lowercase						,				__lowercase						,				__lowercase						,				dropout=__lowercase						,				activation_fn='''gelu'''						,				attention_bias=__lowercase						,				)
                for d in range(__lowercase )
            ] )

        if norm_in_type == "layer":
          __UpperCAmelCase    :						Optional[Any]       =						nn.LayerNorm(__lowercase )
        elif norm_in_type is None:
          __UpperCAmelCase    :						List[Any]       =						None
        else:
          raise ValueError(f'''Unsupported norm_in_type: {norm_in_type}.''' )

        __UpperCAmelCase    :						str       =						nn.LayerNorm(__lowercase )

        __UpperCAmelCase    :						List[Any]       =						nn.Linear(__lowercase						,				__lowercase )

        __UpperCAmelCase    :						List[Any]       =						torch.full(
            [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]						,				-1_0_0_0_0.0 )
        causal_attention_mask.triu_(1 )
        __UpperCAmelCase    :						Optional[Any]       =						causal_attention_mask[None, ...]
        self.register_buffer('''causal_attention_mask'''						,				__lowercase						,				persistent=__lowercase )

        __UpperCAmelCase    :						Any       =						nn.Parameter(torch.zeros(1						,				__lowercase ) )
        __UpperCAmelCase    :						List[str]       =						nn.Parameter(torch.zeros(1						,				__lowercase ) )

      @property
      # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						{}

        def fn_recursive_add_processors(__lowercase       :      str						,				__lowercase       :      torch.nn.Module						,				__lowercase       :      Dict[str, AttentionProcessor] ):
          if hasattr(__lowercase						,				'''set_processor''' ):
            __UpperCAmelCase    :						Optional[Any]       =						module.processor

          for sub_name, child in module.named_children():
            fn_recursive_add_processors(f'''{name}.{sub_name}'''						,				__lowercase						,				__lowercase )

          return processors

        for name, module in self.named_children():
          fn_recursive_add_processors(__lowercase						,				__lowercase						,				__lowercase )

        return processors

      def       A_					(							self       :      Any						,				__lowercase       :      Union[AttentionProcessor, Dict[str, AttentionProcessor]] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						len(self.attn_processors.keys() )

        if isinstance(__lowercase						,				__lowercase ) and len(__lowercase ) != count:
          raise ValueError(
              f'''A dict of processors was passed, but the number of processors {len(__lowercase )} does not match the'''
              f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.''' )

        def fn_recursive_attn_processor(__lowercase       :      str						,				__lowercase       :      torch.nn.Module						,				__lowercase       :      int ):
          if hasattr(__lowercase						,				'''set_processor''' ):
            if not isinstance(__lowercase						,				__lowercase ):
              module.set_processor(__lowercase )
            else:
              module.set_processor(processor.pop(f'''{name}.processor''' ) )

          for sub_name, child in module.named_children():
            fn_recursive_attn_processor(f'''{name}.{sub_name}'''						,				__lowercase						,				__lowercase )

        for name, module in self.named_children():
          fn_recursive_attn_processor(__lowercase						,				__lowercase						,				__lowercase )

      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        self.set_attn_processor(AttnProcessor() )

      def       A_					(							self       :      Optional[int]						,				__lowercase       :      Dict						,				__lowercase       :      Union[torch.Tensor, float, int]						,				__lowercase       :      torch.FloatTensor						,				__lowercase       :      Optional[torch.FloatTensor] = None						,				__lowercase       :      Optional[torch.BoolTensor] = None						,				__lowercase       :      bool = True						,				):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						hidden_states.shape[0]

        __UpperCAmelCase    :						Any       =						timestep
        if not torch.is_tensor(__lowercase ):
          __UpperCAmelCase    :						Union[str, Any]       =						torch.tensor([timesteps]						,				dtype=torch.long						,				device=hidden_states.device )
        elif torch.is_tensor(__lowercase ) and len(timesteps.shape ) == 0:
          __UpperCAmelCase    :						Union[str, Any]       =						timesteps[None].to(hidden_states.device )

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        __UpperCAmelCase    :						List[str]       =						timesteps * torch.ones(__lowercase						,				dtype=timesteps.dtype						,				device=timesteps.device )

        __UpperCAmelCase    :						List[str]       =						self.time_proj(__lowercase )

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might be fp16, so we need to cast here.
        __UpperCAmelCase    :						Any       =						timesteps_projected.to(dtype=self.dtype )
        __UpperCAmelCase    :						List[str]       =						self.time_embedding(__lowercase )

        if self.embedding_proj_norm is not None:
          __UpperCAmelCase    :						Dict       =						self.embedding_proj_norm(__lowercase )

        __UpperCAmelCase    :						Optional[int]       =						self.embedding_proj(__lowercase )
        if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
          __UpperCAmelCase    :						Dict       =						self.encoder_hidden_states_proj(__lowercase )
        elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
          raise ValueError('''`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set''' )

        __UpperCAmelCase    :						int       =						self.proj_in(__lowercase )

        __UpperCAmelCase    :						Tuple       =						self.positional_embedding.to(hidden_states.dtype )

        __UpperCAmelCase    :						Optional[Any]       =						[]
        __UpperCAmelCase    :						str       =						0

        if encoder_hidden_states is not None:
          additional_embeds.append(__lowercase )
          additional_embeddings_len += encoder_hidden_states.shape[1]

        if len(proj_embeddings.shape ) == 2:
          __UpperCAmelCase    :						str       =						proj_embeddings[:, None, :]

        if len(hidden_states.shape ) == 2:
          __UpperCAmelCase    :						Optional[int]       =						hidden_states[:, None, :]

        __UpperCAmelCase    :						str       =						additional_embeds + [
            proj_embeddings,
            time_embeddings[:, None, :],
            hidden_states,
        ]

        if self.prd_embedding is not None:
          __UpperCAmelCase    :						Dict       =						self.prd_embedding.to(hidden_states.dtype ).expand(__lowercase						,				-1						,				-1 )
          additional_embeds.append(__lowercase )

        __UpperCAmelCase    :						Optional[Any]       =						torch.cat(
            __lowercase						,				dim=1						,				)

        # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
        __UpperCAmelCase    :						Optional[int]       =						additional_embeddings_len + proj_embeddings.shape[1] + 1
        if positional_embeddings.shape[1] < hidden_states.shape[1]:
          __UpperCAmelCase    :						List[str]       =						F.pad(
              __lowercase						,				(
                  0,
                  0,
                  additional_embeddings_len,
                  self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
              )						,				value=0.0						,				)

        __UpperCAmelCase    :						Dict       =						hidden_states + positional_embeddings

        if attention_mask is not None:
          __UpperCAmelCase    :						Optional[Any]       =						(1 - attention_mask.to(hidden_states.dtype )) * -1_0_0_0_0.0
          __UpperCAmelCase    :						Optional[Any]       =						F.pad(__lowercase						,				(0, self.additional_embeddings)						,				value=0.0 )
          __UpperCAmelCase    :						Dict       =						(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype )
          __UpperCAmelCase    :						Tuple       =						attention_mask.repeat_interleave(self.config.num_attention_heads						,				dim=0 )

        if self.norm_in is not None:
          __UpperCAmelCase    :						str       =						self.norm_in(__lowercase )

        for block in self.transformer_blocks:
          __UpperCAmelCase    :						Dict       =						block(__lowercase						,				attention_mask=__lowercase )

        __UpperCAmelCase    :						Any       =						self.norm_out(__lowercase )

        if self.prd_embedding is not None:
          __UpperCAmelCase    :						List[Any]       =						hidden_states[:, -1]
        else:
          __UpperCAmelCase    :						List[str]       =						hidden_states[:, additional_embeddings_len:]

        __UpperCAmelCase    :						Optional[int]       =						self.proj_to_clip_embeddings(__lowercase )

        if not return_dict:
          return (predicted_image_embedding,)

        return PriorTransformerOutput(predicted_image_embedding=__lowercase )







      def       A_					(							self       :      List[Any]						,				__lowercase       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[str]       =						(prior_latents * self.clip_std) + self.clip_mean
        return prior_latents