"""simple docstring"""


import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class 							a					(		lowerCAmelCase_						,				lowerCAmelCase_ ):


    @register_to_config
    def __init__(       self						:	Union[str, Any] ,					*,
        __lowerCAmelCase						:	int = 4 ,					__lowerCAmelCase						:	int = 768 ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Dict ,					):
      super().__init__()

      _UpperCAmelCase    			=   nn.Parameter(torch.zeros(__lowerCAmelCase       )       )

      # parameters for additional clip time embeddings
      _UpperCAmelCase    			=   nn.Linear(__lowerCAmelCase ,					__lowerCAmelCase       )
      _UpperCAmelCase    			=   nn.Linear(__lowerCAmelCase ,					__lowerCAmelCase       )

      # parameters for encoder hidden states
      _UpperCAmelCase    			=   clip_extra_context_tokens
      _UpperCAmelCase    			=   nn.Linear(
          __lowerCAmelCase ,					self.clip_extra_context_tokens * cross_attention_dim       )
      _UpperCAmelCase    			=   nn.Linear(__lowerCAmelCase ,					__lowerCAmelCase       )
      _UpperCAmelCase    			=   nn.LayerNorm(__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[Any] ,					*, __lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	Dict       ):
      if do_classifier_free_guidance:
        # Add the classifier free guidance embeddings to the image embeddings
        _UpperCAmelCase    			=   image_embeddings.shape[0]
        _UpperCAmelCase    			=   self.learned_classifier_free_guidance_embeddings.unsqueeze(0       )
        _UpperCAmelCase    			=   classifier_free_guidance_embeddings.expand(
            __lowerCAmelCase ,					-1       )
        _UpperCAmelCase    			=   torch.cat([classifier_free_guidance_embeddings, image_embeddings] ,					dim=0       )

      # The image embeddings batch size and the text embeddings batch size are equal
      assert image_embeddings.shape[0] == prompt_embeds.shape[0]

      _UpperCAmelCase    			=   prompt_embeds.shape[0]

      # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
      # adding CLIP embeddings to the existing timestep embedding, ...
      _UpperCAmelCase    			=   self.embedding_proj(__lowerCAmelCase       )
      _UpperCAmelCase    			=   self.clip_image_embeddings_project_to_time_embeddings(__lowerCAmelCase       )
      _UpperCAmelCase    			=   time_projected_image_embeddings + time_projected_prompt_embeds

      # ... and by projecting CLIP embeddings into four
      # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
      _UpperCAmelCase    			=   self.clip_extra_context_tokens_proj(__lowerCAmelCase       )
      _UpperCAmelCase    			=   clip_extra_context_tokens.reshape(__lowerCAmelCase ,					-1 ,					self.clip_extra_context_tokens       )
      _UpperCAmelCase    			=   clip_extra_context_tokens.permute(0 ,					2 ,					1       )

      _UpperCAmelCase    			=   self.encoder_hidden_states_proj(__lowerCAmelCase       )
      _UpperCAmelCase    			=   self.text_encoder_hidden_states_norm(__lowerCAmelCase       )
      _UpperCAmelCase    			=   torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] ,					dim=1       )

      return text_encoder_hidden_states, additive_clip_time_embeddings

"""simple docstring"""


from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
    import datasets


class 							a					(		lowerCAmelCase_ ):


    @slow
    @require_torch
    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" ,					"""prajjwal1/bert-tiny"""       )
      _UpperCAmelCase    			=   BertTokenizer.from_pretrained("""bert-base-uncased"""       )

      _UpperCAmelCase    			=   bertabert.config.encoder.vocab_size
      _UpperCAmelCase    			=   tokenizer.sep_token_id
      _UpperCAmelCase    			=   tokenizer.cls_token_id
      _UpperCAmelCase    			=   128

      _UpperCAmelCase    			=   datasets.load_dataset("""cnn_dailymail""" ,					"""3.0.0""" ,					split="""train[:1%]"""       )
      _UpperCAmelCase    			=   datasets.load_dataset("""cnn_dailymail""" ,					"""3.0.0""" ,					split="""validation[:1%]"""       )

      _UpperCAmelCase    			=   train_dataset.select(range(32       )       )
      _UpperCAmelCase    			=   val_dataset.select(range(16       )       )

      _UpperCAmelCase    			=   4

      def _map_to_encoder_decoder_inputs(__lowerCAmelCase						:	Dict       ):
        # Tokenizer will automatically set [BOS] <text> [EOS]
        _UpperCAmelCase    			=   tokenizer(batch["""article"""] ,					padding="""max_length""" ,					truncation=__lowerCAmelCase ,					max_length=512       )
        _UpperCAmelCase    			=   tokenizer(batch["""highlights"""] ,					padding="""max_length""" ,					truncation=__lowerCAmelCase ,					max_length=128       )
        _UpperCAmelCase    			=   inputs.input_ids
        _UpperCAmelCase    			=   inputs.attention_mask

        _UpperCAmelCase    			=   outputs.input_ids
        _UpperCAmelCase    			=   outputs.input_ids.copy()
        _UpperCAmelCase    			=   [
            [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
        ]
        _UpperCAmelCase    			=   outputs.attention_mask

        assert all(len(__lowerCAmelCase       ) == 512 for x in inputs.input_ids       )
        assert all(len(__lowerCAmelCase       ) == 128 for x in outputs.input_ids       )

        return batch

      def _compute_metrics(__lowerCAmelCase						:	int       ):
        _UpperCAmelCase    			=   pred.label_ids
        _UpperCAmelCase    			=   pred.predictions

        # all unnecessary tokens are removed
        _UpperCAmelCase    			=   tokenizer.batch_decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )
        _UpperCAmelCase    			=   tokenizer.batch_decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )

        _UpperCAmelCase    			=   sum([int(pred_str[i] == label_str[i]       ) for i in range(len(__lowerCAmelCase       )       )]       ) / len(__lowerCAmelCase       )

        return {"accuracy": accuracy}

      # map train dataset
      _UpperCAmelCase    			=   train_dataset.map(
          _map_to_encoder_decoder_inputs ,					batched=__lowerCAmelCase ,					batch_size=__lowerCAmelCase ,					remove_columns=["""article""", """highlights"""] ,					)
      train_dataset.set_format(
          type="""torch""" ,					columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,					)

      # same for validation dataset
      _UpperCAmelCase    			=   val_dataset.map(
          _map_to_encoder_decoder_inputs ,					batched=__lowerCAmelCase ,					batch_size=__lowerCAmelCase ,					remove_columns=["""article""", """highlights"""] ,					)
      val_dataset.set_format(
          type="""torch""" ,					columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,					)

      _UpperCAmelCase    			=   self.get_auto_remove_tmp_dir()

      _UpperCAmelCase    			=   SeqaSeqTrainingArguments(
          output_dir=__lowerCAmelCase ,					per_device_train_batch_size=__lowerCAmelCase ,					per_device_eval_batch_size=__lowerCAmelCase ,					predict_with_generate=__lowerCAmelCase ,					evaluation_strategy="""steps""" ,					do_train=__lowerCAmelCase ,					do_eval=__lowerCAmelCase ,					warmup_steps=0 ,					eval_steps=2 ,					logging_steps=2 ,					)

      # instantiate trainer
      _UpperCAmelCase    			=   SeqaSeqTrainer(
          model=__lowerCAmelCase ,					args=__lowerCAmelCase ,					compute_metrics=_compute_metrics ,					train_dataset=__lowerCAmelCase ,					eval_dataset=__lowerCAmelCase ,					tokenizer=__lowerCAmelCase ,					)

      # start training
      trainer.train()
