





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__     ( lowercase__    ):

      '''simple docstring'''





      @slow
      @require_torch
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny" ,							"prajjwal1/bert-tiny"     )
       UpperCamelCase			:      str 			=     BertTokenizer.from_pretrained("bert-base-uncased"     )

       UpperCamelCase			:      List[str] 			=     bertabert.config.encoder.vocab_size
       UpperCamelCase			:      Tuple 			=     tokenizer.sep_token_id
       UpperCamelCase			:      Tuple 			=     tokenizer.cls_token_id
       UpperCamelCase			:      Optional[int] 			=     128

       UpperCamelCase			:      List[str] 			=     datasets.load_dataset("cnn_dailymail" ,							"3.0.0" ,							split="train[:1%]"     )
       UpperCamelCase			:      List[Any] 			=     datasets.load_dataset("cnn_dailymail" ,							"3.0.0" ,							split="validation[:1%]"     )

       UpperCamelCase			:      List[str] 			=     train_dataset.select(range(32     )     )
       UpperCamelCase			:      Optional[Any] 			=     val_dataset.select(range(16     )     )

       UpperCamelCase			:      Optional[int] 			=     4

       def _map_to_encoder_decoder_inputs(A_     ):
        # Tokenizer will automatically set [BOS] <text> [EOS]
        UpperCamelCase			:      Tuple 			=     tokenizer(batch["article"] ,							padding="max_length" ,							truncation=UpperCAmelCase__ ,							max_length=512     )
        UpperCamelCase			:      Union[str, Any] 			=     tokenizer(batch["highlights"] ,							padding="max_length" ,							truncation=UpperCAmelCase__ ,							max_length=128     )
        UpperCamelCase			:      Optional[int] 			=     inputs.input_ids
        UpperCamelCase			:      List[Any] 			=     inputs.attention_mask

        UpperCamelCase			:      List[Any] 			=     outputs.input_ids
        UpperCamelCase			:      Optional[int] 			=     outputs.input_ids.copy()
        UpperCamelCase			:      List[Any] 			=     [
            [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
        ]
        UpperCamelCase			:      Dict 			=     outputs.attention_mask

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

        return batch

       def _compute_metrics(A_     ):
        UpperCamelCase			:      Optional[Any] 			=     pred.label_ids
        UpperCamelCase			:      Union[str, Any] 			=     pred.predictions

        # all unnecessary tokens are removed
        UpperCamelCase			:      Union[str, Any] 			=     tokenizer.batch_decode(UpperCAmelCase__ ,							skip_special_tokens=UpperCAmelCase__     )
        UpperCamelCase			:      int 			=     tokenizer.batch_decode(UpperCAmelCase__ ,							skip_special_tokens=UpperCAmelCase__     )

        UpperCamelCase			:      str 			=     sum([int(pred_str[i] == label_str[i]     ) for i in range(len(UpperCAmelCase__     )     )]     ) / len(UpperCAmelCase__     )

        return {"accuracy": accuracy}

       # map train dataset
       UpperCamelCase			:      Union[str, Any] 			=     train_dataset.map(
           _map_to_encoder_decoder_inputs ,							batched=UpperCAmelCase__ ,							batch_size=UpperCAmelCase__ ,							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			:      List[str] 			=     val_dataset.map(
           _map_to_encoder_decoder_inputs ,							batched=UpperCAmelCase__ ,							batch_size=UpperCAmelCase__ ,							remove_columns=["article", "highlights"] ,							)
       val_dataset.set_format(
           type="torch" ,							columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"] ,							)

       UpperCamelCase			:      str 			=     self.get_auto_remove_tmp_dir()

       UpperCamelCase			:      int 			=     SeqaSeqTrainingArguments(
           output_dir=UpperCAmelCase__ ,							per_device_train_batch_size=UpperCAmelCase__ ,							per_device_eval_batch_size=UpperCAmelCase__ ,							predict_with_generate=UpperCAmelCase__ ,							evaluation_strategy="steps" ,							do_train=UpperCAmelCase__ ,							do_eval=UpperCAmelCase__ ,							warmup_steps=0 ,							eval_steps=2 ,							logging_steps=2 ,							)

       # instantiate trainer
       UpperCamelCase			:      Union[str, Any] 			=     SeqaSeqTrainer(
           model=UpperCAmelCase__ ,							args=UpperCAmelCase__ ,							compute_metrics=_compute_metrics ,							train_dataset=UpperCAmelCase__ ,							eval_dataset=UpperCAmelCase__ ,							tokenizer=UpperCAmelCase__ ,							)

       # start training
       trainer.train()







import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class       A__     ( __snake_case ,     unittest.TestCase    ):
      _UpperCAmelCase			:str							=     KandinskyVaaPipeline
      _UpperCAmelCase			:str							=     [
          'image_embeds',
          'negative_image_embeds',
      ]
      _UpperCAmelCase			:str							=     ['image_embeds', 'negative_image_embeds']
      _UpperCAmelCase			:List[str]							=     [
          'generator',
          'height',
          'width',
          'latents',
          'guidance_scale',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      _UpperCAmelCase			:List[str]							=     False





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return 32





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return 32





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.time_input_dim





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.time_input_dim * 4





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return 100





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       torch.manual_seed(0     )

       UpperCamelCase			:      List[str] 			=     {
           "in_channels": 4,
           # Out channels is double in channels because predicts mean and variance
           "out_channels": 8,
           "addition_embed_type": "image",
           "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
           "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
           "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
           "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
           "layers_per_block": 1,
           "encoder_hid_dim": self.text_embedder_hidden_size,
           "encoder_hid_dim_type": "image_proj",
           "cross_attention_dim": self.cross_attention_dim,
           "attention_head_dim": 4,
           "resnet_time_scale_shift": "scale_shift",
           "class_embed_type": None,
       }

       UpperCamelCase			:      Dict 			=     UNetaDConditionModel(**A_     )
       return model





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       torch.manual_seed(0     )
       UpperCamelCase			:      Optional[Any] 			=     VQModel(**self.dummy_movq_kwargs     )
       return model





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     self.dummy_unet
       UpperCamelCase			:      Optional[Any] 			=     self.dummy_movq

       UpperCamelCase			:      Dict 			=     DDIMScheduler(
           num_train_timesteps=1000 ,							beta_schedule="linear" ,							beta_start=0.0_00_85 ,							beta_end=0.0_12 ,							clip_sample=A_ ,							set_alpha_to_one=A_ ,							steps_offset=1 ,							prediction_type="epsilon" ,							thresholding=A_ ,							)

       UpperCamelCase			:      Tuple 			=     {
           "unet": unet,
           "scheduler": scheduler,
           "movq": movq,
       }
       return components





      def  __UpperCamelCase(				self ,							A_ ,							A_=0     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     floats_tensor((1, self.text_embedder_hidden_size) ,							rng=random.Random(A_     )     ).to(A_     )
       UpperCamelCase			:      Optional[Any] 			=     floats_tensor((1, self.text_embedder_hidden_size) ,							rng=random.Random(seed + 1     )     ).to(
           A_     )
       if str(A_     ).startswith("mps"     ):
        UpperCamelCase			:      Optional[Any] 			=     torch.manual_seed(A_     )
       else:
        UpperCamelCase			:      List[Any] 			=     torch.Generator(device=A_     ).manual_seed(A_     )
       UpperCamelCase			:      Optional[int] 			=     {
           "image_embeds": image_embeds,
           "negative_image_embeds": negative_image_embeds,
           "generator": generator,
           "height": 64,
           "width": 64,
           "guidance_scale": 4.0,
           "num_inference_steps": 2,
           "output_type": "np",
       }
       return inputs





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     "cpu"

       UpperCamelCase			:      List[str] 			=     self.get_dummy_components()

       UpperCamelCase			:      Tuple 			=     self.pipeline_class(**A_     )
       UpperCamelCase			:      List[str] 			=     pipe.to(A_     )

       pipe.set_progress_bar_config(disable=A_     )

       UpperCamelCase			:      Dict 			=     pipe(**self.get_dummy_inputs(A_     )     )
       UpperCamelCase			:      Optional[int] 			=     output.images

       UpperCamelCase			:      int 			=     pipe(
           **self.get_dummy_inputs(A_     ) ,							return_dict=A_ ,							)[0]

       UpperCamelCase			:      Tuple 			=     image[0, -3:, -3:, -1]
       UpperCamelCase			:      List[Any] 			=     image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       UpperCamelCase			:      int 			=     np.array(
           [0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46]     )

       assert (
           np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2
       ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""

       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1e-2
       ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""



@slow
@require_torch_gpu
class       A__     ( unittest.TestCase    ):





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"     )

       UpperCamelCase			:      Dict 			=     KandinskyVaaPriorPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-prior" ,							torch_dtype=torch.floataa     )
       pipe_prior.to(A_     )

       UpperCamelCase			:      Dict 			=     KandinskyVaaPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-decoder" ,							torch_dtype=torch.floataa     )
       UpperCamelCase			:      Tuple 			=     pipeline.to(A_     )
       pipeline.set_progress_bar_config(disable=A_     )

       UpperCamelCase			:      str 			=     "red cat, 4k photo"

       UpperCamelCase			:      str 			=     torch.Generator(device="cuda"     ).manual_seed(0     )
       UpperCamelCase			,					UpperCamelCase			:      Tuple 			=     pipe_prior(
           A_ ,							generator=A_ ,							num_inference_steps=5 ,							negative_prompt="" ,							).to_tuple()

       UpperCamelCase			:      int 			=     torch.Generator(device="cuda"     ).manual_seed(0     )
       UpperCamelCase			:      Tuple 			=     pipeline(
           image_embeds=A_ ,							negative_image_embeds=A_ ,							generator=A_ ,							num_inference_steps=100 ,							output_type="np" ,							)

       UpperCamelCase			:      Union[str, Any] 			=     output.images[0]

       assert image.shape == (512, 512, 3)

       assert_mean_pixel_difference(A_ ,							A_     )
