






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class     _a							(	UpperCAmelCase__    ,							UpperCAmelCase__    ,							UpperCAmelCase__    ,							unittest.TestCase     ):






  """simple docstring"""





  A_						       = AltDiffusionPipeline
  A_						       = TEXT_TO_IMAGE_PARAMS
  A_						       = TEXT_TO_IMAGE_BATCH_PARAMS
  A_						       = TEXT_TO_IMAGE_IMAGE_PARAMS
  A_						       = TEXT_TO_IMAGE_IMAGE_PARAMS




  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     torch.manual_seed(0			)
     UpperCamelCase_						=  UNetaDConditionModel(
         block_out_channels=(32, 64)		,		layers_per_block=2		,		sample_size=32		,		in_channels=4		,		out_channels=4		,		down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,		up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')		,		cross_attention_dim=32		,		)
     UpperCamelCase_						=  DDIMScheduler(
         beta_start=0.0_0_0_8_5		,		beta_end=0.0_1_2		,		beta_schedule='scaled_linear'		,		clip_sample=_UpperCAmelCase		,		set_alpha_to_one=_UpperCAmelCase		,		)
     torch.manual_seed(0			)
     UpperCamelCase_						=  AutoencoderKL(
         block_out_channels=[32, 64]		,		in_channels=3		,		out_channels=3		,		down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,		up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,		latent_channels=4		,		)

     # TODO: address the non-deterministic text encoder (fails for save-load tests)
     # torch.manual_seed(0)
     # text_encoder_config = RobertaSeriesConfig(
     #     hidden_size=32,
     #     project_dim=32,
     #     intermediate_size=37,
     #     layer_norm_eps=1e-05,
     #     num_attention_heads=4,
     #     num_hidden_layers=5,
     #     vocab_size=5002,
     # )
     # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

     torch.manual_seed(0			)
     UpperCamelCase_						=  CLIPTextConfig(
         bos_token_id=0		,		eos_token_id=2		,		hidden_size=32		,		projection_dim=32		,		intermediate_size=37		,		layer_norm_eps=1e-05		,		num_attention_heads=4		,		num_hidden_layers=5		,		pad_token_id=1		,		vocab_size=5002		,		)
     UpperCamelCase_						=  CLIPTextModel(_UpperCAmelCase			)

     UpperCamelCase_						=  XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'			)
     UpperCamelCase_						=  77

     UpperCamelCase_						=  {
         'unet': unet,
         'scheduler': scheduler,
         'vae': vae,
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'safety_checker': None,
         'feature_extractor': None,
     }
     return components




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase=0			)      ->			Optional[Any]:
     if str(_UpperCAmelCase			).startswith('mps'			):
        UpperCamelCase_						=  torch.manual_seed(_UpperCAmelCase			)
     else:
        UpperCamelCase_						=  torch.Generator(device=_UpperCAmelCase			).manual_seed(_UpperCAmelCase			)
     UpperCamelCase_						=  {
         'prompt': 'A painting of a squirrel eating a burger',
         'generator': generator,
         'num_inference_steps': 2,
         'guidance_scale': 6.0,
         'output_type': 'numpy',
     }
     return inputs




  def  _UpperCAmelCase  (  self			)      ->			Tuple:
     super().test_attention_slicing_forward_pass(expected_max_diff=3e-3			)




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     super().test_inference_batch_single_identical(expected_max_diff=3e-3			)




  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     UpperCamelCase_						=  'cpu'  # ensure determinism for the device-dependent torch.Generator

     UpperCamelCase_						=  self.get_dummy_components()
     torch.manual_seed(0			)
     UpperCamelCase_						=  RobertaSeriesConfig(
         hidden_size=32		,		project_dim=32		,		intermediate_size=37		,		layer_norm_eps=1e-05		,		num_attention_heads=4		,		num_hidden_layers=5		,		vocab_size=5002		,		)
     # TODO: remove after fixing the non-deterministic text encoder
     UpperCamelCase_						=  RobertaSeriesModelWithTransformation(_UpperCAmelCase			)
     UpperCamelCase_						=  text_encoder

     UpperCamelCase_						=  AltDiffusionPipeline(**_UpperCAmelCase			)
     UpperCamelCase_						=  alt_pipe.to(_UpperCAmelCase			)
     alt_pipe.set_progress_bar_config(disable=_UpperCAmelCase			)

     UpperCamelCase_						=  self.get_dummy_inputs(_UpperCAmelCase			)
     UpperCamelCase_						=  'A photo of an astronaut'
     UpperCamelCase_						=  alt_pipe(**_UpperCAmelCase			)
     UpperCamelCase_						=  output.images
     UpperCamelCase_						=  image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     UpperCamelCase_						=  np.array(
         [0.5_7_4_8_1_6_2, 0.6_0_4_4_7_1_4_5, 0.4_8_8_2_1_2_1_7, 0.5_0_1_0_0_6_3_6, 0.5_4_3_1_1_8_5, 0.4_5_7_6_3_6_8_3, 0.4_9_6_5_7_6_9_6, 0.4_8_1_3_2_7_3_3, 0.4_7_5_7_3_0_9_3]			)

     assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2




  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     UpperCamelCase_						=  'cpu'  # ensure determinism for the device-dependent torch.Generator

     UpperCamelCase_						=  self.get_dummy_components()
     UpperCamelCase_						=  PNDMScheduler(skip_prk_steps=_UpperCAmelCase			)
     torch.manual_seed(0			)
     UpperCamelCase_						=  RobertaSeriesConfig(
         hidden_size=32		,		project_dim=32		,		intermediate_size=37		,		layer_norm_eps=1e-05		,		num_attention_heads=4		,		num_hidden_layers=5		,		vocab_size=5002		,		)
     # TODO: remove after fixing the non-deterministic text encoder
     UpperCamelCase_						=  RobertaSeriesModelWithTransformation(_UpperCAmelCase			)
     UpperCamelCase_						=  text_encoder
     UpperCamelCase_						=  AltDiffusionPipeline(**_UpperCAmelCase			)
     UpperCamelCase_						=  alt_pipe.to(_UpperCAmelCase			)
     alt_pipe.set_progress_bar_config(disable=_UpperCAmelCase			)

     UpperCamelCase_						=  self.get_dummy_inputs(_UpperCAmelCase			)
     UpperCamelCase_						=  alt_pipe(**_UpperCAmelCase			)
     UpperCamelCase_						=  output.images
     UpperCamelCase_						=  image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     UpperCamelCase_						=  np.array(
         [0.5_1_6_0_5_0_9_3, 0.5_7_0_7_2_4_1, 0.4_7_3_6_5_5_0_7, 0.5_0_5_7_8_8_8_6, 0.5_6_3_3_8_7_7, 0.4_6_4_2_5_0_3, 0.5_1_8_2_0_8_1, 0.4_8_7_6_3_4_8_4, 0.4_9_0_8_4_2_3_7]			)

     assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2




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






  """simple docstring"""





  def  _UpperCAmelCase  (  self			)      ->			Tuple:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     # make sure here that pndm scheduler skips prk
     UpperCamelCase_						=  AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'		,		safety_checker=_UpperCAmelCase			)
     UpperCamelCase_						=  alt_pipe.to(_UpperCAmelCase			)
     alt_pipe.set_progress_bar_config(disable=_UpperCAmelCase			)

     UpperCamelCase_						=  'A painting of a squirrel eating a burger'
     UpperCamelCase_						=  torch.manual_seed(0			)
     UpperCamelCase_						=  alt_pipe([prompt]		,		generator=_UpperCAmelCase		,		guidance_scale=6.0		,		num_inference_steps=20		,		output_type='np'			)

     UpperCamelCase_						=  output.images

     UpperCamelCase_						=  image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     UpperCamelCase_						=  np.array([0.1_0_1_0, 0.0_8_0_0, 0.0_7_9_4, 0.0_8_8_5, 0.0_8_4_3, 0.0_7_6_2, 0.0_7_6_9, 0.0_7_2_9, 0.0_5_8_6]			)

     assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2




  def  _UpperCAmelCase  (  self			)      ->			Dict:
     UpperCamelCase_						=  DDIMScheduler.from_pretrained('BAAI/AltDiffusion'		,		subfolder='scheduler'			)

     UpperCamelCase_						=  AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'		,		scheduler=_UpperCAmelCase		,		safety_checker=_UpperCAmelCase			)
     UpperCamelCase_						=  alt_pipe.to(_UpperCAmelCase			)
     alt_pipe.set_progress_bar_config(disable=_UpperCAmelCase			)

     UpperCamelCase_						=  'A painting of a squirrel eating a burger'
     UpperCamelCase_						=  torch.manual_seed(0			)

     UpperCamelCase_						=  alt_pipe([prompt]		,		generator=_UpperCAmelCase		,		num_inference_steps=2		,		output_type='numpy'			)
     UpperCamelCase_						=  output.images

     UpperCamelCase_						=  image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     UpperCamelCase_						=  np.array([0.4_0_1_9, 0.4_0_5_2, 0.3_8_1_0, 0.4_1_1_9, 0.3_9_1_6, 0.3_9_8_2, 0.4_6_5_1, 0.4_1_9_5, 0.5_3_2_3]			)

     assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2













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							(	UpperCAmelCase__     ):






  """simple docstring"""





  @slow
  @require_torch
  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     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(_UpperCAmelCase			):
        # Tokenizer will automatically set [BOS] <text> [EOS]
        UpperCamelCase_						=  tokenizer(batch['article']		,		padding='max_length'		,		truncation=_UpperCAmelCase		,		max_length=512			)
        UpperCamelCase_						=  tokenizer(batch['highlights']		,		padding='max_length'		,		truncation=_UpperCAmelCase		,		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(_UpperCAmelCase			) == 512 for x in inputs.input_ids			)
        assert all(len(_UpperCAmelCase			) == 128 for x in outputs.input_ids			)

        return batch

     def _compute_metrics(_UpperCAmelCase			):
        UpperCamelCase_						=  pred.label_ids
        UpperCamelCase_						=  pred.predictions

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

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

        return {"accuracy": accuracy}

     # map train dataset
     UpperCamelCase_						=  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_						=  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_						=  self.get_auto_remove_tmp_dir()

     UpperCamelCase_						=  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_						=  SeqaSeqTrainer(
         model=_UpperCAmelCase		,		args=_UpperCAmelCase		,		compute_metrics=_compute_metrics		,		train_dataset=_UpperCAmelCase		,		eval_dataset=_UpperCAmelCase		,		tokenizer=_UpperCAmelCase		,		)

     # start training
     trainer.train()





