

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE      ,      unittest.TestCase							):
  _a						      =  UnCLIPPipeline
  _a						      =  TEXT_TO_IMAGE_PARAMS - {
      """negative_prompt""",
      """height""",
      """width""",
      """negative_prompt_embeds""",
      """guidance_scale""",
      """prompt_embeds""",
      """cross_attention_kwargs""",
  }
  _a						      =  TEXT_TO_IMAGE_BATCH_PARAMS
  _a						      =  [
      """generator""",
      """return_dict""",
      """prior_num_inference_steps""",
      """decoder_num_inference_steps""",
      """super_res_num_inference_steps""",
  ]
  _a						      =  False




  @property
  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        return 32




  @property
  def   A__					(    self )  ->			List[Any]:
        '''simple docstring'''






        return 32




  @property
  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        return self.time_input_dim




  @property
  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        return self.time_input_dim * 4




  @property
  def   A__					(    self )  ->			List[str]:
        '''simple docstring'''






        return 100




  @property
  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
        return tokenizer




  @property
  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        torch.manual_seed(0 )
        _lowercase     	=CLIPTextConfig(
            bos_token_id=0							,       eos_token_id=2							,       hidden_size=self.text_embedder_hidden_size							,       projection_dim=self.text_embedder_hidden_size							,       intermediate_size=37							,       layer_norm_eps=1e-05							,       num_attention_heads=4							,       num_hidden_layers=5							,       pad_token_id=1							,       vocab_size=1_000							,       )
        return CLIPTextModelWithProjection(lowerCAmelCase )




  @property
  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        torch.manual_seed(0 )

        _lowercase     	={
            'num_attention_heads': 2,
            'attention_head_dim': 12,
            'embedding_dim': self.text_embedder_hidden_size,
            'num_layers': 1,
        }

        _lowercase     	=PriorTransformer(**lowerCAmelCase )
        return model




  @property
  def   A__					(    self )  ->			Any:
        '''simple docstring'''






        torch.manual_seed(0 )

        _lowercase     	={
            'clip_embeddings_dim': self.text_embedder_hidden_size,
            'time_embed_dim': self.time_embed_dim,
            'cross_attention_dim': self.cross_attention_dim,
        }

        _lowercase     	=UnCLIPTextProjModel(**lowerCAmelCase )
        return model




  @property
  def   A__					(    self )  ->			List[str]:
        '''simple docstring'''






        torch.manual_seed(0 )

        _lowercase     	={
            'sample_size': 32,
            # RGB in channels
            'in_channels': 3,
            # Out channels is double in channels because predicts mean and variance
            'out_channels': 6,
            '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,
            'cross_attention_dim': self.cross_attention_dim,
            'attention_head_dim': 4,
            'resnet_time_scale_shift': 'scale_shift',
            'class_embed_type': 'identity',
        }

        _lowercase     	=UNetaDConditionModel(**lowerCAmelCase )
        return model




  @property
  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        return {
            "sample_size": 64,
            "layers_per_block": 1,
            "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
            "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
            "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
            "in_channels": 6,
            "out_channels": 3,
        }




  @property
  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        torch.manual_seed(0 )

        _lowercase     	=UNetaDModel(**self.dummy_super_res_kwargs )
        return model




  @property
  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        torch.manual_seed(1 )

        _lowercase     	=UNetaDModel(**self.dummy_super_res_kwargs )
        return model




  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	=self.dummy_prior
        _lowercase     	=self.dummy_decoder
        _lowercase     	=self.dummy_text_proj
        _lowercase     	=self.dummy_text_encoder
        _lowercase     	=self.dummy_tokenizer
        _lowercase     	=self.dummy_super_res_first
        _lowercase     	=self.dummy_super_res_last

        _lowercase     	=UnCLIPScheduler(
            variance_type='fixed_small_log'							,       prediction_type='sample'							,       num_train_timesteps=1_000							,       clip_sample_range=5.0							,       )

        _lowercase     	=UnCLIPScheduler(
            variance_type='learned_range'							,       prediction_type='epsilon'							,       num_train_timesteps=1_000							,       )

        _lowercase     	=UnCLIPScheduler(
            variance_type='fixed_small_log'							,       prediction_type='epsilon'							,       num_train_timesteps=1_000							,       )

        _lowercase     	={
            'prior': prior,
            'decoder': decoder,
            'text_proj': text_proj,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'super_res_first': super_res_first,
            'super_res_last': super_res_last,
            'prior_scheduler': prior_scheduler,
            'decoder_scheduler': decoder_scheduler,
            'super_res_scheduler': super_res_scheduler,
        }

        return components




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase=0 )  ->			List[str]:
        '''simple docstring'''






        if str(lowerCAmelCase ).startswith('mps' ):
              _lowercase     	=torch.manual_seed(lowerCAmelCase )
        else:
              _lowercase     	=torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
        _lowercase     	={
            'prompt': 'horse',
            'generator': generator,
            'prior_num_inference_steps': 2,
            'decoder_num_inference_steps': 2,
            'super_res_num_inference_steps': 2,
            'output_type': 'numpy',
        }
        return inputs




  def   A__					(    self )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	='cpu'

        _lowercase     	=self.get_dummy_components()

        _lowercase     	=self.pipeline_class(**lowerCAmelCase )
        _lowercase     	=pipe.to(lowerCAmelCase )

        pipe.set_progress_bar_config(disable=lowerCAmelCase )

        _lowercase     	=pipe(**self.get_dummy_inputs(lowerCAmelCase ) )
        _lowercase     	=output.images

        _lowercase     	=pipe(
            **self.get_dummy_inputs(lowerCAmelCase )							,       return_dict=lowerCAmelCase							,       )[0]

        _lowercase     	=image[0, -3:, -3:, -1]
        _lowercase     	=image_from_tuple[0, -3:, -3:, -1]

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

        _lowercase     	=np.array(
            [
                0.9997,
                0.9988,
                0.0028,
                0.9997,
                0.9984,
                0.9965,
                0.0029,
                0.9986,
                0.0025,
            ] )

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




  def   A__					(    self )  ->			int:
        '''simple docstring'''






        _lowercase     	=torch.device('cpu' )





        class 			__lowerCAmelCase					:
          _a						      =  1
        _lowercase     	=self.get_dummy_components()

        _lowercase     	=self.pipeline_class(**lowerCAmelCase )
        _lowercase     	=pipe.to(lowerCAmelCase )

        _lowercase     	=components['prior']
        _lowercase     	=components['decoder']
        _lowercase     	=components['super_res_first']
        _lowercase     	=components['tokenizer']
        _lowercase     	=components['text_encoder']

        _lowercase     	=torch.Generator(device=lowerCAmelCase ).manual_seed(0 )
        _lowercase     	=prior.dtype
        _lowercase     	=1

        _lowercase     	=(batch_size, prior.config.embedding_dim)
        _lowercase     	=pipe.prepare_latents(
            lowerCAmelCase							,       dtype=lowerCAmelCase							,       device=lowerCAmelCase							,       generator=lowerCAmelCase							,       latents=lowerCAmelCase							,       scheduler=DummyScheduler() )
        _lowercase     	=(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
        _lowercase     	=pipe.prepare_latents(
            lowerCAmelCase							,       dtype=lowerCAmelCase							,       device=lowerCAmelCase							,       generator=lowerCAmelCase							,       latents=lowerCAmelCase							,       scheduler=DummyScheduler() )

        _lowercase     	=(
            batch_size,
            super_res_first.config.in_channels // 2,
            super_res_first.config.sample_size,
            super_res_first.config.sample_size,
        )
        _lowercase     	=pipe.prepare_latents(
            lowerCAmelCase							,       dtype=lowerCAmelCase							,       device=lowerCAmelCase							,       generator=lowerCAmelCase							,       latents=lowerCAmelCase							,       scheduler=DummyScheduler() )

        pipe.set_progress_bar_config(disable=lowerCAmelCase )

        _lowercase     	='this is a prompt example'

        _lowercase     	=torch.Generator(device=lowerCAmelCase ).manual_seed(0 )
        _lowercase     	=pipe(
            [prompt]							,       generator=lowerCAmelCase							,       prior_num_inference_steps=2							,       decoder_num_inference_steps=2							,       super_res_num_inference_steps=2							,       prior_latents=lowerCAmelCase							,       decoder_latents=lowerCAmelCase							,       super_res_latents=lowerCAmelCase							,       output_type='np'							,       )
        _lowercase     	=output.images

        _lowercase     	=tokenizer(
            lowerCAmelCase							,       padding='max_length'							,       max_length=tokenizer.model_max_length							,       return_tensors='pt'							,       )
        _lowercase     	=text_encoder(text_inputs.input_ids )
        _lowercase     	=text_inputs.attention_mask

        _lowercase     	=torch.Generator(device=lowerCAmelCase ).manual_seed(0 )
        _lowercase     	=pipe(
            generator=lowerCAmelCase							,       prior_num_inference_steps=2							,       decoder_num_inference_steps=2							,       super_res_num_inference_steps=2							,       prior_latents=lowerCAmelCase							,       decoder_latents=lowerCAmelCase							,       super_res_latents=lowerCAmelCase							,       text_model_output=lowerCAmelCase							,       text_attention_mask=lowerCAmelCase							,       output_type='np'							,       )[0]

        # make sure passing text embeddings manually is identical
        assert np.abs(image - image_from_text ).max() < 1e-4




  @skip_mps
  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=torch_device == 'cpu'

        self._test_attention_slicing_forward_pass(test_max_difference=lowerCAmelCase							,       expected_max_diff=0.01 )




  @skip_mps
  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        _lowercase     	=torch_device == 'cpu'
        _lowercase     	=True
        _lowercase     	=[
            'prior_num_inference_steps',
            'decoder_num_inference_steps',
            'super_res_num_inference_steps',
        ]

        self._test_inference_batch_single_identical(
            test_max_difference=lowerCAmelCase							,       relax_max_difference=lowerCAmelCase							,       additional_params_copy_to_batched_inputs=lowerCAmelCase							,       )




  def   A__					(    self )  ->			int:
        '''simple docstring'''






        _lowercase     	=[
            'prior_num_inference_steps',
            'decoder_num_inference_steps',
            'super_res_num_inference_steps',
        ]

        if torch_device == "mps":
              # TODO: MPS errors with larger batch sizes
              _lowercase     	=[2, 3]
              self._test_inference_batch_consistent(
                  batch_sizes=lowerCAmelCase							,       additional_params_copy_to_batched_inputs=lowerCAmelCase							,       )
        else:
              self._test_inference_batch_consistent(
                  additional_params_copy_to_batched_inputs=lowerCAmelCase )




  @skip_mps
  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        return super().test_dict_tuple_outputs_equivalent()




  @skip_mps
  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        return super().test_save_load_local()




  @skip_mps
  def   A__					(    self )  ->			List[str]:
        '''simple docstring'''






        return super().test_save_load_optional_components()





@nightly
class 			__lowerCAmelCase					( unittest.TestCase							):




  def   A__					(    self )  ->			int:
        '''simple docstring'''






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




  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/unclip/karlo_v1_alpha_horse_cpu.npy' )

        _lowercase     	=UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha' )
        pipeline.set_progress_bar_config(disable=lowerCAmelCase )

        _lowercase     	=torch.manual_seed(0 )
        _lowercase     	=pipeline(
            'horse'							,       num_images_per_prompt=1							,       generator=lowerCAmelCase							,       output_type='np'							,       )

        _lowercase     	=output.images[0]

        assert image.shape == (256, 256, 3)
        assert np.abs(expected_image - image ).max() < 1e-1





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




  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






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




  def   A__					(    self )  ->			List[str]:
        '''simple docstring'''






        _lowercase     	=load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/unclip/karlo_v1_alpha_horse_fp16.npy' )

        _lowercase     	=UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'							,       torch_dtype=torch.floataa )
        _lowercase     	=pipeline.to(lowerCAmelCase )
        pipeline.set_progress_bar_config(disable=lowerCAmelCase )

        _lowercase     	=torch.Generator(device='cpu' ).manual_seed(0 )
        _lowercase     	=pipeline(
            'horse'							,       generator=lowerCAmelCase							,       output_type='np'							,       )

        _lowercase     	=output.images[0]

        assert image.shape == (256, 256, 3)

        assert_mean_pixel_difference(lowerCAmelCase							,       lowerCAmelCase )




  def   A__					(    self )  ->			List[str]:
        '''simple docstring'''






        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        _lowercase     	=UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'							,       torch_dtype=torch.floataa )
        _lowercase     	=pipe.to(lowerCAmelCase )
        pipe.set_progress_bar_config(disable=lowerCAmelCase )
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        _lowercase     	=pipe(
            'horse'							,       num_images_per_prompt=1							,       prior_num_inference_steps=2							,       decoder_num_inference_steps=2							,       super_res_num_inference_steps=2							,       output_type='np'							,       )

        _lowercase     	=torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 10**9





import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


lowercase_     =       logging.get_logger(__name__)





class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE							):




  def __init__(    self							,       *lowerCAmelCase							,       **lowerCAmelCase )  ->			None:
        '''simple docstring'''






        warnings.warn(
            'The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
            ' use ViTImageProcessor instead.'							,       lowerCAmelCase							,       )
        super().__init__(*lowerCAmelCase							,       **lowerCAmelCase )


