

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					(    lowerCamelCase__,		unittest.TestCase  ):



 '''simple docstring'''
 _lowerCamelCase  :		str			      =	UnCLIPPipeline
 _lowerCamelCase  :		Optional[Any]			      =	TEXT_TO_IMAGE_PARAMS - {
     'negative_prompt',
     'height',
     'width',
     'negative_prompt_embeds',
     'guidance_scale',
     'prompt_embeds',
     'cross_attention_kwargs',
 }
 _lowerCamelCase  :		List[Any]			      =	TEXT_TO_IMAGE_BATCH_PARAMS
 _lowerCamelCase  :		List[Any]			      =	[
     'generator',
     'return_dict',
     'prior_num_inference_steps',
     'decoder_num_inference_steps',
     'super_res_num_inference_steps',
 ]
 _lowerCamelCase  :		List[Any]			      =	False

 @property
 def    __snake_case   (     self    :						int					)	->							Tuple:
        return 3_2

 @property
 def    __snake_case   (     self    :						Tuple					)	->							Optional[Any]:
        return 3_2

 @property
 def    __snake_case   (     self    :						int					)	->							Optional[Any]:
        return self.time_input_dim

 @property
 def    __snake_case   (     self    :						Any					)	->							int:
        return self.time_input_dim * 4

 @property
 def    __snake_case   (     self    :						Tuple					)	->							List[Any]:
        return 1_0_0

 @property
 def    __snake_case   (     self    :						List[str]					)	->							List[str]:
        _UpperCamelCase       :    Optional[Any]					  =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)
        return tokenizer

 @property
 def    __snake_case   (     self    :						List[Any]					)	->							Optional[Any]:
        torch.manual_seed(0					)
        _UpperCamelCase       :    Dict					  =					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=3_7    ,     layer_norm_eps=1e-05    ,     num_attention_heads=4    ,     num_hidden_layers=5    ,     pad_token_id=1    ,     vocab_size=1_0_0_0    ,     )
        return CLIPTextModelWithProjection(lowercase_					)

 @property
 def    __snake_case   (     self    :						Tuple					)	->							Optional[Any]:
        torch.manual_seed(0					)

        _UpperCamelCase       :    Tuple					  =					{
            'num_attention_heads': 2,
            'attention_head_dim': 1_2,
            'embedding_dim': self.text_embedder_hidden_size,
            'num_layers': 1,
        }

        _UpperCamelCase       :    Optional[Any]					  =					PriorTransformer(**lowercase_					)
        return model

 @property
 def    __snake_case   (     self    :						Dict					)	->							Union[str, Any]:
        torch.manual_seed(0					)

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

        _UpperCamelCase       :    Optional[int]					  =					UnCLIPTextProjModel(**lowercase_					)
        return model

 @property
 def    __snake_case   (     self    :						Any					)	->							str:
        torch.manual_seed(0					)

        _UpperCamelCase       :    Dict					  =					{
            'sample_size': 3_2,
            # 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',
        }

        _UpperCamelCase       :    List[str]					  =					UNetaDConditionModel(**lowercase_					)
        return model

 @property
 def    __snake_case   (     self    :						Union[str, Any]					)	->							Any:
        return {
            "sample_size": 6_4,
            "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    __snake_case   (     self    :						Union[str, Any]					)	->							List[str]:
        torch.manual_seed(0					)

        _UpperCamelCase       :    str					  =					UNetaDModel(**self.dummy_super_res_kwargs					)
        return model

 @property
 def    __snake_case   (     self    :						List[str]					)	->							str:
        torch.manual_seed(1					)

        _UpperCamelCase       :    List[Any]					  =					UNetaDModel(**self.dummy_super_res_kwargs					)
        return model

 def    __snake_case   (     self    :						Any					)	->							List[str]:
        _UpperCamelCase       :    List[Any]					  =					self.dummy_prior
        _UpperCamelCase       :    int					  =					self.dummy_decoder
        _UpperCamelCase       :    List[Any]					  =					self.dummy_text_proj
        _UpperCamelCase       :    Optional[Any]					  =					self.dummy_text_encoder
        _UpperCamelCase       :    List[Any]					  =					self.dummy_tokenizer
        _UpperCamelCase       :    int					  =					self.dummy_super_res_first
        _UpperCamelCase       :    List[str]					  =					self.dummy_super_res_last

        _UpperCamelCase       :    List[str]					  =					UnCLIPScheduler(
            variance_type='fixed_small_log'    ,     prediction_type='sample'    ,     num_train_timesteps=1_0_0_0    ,     clip_sample_range=5.0    ,     )

        _UpperCamelCase       :    Optional[int]					  =					UnCLIPScheduler(
            variance_type='learned_range'    ,     prediction_type='epsilon'    ,     num_train_timesteps=1_0_0_0    ,     )

        _UpperCamelCase       :    Dict					  =					UnCLIPScheduler(
            variance_type='fixed_small_log'    ,     prediction_type='epsilon'    ,     num_train_timesteps=1_0_0_0    ,     )

        _UpperCamelCase       :    Tuple					  =					{
            '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    __snake_case   (     self    :						int    ,     lowerCamelCase    :						Tuple    ,     lowerCamelCase    :						Any=0					)	->							Optional[int]:






        if str(lowercase_					).startswith('mps'					):
               _UpperCamelCase       :    int					  =					torch.manual_seed(lowercase_					)
        else:
               _UpperCamelCase       :    Dict					  =					torch.Generator(device=lowercase_					).manual_seed(lowercase_					)
        _UpperCamelCase       :    Tuple					  =					{
            '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    __snake_case   (     self    :						List[Any]					)	->							List[str]:
        _UpperCamelCase       :    int					  =					'cpu'

        _UpperCamelCase       :    Tuple					  =					self.get_dummy_components()

        _UpperCamelCase       :    str					  =					self.pipeline_class(**lowercase_					)
        _UpperCamelCase       :    Optional[int]					  =					pipe.to(lowercase_					)

        pipe.set_progress_bar_config(disable=lowercase_					)

        _UpperCamelCase       :    Union[str, Any]					  =					pipe(**self.get_dummy_inputs(lowercase_					)					)
        _UpperCamelCase       :    List[str]					  =					output.images

        _UpperCamelCase       :    List[str]					  =					pipe(
            **self.get_dummy_inputs(lowercase_					)    ,     return_dict=lowercase_    ,     )[0]

        _UpperCamelCase       :    Dict					  =					image[0, -3:, -3:, -1]
        _UpperCamelCase       :    Optional[Any]					  =					image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)

        _UpperCamelCase       :    Optional[Any]					  =					np.array(
            [
                0.9_9_9_7,
                0.9_9_8_8,
                0.0_0_2_8,
                0.9_9_9_7,
                0.9_9_8_4,
                0.9_9_6_5,
                0.0_0_2_9,
                0.9_9_8_6,
                0.0_0_2_5,
            ]					)

        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    __snake_case   (     self    :						Optional[int]					)	->							List[Any]:
        _UpperCamelCase       :    Union[str, Any]					  =					torch.device('cpu'					)




        class 				__lowerCAmelCase					:



         '''simple docstring'''
         _lowerCamelCase  :		str			      =	1







        _UpperCamelCase       :    Union[str, Any]					  =					self.get_dummy_components()

        _UpperCamelCase       :    Tuple					  =					self.pipeline_class(**lowercase_					)
        _UpperCamelCase       :    Dict					  =					pipe.to(lowercase_					)

        _UpperCamelCase       :    Optional[Any]					  =					components['prior']
        _UpperCamelCase       :    Dict					  =					components['decoder']
        _UpperCamelCase       :    Optional[int]					  =					components['super_res_first']
        _UpperCamelCase       :    Any					  =					components['tokenizer']
        _UpperCamelCase       :    List[str]					  =					components['text_encoder']

        _UpperCamelCase       :    Optional[Any]					  =					torch.Generator(device=lowercase_					).manual_seed(0					)
        _UpperCamelCase       :    Tuple					  =					prior.dtype
        _UpperCamelCase       :    List[str]					  =					1

        _UpperCamelCase       :    Tuple					  =					(batch_size, prior.config.embedding_dim)
        _UpperCamelCase       :    Optional[int]					  =					pipe.prepare_latents(
            lowercase_    ,     dtype=lowercase_    ,     device=lowercase_    ,     generator=lowercase_    ,     latents=lowercase_    ,     scheduler=DummyScheduler()					)
        _UpperCamelCase       :    Optional[int]					  =					(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
        _UpperCamelCase       :    List[str]					  =					pipe.prepare_latents(
            lowercase_    ,     dtype=lowercase_    ,     device=lowercase_    ,     generator=lowercase_    ,     latents=lowercase_    ,     scheduler=DummyScheduler()					)

        _UpperCamelCase       :    Dict					  =					(
            batch_size,
            super_res_first.config.in_channels // 2,
            super_res_first.config.sample_size,
            super_res_first.config.sample_size,
        )
        _UpperCamelCase       :    Tuple					  =					pipe.prepare_latents(
            lowercase_    ,     dtype=lowercase_    ,     device=lowercase_    ,     generator=lowercase_    ,     latents=lowercase_    ,     scheduler=DummyScheduler()					)

        pipe.set_progress_bar_config(disable=lowercase_					)

        _UpperCamelCase       :    Optional[Any]					  =					'this is a prompt example'

        _UpperCamelCase       :    List[Any]					  =					torch.Generator(device=lowercase_					).manual_seed(0					)
        _UpperCamelCase       :    Any					  =					pipe(
            [prompt]    ,     generator=lowercase_    ,     prior_num_inference_steps=2    ,     decoder_num_inference_steps=2    ,     super_res_num_inference_steps=2    ,     prior_latents=lowercase_    ,     decoder_latents=lowercase_    ,     super_res_latents=lowercase_    ,     output_type='np'    ,     )
        _UpperCamelCase       :    Dict					  =					output.images

        _UpperCamelCase       :    Optional[Any]					  =					tokenizer(
            lowercase_    ,     padding='max_length'    ,     max_length=tokenizer.model_max_length    ,     return_tensors='pt'    ,     )
        _UpperCamelCase       :    Optional[int]					  =					text_encoder(text_inputs.input_ids					)
        _UpperCamelCase       :    Dict					  =					text_inputs.attention_mask

        _UpperCamelCase       :    int					  =					torch.Generator(device=lowercase_					).manual_seed(0					)
        _UpperCamelCase       :    List[str]					  =					pipe(
            generator=lowercase_    ,     prior_num_inference_steps=2    ,     decoder_num_inference_steps=2    ,     super_res_num_inference_steps=2    ,     prior_latents=lowercase_    ,     decoder_latents=lowercase_    ,     super_res_latents=lowercase_    ,     text_model_output=lowercase_    ,     text_attention_mask=lowercase_    ,     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    __snake_case   (     self    :						Optional[Any]					)	->							Union[str, Any]:
        _UpperCamelCase       :    Tuple					  =					torch_device == 'cpu'

        self._test_attention_slicing_forward_pass(test_max_difference=lowercase_    ,     expected_max_diff=0.0_1					)

 @skip_mps
 def    __snake_case   (     self    :						List[str]					)	->							Union[str, Any]:
        _UpperCamelCase       :    Optional[int]					  =					torch_device == 'cpu'
        _UpperCamelCase       :    Tuple					  =					True
        _UpperCamelCase       :    str					  =					[
            'prior_num_inference_steps',
            'decoder_num_inference_steps',
            'super_res_num_inference_steps',
        ]

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

 def    __snake_case   (     self    :						Tuple					)	->							Tuple:
        _UpperCamelCase       :    str					  =					[
            'prior_num_inference_steps',
            'decoder_num_inference_steps',
            'super_res_num_inference_steps',
        ]

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

 @skip_mps
 def    __snake_case   (     self    :						List[Any]					)	->							Any:
        return super().test_dict_tuple_outputs_equivalent()

 @skip_mps
 def    __snake_case   (     self    :						str					)	->							Any:
        return super().test_save_load_local()

 @skip_mps
 def    __snake_case   (     self    :						Dict					)	->							Any:
        return super().test_save_load_optional_components()







@nightly
class 				__lowerCAmelCase					(    unittest.TestCase  ):



 '''simple docstring'''

 def    __snake_case   (     self    :						List[str]					)	->							Any:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

 def    __snake_case   (     self    :						Any					)	->							Tuple:
        _UpperCamelCase       :    str					  =					load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/unclip/karlo_v1_alpha_horse_cpu.npy'					)

        _UpperCamelCase       :    str					  =					UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'					)
        pipeline.set_progress_bar_config(disable=lowercase_					)

        _UpperCamelCase       :    Union[str, Any]					  =					torch.manual_seed(0					)
        _UpperCamelCase       :    int					  =					pipeline(
            'horse'    ,     num_images_per_prompt=1    ,     generator=lowercase_    ,     output_type='np'    ,     )

        _UpperCamelCase       :    Dict					  =					output.images[0]

        assert image.shape == (2_5_6, 2_5_6, 3)
        assert np.abs(expected_image - image					).max() < 1e-1







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



 '''simple docstring'''

 def    __snake_case   (     self    :						List[Any]					)	->							List[str]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

 def    __snake_case   (     self    :						Any					)	->							Tuple:
        _UpperCamelCase       :    List[Any]					  =					load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/unclip/karlo_v1_alpha_horse_fp16.npy'					)

        _UpperCamelCase       :    Optional[Any]					  =					UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'    ,     torch_dtype=torch.floataa					)
        _UpperCamelCase       :    Any					  =					pipeline.to(lowercase_					)
        pipeline.set_progress_bar_config(disable=lowercase_					)

        _UpperCamelCase       :    str					  =					torch.Generator(device='cpu'					).manual_seed(0					)
        _UpperCamelCase       :    int					  =					pipeline(
            'horse'    ,     generator=lowercase_    ,     output_type='np'    ,     )

        _UpperCamelCase       :    Any					  =					output.images[0]

        assert image.shape == (2_5_6, 2_5_6, 3)

        assert_mean_pixel_difference(lowercase_    ,     lowercase_					)

 def    __snake_case   (     self    :						List[str]					)	->							Dict:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        _UpperCamelCase       :    List[Any]					  =					UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'    ,     torch_dtype=torch.floataa					)
        _UpperCamelCase       :    str					  =					pipe.to(lowercase_					)
        pipe.set_progress_bar_config(disable=lowercase_					)
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        _UpperCamelCase       :    Optional[int]					  =					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'    ,     )

        _UpperCamelCase       :    Optional[int]					  =					torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 1_0**9




def SCREAMING_SNAKE_CASE			(       lowercase_:		int			):
       if not isinstance(lowercase_						,  lowercase_			):
              _UpperCamelCase       :    Optional[int]					  =					(
                  'operation can not be conducted on a object of type '
                  F"{type(lowercase_			).__name__}"
              )
              raise TypeError(lowercase_			)
       _UpperCamelCase       :    List[Any]					  =					''
       for _ in range(0						,  32			):
              bit_string += str(number % 2			)
              _UpperCamelCase       :    Dict					  =					number >> 1
       return bit_string


def SCREAMING_SNAKE_CASE			(       lowercase_:		int			):
       if number < 0:
              raise ValueError('the value of input must be positive'			)
       elif isinstance(lowercase_						,  lowercase_			):
              raise TypeError('Input value must be a \'int\' type'			)
       elif isinstance(lowercase_						,  lowercase_			):
              raise TypeError('\'<\' not supported between instances of \'str\' and \'int\''			)
       _UpperCamelCase       :    List[Any]					  =					0
       # iterator over [1 to 32],since we are dealing with 32 bit integer
       for _ in range(1						,  33			):
              # left shift the bits by unity
              _UpperCamelCase       :    Tuple					  =					result << 1
              # get the end bit
              _UpperCamelCase       :    List[Any]					  =					number % 2
              # right shift the bits by unity
              _UpperCamelCase       :    Dict					  =					number >> 1
              # add that bit to our ans
              _UpperCamelCase       :    int					  =					result | end_bit
       return get_reverse_bit_string(lowercase_			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()



