





"""simple docstring"""





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 			snake_case		(   __UpperCAmelCase ,     unittest.TestCase   ):
      lowerCamelCase__						= UnCLIPPipeline
      lowerCamelCase__						= TEXT_TO_IMAGE_PARAMS - {
          '''negative_prompt''',
          '''height''',
          '''width''',
          '''negative_prompt_embeds''',
          '''guidance_scale''',
          '''prompt_embeds''',
          '''cross_attention_kwargs''',
      }
      lowerCamelCase__						= TEXT_TO_IMAGE_BATCH_PARAMS
      lowerCamelCase__						= [
          '''generator''',
          '''return_dict''',
          '''prior_num_inference_steps''',
          '''decoder_num_inference_steps''',
          '''super_res_num_inference_steps''',
      ]
      lowerCamelCase__						= False

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						):
       return 3_2

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						):
       return 3_2

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						):
       return self.time_input_dim

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						):
       return self.time_input_dim * 4

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						):
       return 1_0_0

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

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						):
       torch.manual_seed(0						)
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       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(_lowerCamelCase						)

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       torch.manual_seed(0						)

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

       __SCREAMING_SNAKE_CASE :			Any												=       PriorTransformer(**_lowerCamelCase						)
       return model

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						):
       torch.manual_seed(0						)

       __SCREAMING_SNAKE_CASE :			List[str]												=       {
           '''clip_embeddings_dim''': self.text_embedder_hidden_size,
           '''time_embed_dim''': self.time_embed_dim,
           '''cross_attention_dim''': self.cross_attention_dim,
       }

       __SCREAMING_SNAKE_CASE :			Dict												=       UnCLIPTextProjModel(**_lowerCamelCase						)
       return model

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       torch.manual_seed(0						)

       __SCREAMING_SNAKE_CASE :			int												=       {
           '''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''',
       }

       __SCREAMING_SNAKE_CASE :			str												=       UNetaDConditionModel(**_lowerCamelCase						)
       return model

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, 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 						SCREAMING_SNAKE_CASE_			(			self   :Any						):
       torch.manual_seed(0						)

       __SCREAMING_SNAKE_CASE :			Tuple												=       UNetaDModel(**self.dummy_super_res_kwargs						)
       return model

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       # seeded differently to get different unet than `self.dummy_super_res_first`
       torch.manual_seed(1						)

       __SCREAMING_SNAKE_CASE :			Any												=       UNetaDModel(**self.dummy_super_res_kwargs						)
       return model

      def 						SCREAMING_SNAKE_CASE_			(			self   :Dict						):
       __SCREAMING_SNAKE_CASE :			str												=       self.dummy_prior
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.dummy_decoder
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.dummy_text_proj
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self.dummy_text_encoder
       __SCREAMING_SNAKE_CASE :			Tuple												=       self.dummy_tokenizer
       __SCREAMING_SNAKE_CASE :			int												=       self.dummy_super_res_first
       __SCREAMING_SNAKE_CASE :			List[str]												=       self.dummy_super_res_last

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

       __SCREAMING_SNAKE_CASE :			List[str]												=       UnCLIPScheduler(
           variance_type='''learned_range'''						,							prediction_type='''epsilon'''						,							num_train_timesteps=1_0_0_0						,							)

       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       UnCLIPScheduler(
           variance_type='''fixed_small_log'''						,							prediction_type='''epsilon'''						,							num_train_timesteps=1_0_0_0						,							)

       __SCREAMING_SNAKE_CASE :			List[str]												=       {
           '''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 						SCREAMING_SNAKE_CASE_			(			self   :str						,							_lowerCamelCase   :Optional[Any]						,							_lowerCamelCase   :List[Any]=0						):
       if str(_lowerCamelCase						).startswith('''mps'''						):
        __SCREAMING_SNAKE_CASE :			int												=       torch.manual_seed(_lowerCamelCase						)
       else:
        __SCREAMING_SNAKE_CASE :			List[Any]												=       torch.Generator(device=_lowerCamelCase						).manual_seed(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			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 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						):
       __SCREAMING_SNAKE_CASE :			str												=       '''cpu'''

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

       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.pipeline_class(**_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			List[str]												=       pipe.to(_lowerCamelCase						)

       pipe.set_progress_bar_config(disable=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Dict												=       pipe(**self.get_dummy_inputs(_lowerCamelCase						)						)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       output.images

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

       __SCREAMING_SNAKE_CASE :			List[Any]												=       image[0, -3:, -3:, -1]
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       image_from_tuple[0, -3:, -3:, -1]

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

       __SCREAMING_SNAKE_CASE :			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 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						):
       __SCREAMING_SNAKE_CASE :			Tuple												=       torch.device('''cpu'''						)





       class 			snake_case		:
             lowerCamelCase__						= 1







       __SCREAMING_SNAKE_CASE :			List[Any]												=       self.get_dummy_components()

       __SCREAMING_SNAKE_CASE :			List[str]												=       self.pipeline_class(**_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       pipe.to(_lowerCamelCase						)

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

       __SCREAMING_SNAKE_CASE :			Dict												=       torch.Generator(device=_lowerCamelCase						).manual_seed(0						)
       __SCREAMING_SNAKE_CASE :			int												=       prior.dtype
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       1

       __SCREAMING_SNAKE_CASE :			List[str]												=       (batch_size, prior.config.embedding_dim)
       __SCREAMING_SNAKE_CASE :			Any												=       pipe.prepare_latents(
           _lowerCamelCase						,							dtype=_lowerCamelCase						,							device=_lowerCamelCase						,							generator=_lowerCamelCase						,							latents=_lowerCamelCase						,							scheduler=DummyScheduler()						)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
       __SCREAMING_SNAKE_CASE :			int												=       pipe.prepare_latents(
           _lowerCamelCase						,							dtype=_lowerCamelCase						,							device=_lowerCamelCase						,							generator=_lowerCamelCase						,							latents=_lowerCamelCase						,							scheduler=DummyScheduler()						)

       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       (
           batch_size,
           super_res_first.config.in_channels // 2,
           super_res_first.config.sample_size,
           super_res_first.config.sample_size,
       )
       __SCREAMING_SNAKE_CASE :			str												=       pipe.prepare_latents(
           _lowerCamelCase						,							dtype=_lowerCamelCase						,							device=_lowerCamelCase						,							generator=_lowerCamelCase						,							latents=_lowerCamelCase						,							scheduler=DummyScheduler()						)

       pipe.set_progress_bar_config(disable=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			int												=       '''this is a prompt example'''

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       torch.Generator(device=_lowerCamelCase						).manual_seed(0						)
       __SCREAMING_SNAKE_CASE :			str												=       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'''						,							)
       __SCREAMING_SNAKE_CASE :			Tuple												=       output.images

       __SCREAMING_SNAKE_CASE :			List[Any]												=       tokenizer(
           _lowerCamelCase						,							padding='''max_length'''						,							max_length=tokenizer.model_max_length						,							return_tensors='''pt'''						,							)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       text_encoder(text_inputs.input_ids						)
       __SCREAMING_SNAKE_CASE :			List[str]												=       text_inputs.attention_mask

       __SCREAMING_SNAKE_CASE :			Tuple												=       torch.Generator(device=_lowerCamelCase						).manual_seed(0						)
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       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 						SCREAMING_SNAKE_CASE_			(			self   :Dict						):
       __SCREAMING_SNAKE_CASE :			Dict												=       torch_device == '''cpu'''

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

      @skip_mps
      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       torch_device == '''cpu'''
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       True
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       [
           '''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 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       __SCREAMING_SNAKE_CASE :			List[Any]												=       [
           '''prior_num_inference_steps''',
           '''decoder_num_inference_steps''',
           '''super_res_num_inference_steps''',
       ]

       if torch_device == "mps":
        # TODO: MPS errors with larger batch sizes
        __SCREAMING_SNAKE_CASE :			Optional[Any]												=       [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 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       return super().test_dict_tuple_outputs_equivalent()

      @skip_mps
      def 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       return super().test_save_load_local()



      @skip_mps
      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						):
       return super().test_save_load_optional_components()







@nightly
class 			snake_case		(   unittest.TestCase   ):

      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



      def 						SCREAMING_SNAKE_CASE_			(			self   :Dict						):
       __SCREAMING_SNAKE_CASE :			List[str]												=       load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/unclip/karlo_v1_alpha_horse_cpu.npy'''						)

       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''						)
       pipeline.set_progress_bar_config(disable=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       torch.manual_seed(0						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       pipeline(
           '''horse'''						,							num_images_per_prompt=1						,							generator=_lowerCamelCase						,							output_type='''np'''						,							)

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       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 			snake_case		(   unittest.TestCase   ):

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

      def 						SCREAMING_SNAKE_CASE_			(			self   :Dict						):
       __SCREAMING_SNAKE_CASE :			Tuple												=       load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/unclip/karlo_v1_alpha_horse_fp16.npy'''						)

       __SCREAMING_SNAKE_CASE :			List[Any]												=       UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''						,							torch_dtype=torch.floataa						)
       __SCREAMING_SNAKE_CASE :			str												=       pipeline.to(_lowerCamelCase						)
       pipeline.set_progress_bar_config(disable=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       torch.Generator(device='''cpu'''						).manual_seed(0						)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       pipeline(
           '''horse'''						,							generator=_lowerCamelCase						,							output_type='''np'''						,							)

       __SCREAMING_SNAKE_CASE :			Any												=       output.images[0]

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

       assert_mean_pixel_difference(_lowerCamelCase						,							_lowerCamelCase						)



      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       __SCREAMING_SNAKE_CASE :			str												=       UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''						,							torch_dtype=torch.floataa						)
       __SCREAMING_SNAKE_CASE :			Any												=       pipe.to(_lowerCamelCase						)
       pipe.set_progress_bar_config(disable=_lowerCamelCase						)
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       __SCREAMING_SNAKE_CASE :			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'''						,							)

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








"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCamelCase       =	{
    '''configuration_mobilenet_v1''': [
        '''MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV1Config''',
        '''MobileNetV1OnnxConfig''',
    ],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase       =	['''MobileNetV1FeatureExtractor''']
    _lowerCamelCase       =	['''MobileNetV1ImageProcessor''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase       =	[
        '''MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MobileNetV1ForImageClassification''',
        '''MobileNetV1Model''',
        '''MobileNetV1PreTrainedModel''',
        '''load_tf_weights_in_mobilenet_v1''',
    ]


if TYPE_CHECKING:
    from .configuration_mobilenet_va import (
        MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
        MobileNetVaConfig,
        MobileNetVaOnnxConfig,
    )

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
        from .image_processing_mobilenet_va import MobileNetVaImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mobilenet_va import (
            MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
            MobileNetVaForImageClassification,
            MobileNetVaModel,
            MobileNetVaPreTrainedModel,
            load_tf_weights_in_mobilenet_va,
        )


else:
    import sys

    _lowerCamelCase       =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

