


'''simple docstring'''







import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

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


enable_full_determinism()


class 			__lowerCamelCase			(	a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   LDMTextToImagePipeline
    a													=   TEXT_TO_IMAGE_PARAMS - {
        "negative_prompt",
        "negative_prompt_embeds",
        "cross_attention_kwargs",
        "prompt_embeds",
    }
    a													=   PipelineTesterMixin.required_optional_params - {
        "num_images_per_prompt",
        "callback",
        "callback_steps",
    }
    a													=   TEXT_TO_IMAGE_BATCH_PARAMS
    a													=   False


    def        A   (  self      :							Optional[Any]):
           torch.manual_seed(0)
           _A					:     Optional[int]     = 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				,							)
           _A					:     Optional[Any]     = DDIMScheduler(
               beta_start=0.0_0085				,							beta_end=0.012				,							beta_schedule='scaled_linear'				,							clip_sample=SCREAMING_SNAKE_CASE				,							set_alpha_to_one=SCREAMING_SNAKE_CASE				,							)
           torch.manual_seed(0)
           _A					:     int     = 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				,							)
           torch.manual_seed(0)
           _A					:     int     = CLIPTextConfig(
               bos_token_id=0				,							eos_token_id=2				,							hidden_size=32				,							intermediate_size=37				,							layer_norm_eps=1e-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1000				,							)
           _A					:     Tuple     = CLIPTextModel(SCREAMING_SNAKE_CASE)
           _A					:     Tuple     = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

           _A					:     Optional[int]     = {
               'unet': unet,
               'scheduler': scheduler,
               'vqvae': vae,
               'bert': text_encoder,
               'tokenizer': tokenizer,
           }
           return components


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Tuple=0):
           if str(SCREAMING_SNAKE_CASE).startswith('mps'):
                  _A					:     int     = torch.manual_seed(SCREAMING_SNAKE_CASE)
           else:
                  _A					:     Optional[Any]     = torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)
           _A					:     Union[str, Any]     = {
               '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        A   (  self      :							int):
           _A					:     Optional[Any]     = 'cpu'  # ensure determinism for the device-dependent torch.Generator

           _A					:     str     = self.get_dummy_components()
           _A					:     Dict     = LDMTextToImagePipeline(**SCREAMING_SNAKE_CASE)
           pipe.to(SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

           _A					:     List[str]     = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
           _A					:     Dict     = pipe(**SCREAMING_SNAKE_CASE).images
           _A					:     Any     = image[0, -3:, -3:, -1]

           assert image.shape == (1, 16, 16, 3)
           _A					:     str     = np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3




@slow
@require_torch_gpu
class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


    def        A   (  self      :							List[str]):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=torch.floataa				,							SCREAMING_SNAKE_CASE      :							List[str]=0):
           _A					:     List[str]     = torch.manual_seed(SCREAMING_SNAKE_CASE)
           _A					:     Optional[Any]     = np.random.RandomState(SCREAMING_SNAKE_CASE).standard_normal((1, 4, 32, 32))
           _A					:     str     = torch.from_numpy(SCREAMING_SNAKE_CASE).to(device=SCREAMING_SNAKE_CASE				,							dtype=SCREAMING_SNAKE_CASE)
           _A					:     Union[str, Any]     = {
               'prompt': 'A painting of a squirrel eating a burger',
               'latents': latents,
               'generator': generator,
               'num_inference_steps': 3,
               'guidance_scale': 6.0,
               'output_type': 'numpy',
           }
           return inputs


    def        A   (  self      :							Union[str, Any]):
           _A					:     Tuple     = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256').to(SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

           _A					:     str     = self.get_inputs(SCREAMING_SNAKE_CASE)
           _A					:     Any     = pipe(**SCREAMING_SNAKE_CASE).images
           _A					:     Tuple     = image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 256, 256, 3)
           _A					:     Union[str, Any]     = np.array([0.5_1825, 0.5_2850, 0.5_2543, 0.5_4258, 0.5_2304, 0.5_2569, 0.5_4363, 0.5_5276, 0.5_6878])
           _A					:     int     = np.abs(expected_slice - image_slice).max()
           assert max_diff < 1e-3




@nightly
@require_torch_gpu
class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


    def        A   (  self      :							Dict):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							Any=torch.floataa				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=0):
           _A					:     Tuple     = torch.manual_seed(SCREAMING_SNAKE_CASE)
           _A					:     Optional[Any]     = np.random.RandomState(SCREAMING_SNAKE_CASE).standard_normal((1, 4, 32, 32))
           _A					:     List[Any]     = torch.from_numpy(SCREAMING_SNAKE_CASE).to(device=SCREAMING_SNAKE_CASE				,							dtype=SCREAMING_SNAKE_CASE)
           _A					:     List[Any]     = {
               'prompt': 'A painting of a squirrel eating a burger',
               'latents': latents,
               'generator': generator,
               'num_inference_steps': 50,
               'guidance_scale': 6.0,
               'output_type': 'numpy',
           }
           return inputs


    def        A   (  self      :							Any):
           _A					:     int     = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256').to(SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

           _A					:     Any     = self.get_inputs(SCREAMING_SNAKE_CASE)
           _A					:     Dict     = pipe(**SCREAMING_SNAKE_CASE).images[0]

           _A					:     Optional[Any]     = load_numpy(
               'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy')
           _A					:     Optional[int]     = np.abs(expected_image - image).max()
           assert max_diff < 1e-3










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A				:					str    						=			{
    '''configuration_blip''': [
        '''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlipConfig''',
        '''BlipTextConfig''',
        '''BlipVisionConfig''',
    ],
    '''processing_blip''': ['''BlipProcessor'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Optional[Any]    						=			['''BlipImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[Any]    						=			[
       '''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''BlipModel''',
       '''BlipPreTrainedModel''',
       '''BlipForConditionalGeneration''',
       '''BlipForQuestionAnswering''',
       '''BlipVisionModel''',
       '''BlipTextModel''',
       '''BlipForImageTextRetrieval''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Union[str, Any]    						=			[
       '''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFBlipModel''',
       '''TFBlipPreTrainedModel''',
       '''TFBlipForConditionalGeneration''',
       '''TFBlipForQuestionAnswering''',
       '''TFBlipVisionModel''',
       '''TFBlipTextModel''',
       '''TFBlipForImageTextRetrieval''',
   ]

if TYPE_CHECKING:
   from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
   from .processing_blip import BlipProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_blip import BlipImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_blip import (
          BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          BlipForConditionalGeneration,
          BlipForImageTextRetrieval,
          BlipForQuestionAnswering,
          BlipModel,
          BlipPreTrainedModel,
          BlipTextModel,
          BlipVisionModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_blip import (
          TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFBlipForConditionalGeneration,
          TFBlipForImageTextRetrieval,
          TFBlipForQuestionAnswering,
          TFBlipModel,
          TFBlipPreTrainedModel,
          TFBlipTextModel,
          TFBlipVisionModel,
      )

else:
   import sys

   A				:					Optional[int]    						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






