




'''simple docstring'''



import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np







class 		lowercase_   :


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"   )

      torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"   )

      torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				UNetaDConditionModel(
          sample_size=3_2  ,				layers_per_block=1  ,				block_out_channels=[3_2, 6_4]  ,				down_block_types=[
              "ResnetDownsampleBlock2D",
              "SimpleCrossAttnDownBlock2D",
          ]  ,				mid_block_type="UNetMidBlock2DSimpleCrossAttn"  ,				up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]  ,				in_channels=3  ,				out_channels=6  ,				cross_attention_dim=3_2  ,				encoder_hid_dim=3_2  ,				attention_head_dim=8  ,				addition_embed_type="text"  ,				addition_embed_type_num_heads=2  ,				cross_attention_norm="group_norm"  ,				resnet_time_scale_shift="scale_shift"  ,				act_fn="gelu"  ,				)
      unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

      torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				DDPMScheduler(
          num_train_timesteps=1_0_0_0  ,				beta_schedule="squaredcos_cap_v2"  ,				beta_start=0.0_0_0_1  ,				beta_end=0.0_2  ,				thresholding=__lowerCamelCase  ,				dynamic_thresholding_ratio=0.9_5  ,				sample_max_value=1.0  ,				prediction_type="epsilon"  ,				variance_type="learned_range"  ,				)

      torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				IFWatermarker()

      return {
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "unet": unet,
          "scheduler": scheduler,
          "watermarker": watermarker,
          "safety_checker": None,
          "feature_extractor": None,
      }



   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"   )

      torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"   )

      torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				UNetaDConditionModel(
          sample_size=3_2  ,				layers_per_block=[1, 2]  ,				block_out_channels=[3_2, 6_4]  ,				down_block_types=[
              "ResnetDownsampleBlock2D",
              "SimpleCrossAttnDownBlock2D",
          ]  ,				mid_block_type="UNetMidBlock2DSimpleCrossAttn"  ,				up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]  ,				in_channels=6  ,				out_channels=6  ,				cross_attention_dim=3_2  ,				encoder_hid_dim=3_2  ,				attention_head_dim=8  ,				addition_embed_type="text"  ,				addition_embed_type_num_heads=2  ,				cross_attention_norm="group_norm"  ,				resnet_time_scale_shift="scale_shift"  ,				act_fn="gelu"  ,				class_embed_type="timestep"  ,				mid_block_scale_factor=1.4_1_4  ,				time_embedding_act_fn="gelu"  ,				time_embedding_dim=3_2  ,				)
      unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

      torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				DDPMScheduler(
          num_train_timesteps=1_0_0_0  ,				beta_schedule="squaredcos_cap_v2"  ,				beta_start=0.0_0_0_1  ,				beta_end=0.0_2  ,				thresholding=__lowerCamelCase  ,				dynamic_thresholding_ratio=0.9_5  ,				sample_max_value=1.0  ,				prediction_type="epsilon"  ,				variance_type="learned_range"  ,				)

      torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				DDPMScheduler(
          num_train_timesteps=1_0_0_0  ,				beta_schedule="squaredcos_cap_v2"  ,				beta_start=0.0_0_0_1  ,				beta_end=0.0_2  ,				)

      torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				IFWatermarker()

      return {
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "unet": unet,
          "scheduler": scheduler,
          "image_noising_scheduler": image_noising_scheduler,
          "watermarker": watermarker,
          "safety_checker": None,
          "feature_extractor": None,
      }



   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.get_dummy_components()
      _SCREAMING_SNAKE_CASE	=				self.pipeline_class(**__lowerCamelCase   )
      pipe.to(__lowerCamelCase   )
      pipe.set_progress_bar_config(disable=__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				self.get_dummy_inputs(__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				inputs["prompt"]
      _SCREAMING_SNAKE_CASE	=				inputs["generator"]
      _SCREAMING_SNAKE_CASE	=				inputs["num_inference_steps"]
      _SCREAMING_SNAKE_CASE	=				inputs["output_type"]

      if "image" in inputs:
         _SCREAMING_SNAKE_CASE	=				inputs["image"]
      else:
         _SCREAMING_SNAKE_CASE	=				None

      if "mask_image" in inputs:
         _SCREAMING_SNAKE_CASE	=				inputs["mask_image"]
      else:
         _SCREAMING_SNAKE_CASE	=				None

      if "original_image" in inputs:
         _SCREAMING_SNAKE_CASE	=				inputs["original_image"]
      else:
         _SCREAMING_SNAKE_CASE	=				None

      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				pipe.encode_prompt(__lowerCamelCase   )

      # inputs with prompt converted to embeddings
      _SCREAMING_SNAKE_CASE	=				{
          "prompt_embeds": prompt_embeds,
          "negative_prompt_embeds": negative_prompt_embeds,
          "generator": generator,
          "num_inference_steps": num_inference_steps,
          "output_type": output_type,
      }

      if image is not None:
         _SCREAMING_SNAKE_CASE	=				image

      if mask_image is not None:
         _SCREAMING_SNAKE_CASE	=				mask_image

      if original_image is not None:
         _SCREAMING_SNAKE_CASE	=				original_image

      # set all optional components to None
      for optional_component in pipe._optional_components:
         setattr(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				pipe(**__lowerCamelCase   )[0]

      with tempfile.TemporaryDirectory() as tmpdir:
         pipe.save_pretrained(__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				self.pipeline_class.from_pretrained(__lowerCamelCase   )
         pipe_loaded.to(__lowerCamelCase   )
         pipe_loaded.set_progress_bar_config(disable=__lowerCamelCase   )

      pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

      for optional_component in pipe._optional_components:
         self.assertTrue(
             getattr(__lowerCamelCase  ,				__lowerCamelCase   ) is None  ,				F"""`{optional_component}` did not stay set to None after loading."""  ,				)

      _SCREAMING_SNAKE_CASE	=				self.get_dummy_inputs(__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				inputs["generator"]
      _SCREAMING_SNAKE_CASE	=				inputs["num_inference_steps"]
      _SCREAMING_SNAKE_CASE	=				inputs["output_type"]

      # inputs with prompt converted to embeddings
      _SCREAMING_SNAKE_CASE	=				{
          "prompt_embeds": prompt_embeds,
          "negative_prompt_embeds": negative_prompt_embeds,
          "generator": generator,
          "num_inference_steps": num_inference_steps,
          "output_type": output_type,
      }

      if image is not None:
         _SCREAMING_SNAKE_CASE	=				image

      if mask_image is not None:
         _SCREAMING_SNAKE_CASE	=				mask_image

      if original_image is not None:
         _SCREAMING_SNAKE_CASE	=				original_image

      _SCREAMING_SNAKE_CASE	=				pipe_loaded(**__lowerCamelCase   )[0]

      _SCREAMING_SNAKE_CASE	=				np.abs(to_np(__lowerCamelCase   ) - to_np(__lowerCamelCase   )   ).max()
      self.assertLess(__lowerCamelCase  ,				1e-4   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.get_dummy_components()
      _SCREAMING_SNAKE_CASE	=				self.pipeline_class(**__lowerCamelCase   )
      pipe.to(__lowerCamelCase   )
      pipe.set_progress_bar_config(disable=__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				self.get_dummy_inputs(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				pipe(**__lowerCamelCase   )[0]

      with tempfile.TemporaryDirectory() as tmpdir:
         pipe.save_pretrained(__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				self.pipeline_class.from_pretrained(__lowerCamelCase   )
         pipe_loaded.to(__lowerCamelCase   )
         pipe_loaded.set_progress_bar_config(disable=__lowerCamelCase   )

      pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

      _SCREAMING_SNAKE_CASE	=				self.get_dummy_inputs(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				pipe_loaded(**__lowerCamelCase   )[0]

      _SCREAMING_SNAKE_CASE	=				np.abs(to_np(__lowerCamelCase   ) - to_np(__lowerCamelCase   )   ).max()
      self.assertLess(__lowerCamelCase  ,				1e-4   )







'''simple docstring'''



from typing import TYPE_CHECKING

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


lowerCamelCase_			=						{
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						['BridgeTowerImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'BridgeTowerForContrastiveLearning',
     'BridgeTowerForImageAndTextRetrieval',
     'BridgeTowerForMaskedLM',
     'BridgeTowerModel',
     'BridgeTowerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_bridgetower import (
     BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BridgeTowerConfig,
     BridgeTowerTextConfig,
     BridgeTowerVisionConfig,
 )
 from .processing_bridgetower import BridgeTowerProcessor

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bridgetower import BridgeTowerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bridgetower import (
      BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
      BridgeTowerForContrastiveLearning,
      BridgeTowerForImageAndTextRetrieval,
      BridgeTowerForMaskedLM,
      BridgeTowerModel,
      BridgeTowerPreTrainedModel,
  )


else:
 import sys

 lowerCamelCase_			=						_LazyModule(__name__, globals()['__file__'], _import_structure)

