





"""simple docstring"""





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__					=   {
    "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:
       SCREAMING_SNAKE_CASE__					=   ["BridgeTowerImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__					=   [
           "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

       SCREAMING_SNAKE_CASE__					=   _LazyModule(__name__, globals()["__file__"], _import_structure)













"""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					:





      def     _snake_case			(       self )		->     List[Any]:
            torch.manual_seed(0 )
            lowerCAmelCase  						=       TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )

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

            torch.manual_seed(0 )
            lowerCAmelCase  						=       UNetaDConditionModel(
                sample_size=32      ,						layers_per_block=1      ,						block_out_channels=[32, 64]      ,						down_block_types=[
                    """ResnetDownsampleBlock2D""",
                    """SimpleCrossAttnDownBlock2D""",
                ]      ,						mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""      ,						up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]      ,						in_channels=3      ,						out_channels=6      ,						cross_attention_dim=32      ,						encoder_hid_dim=32      ,						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 )
            lowerCAmelCase  						=       DDPMScheduler(
                num_train_timesteps=1_000      ,						beta_schedule="""squaredcos_cap_v2"""      ,						beta_start=0.0_001      ,						beta_end=0.02      ,						thresholding=lowercase      ,						dynamic_thresholding_ratio=0.95      ,						sample_max_value=1.0      ,						prediction_type="""epsilon"""      ,						variance_type="""learned_range"""      ,						)

            torch.manual_seed(0 )
            lowerCAmelCase  						=       IFWatermarker()

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





      def     _snake_case			(       self )		->     int:
            torch.manual_seed(0 )
            lowerCAmelCase  						=       TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )

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

            torch.manual_seed(0 )
            lowerCAmelCase  						=       UNetaDConditionModel(
                sample_size=32      ,						layers_per_block=[1, 2]      ,						block_out_channels=[32, 64]      ,						down_block_types=[
                    """ResnetDownsampleBlock2D""",
                    """SimpleCrossAttnDownBlock2D""",
                ]      ,						mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""      ,						up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]      ,						in_channels=6      ,						out_channels=6      ,						cross_attention_dim=32      ,						encoder_hid_dim=32      ,						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=1.414      ,						time_embedding_act_fn="""gelu"""      ,						time_embedding_dim=32      ,						)
            unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

            torch.manual_seed(0 )
            lowerCAmelCase  						=       DDPMScheduler(
                num_train_timesteps=1_000      ,						beta_schedule="""squaredcos_cap_v2"""      ,						beta_start=0.0_001      ,						beta_end=0.02      ,						thresholding=lowercase      ,						dynamic_thresholding_ratio=0.95      ,						sample_max_value=1.0      ,						prediction_type="""epsilon"""      ,						variance_type="""learned_range"""      ,						)

            torch.manual_seed(0 )
            lowerCAmelCase  						=       DDPMScheduler(
                num_train_timesteps=1_000      ,						beta_schedule="""squaredcos_cap_v2"""      ,						beta_start=0.0_001      ,						beta_end=0.02      ,						)

            torch.manual_seed(0 )
            lowerCAmelCase  						=       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     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       self.get_dummy_components()
            lowerCAmelCase  						=       self.pipeline_class(**lowercase )
            pipe.to(lowercase )
            pipe.set_progress_bar_config(disable=lowercase )

            lowerCAmelCase  						=       self.get_dummy_inputs(lowercase )

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

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

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

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

            lowerCAmelCase						,		lowerCAmelCase  						=       pipe.encode_prompt(lowercase )

            # inputs with prompt converted to embeddings
            lowerCAmelCase  						=       {
                """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:
                  lowerCAmelCase  						=       image

            if mask_image is not None:
                  lowerCAmelCase  						=       mask_image

            if original_image is not None:
                  lowerCAmelCase  						=       original_image

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

            lowerCAmelCase  						=       pipe(**lowercase )[0]

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

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

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

            lowerCAmelCase  						=       self.get_dummy_inputs(lowercase )

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

            # inputs with prompt converted to embeddings
            lowerCAmelCase  						=       {
                """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:
                  lowerCAmelCase  						=       image

            if mask_image is not None:
                  lowerCAmelCase  						=       mask_image

            if original_image is not None:
                  lowerCAmelCase  						=       original_image

            lowerCAmelCase  						=       pipe_loaded(**lowercase )[0]

            lowerCAmelCase  						=       np.abs(to_np(lowercase ) - to_np(lowercase ) ).max()
            self.assertLess(lowercase      ,						1e-4 )





      def     _snake_case			(       self )		->     Optional[int]:
            lowerCAmelCase  						=       self.get_dummy_components()
            lowerCAmelCase  						=       self.pipeline_class(**lowercase )
            pipe.to(lowercase )
            pipe.set_progress_bar_config(disable=lowercase )

            lowerCAmelCase  						=       self.get_dummy_inputs(lowercase )
            lowerCAmelCase  						=       pipe(**lowercase )[0]

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

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

            lowerCAmelCase  						=       self.get_dummy_inputs(lowercase )
            lowerCAmelCase  						=       pipe_loaded(**lowercase )[0]

            lowerCAmelCase  						=       np.abs(to_np(lowercase ) - to_np(lowercase ) ).max()
            self.assertLess(lowercase      ,						1e-4 )






