



"""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    SCREAMING_SNAKE_CASE__						:

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    torch.manual_seed(0		)
    UpperCAmelCase			:      Optional[int]			=			TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""		)

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

    torch.manual_seed(0		)
    UpperCAmelCase			:      Union[str, Any]			=			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		)
    UpperCAmelCase			:      Optional[Any]			=			DDPMScheduler(
        num_train_timesteps=1000   ,  beta_schedule="""squaredcos_cap_v2"""   ,  beta_start=0.0001   ,  beta_end=0.02   ,  thresholding=_SCREAMING_SNAKE_CASE   ,  dynamic_thresholding_ratio=0.95   ,  sample_max_value=1.0   ,  prediction_type="""epsilon"""   ,  variance_type="""learned_range"""   ,  )

    torch.manual_seed(0		)
    UpperCAmelCase			:      int			=			IFWatermarker()

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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   str:





    '''simple docstring'''




    torch.manual_seed(0		)
    UpperCAmelCase			:      Dict			=			TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""		)

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

    torch.manual_seed(0		)
    UpperCAmelCase			:      Tuple			=			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		)
    UpperCAmelCase			:      Any			=			DDPMScheduler(
        num_train_timesteps=1000   ,  beta_schedule="""squaredcos_cap_v2"""   ,  beta_start=0.0001   ,  beta_end=0.02   ,  thresholding=_SCREAMING_SNAKE_CASE   ,  dynamic_thresholding_ratio=0.95   ,  sample_max_value=1.0   ,  prediction_type="""epsilon"""   ,  variance_type="""learned_range"""   ,  )

    torch.manual_seed(0		)
    UpperCAmelCase			:      str			=			DDPMScheduler(
        num_train_timesteps=1000   ,  beta_schedule="""squaredcos_cap_v2"""   ,  beta_start=0.0001   ,  beta_end=0.02   ,  )

    torch.manual_seed(0		)
    UpperCAmelCase			:      str			=			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 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			self.get_dummy_components()
    UpperCAmelCase			:      str			=			self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
    pipe.to(_SCREAMING_SNAKE_CASE		)
    pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      List[Any]			=			self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      List[Any]			=			inputs["""prompt"""]
    UpperCAmelCase			:      str			=			inputs["""generator"""]
    UpperCAmelCase			:      int			=			inputs["""num_inference_steps"""]
    UpperCAmelCase			:      Union[str, Any]			=			inputs["""output_type"""]

    if "image" in inputs:
     UpperCAmelCase			:      str			=			inputs["""image"""]
    else:
     UpperCAmelCase			:      List[Any]			=			None

    if "mask_image" in inputs:
     UpperCAmelCase			:      List[str]			=			inputs["""mask_image"""]
    else:
     UpperCAmelCase			:      List[str]			=			None

    if "original_image" in inputs:
     UpperCAmelCase			:      Tuple			=			inputs["""original_image"""]
    else:
     UpperCAmelCase			:      Dict			=			None

    UpperCAmelCase			,	UpperCAmelCase			:      str			=			pipe.encode_prompt(_SCREAMING_SNAKE_CASE		)

    # inputs with prompt converted to embeddings
    UpperCAmelCase			:      int			=			{
        """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:
     UpperCAmelCase			:      Union[str, Any]			=			image

    if mask_image is not None:
     UpperCAmelCase			:      int			=			mask_image

    if original_image is not None:
     UpperCAmelCase			:      Optional[int]			=			original_image

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

    UpperCAmelCase			:      str			=			pipe(**_SCREAMING_SNAKE_CASE		)[0]

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

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

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

    UpperCAmelCase			:      Union[str, Any]			=			self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[Any]			=			inputs["""generator"""]
    UpperCAmelCase			:      Optional[Any]			=			inputs["""num_inference_steps"""]
    UpperCAmelCase			:      str			=			inputs["""output_type"""]

    # inputs with prompt converted to embeddings
    UpperCAmelCase			:      List[str]			=			{
        """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:
     UpperCAmelCase			:      Optional[int]			=			image

    if mask_image is not None:
     UpperCAmelCase			:      int			=			mask_image

    if original_image is not None:
     UpperCAmelCase			:      List[Any]			=			original_image

    UpperCAmelCase			:      Optional[Any]			=			pipe_loaded(**_SCREAMING_SNAKE_CASE		)[0]

    UpperCAmelCase			:      str			=			np.abs(to_np(_SCREAMING_SNAKE_CASE		) - to_np(_SCREAMING_SNAKE_CASE		)		).max()
    self.assertLess(_SCREAMING_SNAKE_CASE   ,  1E-4		)




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			self.get_dummy_components()
    UpperCAmelCase			:      Tuple			=			self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
    pipe.to(_SCREAMING_SNAKE_CASE		)
    pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      int			=			self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			pipe(**_SCREAMING_SNAKE_CASE		)[0]

    with tempfile.TemporaryDirectory() as tmpdir:
     pipe.save_pretrained(_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      List[str]			=			self.pipeline_class.from_pretrained(_SCREAMING_SNAKE_CASE		)
     pipe_loaded.to(_SCREAMING_SNAKE_CASE		)
     pipe_loaded.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

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

    UpperCAmelCase			:      Union[str, Any]			=			self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[int]			=			pipe_loaded(**_SCREAMING_SNAKE_CASE		)[0]

    UpperCAmelCase			:      int			=			np.abs(to_np(_SCREAMING_SNAKE_CASE		) - to_np(_SCREAMING_SNAKE_CASE		)		).max()
    self.assertLess(_SCREAMING_SNAKE_CASE   ,  1E-4		)





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import _LazyModule


A:     Dict										=	{"tokenization_bertweet": ["BertweetTokenizer"]}


if TYPE_CHECKING:
      from .tokenization_bertweet import BertweetTokenizer

else:
      import sys

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