
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        lowerCAmelCase__      :

						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

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

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

								torch.manual_seed(0				)
								_UpperCamelCase						    =	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						    =	DDPMScheduler(
								    num_train_timesteps=10_00       ,							beta_schedule="""squaredcos_cap_v2"""       ,							beta_start=0.0001       ,							beta_end=0.02       ,							thresholding=a       ,							dynamic_thresholding_ratio=0.95       ,							sample_max_value=1.0       ,							prediction_type="""epsilon"""       ,							variance_type="""learned_range"""       ,							)

								torch.manual_seed(0				)
								_UpperCamelCase						    =	IFWatermarker()

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

						def  A_				(			self				)					->      int:


								'''simple docstring'''

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

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

								torch.manual_seed(0				)
								_UpperCamelCase						    =	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						    =	DDPMScheduler(
								    num_train_timesteps=10_00       ,							beta_schedule="""squaredcos_cap_v2"""       ,							beta_start=0.0001       ,							beta_end=0.02       ,							thresholding=a       ,							dynamic_thresholding_ratio=0.95       ,							sample_max_value=1.0       ,							prediction_type="""epsilon"""       ,							variance_type="""learned_range"""       ,							)

								torch.manual_seed(0				)
								_UpperCamelCase						    =	DDPMScheduler(
								    num_train_timesteps=10_00       ,							beta_schedule="""squaredcos_cap_v2"""       ,							beta_start=0.0001       ,							beta_end=0.02       ,							)

								torch.manual_seed(0				)
								_UpperCamelCase						    =	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  A_				(			self				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_dummy_components()
								_UpperCamelCase						    =	self.pipeline_class(**a				)
								pipe.to(a				)
								pipe.set_progress_bar_config(disable=a				)

								_UpperCamelCase						    =	self.get_dummy_inputs(a				)

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

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

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

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

								_UpperCamelCase    ,	_UpperCamelCase						    =	pipe.encode_prompt(a				)

								# inputs with prompt converted to embeddings
								_UpperCamelCase						    =	{
								    """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						    =	image

								if mask_image is not None:
										_UpperCamelCase						    =	mask_image

								if original_image is not None:
										_UpperCamelCase						    =	original_image

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

								_UpperCamelCase						    =	pipe(**a				)[0]

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

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

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

								_UpperCamelCase						    =	self.get_dummy_inputs(a				)

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

								# inputs with prompt converted to embeddings
								_UpperCamelCase						    =	{
								    """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						    =	image

								if mask_image is not None:
										_UpperCamelCase						    =	mask_image

								if original_image is not None:
										_UpperCamelCase						    =	original_image

								_UpperCamelCase						    =	pipe_loaded(**a				)[0]

								_UpperCamelCase						    =	np.abs(to_np(a				) - to_np(a				)				).max()
								self.assertLess(a       ,							1e-4				)







						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_dummy_components()
								_UpperCamelCase						    =	self.pipeline_class(**a				)
								pipe.to(a				)
								pipe.set_progress_bar_config(disable=a				)

								_UpperCamelCase						    =	self.get_dummy_inputs(a				)
								_UpperCamelCase						    =	pipe(**a				)[0]

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

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

								_UpperCamelCase						    =	self.get_dummy_inputs(a				)
								_UpperCamelCase						    =	pipe_loaded(**a				)[0]

								_UpperCamelCase						    =	np.abs(to_np(a				) - to_np(a				)				).max()
								self.assertLess(a       ,							1e-4				)





from typing import TYPE_CHECKING

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


lowerCamelCase__	=							{"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__	=							["YolosFeatureExtractor"]
		lowerCamelCase__	=							["YolosImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__	=							[
		    "YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "YolosForObjectDetection",
		    "YolosModel",
		    "YolosPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_yolos import YolosFeatureExtractor
				from .image_processing_yolos import YolosImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_yolos import (
				    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    YolosForObjectDetection,
				    YolosModel,
				    YolosPreTrainedModel,
				)


else:
		import sys

		lowerCamelCase__	=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



