


"""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 					__lowerCAmelCase					:


		'''simple docstring'''



		def 			__UpperCAmelCase       (  self      ):
				torch.manual_seed(0      )
				__a           =			TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''      )

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

				torch.manual_seed(0      )
				__a           =			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      )
				__a           =			DDPMScheduler(
				    num_train_timesteps=1_000							, 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      )
				__a           =			IFWatermarker()

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

		def 			__UpperCAmelCase       (  self      ):
				torch.manual_seed(0      )
				__a           =			TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''      )

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

				torch.manual_seed(0      )
				__a           =			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      )
				__a           =			DDPMScheduler(
				    num_train_timesteps=1_000							, 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      )
				__a           =			DDPMScheduler(
				    num_train_timesteps=1_000							, beta_schedule='''squaredcos_cap_v2'''							, beta_start=0.0001							, beta_end=0.02							, )

				torch.manual_seed(0      )
				__a           =			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 			__UpperCAmelCase       (  self      ):
				__a           =			self.get_dummy_components()
				__a           =			self.pipeline_class(**_a      )
				pipe.to(_a      )
				pipe.set_progress_bar_config(disable=_a      )

				__a           =			self.get_dummy_inputs(_a      )

				__a           =			inputs['''prompt''']
				__a           =			inputs['''generator''']
				__a           =			inputs['''num_inference_steps''']
				__a           =			inputs['''output_type''']

				if "image" in inputs:
						__a           =			inputs['''image''']
				else:
						__a           =			None

				if "mask_image" in inputs:
						__a           =			inputs['''mask_image''']
				else:
						__a           =			None

				if "original_image" in inputs:
						__a           =			inputs['''original_image''']
				else:
						__a           =			None

				__a      ,     __a           =			pipe.encode_prompt(_a      )

				# inputs with prompt converted to embeddings
				__a           =			{
				    '''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:
						__a           =			image

				if mask_image is not None:
						__a           =			mask_image

				if original_image is not None:
						__a           =			original_image

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

				__a           =			pipe(**_a      )[0]

				with tempfile.TemporaryDirectory() as tmpdir:
						pipe.save_pretrained(_a      )
						__a           =			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.'''							, )

				__a           =			self.get_dummy_inputs(_a      )

				__a           =			inputs['''generator''']
				__a           =			inputs['''num_inference_steps''']
				__a           =			inputs['''output_type''']

				# inputs with prompt converted to embeddings
				__a           =			{
				    '''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:
						__a           =			image

				if mask_image is not None:
						__a           =			mask_image

				if original_image is not None:
						__a           =			original_image

				__a           =			pipe_loaded(**_a      )[0]

				__a           =			np.abs(to_np(_a      ) - to_np(_a      )      ).max()
				self.assertLess(_a							, 1E-4      )





		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.get_dummy_components()
				__a           =			self.pipeline_class(**_a      )
				pipe.to(_a      )
				pipe.set_progress_bar_config(disable=_a      )

				__a           =			self.get_dummy_inputs(_a      )
				__a           =			pipe(**_a      )[0]

				with tempfile.TemporaryDirectory() as tmpdir:
						pipe.save_pretrained(_a      )
						__a           =			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

				__a           =			self.get_dummy_inputs(_a      )
				__a           =			pipe_loaded(**_a      )[0]

				__a           =			np.abs(to_np(_a      ) - to_np(_a      )      ).max()
				self.assertLess(_a							, 1E-4      )











'''simple docstring'''





import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__a          =		logging.get_logger(__name__)







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    def __init__(    self					:     List[str] ,     *snake_case_					:     str ,     **snake_case_					:     List[str]						):
          warnings.warn(
              """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
              """ use BeitImageProcessor instead.""" ,     snake_case_ ,     )
          super().__init__(*snake_case_ ,     **snake_case_						)
