





'''simple docstring'''






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class    lowerCamelCase  (      _A      ,   _A      ,   _A      ,   unittest.TestCase       ):
	snake_case_      =   AltDiffusionPipeline
	snake_case_      =   TEXT_TO_IMAGE_PARAMS
	snake_case_      =   TEXT_TO_IMAGE_BATCH_PARAMS
	snake_case_      =   TEXT_TO_IMAGE_IMAGE_PARAMS
	snake_case_      =   TEXT_TO_IMAGE_IMAGE_PARAMS



	def   _lowerCamelCase    (   self	):
			torch.manual_seed(0	)
			lowerCAmelCase     :    str						=   UNetaDConditionModel(
			    block_out_channels=(32, 64)		,      layers_per_block=2		,      sample_size=32		,      in_channels=4		,      out_channels=4		,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,      up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,      cross_attention_dim=32		,      )
			lowerCAmelCase     :    str						=   DDIMScheduler(
			    beta_start=0.00085		,      beta_end=0.012		,      beta_schedule="scaled_linear"		,      clip_sample=a_		,      set_alpha_to_one=a_		,      )
			torch.manual_seed(0	)
			lowerCAmelCase     :    int						=   AutoencoderKL(
			    block_out_channels=[32, 64]		,      in_channels=3		,      out_channels=3		,      down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,      up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,      latent_channels=4		,      )

			# TODO: address the non-deterministic text encoder (fails for save-load tests)
			# torch.manual_seed(0)
			# text_encoder_config = RobertaSeriesConfig(
			#     hidden_size=32,
			#     project_dim=32,
			#     intermediate_size=37,
			#     layer_norm_eps=1e-05,
			#     num_attention_heads=4,
			#     num_hidden_layers=5,
			#     vocab_size=5002,
			# )
			# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

			torch.manual_seed(0	)
			lowerCAmelCase     :    str						=   CLIPTextConfig(
			    bos_token_id=0		,      eos_token_id=2		,      hidden_size=32		,      projection_dim=32		,      intermediate_size=37		,      layer_norm_eps=1e-0_5		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=5_002		,      )
			lowerCAmelCase     :    Dict						=   CLIPTextModel(a_	)

			lowerCAmelCase     :    List[Any]						=   XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"	)
			lowerCAmelCase     :    Union[str, Any]						=   77

			lowerCAmelCase     :    List[str]						=   {
			    "unet": unet,
			    "scheduler": scheduler,
			    "vae": vae,
			    "text_encoder": text_encoder,
			    "tokenizer": tokenizer,
			    "safety_checker": None,
			    "feature_extractor": None,
			}
			return components



	def   _lowerCamelCase    (   self		,      a_		,      a_=0	):
			if str(a_	).startswith("mps"	):
					lowerCAmelCase     :    List[str]						=   torch.manual_seed(a_	)
			else:
					lowerCAmelCase     :    Optional[int]						=   torch.Generator(device=a_	).manual_seed(a_	)
			lowerCAmelCase     :    List[Any]						=   {
			    "prompt": "A painting of a squirrel eating a burger",
			    "generator": generator,
			    "num_inference_steps": 2,
			    "guidance_scale": 6.0,
			    "output_type": "numpy",
			}
			return inputs



	def   _lowerCamelCase    (   self	):
			super().test_attention_slicing_forward_pass(expected_max_diff=3e-3	)



	def   _lowerCamelCase    (   self	):
			super().test_inference_batch_single_identical(expected_max_diff=3e-3	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[Any]						=   "cpu"  # ensure determinism for the device-dependent torch.Generator

			lowerCAmelCase     :    List[str]						=   self.get_dummy_components()
			torch.manual_seed(0	)
			lowerCAmelCase     :    Dict						=   RobertaSeriesConfig(
			    hidden_size=32		,      project_dim=32		,      intermediate_size=37		,      layer_norm_eps=1e-0_5		,      num_attention_heads=4		,      num_hidden_layers=5		,      vocab_size=5_002		,      )
			# TODO: remove after fixing the non-deterministic text encoder
			lowerCAmelCase     :    Optional[Any]						=   RobertaSeriesModelWithTransformation(a_	)
			lowerCAmelCase     :    Union[str, Any]						=   text_encoder

			lowerCAmelCase     :    int						=   AltDiffusionPipeline(**a_	)
			lowerCAmelCase     :    Any						=   alt_pipe.to(a_	)
			alt_pipe.set_progress_bar_config(disable=a_	)

			lowerCAmelCase     :    Union[str, Any]						=   self.get_dummy_inputs(a_	)
			lowerCAmelCase     :    Optional[Any]						=   "A photo of an astronaut"
			lowerCAmelCase     :    Any						=   alt_pipe(**a_	)
			lowerCAmelCase     :    Union[str, Any]						=   output.images
			lowerCAmelCase     :    Dict						=   image[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			lowerCAmelCase     :    Optional[int]						=   np.array(
			    [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]	)

			assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   "cpu"  # ensure determinism for the device-dependent torch.Generator

			lowerCAmelCase     :    int						=   self.get_dummy_components()
			lowerCAmelCase     :    Optional[int]						=   PNDMScheduler(skip_prk_steps=a_	)
			torch.manual_seed(0	)
			lowerCAmelCase     :    Tuple						=   RobertaSeriesConfig(
			    hidden_size=32		,      project_dim=32		,      intermediate_size=37		,      layer_norm_eps=1e-0_5		,      num_attention_heads=4		,      num_hidden_layers=5		,      vocab_size=5_002		,      )
			# TODO: remove after fixing the non-deterministic text encoder
			lowerCAmelCase     :    int						=   RobertaSeriesModelWithTransformation(a_	)
			lowerCAmelCase     :    int						=   text_encoder
			lowerCAmelCase     :    Optional[int]						=   AltDiffusionPipeline(**a_	)
			lowerCAmelCase     :    Tuple						=   alt_pipe.to(a_	)
			alt_pipe.set_progress_bar_config(disable=a_	)

			lowerCAmelCase     :    str						=   self.get_dummy_inputs(a_	)
			lowerCAmelCase     :    Optional[Any]						=   alt_pipe(**a_	)
			lowerCAmelCase     :    Optional[int]						=   output.images
			lowerCAmelCase     :    Optional[int]						=   image[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			lowerCAmelCase     :    Dict						=   np.array(
			    [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]	)

			assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2






@slow
@require_torch_gpu
class    lowerCamelCase  (      unittest.TestCase       ):



	def   _lowerCamelCase    (   self	):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()



	def   _lowerCamelCase    (   self	):
			# make sure here that pndm scheduler skips prk
			lowerCAmelCase     :    Optional[int]						=   AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"		,      safety_checker=a_	)
			lowerCAmelCase     :    Optional[Any]						=   alt_pipe.to(a_	)
			alt_pipe.set_progress_bar_config(disable=a_	)

			lowerCAmelCase     :    str						=   "A painting of a squirrel eating a burger"
			lowerCAmelCase     :    int						=   torch.manual_seed(0	)
			lowerCAmelCase     :    int						=   alt_pipe([prompt]		,      generator=a_		,      guidance_scale=6.0		,      num_inference_steps=20		,      output_type="np"	)

			lowerCAmelCase     :    Optional[Any]						=   output.images

			lowerCAmelCase     :    Optional[Any]						=   image[0, -3:, -3:, -1]

			assert image.shape == (1, 512, 512, 3)
			lowerCAmelCase     :    Optional[Any]						=   np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]	)

			assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   DDIMScheduler.from_pretrained("BAAI/AltDiffusion"		,      subfolder="scheduler"	)

			lowerCAmelCase     :    List[Any]						=   AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"		,      scheduler=a_		,      safety_checker=a_	)
			lowerCAmelCase     :    Tuple						=   alt_pipe.to(a_	)
			alt_pipe.set_progress_bar_config(disable=a_	)

			lowerCAmelCase     :    Any						=   "A painting of a squirrel eating a burger"
			lowerCAmelCase     :    str						=   torch.manual_seed(0	)

			lowerCAmelCase     :    Dict						=   alt_pipe([prompt]		,      generator=a_		,      num_inference_steps=2		,      output_type="numpy"	)
			lowerCAmelCase     :    List[Any]						=   output.images

			lowerCAmelCase     :    int						=   image[0, -3:, -3:, -1]

			assert image.shape == (1, 512, 512, 3)
			lowerCAmelCase     :    Optional[int]						=   np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]	)

			assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2










'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


lowerCAmelCase       		=      logging.get_logger(__name__)
class 		lowerCamelCase  (      _A       ):



	def __init__(   self		,      *a_		,      **a_	):
			warnings.warn(
			    "The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
			    " Please use ChineseCLIPImageProcessor instead."		,      a_		,      )
			super().__init__(*a_		,      **a_	)



