'''simple docstring'''
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
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 PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class       __lowercase		(    A_      ,				A_      ,				unittest.TestCase    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    StableDiffusionXLPipeline
     SCREAMING_SNAKE_CASE__		 =    TEXT_TO_IMAGE_PARAMS
     SCREAMING_SNAKE_CASE__		 =    TEXT_TO_IMAGE_BATCH_PARAMS
     SCREAMING_SNAKE_CASE__		 =    TEXT_TO_IMAGE_IMAGE_PARAMS
     SCREAMING_SNAKE_CASE__		 =    TEXT_TO_IMAGE_IMAGE_PARAMS







     def lowerCAmelCase  (		self     ):
         torch.manual_seed(0     )
         __UpperCamelCase						:						Any		     =	UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)  ,      layers_per_block=2  ,      sample_size=3_2  ,      in_channels=4  ,      out_channels=4  ,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')  ,      attention_head_dim=(2, 4)  ,      use_linear_projection=_A  ,      addition_embed_type='text_time'  ,      addition_time_embed_dim=8  ,      transformer_layers_per_block=(1, 2)  ,      projection_class_embeddings_input_dim=8_0  ,      cross_attention_dim=6_4  ,      )
         __UpperCamelCase						:						Dict		     =	EulerDiscreteScheduler(
             beta_start=0.0_0_0_8_5  ,      beta_end=0.0_1_2  ,      steps_offset=1  ,      beta_schedule='scaled_linear'  ,      timestep_spacing='leading'  ,      )
         torch.manual_seed(0     )
         __UpperCamelCase						:						Optional[Any]		     =	AutoencoderKL(
             block_out_channels=[3_2, 6_4]  ,      in_channels=3  ,      out_channels=3  ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,      latent_channels=4  ,      sample_size=1_2_8  ,      )
         torch.manual_seed(0     )
         __UpperCamelCase						:						str		     =	CLIPTextConfig(
             bos_token_id=0  ,      eos_token_id=2  ,      hidden_size=3_2  ,      intermediate_size=3_7  ,      layer_norm_eps=1E-0_5  ,      num_attention_heads=4  ,      num_hidden_layers=5  ,      pad_token_id=1  ,      vocab_size=1_0_0_0  ,      hidden_act='gelu'  ,      projection_dim=3_2  ,      )
         __UpperCamelCase						:						str		     =	CLIPTextModel(_A     )
         __UpperCamelCase						:						List[str]		     =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  ,      local_files_only=_A     )

         __UpperCamelCase						:						Union[str, Any]		     =	CLIPTextModelWithProjection(_A     )
         __UpperCamelCase						:						int		     =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  ,      local_files_only=_A     )

         __UpperCamelCase						:						Dict		     =	{
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'text_encoder_2': text_encoder_a,
             'tokenizer_2': tokenizer_a,
             # "safety_checker": None,
             # "feature_extractor": None,
         }
         return components







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase=0     ):
         if str(_A     ).startswith('mps'     ):
             __UpperCamelCase						:						List[str]		     =	torch.manual_seed(_A     )
         else:
             __UpperCamelCase						:						Dict		     =	torch.Generator(device=_A     ).manual_seed(_A     )
         __UpperCamelCase						:						str		     =	{
             'prompt': 'A painting of a squirrel eating a burger',
             'generator': generator,
             'num_inference_steps': 2,
             'guidance_scale': 5.0,
             'output_type': 'numpy',
         }
         return inputs







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Tuple		     =	'cpu'  # ensure determinism for the device-dependent torch.Generator
         __UpperCamelCase						:						Union[str, Any]		     =	self.get_dummy_components()
         __UpperCamelCase						:						Optional[Any]		     =	StableDiffusionXLPipeline(**_A     )
         __UpperCamelCase						:						int		     =	sd_pipe.to(_A     )
         sd_pipe.set_progress_bar_config(disable=_A     )

         __UpperCamelCase						:						Any		     =	self.get_dummy_inputs(_A     )
         __UpperCamelCase						:						List[str]		     =	sd_pipe(**_A     ).images
         __UpperCamelCase						:						List[str]		     =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)
         __UpperCamelCase						:						Tuple		     =	np.array([0.5_8_7_3, 0.6_1_2_8, 0.4_7_9_7, 0.5_1_2_2, 0.5_6_7_4, 0.4_6_3_9, 0.5_2_2_7, 0.5_1_4_9, 0.4_7_4_7]     )

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







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Union[str, Any]		     =	self.get_dummy_components()
         __UpperCamelCase						:						Optional[int]		     =	StableDiffusionXLPipeline(**_A     )
         __UpperCamelCase						:						Tuple		     =	sd_pipe.to(_A     )
         __UpperCamelCase						:						Any		     =	sd_pipe.to(_A     )
         sd_pipe.set_progress_bar_config(disable=_A     )

         # forward without prompt embeds
         __UpperCamelCase						:						str		     =	self.get_dummy_inputs(_A     )
         __UpperCamelCase						:						Optional[Any]		     =	3 * ['this is a negative prompt']
         __UpperCamelCase						:						Any		     =	negative_prompt
         __UpperCamelCase						:						List[str]		     =	3 * [inputs['prompt']]

         __UpperCamelCase						:						Tuple		     =	sd_pipe(**_A     )
         __UpperCamelCase						:						Dict		     =	output.images[0, -3:, -3:, -1]

         # forward with prompt embeds
         __UpperCamelCase						:						Tuple		     =	self.get_dummy_inputs(_A     )
         __UpperCamelCase						:						str		     =	3 * ['this is a negative prompt']
         __UpperCamelCase						:						int		     =	3 * [inputs.pop('prompt'     )]

         (
             (
             __UpperCamelCase				
         )     ,       (
             __UpperCamelCase				
         )     ,       (
             __UpperCamelCase				
         )     ,       (
             __UpperCamelCase				
         )     ,       				
         )						:						Union[str, Any]		     =	sd_pipe.encode_prompt(_A  ,      negative_prompt=_A     )

         __UpperCamelCase						:						Dict		     =	sd_pipe(
             **_A  ,      prompt_embeds=_A  ,      negative_prompt_embeds=_A  ,      pooled_prompt_embeds=_A  ,      negative_pooled_prompt_embeds=_A  ,      )
         __UpperCamelCase						:						List[Any]		     =	output.images[0, -3:, -3:, -1]

         # make sure that it's equal
         assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()     ).max() < 1E-4







     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     )






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







     """simple docstring"""






     def lowerCAmelCase  (		self     ):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase="cpu"  ,      _lowerCamelCase=torch.floataa  ,      _lowerCamelCase=0     ):
         __UpperCamelCase						:						Optional[int]		     =	torch.Generator(device=_A     ).manual_seed(_A     )
         __UpperCamelCase						:						List[Any]		     =	np.random.RandomState(_A     ).standard_normal((1, 4, 6_4, 6_4)     )
         __UpperCamelCase						:						List[str]		     =	torch.from_numpy(_A     ).to(device=_A  ,      dtype=_A     )
         __UpperCamelCase						:						Optional[Any]		     =	{
             'prompt': 'a photograph of an astronaut riding a horse',
             'latents': latents,
             'generator': generator,
             'num_inference_steps': 3,
             'guidance_scale': 7.5,
             'output_type': 'numpy',
         }
         return inputs







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[str]		     =	DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'     )
         pipe.to(_A     )
         pipe.set_progress_bar_config(disable=_A     )

         __UpperCamelCase						:						Optional[Any]		     =	self.get_inputs(_A     )
         __UpperCamelCase						:						Optional[Any]		     =	pipe(**_A     ).images
         __UpperCamelCase						:						Union[str, Any]		     =	image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         __UpperCamelCase						:						Union[str, Any]		     =	np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]     )
         assert np.abs(image_slice - expected_slice     ).max() < 7E-3





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A       =					logging.get_logger(__name__)

_A       =					{
    "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}







class 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				List[Any] 	=						"cpmant"





     def __init__(self :       List[Any]							,       _A :       int = 3_0_7_2_0							,       _A :       int = 4_0_9_6							,       _A :       int = 3_2							,       _A :       int = 1_2_8							,       _A :       int = 1_0_2_4_0							,       _A :       int = 4_8							,       _A :       int = 0.0							,       _A :       int = 5_1_2							,       _A :       int = 2_0_4_8							,       _A :       int = 1E-6							,       _A :       float = 1.0							,       _A :       int = 3_2							,       _A :       int = 3_2							,       _A :       int = 3_2							,       _A :       bool = True							,       **_A :       Dict							,       )		->		Dict:
         super().__init__(**_A  )
         snake_case						=   prompt_types
         snake_case						=   prompt_length
         snake_case						=   segment_types
         snake_case						=   hidden_size
         snake_case						=   num_attention_heads
         snake_case						=   dim_head
         snake_case						=   dim_ff
         snake_case						=   num_hidden_layers
         snake_case						=   position_bias_num_buckets
         snake_case						=   position_bias_max_distance
         snake_case						=   dropout_p
         snake_case						=   eps
         snake_case						=   use_cache
         snake_case						=   vocab_size
         snake_case						=   init_std

