
import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 				_lowerCamelCase (      UpperCamelCase_	,							UpperCamelCase_	,							unittest.TestCase ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  StableDiffusionXLImgaImgPipeline
     SCREAMING_SNAKE_CASE_		   =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
     SCREAMING_SNAKE_CASE_		   =  PipelineTesterMixin.required_optional_params - {'''latents'''}
     SCREAMING_SNAKE_CASE_		   =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     SCREAMING_SNAKE_CASE_		   =  IMAGE_TO_IMAGE_IMAGE_PARAMS
     SCREAMING_SNAKE_CASE_		   =  IMAGE_TO_IMAGE_IMAGE_PARAMS






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[str]:




           """simple docstring"""







           torch.manual_seed(0  )
           UpperCamelCase__    :   List[str]					=				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=__SCREAMING_SNAKE_CASE						,						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__    :   Optional[int]					=				EulerDiscreteScheduler(
               beta_start=0.00085						,						beta_end=0.012						,						steps_offset=1						,						beta_schedule='''scaled_linear'''						,						timestep_spacing='''leading'''						,						)
           torch.manual_seed(0  )
           UpperCamelCase__    :   Union[str, 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__    :   List[Any]					=				CLIPTextConfig(
               bos_token_id=0						,						eos_token_id=2						,						hidden_size=3_2						,						intermediate_size=3_7						,						layer_norm_eps=1e-05						,						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__    :   List[Any]					=				CLIPTextModel(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[int]					=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						,						local_files_only=__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Dict					=				CLIPTextModelWithProjection(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[int]					=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						,						local_files_only=__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   str					=				{
               '''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    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=0  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				floats_tensor((1, 3, 3_2, 3_2)						,						rng=random.Random(__SCREAMING_SNAKE_CASE  )  ).to(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[int]					=				image / 2 + 0.5
           if str(__SCREAMING_SNAKE_CASE  ).startswith('''mps'''  ):
                 UpperCamelCase__    :   Tuple					=				torch.manual_seed(__SCREAMING_SNAKE_CASE  )
           else:
                 UpperCamelCase__    :   Dict					=				torch.Generator(device=__SCREAMING_SNAKE_CASE  ).manual_seed(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[Any]					=				{
               '''prompt''': '''A painting of a squirrel eating a burger''',
               '''image''': image,
               '''generator''': generator,
               '''num_inference_steps''': 2,
               '''guidance_scale''': 5.0,
               '''output_type''': '''numpy''',
               '''strength''': 0.75,
           }
           return inputs






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
           UpperCamelCase__    :   List[str]					=				self.get_dummy_components()
           UpperCamelCase__    :   Tuple					=				StableDiffusionXLImgaImgPipeline(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[str]					=				sd_pipe.to(__SCREAMING_SNAKE_CASE  )
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   str					=				self.get_dummy_inputs(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[Any]					=				sd_pipe(**__SCREAMING_SNAKE_CASE  ).images
           UpperCamelCase__    :   Optional[int]					=				image[0, -3:, -3:, -1]

           assert image.shape == (1, 3_2, 3_2, 3)

           UpperCamelCase__    :   Optional[Any]					=				np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]  )

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






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           super().test_attention_slicing_forward_pass(expected_max_diff=3e-3  )






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




           """simple docstring"""







           super().test_inference_batch_single_identical(expected_max_diff=3e-3  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[str]:




           """simple docstring"""







           pass






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[int]:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				self.get_dummy_components()
           UpperCamelCase__    :   Tuple					=				StableDiffusionXLImgaImgPipeline(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				sd_pipe.to(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				sd_pipe.to(__SCREAMING_SNAKE_CASE  )
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

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

           UpperCamelCase__    :   Any					=				sd_pipe(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[Any]					=				output.images[0, -3:, -3:, -1]

           # forward with prompt embeds
           UpperCamelCase__    :   Any					=				self.get_dummy_inputs(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				3 * ['''this is a negative prompt''']
           UpperCamelCase__    :   List[str]					=				3 * [inputs.pop('''prompt'''  )]

           (
               (
               UpperCamelCase__
           )				,(
               UpperCamelCase__
           )				,(
               UpperCamelCase__
           )				,(
               UpperCamelCase__
           )				,
           )    :   Tuple					=				sd_pipe.encode_prompt(__SCREAMING_SNAKE_CASE						,						negative_prompt=__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Union[str, Any]					=				sd_pipe(
               **__SCREAMING_SNAKE_CASE						,						prompt_embeds=__SCREAMING_SNAKE_CASE						,						negative_prompt_embeds=__SCREAMING_SNAKE_CASE						,						pooled_prompt_embeds=__SCREAMING_SNAKE_CASE						,						negative_pooled_prompt_embeds=__SCREAMING_SNAKE_CASE						,						)
           UpperCamelCase__    :   Dict					=				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




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





     """simple docstring"""







     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[str]:




           """simple docstring"""







           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE="cpu"						,						__SCREAMING_SNAKE_CASE=torch.floataa						,						__SCREAMING_SNAKE_CASE=0  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   str					=				torch.Generator(device=__SCREAMING_SNAKE_CASE  ).manual_seed(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[Any]					=				np.random.RandomState(__SCREAMING_SNAKE_CASE  ).standard_normal((1, 4, 6_4, 6_4)  )
           UpperCamelCase__    :   str					=				torch.from_numpy(__SCREAMING_SNAKE_CASE  ).to(device=__SCREAMING_SNAKE_CASE						,						dtype=__SCREAMING_SNAKE_CASE  )
           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    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''  )
           pipe.to(__SCREAMING_SNAKE_CASE  )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   List[Any]					=				self.get_inputs(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Any					=				pipe(**__SCREAMING_SNAKE_CASE  ).images
           UpperCamelCase__    :   Optional[Any]					=				image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 5_1_2, 5_1_2, 3)
           UpperCamelCase__    :   Union[str, Any]					=				np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]  )
           assert np.abs(image_slice - expected_slice  ).max() < 7e-3




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


lowerCamelCase      =logging.get_logger(__name__)

lowerCamelCase      ={
    "google/realm-cc-news-pretrained-embedder": (
        "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-encoder": (
        "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-scorer": (
        "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-openqa": (
        "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"
    ),
    "google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json",
    "google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json",
    "google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json",
    "google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json",
    # See all REALM models at https://huggingface.co/models?filter=realm
}




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  '''realm'''






     def __init__(							self						,						__SCREAMING_SNAKE_CASE=3_0_5_2_2						,						__SCREAMING_SNAKE_CASE=7_6_8						,						__SCREAMING_SNAKE_CASE=1_2_8						,						__SCREAMING_SNAKE_CASE=1_2						,						__SCREAMING_SNAKE_CASE=1_2						,						__SCREAMING_SNAKE_CASE=8						,						__SCREAMING_SNAKE_CASE=3_0_7_2						,						__SCREAMING_SNAKE_CASE="gelu_new"						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=5_1_2						,						__SCREAMING_SNAKE_CASE=2						,						__SCREAMING_SNAKE_CASE=0.02						,						__SCREAMING_SNAKE_CASE=1e-12						,						__SCREAMING_SNAKE_CASE=2_5_6						,						__SCREAMING_SNAKE_CASE=1_0						,						__SCREAMING_SNAKE_CASE=1e-3						,						__SCREAMING_SNAKE_CASE=5						,						__SCREAMING_SNAKE_CASE=3_2_0						,						__SCREAMING_SNAKE_CASE=1_3_3_5_3_7_1_8						,						__SCREAMING_SNAKE_CASE=5_0_0_0						,						__SCREAMING_SNAKE_CASE=1						,						__SCREAMING_SNAKE_CASE=0						,						__SCREAMING_SNAKE_CASE=2						,						**__SCREAMING_SNAKE_CASE						,						)  ->   List[str]:




           """simple docstring"""







           super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE						,						bos_token_id=__SCREAMING_SNAKE_CASE						,						eos_token_id=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )

           # Common config
           UpperCamelCase__    :   int					=				vocab_size
           UpperCamelCase__    :   Any					=				max_position_embeddings
           UpperCamelCase__    :   List[str]					=				hidden_size
           UpperCamelCase__    :   Union[str, Any]					=				retriever_proj_size
           UpperCamelCase__    :   int					=				num_hidden_layers
           UpperCamelCase__    :   Union[str, Any]					=				num_attention_heads
           UpperCamelCase__    :   str					=				num_candidates
           UpperCamelCase__    :   int					=				intermediate_size
           UpperCamelCase__    :   Dict					=				hidden_act
           UpperCamelCase__    :   Any					=				hidden_dropout_prob
           UpperCamelCase__    :   Any					=				attention_probs_dropout_prob
           UpperCamelCase__    :   Any					=				initializer_range
           UpperCamelCase__    :   Optional[Any]					=				type_vocab_size
           UpperCamelCase__    :   List[Any]					=				layer_norm_eps

           # Reader config
           UpperCamelCase__    :   List[Any]					=				span_hidden_size
           UpperCamelCase__    :   List[Any]					=				max_span_width
           UpperCamelCase__    :   Optional[Any]					=				reader_layer_norm_eps
           UpperCamelCase__    :   Optional[Any]					=				reader_beam_size
           UpperCamelCase__    :   int					=				reader_seq_len

           # Retrieval config
           UpperCamelCase__    :   List[str]					=				num_block_records
           UpperCamelCase__    :   Union[str, Any]					=				searcher_beam_size


