





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       (			__SCREAMING_SNAKE_CASE,     __SCREAMING_SNAKE_CASE,     unittest.TestCase     ):
			lowerCamelCase_     :     Optional[int]       			=      StableDiffusionXLImgaImgPipeline
			lowerCamelCase_     :     Dict       			=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
			lowerCamelCase_     :     str       			=      PipelineTesterMixin.required_optional_params - {'''latents'''}
			lowerCamelCase_     :     Optional[Any]       			=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			lowerCamelCase_     :     int       			=      IMAGE_TO_IMAGE_IMAGE_PARAMS
			lowerCamelCase_     :     Dict       			=      IMAGE_TO_IMAGE_IMAGE_PARAMS






			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							torch.manual_seed(0    )
							snake_case_						:    Tuple				=  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''')					,							attention_head_dim=(2, 4)					,							use_linear_projection=UpperCAmelCase_					,							addition_embed_type='''text_time'''					,							addition_time_embed_dim=8					,							transformer_layers_per_block=(1, 2)					,							projection_class_embeddings_input_dim=80					,							cross_attention_dim=64					,							)
							snake_case_						:    Tuple				=  EulerDiscreteScheduler(
							    beta_start=0.00_085					,							beta_end=0.012					,							steps_offset=1					,							beta_schedule='''scaled_linear'''					,							timestep_spacing='''leading'''					,							)
							torch.manual_seed(0    )
							snake_case_						:    Tuple				=  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					,							sample_size=128					,							)
							torch.manual_seed(0    )
							snake_case_						:    Union[str, Any]				=  CLIPTextConfig(
							    bos_token_id=0					,							eos_token_id=2					,							hidden_size=32					,							intermediate_size=37					,							layer_norm_eps=1e-05					,							num_attention_heads=4					,							num_hidden_layers=5					,							pad_token_id=1					,							vocab_size=1000					,							hidden_act='''gelu'''					,							projection_dim=32					,							)
							snake_case_						:    Optional[Any]				=  CLIPTextModel(UpperCAmelCase_    )
							snake_case_						:    List[str]				=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					,							local_files_only=UpperCAmelCase_    )

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

							snake_case_						:    int				=  {
							    "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					,							__magic_name__					,							__magic_name__=0    )     ->   List[Any]:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  floats_tensor((1, 3, 32, 32)					,							rng=random.Random(UpperCAmelCase_    )    ).to(UpperCAmelCase_    )
							snake_case_						:    Any				=  image / 2 + 0.5
							if str(UpperCAmelCase_    ).startswith('''mps'''    ):
											snake_case_						:    str				=  torch.manual_seed(UpperCAmelCase_    )
							else:
											snake_case_						:    List[str]				=  torch.Generator(device=UpperCAmelCase_    ).manual_seed(UpperCAmelCase_    )
							snake_case_						:    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 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    str				=  "cpu"  # ensure determinism for the device-dependent torch.Generator
							snake_case_						:    List[str]				=  self.get_dummy_components()
							snake_case_						:    Union[str, Any]				=  StableDiffusionXLImgaImgPipeline(**UpperCAmelCase_    )
							snake_case_						:    Dict				=  sd_pipe.to(UpperCAmelCase_    )
							sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_    )

							snake_case_						:    Dict				=  self.get_dummy_inputs(UpperCAmelCase_    )
							snake_case_						:    Union[str, Any]				=  sd_pipe(**UpperCAmelCase_    ).images
							snake_case_						:    int				=  image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)

							snake_case_						:    List[Any]				=  np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165]    )

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






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


							'''simple docstring'''



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






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


							'''simple docstring'''



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






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


							'''simple docstring'''



							pass






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  self.get_dummy_components()
							snake_case_						:    Dict				=  StableDiffusionXLImgaImgPipeline(**UpperCAmelCase_    )
							snake_case_						:    str				=  sd_pipe.to(UpperCAmelCase_    )
							snake_case_						:    List[Any]				=  sd_pipe.to(UpperCAmelCase_    )
							sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_    )

							# forward without prompt embeds
							snake_case_						:    int				=  self.get_dummy_inputs(UpperCAmelCase_    )
							snake_case_						:    List[Any]				=  3 * ["this is a negative prompt"]
							snake_case_						:    Tuple				=  negative_prompt
							snake_case_						:    int				=  3 * [inputs["prompt"]]

							snake_case_						:    Tuple				=  sd_pipe(**UpperCAmelCase_    )
							snake_case_						:    Tuple				=  output.images[0, -3:, -3:, -1]

							# forward with prompt embeds
							snake_case_						:    Union[str, Any]				=  self.get_dummy_inputs(UpperCAmelCase_    )
							snake_case_						:    Dict				=  3 * ["this is a negative prompt"]
							snake_case_						:    Any				=  3 * [inputs.pop('''prompt'''    )]

							(
							    snake_case_					
							)						:    Tuple				=  sd_pipe.encode_prompt(UpperCAmelCase_					,							negative_prompt=UpperCAmelCase_    )

							snake_case_						:    int				=  sd_pipe(
							    **UpperCAmelCase_					,							prompt_embeds=UpperCAmelCase_					,							negative_prompt_embeds=UpperCAmelCase_					,							pooled_prompt_embeds=UpperCAmelCase_					,							negative_pooled_prompt_embeds=UpperCAmelCase_					,							)
							snake_case_						:    Optional[int]				=  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     ):






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


							'''simple docstring'''



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






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


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  torch.Generator(device=UpperCAmelCase_    ).manual_seed(UpperCAmelCase_    )
							snake_case_						:    Union[str, Any]				=  np.random.RandomState(UpperCAmelCase_    ).standard_normal((1, 4, 64, 64)    )
							snake_case_						:    Union[str, Any]				=  torch.from_numpy(UpperCAmelCase_    ).to(device=UpperCAmelCase_					,							dtype=UpperCAmelCase_    )
							snake_case_						:    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    )     ->   List[str]:


							'''simple docstring'''



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

							snake_case_						:    int				=  self.get_inputs(UpperCAmelCase_    )
							snake_case_						:    str				=  pipe(**UpperCAmelCase_    ).images
							snake_case_						:    Tuple				=  image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							snake_case_						:    int				=  np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506]    )
							assert np.abs(image_slice - expected_slice    ).max() < 7e-3







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


__A									=				logging.get_logger(__name__)

__A									=				{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A									=				{
    "vocab_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

__A									=				{
    "yjernite/retribert-base-uncased": 512,
}


__A									=				{
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			VOCAB_FILES_NAMES
 lowercase_		=			PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=			PRETRAINED_INIT_CONFIGURATION
 lowercase_		=			RetriBertTokenizer
 lowercase_		=			["input_ids", "attention_mask"]

 def __init__(self				:   int       , UpperCAmelCase_				:   List[str]=None       , UpperCAmelCase_				:   str=None       , UpperCAmelCase_				:   Optional[Any]=True       , UpperCAmelCase_				:   Union[str, Any]="[UNK]"       , UpperCAmelCase_				:   Any="[SEP]"       , UpperCAmelCase_				:   List[str]="[PAD]"       , UpperCAmelCase_				:   Optional[Any]="[CLS]"       , UpperCAmelCase_				:   Optional[Any]="[MASK]"       , UpperCAmelCase_				:   Dict=True       , UpperCAmelCase_				:   str=None       , **UpperCAmelCase_				:   str       , )       ->List[Any]:



   '''simple docstring'''



   super().__init__(
       UpperCAmelCase_       , tokenizer_file=UpperCAmelCase_       , do_lower_case=UpperCAmelCase_       , unk_token=UpperCAmelCase_       , sep_token=UpperCAmelCase_       , pad_token=UpperCAmelCase_       , cls_token=UpperCAmelCase_       , mask_token=UpperCAmelCase_       , tokenize_chinese_chars=UpperCAmelCase_       , strip_accents=UpperCAmelCase_       , **UpperCAmelCase_       , )

   lowerCamelCase__:      List[Any]			  =json.loads(self.backend_tokenizer.normalizer.__getstate__())
   if (
       normalizer_state.get("lowercase"       , UpperCAmelCase_) != do_lower_case
       or normalizer_state.get("strip_accents"       , UpperCAmelCase_) != strip_accents
       or normalizer_state.get("handle_chinese_chars"       , UpperCAmelCase_) != tokenize_chinese_chars
   ):
     lowerCamelCase__:      Dict			  =getattr(UpperCAmelCase_       , normalizer_state.pop("type"))
     lowerCamelCase__:      int			  =do_lower_case
     lowerCamelCase__:      int			  =strip_accents
     lowerCamelCase__:      List[str]			  =tokenize_chinese_chars
     lowerCamelCase__:      Tuple			  =normalizer_class(**UpperCAmelCase_)

   lowerCamelCase__:      Any			  =do_lower_case

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   Optional[Any]=None)       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =[self.cls_token_id] + token_ids_a + [self.sep_token_id]

   if token_ids_a:
     output += token_ids_a + [self.sep_token_id]

   return output

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None)       ->List[int]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =[self.sep_token_id]
   lowerCamelCase__:      Optional[int]			  =[self.cls_token_id]
   if token_ids_a is None:
     return len(cls + token_ids_a + sep) * [0]
   return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]







 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Optional[str] = None)       ->Tuple[str]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self._tokenizer.model.save(UpperCAmelCase_       , name=UpperCAmelCase_)
   return tuple(UpperCAmelCase_)

