


"""simple docstring"""




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


snake_case    :   Tuple	    =					logging.get_logger(__name__)

snake_case    :   str	    =					{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case    :   List[str]	    =					{
    '''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'''
        ),
    },
}

snake_case    :   Optional[Any]	    =					{
    '''yjernite/retribert-base-uncased''': 5_12,
}


snake_case    :   Optional[int]	    =					{
    '''yjernite/retribert-base-uncased''': {'''do_lower_case''': True},
}







class 		_snake_case  (				__snake_case							):
 SCREAMING_SNAKE_CASE__				=	VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE__				=	PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE__				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE__				=	PRETRAINED_INIT_CONFIGURATION
 SCREAMING_SNAKE_CASE__				=	RetriBertTokenizer
 SCREAMING_SNAKE_CASE__				=	['input_ids', 'attention_mask']




 def __init__(		self ,  _lowerCamelCase=None ,  _lowerCamelCase=None ,  _lowerCamelCase=True ,  _lowerCamelCase="[UNK]" ,  _lowerCamelCase="[SEP]" ,  _lowerCamelCase="[PAD]" ,  _lowerCamelCase="[CLS]" ,  _lowerCamelCase="[MASK]" ,  _lowerCamelCase=True ,  _lowerCamelCase=None ,  **_lowerCamelCase ,  ):
       super().__init__(
           _lowerCamelCase ,  tokenizer_file=_lowerCamelCase ,  do_lower_case=_lowerCamelCase ,  unk_token=_lowerCamelCase ,  sep_token=_lowerCamelCase ,  pad_token=_lowerCamelCase ,  cls_token=_lowerCamelCase ,  mask_token=_lowerCamelCase ,  tokenize_chinese_chars=_lowerCamelCase ,  strip_accents=_lowerCamelCase ,  **_lowerCamelCase ,  )

       a						:List[Any]										=	json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
       if (
           normalizer_state.get('''lowercase''' ,  _lowerCamelCase   ) != do_lower_case
           or normalizer_state.get('''strip_accents''' ,  _lowerCamelCase   ) != strip_accents
           or normalizer_state.get('''handle_chinese_chars''' ,  _lowerCamelCase   ) != tokenize_chinese_chars
       ):
             a						:Optional[int]										=	getattr(_lowerCamelCase ,  normalizer_state.pop('''type'''   )   )
             a						:List[Any]										=	do_lower_case
             a						:List[Any]										=	strip_accents
             a						:List[Any]										=	tokenize_chinese_chars
             a						:Optional[Any]										=	normalizer_class(**_lowerCamelCase   )

       a						:int										=	do_lower_case




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=None   ):
       a						:Union[str, 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 ,  _lowerCamelCase ,  _lowerCamelCase = None   ):
       a						:Optional[int]										=	[self.sep_token_id]
       a						:Any										=	[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 ,  _lowerCamelCase ,  _lowerCamelCase = None   ):
       a						:Tuple										=	self._tokenizer.model.save(_lowerCamelCase ,  name=_lowerCamelCase   )
       return tuple(_lowerCamelCase   )









import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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 		_snake_case  (				_snake_case						,   _snake_case						,   unittest.TestCase							):
 SCREAMING_SNAKE_CASE__				=	CycleDiffusionPipeline
 SCREAMING_SNAKE_CASE__				=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
     'negative_prompt',
     'height',
     'width',
     'negative_prompt_embeds',
 }
 SCREAMING_SNAKE_CASE__				=	PipelineTesterMixin.required_optional_params - {'latents'}
 SCREAMING_SNAKE_CASE__				=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}							)
 SCREAMING_SNAKE_CASE__				=	IMAGE_TO_IMAGE_IMAGE_PARAMS
 SCREAMING_SNAKE_CASE__				=	IMAGE_TO_IMAGE_IMAGE_PARAMS




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       torch.manual_seed(0   )
       a						:Optional[Any]										=	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 ,  )
       a						:List[str]										=	DDIMScheduler(
           beta_start=0.0_0085 ,  beta_end=0.012 ,  beta_schedule='''scaled_linear''' ,  num_train_timesteps=1000 ,  clip_sample=_lowerCamelCase ,  set_alpha_to_one=_lowerCamelCase ,  )
       torch.manual_seed(0   )
       a						:List[str]										=	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 ,  )
       torch.manual_seed(0   )
       a						:Dict										=	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 ,  )
       a						:str										=	CLIPTextModel(_lowerCamelCase   )
       a						:List[str]										=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

       a						:Union[str, Any]										=	{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=0   ):
       a						:Tuple										=	floats_tensor((1, 3, 32, 32) ,  rng=random.Random(_lowerCamelCase   )   ).to(_lowerCamelCase   )
       a						:Tuple										=	image / 2 + 0.5
       if str(_lowerCamelCase   ).startswith('''mps'''   ):
             a						:List[str]										=	torch.manual_seed(_lowerCamelCase   )
       else:
             a						:Any										=	torch.Generator(device=_lowerCamelCase   ).manual_seed(_lowerCamelCase   )
       a						:int										=	{
           '''prompt''': '''An astronaut riding an elephant''',
           '''source_prompt''': '''An astronaut riding a horse''',
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''eta''': 0.1,
           '''strength''': 0.8,
           '''guidance_scale''': 3,
           '''source_guidance_scale''': 1,
           '''output_type''': '''numpy''',
       }
       return inputs




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Union[str, Any]										=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator

       a						:Optional[Any]										=	self.get_dummy_components()
       a						:Dict										=	CycleDiffusionPipeline(**_lowerCamelCase   )
       a						:Optional[Any]										=	pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:List[str]										=	self.get_dummy_inputs(_lowerCamelCase   )
       a						:Any										=	pipe(**_lowerCamelCase   )
       a						:List[Any]										=	output.images

       a						:str										=	images[0, -3:, -3:, -1]

       assert images.shape == (1, 32, 32, 3)
       a						:List[Any]										=	np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]   )

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




 @unittest.skipIf(torch_device != '''cuda''' ,  '''This test requires a GPU'''   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[int]										=	self.get_dummy_components()
       for name, module in components.items():
             if hasattr(_lowerCamelCase ,  '''half'''   ):
                   a						:Union[str, Any]										=	module.half()
       a						:List[Any]										=	CycleDiffusionPipeline(**_lowerCamelCase   )
       a						:Dict										=	pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:Tuple										=	self.get_dummy_inputs(_lowerCamelCase   )
       a						:Optional[int]										=	pipe(**_lowerCamelCase   )
       a						:Optional[Any]										=	output.images

       a						:List[Any]										=	images[0, -3:, -3:, -1]

       assert images.shape == (1, 32, 32, 3)
       a						:str										=	np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]   )

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




 @skip_mps
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_save_load_local()




 @unittest.skip('''non-deterministic pipeline'''   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_inference_batch_single_identical()




 @skip_mps
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_dict_tuple_outputs_equivalent()




 @skip_mps
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_save_load_optional_components()




 @skip_mps
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_attention_slicing_forward_pass()







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




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




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:str										=	load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/cycle-diffusion/black_colored_car.png'''   )
       a						:Optional[int]										=	load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''   )
       a						:Optional[Any]										=	init_image.resize((512, 512)   )

       a						:List[str]										=	'''CompVis/stable-diffusion-v1-4'''
       a						:List[str]										=	DDIMScheduler.from_pretrained(_lowerCamelCase ,  subfolder='''scheduler'''   )
       a						:Tuple										=	CycleDiffusionPipeline.from_pretrained(
           _lowerCamelCase ,  scheduler=_lowerCamelCase ,  safety_checker=_lowerCamelCase ,  torch_dtype=torch.floataa ,  revision='''fp16'''   )

       pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )
       pipe.enable_attention_slicing()

       a						:Optional[Any]										=	'''A black colored car'''
       a						:Any										=	'''A blue colored car'''

       a						:str										=	torch.manual_seed(0   )
       a						:List[Any]										=	pipe(
           prompt=_lowerCamelCase ,  source_prompt=_lowerCamelCase ,  image=_lowerCamelCase ,  num_inference_steps=100 ,  eta=0.1 ,  strength=0.85 ,  guidance_scale=3 ,  source_guidance_scale=1 ,  generator=_lowerCamelCase ,  output_type='''np''' ,  )
       a						:int										=	output.images

       # the values aren't exactly equal, but the images look the same visually
       assert np.abs(image - expected_image   ).max() < 5e-1




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:str										=	load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/cycle-diffusion/black_colored_car.png'''   )
       a						:Optional[int]										=	load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''   )
       a						:List[str]										=	init_image.resize((512, 512)   )

       a						:List[str]										=	'''CompVis/stable-diffusion-v1-4'''
       a						:Any										=	DDIMScheduler.from_pretrained(_lowerCamelCase ,  subfolder='''scheduler'''   )
       a						:int										=	CycleDiffusionPipeline.from_pretrained(_lowerCamelCase ,  scheduler=_lowerCamelCase ,  safety_checker=_lowerCamelCase   )

       pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )
       pipe.enable_attention_slicing()

       a						:Optional[int]										=	'''A black colored car'''
       a						:Any										=	'''A blue colored car'''

       a						:Optional[int]										=	torch.manual_seed(0   )
       a						:Union[str, Any]										=	pipe(
           prompt=_lowerCamelCase ,  source_prompt=_lowerCamelCase ,  image=_lowerCamelCase ,  num_inference_steps=100 ,  eta=0.1 ,  strength=0.85 ,  guidance_scale=3 ,  source_guidance_scale=1 ,  generator=_lowerCamelCase ,  output_type='''np''' ,  )
       a						:Optional[int]										=	output.images

       assert np.abs(image - expected_image   ).max() < 2e-2





