


"""simple docstring"""







import gc
import unittest

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

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class  __A							(			_SCREAMING_SNAKE_CASE,       unittest.TestCase     ):






 """simple docstring"""




 __lowerCAmelCase					   =    UnCLIPPipeline
 __lowerCAmelCase					   =    TEXT_TO_IMAGE_PARAMS - {
     "negative_prompt",
     "height",
     "width",
     "negative_prompt_embeds",
     "guidance_scale",
     "prompt_embeds",
     "cross_attention_kwargs",
 }
 __lowerCAmelCase					   =    TEXT_TO_IMAGE_BATCH_PARAMS
 __lowerCAmelCase					   =    [
     "generator",
     "return_dict",
     "prior_num_inference_steps",
     "decoder_num_inference_steps",
     "super_res_num_inference_steps",
 ]
 __lowerCAmelCase					   =    False


 @property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	str:
     return 32


 @property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[str]:
     return 32


 @property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[Any]:
     return self.time_input_dim


 @property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[str]:
     return self.time_input_dim * 4


 @property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict:
     return 100


 @property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Tuple:
     a											=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)
     return tokenizer


 @property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Union[str, Any]:
     torch.manual_seed(0		)
     a											=CLIPTextConfig(
         bos_token_id=0					,       eos_token_id=2					,       hidden_size=self.text_embedder_hidden_size					,       projection_dim=self.text_embedder_hidden_size					,       intermediate_size=37					,       layer_norm_eps=1E-0_5					,       num_attention_heads=4					,       num_hidden_layers=5					,       pad_token_id=1					,       vocab_size=1000					,       )
     return CLIPTextModelWithProjection(__A		)


 @property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	int:
     torch.manual_seed(0		)

     a											={
         '''num_attention_heads''': 2,
         '''attention_head_dim''': 12,
         '''embedding_dim''': self.text_embedder_hidden_size,
         '''num_layers''': 1,
     }

     a											=PriorTransformer(**__A		)
     return model


 @property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[Any]:
     torch.manual_seed(0		)

     a											={
         '''clip_embeddings_dim''': self.text_embedder_hidden_size,
         '''time_embed_dim''': self.time_embed_dim,
         '''cross_attention_dim''': self.cross_attention_dim,
     }

     a											=UnCLIPTextProjModel(**__A		)
     return model


 @property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[Any]:
     torch.manual_seed(0		)

     a											={
         '''sample_size''': 32,
         # RGB in channels
         '''in_channels''': 3,
         # Out channels is double in channels because predicts mean and variance
         '''out_channels''': 6,
         '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
         '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
         '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
         '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
         '''layers_per_block''': 1,
         '''cross_attention_dim''': self.cross_attention_dim,
         '''attention_head_dim''': 4,
         '''resnet_time_scale_shift''': '''scale_shift''',
         '''class_embed_type''': '''identity''',
     }

     a											=UNetaDConditionModel(**__A		)
     return model


 @property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[str]:
     return {
         "sample_size": 64,
         "layers_per_block": 1,
         "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
         "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
         "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
         "in_channels": 6,
         "out_channels": 3,
     }


 @property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[Any]:
     torch.manual_seed(0		)

     a											=UNetaDModel(**self.dummy_super_res_kwargs		)
     return model


 @property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[Any]:
     # seeded differently to get different unet than `self.dummy_super_res_first`
     torch.manual_seed(1		)

     a											=UNetaDModel(**self.dummy_super_res_kwargs		)
     return model


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Tuple:
     a											=self.dummy_prior
     a											=self.dummy_decoder
     a											=self.dummy_text_proj
     a											=self.dummy_text_encoder
     a											=self.dummy_tokenizer
     a											=self.dummy_super_res_first
     a											=self.dummy_super_res_last

     a											=UnCLIPScheduler(
         variance_type='''fixed_small_log'''					,       prediction_type='''sample'''					,       num_train_timesteps=1000					,       clip_sample_range=5.0					,       )

     a											=UnCLIPScheduler(
         variance_type='''learned_range'''					,       prediction_type='''epsilon'''					,       num_train_timesteps=1000					,       )

     a											=UnCLIPScheduler(
         variance_type='''fixed_small_log'''					,       prediction_type='''epsilon'''					,       num_train_timesteps=1000					,       )

     a											={
         '''prior''': prior,
         '''decoder''': decoder,
         '''text_proj''': text_proj,
         '''text_encoder''': text_encoder,
         '''tokenizer''': tokenizer,
         '''super_res_first''': super_res_first,
         '''super_res_last''': super_res_last,
         '''prior_scheduler''': prior_scheduler,
         '''decoder_scheduler''': decoder_scheduler,
         '''super_res_scheduler''': super_res_scheduler,
     }

     return components


 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A=0		)   ->	int:
     if str(__A		).startswith('''mps'''		):
         a											=torch.manual_seed(__A		)
     else:
         a											=torch.Generator(device=__A		).manual_seed(__A		)
     a											={
         '''prompt''': '''horse''',
         '''generator''': generator,
         '''prior_num_inference_steps''': 2,
         '''decoder_num_inference_steps''': 2,
         '''super_res_num_inference_steps''': 2,
         '''output_type''': '''numpy''',
     }
     return inputs


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[Any]:
     a											='''cpu'''

     a											=self.get_dummy_components()

     a											=self.pipeline_class(**__A		)
     a											=pipe.to(__A		)

     pipe.set_progress_bar_config(disable=__A		)

     a											=pipe(**self.get_dummy_inputs(__A		)		)
     a											=output.images

     a											=pipe(
         **self.get_dummy_inputs(__A		)					,       return_dict=__A					,       )[0]

     a											=image[0, -3:, -3:, -1]
     a											=image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     a											=np.array(
         [
             0.9_997,
             0.9_988,
             0.0_028,
             0.9_997,
             0.9_984,
             0.9_965,
             0.0_029,
             0.9_986,
             0.0_025,
         ]		)

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


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	str:
     a											=torch.device('''cpu'''		)






     class  __A							:






      """simple docstring"""




      __lowerCAmelCase					   =    1

     a											=self.get_dummy_components()

     a											=self.pipeline_class(**__A		)
     a											=pipe.to(__A		)

     a											=components['''prior''']
     a											=components['''decoder''']
     a											=components['''super_res_first''']
     a											=components['''tokenizer''']
     a											=components['''text_encoder''']

     a											=torch.Generator(device=__A		).manual_seed(0		)
     a											=prior.dtype
     a											=1

     a											=(batch_size, prior.config.embedding_dim)
     a											=pipe.prepare_latents(
         __A					,       dtype=__A					,       device=__A					,       generator=__A					,       latents=__A					,       scheduler=DummyScheduler()		)
     a											=(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
     a											=pipe.prepare_latents(
         __A					,       dtype=__A					,       device=__A					,       generator=__A					,       latents=__A					,       scheduler=DummyScheduler()		)

     a											=(
         batch_size,
         super_res_first.config.in_channels // 2,
         super_res_first.config.sample_size,
         super_res_first.config.sample_size,
     )
     a											=pipe.prepare_latents(
         __A					,       dtype=__A					,       device=__A					,       generator=__A					,       latents=__A					,       scheduler=DummyScheduler()		)

     pipe.set_progress_bar_config(disable=__A		)

     a											='''this is a prompt example'''

     a											=torch.Generator(device=__A		).manual_seed(0		)
     a											=pipe(
         [prompt]					,       generator=__A					,       prior_num_inference_steps=2					,       decoder_num_inference_steps=2					,       super_res_num_inference_steps=2					,       prior_latents=__A					,       decoder_latents=__A					,       super_res_latents=__A					,       output_type='''np'''					,       )
     a											=output.images

     a											=tokenizer(
         __A					,       padding='''max_length'''					,       max_length=tokenizer.model_max_length					,       return_tensors='''pt'''					,       )
     a											=text_encoder(text_inputs.input_ids		)
     a											=text_inputs.attention_mask

     a											=torch.Generator(device=__A		).manual_seed(0		)
     a											=pipe(
         generator=__A					,       prior_num_inference_steps=2					,       decoder_num_inference_steps=2					,       super_res_num_inference_steps=2					,       prior_latents=__A					,       decoder_latents=__A					,       super_res_latents=__A					,       text_model_output=__A					,       text_attention_mask=__A					,       output_type='''np'''					,       )[0]

     # make sure passing text embeddings manually is identical
     assert np.abs(image - image_from_text		).max() < 1E-4


 @skip_mps
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[Any]:
     a											=torch_device == '''cpu'''

     self._test_attention_slicing_forward_pass(test_max_difference=__A					,       expected_max_diff=0.01		)


 @skip_mps
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	int:
     a											=torch_device == '''cpu'''
     a											=True
     a											=[
         '''prior_num_inference_steps''',
         '''decoder_num_inference_steps''',
         '''super_res_num_inference_steps''',
     ]

     self._test_inference_batch_single_identical(
         test_max_difference=__A					,       relax_max_difference=__A					,       additional_params_copy_to_batched_inputs=__A					,       )


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[str]:
     a											=[
         '''prior_num_inference_steps''',
         '''decoder_num_inference_steps''',
         '''super_res_num_inference_steps''',
     ]

     if torch_device == "mps":
         # TODO: MPS errors with larger batch sizes
         a											=[2, 3]
         self._test_inference_batch_consistent(
             batch_sizes=__A					,       additional_params_copy_to_batched_inputs=__A					,       )
     else:
         self._test_inference_batch_consistent(
             additional_params_copy_to_batched_inputs=__A		)


 @skip_mps
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[Any]:
     return super().test_dict_tuple_outputs_equivalent()


 @skip_mps
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Tuple:
     return super().test_save_load_local()





 @skip_mps
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Tuple:
     return super().test_save_load_optional_components()






@nightly
class  __A							(			unittest.TestCase     ):






 """simple docstring"""




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





 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[Any]:
     a											=load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/unclip/karlo_v1_alpha_horse_cpu.npy'''		)

     a											=UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''		)
     pipeline.set_progress_bar_config(disable=__A		)

     a											=torch.manual_seed(0		)
     a											=pipeline(
         '''horse'''					,       num_images_per_prompt=1					,       generator=__A					,       output_type='''np'''					,       )

     a											=output.images[0]

     assert image.shape == (256, 256, 3)
     assert np.abs(expected_image - image		).max() < 1E-1






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






 """simple docstring"""




 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Union[str, Any]:
     a											=load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/unclip/karlo_v1_alpha_horse_fp16.npy'''		)

     a											=UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''					,       torch_dtype=torch.floataa		)
     a											=pipeline.to(__A		)
     pipeline.set_progress_bar_config(disable=__A		)

     a											=torch.Generator(device='''cpu'''		).manual_seed(0		)
     a											=pipeline(
         '''horse'''					,       generator=__A					,       output_type='''np'''					,       )

     a											=output.images[0]

     assert image.shape == (256, 256, 3)

     assert_mean_pixel_difference(__A					,       __A		)





 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict:
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     a											=UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''					,       torch_dtype=torch.floataa		)
     a											=pipe.to(__A		)
     pipe.set_progress_bar_config(disable=__A		)
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     a											=pipe(
         '''horse'''					,       num_images_per_prompt=1					,       prior_num_inference_steps=2					,       decoder_num_inference_steps=2					,       super_res_num_inference_steps=2					,       output_type='''np'''					,       )

     a											=torch.cuda.max_memory_allocated()
     # make sure that less than 7 GB is allocated
     assert mem_bytes < 7 * 10**9



"""simple docstring"""







from __future__ import annotations

import random
import string






class  __A							:






 """simple docstring"""




 def __init__(						self					,       __A = None		)   ->	None:
     a											=passcode or self.__passcode_creator()
     a											=self.__make_key_list()
     a											=self.__make_shift_key()


 def __str__(						self		)   ->	str:
     return "".join(self.__passcode		)


 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	list[int]:
     for i in range(1					,       len(__A		)					,       2		):
         iterlist[i] *= -1
     return iterlist


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	list[str]:
     a											=string.ascii_letters + string.digits
     a											=[random.choice(__A		) for _ in range(random.randint(10					,       20		)		)]
     return password


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	list[str]:
     a											=(
         string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
     )

     a											=[]

     # creates points known as breakpoints to break the key_list_options at those
     # points and pivot each substring
     a											=sorted(set(self.__passcode		)		)
     a											=[]

     # algorithm for creating a new shuffled list, keys_l, out of key_list_options
     for i in key_list_options:
         temp_list.extend(__A		)

         # checking breakpoints at which to pivot temporary sublist and add it into
         # keys_l
         if i in breakpoints or i == key_list_options[-1]:
             keys_l.extend(temp_list[::-1]		)
             temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
     return keys_l


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	int:
     a											=sum(self.__neg_pos([ord(__A		) for x in self.__passcode]		)		)
     return num if num > 0 else len(self.__passcode		)


 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	str:
     a											=''''''

     # decoding shift like Caesar cipher algorithm implementing negative shift or
     # reverse shift or left shift
     for i in encoded_message:
         a											=self.__key_list.index(__A		)
         decoded_message += self.__key_list[
             (position - self.__shift_key) % -len(self.__key_list		)
         ]

     return decoded_message





 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	str:
     a											=''''''

     # encoding shift like Caesar cipher algorithm implementing positive shift or
     # forward shift or right shift
     for i in plaintext:
         a											=self.__key_list.index(__A		)
         encoded_message += self.__key_list[
             (position + self.__shift_key) % len(self.__key_list		)
         ]

     return encoded_message

def _A		(		lowercase = "Hello, this is a modified Caesar cipher"						):







    """simple docstring"""
    a											=ShuffledShiftCipher()
    return cipa.decrypt(cipa.encrypt(lowercase						)						)


if __name__ == "__main__":
       import doctest

       doctest.testmod()