

import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 					A__(a_,     unittest.TestCase       ):
						"""simple docstring"""






						_A						:							int									= KandinskyVaaPriorEmbaEmbPipeline
						_A						:							List[str]									= ['''prompt''', '''image''']
						_A						:							str									= ['''prompt''', '''image''']
						_A						:							Optional[Any]									= [
						    '''num_images_per_prompt''',
						    '''strength''',
						    '''generator''',
						    '''num_inference_steps''',
						    '''latents''',
						    '''negative_prompt''',
						    '''guidance_scale''',
						    '''output_type''',
						    '''return_dict''',
						]
						_A						:							Optional[Any]									= False





						@property
						def  UpperCamelCase__				(						self						)    ->   Union[str, Any]:
									return 32





						@property
						def  UpperCamelCase__				(						self						)    ->   List[Any]:
									return 32





						@property
						def  UpperCamelCase__				(						self						)    ->   Any:
									return self.time_input_dim





						@property
						def  UpperCamelCase__				(						self						)    ->   str:
									return self.time_input_dim * 4





						@property
						def  UpperCamelCase__				(						self						)    ->   Optional[Any]:
									return 100





						@property
						def  UpperCamelCase__				(						self						)    ->   Tuple:
									a_	:	Union[str, Any]          =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)
									return tokenizer





						@property
						def  UpperCamelCase__				(						self						)    ->   Tuple:
									torch.manual_seed(0						)
									a_	:	int          =   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-05     ,			num_attention_heads=4     ,			num_hidden_layers=5     ,			pad_token_id=1     ,			vocab_size=1_000     ,			)
									return CLIPTextModelWithProjection(_lowercase						)





						@property
						def  UpperCamelCase__				(						self						)    ->   Any:
									torch.manual_seed(0						)

									a_	:	int          =   {
									    """num_attention_heads""": 2,
									    """attention_head_dim""": 12,
									    """embedding_dim""": self.text_embedder_hidden_size,
									    """num_layers""": 1,
									}

									a_	:	List[Any]          =   PriorTransformer(**_lowercase						)
									# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
									a_	:	int          =   nn.Parameter(torch.ones(model.clip_std.shape						)						)
									return model





						@property
						def  UpperCamelCase__				(						self						)    ->   Optional[int]:
									torch.manual_seed(0						)
									a_	:	int          =   CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size     ,			image_size=224     ,			projection_dim=self.text_embedder_hidden_size     ,			intermediate_size=37     ,			num_attention_heads=4     ,			num_channels=3     ,			num_hidden_layers=5     ,			patch_size=14     ,			)

									a_	:	Any          =   CLIPVisionModelWithProjection(_lowercase						)
									return model





						@property
						def  UpperCamelCase__				(						self						)    ->   Dict:
									a_	:	List[str]          =   CLIPImageProcessor(
									    crop_size=224     ,			do_center_crop=_lowercase     ,			do_normalize=_lowercase     ,			do_resize=_lowercase     ,			image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]     ,			image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]     ,			resample=3     ,			size=224     ,			)

									return image_processor





						def  UpperCamelCase__				(						self						)    ->   Any:
									a_	:	Dict          =   self.dummy_prior
									a_	:	Tuple          =   self.dummy_image_encoder
									a_	:	Optional[int]          =   self.dummy_text_encoder
									a_	:	Any          =   self.dummy_tokenizer
									a_	:	int          =   self.dummy_image_processor

									a_	:	Optional[Any]          =   UnCLIPScheduler(
									    variance_type="""fixed_small_log"""     ,			prediction_type="""sample"""     ,			num_train_timesteps=1_000     ,			clip_sample=_lowercase     ,			clip_sample_range=10.0     ,			)

									a_	:	Dict          =   {
									    """prior""": prior,
									    """image_encoder""": image_encoder,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """scheduler""": scheduler,
									    """image_processor""": image_processor,
									}

									return components





						def  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase=0						)    ->   int:
									if str(_lowercase						).startswith("""mps"""						):
												a_	:	Dict          =   torch.manual_seed(_lowercase						)
									else:
												a_	:	int          =   torch.Generator(device=_lowercase						).manual_seed(_lowercase						)

									a_	:	int          =   floats_tensor((1, 3, 64, 64)     ,			rng=random.Random(_lowercase						)						).to(_lowercase						)
									a_	:	Optional[Any]          =   image.cpu().permute(0     ,			2     ,			3     ,			1						)[0]
									a_	:	Dict          =   Image.fromarray(np.uinta(_lowercase						)						).convert("""RGB"""						).resize((256, 256)						)

									a_	:	str          =   {
									    """prompt""": """horse""",
									    """image""": init_image,
									    """strength""": 0.5,
									    """generator""": generator,
									    """guidance_scale""": 4.0,
									    """num_inference_steps""": 2,
									    """output_type""": """np""",
									}
									return inputs





						def  UpperCamelCase__				(						self						)    ->   str:
									a_	:	List[Any]          =   """cpu"""

									a_	:	Union[str, Any]          =   self.get_dummy_components()

									a_	:	Dict          =   self.pipeline_class(**_lowercase						)
									a_	:	Union[str, Any]          =   pipe.to(_lowercase						)

									pipe.set_progress_bar_config(disable=_lowercase						)

									a_	:	Any          =   pipe(**self.get_dummy_inputs(_lowercase						)						)
									a_	:	Any          =   output.image_embeds

									a_	:	List[Any]          =   pipe(
									    **self.get_dummy_inputs(_lowercase						)     ,			return_dict=_lowercase     ,			)[0]

									a_	:	Optional[Any]          =   image[0, -10:]
									a_	:	Optional[Any]          =   image_from_tuple[0, -10:]

									assert image.shape == (1, 32)

									a_	:	Union[str, Any]          =   np.array(
									    [
									        0.1_0_7_1_2_8_4,
									        1.3_3_3_0_2_7_1,
									        0.6_1_2_6_0_2_2_3,
									        -0.6_6_9_1_0_6_5,
									        -0.3_8_4_6_8_5_2,
									        -1.0_3_0_3_6_6_1,
									        0.2_2_7_1_6_1_1_1,
									        0.0_3_3_4_8_9_0_1,
									        0.3_0_0_4_0_6_7_5,
									        -0.2_4_8_0_5_0_2_9,
									    ]						)

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





						@skip_mps
						def  UpperCamelCase__				(						self						)    ->   List[Any]:
									a_	:	List[Any]          =   torch_device == """cpu"""
									a_	:	List[str]          =   True
									a_	:	int          =   False

									self._test_inference_batch_single_identical(
									    test_max_difference=_lowercase     ,			relax_max_difference=_lowercase     ,			test_mean_pixel_difference=_lowercase     ,			)





						@skip_mps
						def  UpperCamelCase__				(						self						)    ->   List[Any]:
									a_	:	Dict          =   torch_device == """cpu"""
									a_	:	str          =   False

									self._test_attention_slicing_forward_pass(
									    test_max_difference=_lowercase     ,			test_mean_pixel_difference=_lowercase     ,			)





import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand


def 				_UpperCAmelCase							(	a__):

			'''simple docstring'''



			return EnvironmentCommand()



class 					A__(a_       ):
						"""simple docstring"""






						@staticmethod
						def  UpperCamelCase__				(						_lowercase						)    ->   Optional[Any]:
									a_	:	List[str]          =   parser.add_parser("""env"""     ,			help="""Print relevant system environment info."""						)
									download_parser.set_defaults(func=_lowercase						)





						def  UpperCamelCase__				(						self						)    ->   int:
									a_	:	List[str]          =   {
									    """`datasets` version""": version,
									    """Platform""": platform.platform(),
									    """Python version""": platform.python_version(),
									    """Huggingface_hub version""": huggingface_hub.__version__,
									    """PyArrow version""": pyarrow.__version__,
									    """Pandas version""": pandas.__version__,
									}

									print("""\nCopy-and-paste the text below in your GitHub issue.\n"""						)
									print(self.format_dict(_lowercase						)						)

									return info





						@staticmethod
						def  UpperCamelCase__				(						_lowercase						)    ->   Dict:
									return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]						) + "\n"


