



from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     =""""""
		snake_case_     ="""hf-legacy"""  # "hf://"" is reserved for hffs



		def __init__(self		,__lowerCamelCase = None		,__lowerCamelCase = None		,**__lowerCamelCase		,)					->      List[str]:

						"""simple docstring"""






						super().__init__(self		,**__lowerCamelCase			)
						lowerCAmelCase__					:    Tuple												=					repo_info
						lowerCAmelCase__					:    Any												=					token
						lowerCAmelCase__					:    Optional[Any]												=					None



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						if self.dir_cache is None:
										lowerCAmelCase__					:    Dict												=					{}
										for hf_file in self.repo_info.siblings:
														# TODO(QL): add sizes
														lowerCAmelCase__					:    Tuple												=					{
														    '''name''': hf_file.rfilename,
														    '''size''': None,
														    '''type''': '''file''',
														}
														self.dir_cache.update(
														    {
														        str(__lowerCamelCase			): {'''name''': str(__lowerCamelCase			), '''size''': None, '''type''': '''directory'''}
														        for d in list(PurePosixPath(hf_file.rfilename			).parents			)[:-1]
														    }			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase = "rb"		,**__lowerCamelCase		,)					->      Optional[int]:

						"""simple docstring"""






						if not isinstance(self.repo_info		,__lowerCamelCase			):
										raise NotImplementedError(f"""Open is only implemented for dataset repositories, but got {self.repo_info}"""			)
						lowerCAmelCase__					:    Optional[int]												=					hf_hub_url(self.repo_info.id		,__lowerCamelCase		,revision=self.repo_info.sha			)
						return fsspec.open(
						    __lowerCamelCase		,mode=__lowerCamelCase		,headers=get_authentication_headers_for_url(__lowerCamelCase		,use_auth_token=self.token			)		,client_kwargs={'''trust_env''': True}		,).open()



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,**__lowerCamelCase			)					->      Optional[int]:

						"""simple docstring"""






						self._get_dirs()
						lowerCAmelCase__					:    Optional[Any]												=					self._strip_protocol(__lowerCamelCase			)
						if path in self.dir_cache:
										return self.dir_cache[path]
						else:
										raise FileNotFoundError(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=False		,**__lowerCamelCase			)					->      Tuple:

						"""simple docstring"""






						self._get_dirs()
						lowerCAmelCase__					:    Optional[int]												=					PurePosixPath(path.strip('''/'''			)			)
						lowerCAmelCase__					:    Tuple												=					{}
						for p, f in self.dir_cache.items():
										lowerCAmelCase__					:    Union[str, Any]												=					PurePosixPath(p.strip('''/'''			)			)
										lowerCAmelCase__					:    Union[str, Any]												=					p.parent
										if root == path:
														lowerCAmelCase__					:    List[Any]												=					f
						lowerCAmelCase__					:    Tuple												=					list(paths.values()			)
						if detail:
										return out
						else:
										return sorted(f['''name'''] for f in out			)






import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 	lowerCamelCase__      (							lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =KandinskyVaaPriorPipeline
		snake_case_     =["""prompt"""]
		snake_case_     =["""prompt""", """negative_prompt"""]
		snake_case_     =[
		    """num_images_per_prompt""",
		    """generator""",
		    """num_inference_steps""",
		    """latents""",
		    """negative_prompt""",
		    """guidance_scale""",
		    """output_type""",
		    """return_dict""",
		]
		snake_case_     =False



		@property
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						return 32



		@property
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						return 32



		@property
		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						return self.time_input_dim



		@property
		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						return self.time_input_dim * 4



		@property
		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						return 1_00



		@property
		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)
						return tokenizer



		@property
		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						torch.manual_seed(0			)
						lowerCAmelCase__					:    str												=					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=10_00		,)
						return CLIPTextModelWithProjection(__lowerCamelCase			)



		@property
		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						torch.manual_seed(0			)

						lowerCAmelCase__					:    List[str]												=					{
						    '''num_attention_heads''': 2,
						    '''attention_head_dim''': 12,
						    '''embedding_dim''': self.text_embedder_hidden_size,
						    '''num_layers''': 1,
						}

						lowerCAmelCase__					:    List[str]												=					PriorTransformer(**__lowerCamelCase			)
						# 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
						lowerCAmelCase__					:    int												=					nn.Parameter(torch.ones(model.clip_std.shape			)			)
						return model



		@property
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						torch.manual_seed(0			)
						lowerCAmelCase__					:    Any												=					CLIPVisionConfig(
						    hidden_size=self.text_embedder_hidden_size		,image_size=2_24		,projection_dim=self.text_embedder_hidden_size		,intermediate_size=37		,num_attention_heads=4		,num_channels=3		,num_hidden_layers=5		,patch_size=14		,)

						lowerCAmelCase__					:    int												=					CLIPVisionModelWithProjection(__lowerCamelCase			)
						return model



		@property
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					CLIPImageProcessor(
						    crop_size=2_24		,do_center_crop=__lowerCamelCase		,do_normalize=__lowerCamelCase		,do_resize=__lowerCamelCase		,image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]		,image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]		,resample=3		,size=2_24		,)

						return image_processor



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					self.dummy_prior
						lowerCAmelCase__					:    List[Any]												=					self.dummy_image_encoder
						lowerCAmelCase__					:    Optional[Any]												=					self.dummy_text_encoder
						lowerCAmelCase__					:    Optional[int]												=					self.dummy_tokenizer
						lowerCAmelCase__					:    str												=					self.dummy_image_processor

						lowerCAmelCase__					:    Union[str, Any]												=					UnCLIPScheduler(
						    variance_type='''fixed_small_log'''		,prediction_type='''sample'''		,num_train_timesteps=10_00		,clip_sample=__lowerCamelCase		,clip_sample_range=10.0		,)

						lowerCAmelCase__					:    int												=					{
						    '''prior''': prior,
						    '''image_encoder''': image_encoder,
						    '''text_encoder''': text_encoder,
						    '''tokenizer''': tokenizer,
						    '''scheduler''': scheduler,
						    '''image_processor''': image_processor,
						}

						return components



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=0			)					->      int:

						"""simple docstring"""






						if str(__lowerCamelCase			).startswith('''mps'''			):
										lowerCAmelCase__					:    Dict												=					torch.manual_seed(__lowerCamelCase			)
						else:
										lowerCAmelCase__					:    Any												=					torch.Generator(device=__lowerCamelCase			).manual_seed(__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[Any]												=					{
						    '''prompt''': '''horse''',
						    '''generator''': generator,
						    '''guidance_scale''': 4.0,
						    '''num_inference_steps''': 2,
						    '''output_type''': '''np''',
						}
						return inputs



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

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					'''cpu'''

						lowerCAmelCase__					:    Optional[int]												=					self.get_dummy_components()

						lowerCAmelCase__					:    int												=					self.pipeline_class(**__lowerCamelCase			)
						lowerCAmelCase__					:    int												=					pipe.to(__lowerCamelCase			)

						pipe.set_progress_bar_config(disable=__lowerCamelCase			)

						lowerCAmelCase__					:    List[Any]												=					pipe(**self.get_dummy_inputs(__lowerCamelCase			)			)
						lowerCAmelCase__					:    Union[str, Any]												=					output.image_embeds

						lowerCAmelCase__					:    Tuple												=					pipe(
						    **self.get_dummy_inputs(__lowerCamelCase			)		,return_dict=__lowerCamelCase		,)[0]

						lowerCAmelCase__					:    Union[str, Any]												=					image[0, -10:]
						lowerCAmelCase__					:    str												=					image_from_tuple[0, -10:]

						assert image.shape == (1, 32)

						lowerCAmelCase__					:    int												=					np.array(
						    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]			)

						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 		lowerCAmelCase__				(self			)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					torch_device == '''cpu'''
						lowerCAmelCase__					:    List[Any]												=					True
						lowerCAmelCase__					:    Optional[int]												=					False

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



		@skip_mps
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					torch_device == '''cpu'''
						lowerCAmelCase__					:    int												=					False

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

