import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()







class 	__snake_case      (   lowercase_    ,      unittest.TestCase      ):
 __lowerCamelCase			:      Tuple           =					PriorTransformer
 __lowerCamelCase			:      Dict           =					"""hidden_states"""
 @property
 def 		UpperCAmelCase__				(     self  )		->							Dict:
       '''simple docstring'''





       UpperCAmelCase			:       str			     =4
       UpperCAmelCase			:       Tuple			     =8
       UpperCAmelCase			:       List[Any]			     =7

       UpperCAmelCase			:       str			     =floats_tensor((batch_size, embedding_dim)  ).to(__UpperCamelCase  )

       UpperCAmelCase			:       Any			     =floats_tensor((batch_size, embedding_dim)  ).to(__UpperCamelCase  )
       UpperCAmelCase			:       Optional[Any]			     =floats_tensor((batch_size, num_embeddings, embedding_dim)  ).to(__UpperCamelCase  )

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }
 def 		UpperCAmelCase__				(     self						,      snake_case__=0  )		->							Tuple:
       '''simple docstring'''





       torch.manual_seed(__UpperCamelCase  )
       UpperCAmelCase			:       int			     =4
       UpperCAmelCase			:       List[Any]			     =8
       UpperCAmelCase			:       Optional[int]			     =7

       UpperCAmelCase			:       str			     =torch.randn((batch_size, embedding_dim)  ).to(__UpperCamelCase  )

       UpperCAmelCase			:       Optional[Any]			     =torch.randn((batch_size, embedding_dim)  ).to(__UpperCamelCase  )
       UpperCAmelCase			:       List[str]			     =torch.randn((batch_size, num_embeddings, embedding_dim)  ).to(__UpperCamelCase  )

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }
 @property
 def 		UpperCAmelCase__				(     self  )		->							int:
       '''simple docstring'''





       return (4, 8)
 @property
 def 		UpperCAmelCase__				(     self  )		->							str:
       '''simple docstring'''





       return (4, 8)
 def 		UpperCAmelCase__				(     self  )		->							int:
       '''simple docstring'''





       UpperCAmelCase			:       Optional[int]			     ={
           '''num_attention_heads''': 2,
           '''attention_head_dim''': 4,
           '''num_layers''': 2,
           '''embedding_dim''': 8,
           '''num_embeddings''': 7,
           '''additional_embeddings''': 4,
       }
       UpperCAmelCase			:       Optional[Any]			     =self.dummy_input
       return init_dict, inputs_dict
 def 		UpperCAmelCase__				(     self  )		->							Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase     ,  UpperCAmelCase			:       Optional[int]			     =PriorTransformer.from_pretrained(
           '''hf-internal-testing/prior-dummy'''						,      output_loading_info=__UpperCamelCase  )
       self.assertIsNotNone(__UpperCamelCase  )
       self.assertEqual(len(loading_info['''missing_keys''']  )						,      0  )

       model.to(__UpperCamelCase  )
       UpperCAmelCase			:       List[str]			     =model(**self.dummy_input  )[0]

       assert hidden_states is not None, "Make sure output is not None"
 def 		UpperCAmelCase__				(     self  )		->							Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase     ,  UpperCAmelCase			:       Any			     =self.prepare_init_args_and_inputs_for_common()

       UpperCAmelCase			:       Union[str, Any]			     =self.model_class(**__UpperCamelCase  )
       UpperCAmelCase			:       Optional[Any]			     =inspect.signature(model.forward  )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       UpperCAmelCase			:       List[str]			     =[*signature.parameters.keys()]

       UpperCAmelCase			:       int			     =['''hidden_states''', '''timestep''']
       self.assertListEqual(arg_names[:2]						,      __UpperCamelCase  )






 def 		UpperCAmelCase__				(     self  )		->							Optional[int]:
       '''simple docstring'''





       UpperCAmelCase			:       str			     =PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''  )
       UpperCAmelCase			:       Tuple			     =model.to(__UpperCamelCase  )

       if hasattr(__UpperCamelCase						,      '''set_default_attn_processor'''  ):
             model.set_default_attn_processor()

       UpperCAmelCase			:       List[Any]			     =self.get_dummy_seed_input()

       with torch.no_grad():
             UpperCAmelCase			:       List[Any]			     =model(**__UpperCamelCase  )[0]

       UpperCAmelCase			:       Dict			     =output[0, :5].flatten().cpu()
       print(__UpperCamelCase  )

       # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
       # the expected output slices are not the same for CPU and GPU.
       UpperCAmelCase			:       Dict			     =torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]  )
       self.assertTrue(torch_all_close(__UpperCamelCase						,      __UpperCamelCase						,      rtol=1e-2  )  )







@slow
class 	__snake_case      (   unittest.TestCase      ):
 def 		UpperCAmelCase__				(     self						,      snake_case__=1						,      snake_case__=768						,      snake_case__=77						,      snake_case__=0  )		->							Optional[Any]:
       '''simple docstring'''





       torch.manual_seed(__UpperCamelCase  )
       UpperCAmelCase			:       str			     =batch_size
       UpperCAmelCase			:       str			     =embedding_dim
       UpperCAmelCase			:       List[str]			     =num_embeddings

       UpperCAmelCase			:       Dict			     =torch.randn((batch_size, embedding_dim)  ).to(__UpperCamelCase  )

       UpperCAmelCase			:       List[str]			     =torch.randn((batch_size, embedding_dim)  ).to(__UpperCamelCase  )
       UpperCAmelCase			:       str			     =torch.randn((batch_size, num_embeddings, embedding_dim)  ).to(__UpperCamelCase  )

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }
 def 		UpperCAmelCase__				(     self  )		->							str:
       '''simple docstring'''





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






 @parameterized.expand(
     [
         # fmt: off
         [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
         [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
         # fmt: on
     ]  )
 def 		UpperCAmelCase__				(     self						,      snake_case__						,      snake_case__  )		->							List[str]:
       '''simple docstring'''





       UpperCAmelCase			:       str			     =PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''						,      subfolder='''prior'''  )
       model.to(__UpperCamelCase  )
       UpperCAmelCase			:       Optional[Any]			     =self.get_dummy_seed_input(seed=__UpperCamelCase  )

       with torch.no_grad():
             UpperCAmelCase			:       int			     =model(**__UpperCamelCase  )[0]

       assert list(sample.shape  ) == [1, 768]

       UpperCAmelCase			:       Optional[Any]			     =sample[0, :8].flatten().cpu()
       print(__UpperCamelCase  )
       UpperCAmelCase			:       List[str]			     =torch.tensor(__UpperCamelCase  )

       assert torch_all_close(__UpperCamelCase						,      __UpperCamelCase						,      atol=1e-3  )







"""simple docstring"""
_snake_case     :		Optional[int]       						= [
    'DownloadConfig',
    'DownloadManager',
    'DownloadMode',
    'StreamingDownloadManager',
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager


