




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 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE	,       unittest.TestCase ):


 '''simple docstring'''







 lowercase_       =   PriorTransformer
 lowercase_       =   "hidden_states"



 @property
 def 				lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_     						=      4
       SCREAMING_SNAKE_CASE_     						=      8
       SCREAMING_SNAKE_CASE_     						=      7

       SCREAMING_SNAKE_CASE_     						=      floats_tensor((batch_size, embedding_dim)						).to(_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      floats_tensor((batch_size, embedding_dim)						).to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      floats_tensor((batch_size, num_embeddings, embedding_dim)						).to(_lowerCAmelCase						)

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }



 def 				lowerCAmelCase_  (							self  :		Optional[Any]    ,    _lowerCAmelCase  :		Dict=0						):
       torch.manual_seed(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      4
       SCREAMING_SNAKE_CASE_     						=      8
       SCREAMING_SNAKE_CASE_     						=      7

       SCREAMING_SNAKE_CASE_     						=      torch.randn((batch_size, embedding_dim)						).to(_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      torch.randn((batch_size, embedding_dim)						).to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      torch.randn((batch_size, num_embeddings, embedding_dim)						).to(_lowerCAmelCase						)

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }



 @property
 def 				lowerCAmelCase_  (							self  :		Tuple						):
       return (4, 8)



 @property
 def 				lowerCAmelCase_  (							self  :		int						):
       return (4, 8)



 def 				lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_     						=      {
           'num_attention_heads': 2,
           'attention_head_dim': 4,
           'num_layers': 2,
           'embedding_dim': 8,
           'num_embeddings': 7,
           'additional_embeddings': 4,
       }
       SCREAMING_SNAKE_CASE_     						=      self.dummy_input
       return init_dict, inputs_dict



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

       model.to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      model(**self.dummy_input						)[0]

       assert hidden_states is not None, "Make sure output is not None"



 def 				lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.prepare_init_args_and_inputs_for_common()

       SCREAMING_SNAKE_CASE_     						=      self.model_class(**_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      inspect.signature(model.forward						)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       SCREAMING_SNAKE_CASE_     						=      [*signature.parameters.keys()]

       SCREAMING_SNAKE_CASE_     						=      ['hidden_states', 'timestep']
       self.assertListEqual(arg_names[:2]    ,    _lowerCAmelCase						)




 def 				lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_     						=      PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'						)
       SCREAMING_SNAKE_CASE_     						=      model.to(_lowerCAmelCase						)

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

       SCREAMING_SNAKE_CASE_     						=      self.get_dummy_seed_input()

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_     						=      model(**_lowerCAmelCase						)[0]

       SCREAMING_SNAKE_CASE_     						=      output[0, :5].flatten().cpu()
       print(_lowerCAmelCase						)

       # 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.
       SCREAMING_SNAKE_CASE_     						=      torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]						)
       self.assertTrue(torch_all_close(_lowerCAmelCase    ,    _lowerCAmelCase    ,    rtol=1E-2						)						)





@slow
class 	lowerCamelCase_							(				unittest.TestCase ):


 '''simple docstring'''







 def 				lowerCAmelCase_  (							self  :		int    ,    _lowerCAmelCase  :		Any=1    ,    _lowerCAmelCase  :		Optional[int]=768    ,    _lowerCAmelCase  :		Tuple=77    ,    _lowerCAmelCase  :		Optional[Any]=0						):
       torch.manual_seed(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      batch_size
       SCREAMING_SNAKE_CASE_     						=      embedding_dim
       SCREAMING_SNAKE_CASE_     						=      num_embeddings

       SCREAMING_SNAKE_CASE_     						=      torch.randn((batch_size, embedding_dim)						).to(_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      torch.randn((batch_size, embedding_dim)						).to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      torch.randn((batch_size, num_embeddings, embedding_dim)						).to(_lowerCAmelCase						)

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }



 def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       # clean up the VRAM after each test
       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 				lowerCAmelCase_  (							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		Dict						):
       SCREAMING_SNAKE_CASE_     						=      PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'    ,    subfolder='prior'						)
       model.to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      self.get_dummy_seed_input(seed=_lowerCAmelCase						)

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_     						=      model(**_lowerCAmelCase						)[0]

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

       SCREAMING_SNAKE_CASE_     						=      sample[0, :8].flatten().cpu()
       print(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      torch.tensor(_lowerCAmelCase						)

       assert torch_all_close(_lowerCAmelCase    ,    _lowerCAmelCase    ,    atol=1E-3						)





import random
from typing import Any

def  UpperCAmelCase_							(				__UpperCAmelCase						:     list							) ->			list[Any]:
      for _ in range(len(__UpperCAmelCase							)							):
            SCREAMING_SNAKE_CASE_     						=      random.randint(0      ,      len(__UpperCAmelCase							) - 1							)
            SCREAMING_SNAKE_CASE_     						=      random.randint(0      ,      len(__UpperCAmelCase							) - 1							)
            SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      data[b], data[a]
      return data


if __name__ == "__main__":
   lowerCamelCase__			:       Union[str, Any]         =							[0, 1, 2, 3, 4, 5, 6, 7]
   lowerCamelCase__			:       Optional[int]         =							['python', 'says', 'hello', '!']
   print('Fisher-Yates Shuffle:')
   print('List', integers, strings)
   print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))