





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							(					__UpperCamelCase,  unittest.TestCase			):
 UpperCAmelCase__													=		PriorTransformer
 UpperCAmelCase__													=		"""hidden_states"""
 @property
 def      A_	(      self  :      List[str]	)			->	str:
     lowerCamelCase__    :      Union[str, Any]					 =		4
     lowerCamelCase__    :      List[str]					 =		8
     lowerCamelCase__    :      List[Any]					 =		7

     lowerCamelCase__    :      List[str]					 =		floats_tensor((batch_size, embedding_dim)	).to(UpperCAmelCase	)

     lowerCamelCase__    :      List[Any]					 =		floats_tensor((batch_size, embedding_dim)	).to(UpperCAmelCase	)
     lowerCamelCase__    :      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      A_	(      self  :      Union[str, Any]			,     UpperCAmelCase  :      Union[str, Any]=0	)			->	int:
     torch.manual_seed(UpperCAmelCase	)
     lowerCamelCase__    :      int					 =		4
     lowerCamelCase__    :      Dict					 =		8
     lowerCamelCase__    :      Dict					 =		7

     lowerCamelCase__    :      List[Any]					 =		torch.randn((batch_size, embedding_dim)	).to(UpperCAmelCase	)

     lowerCamelCase__    :      str					 =		torch.randn((batch_size, embedding_dim)	).to(UpperCAmelCase	)
     lowerCamelCase__    :      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      A_	(      self  :      Union[str, Any]	)			->	List[str]:
     return (4, 8)
 @property
 def      A_	(      self  :      List[str]	)			->	Union[str, Any]:
     return (4, 8)
 def      A_	(      self  :      Any	)			->	int:
     lowerCamelCase__    :      Tuple					 =		{
         'num_attention_heads': 2,
         'attention_head_dim': 4,
         'num_layers': 2,
         'embedding_dim': 8,
         'num_embeddings': 7,
         'additional_embeddings': 4,
     }
     lowerCamelCase__    :      List[str]					 =		self.dummy_input
     return init_dict, inputs_dict
 def      A_	(      self  :      List[Any]	)			->	Tuple:
     lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		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	)
     lowerCamelCase__    :      Optional[int]					 =		model(**self.dummy_input	)[0]

     assert hidden_states is not None, "Make sure output is not None"
 def      A_	(      self  :      Tuple	)			->	Tuple:
     lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		self.prepare_init_args_and_inputs_for_common()

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

     lowerCamelCase__    :      List[Any]					 =		['hidden_states', 'timestep']
     self.assertListEqual(arg_names[:2]			,     UpperCAmelCase	)







 def      A_	(      self  :      Optional[int]	)			->	Optional[int]:
     lowerCamelCase__    :      List[str]					 =		PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'	)
     lowerCamelCase__    :      int					 =		model.to(UpperCAmelCase	)

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

     lowerCamelCase__    :      Optional[int]					 =		self.get_dummy_seed_input()

     with torch.no_grad():
         lowerCamelCase__    :      List[str]					 =		model(**UpperCAmelCase	)[0]

     lowerCamelCase__    :      List[str]					 =		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.
     lowerCamelCase__    :      str					 =		torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9]	)
     self.assertTrue(torch_all_close(UpperCAmelCase			,     UpperCAmelCase			,     rtol=1e-2	)	)


@slow
class    lowerCAmelCase							(					unittest.TestCase			):
 def      A_	(      self  :      str			,     UpperCAmelCase  :      Optional[Any]=1			,     UpperCAmelCase  :      List[Any]=768			,     UpperCAmelCase  :      Tuple=77			,     UpperCAmelCase  :      List[Any]=0	)			->	Optional[Any]:
     torch.manual_seed(UpperCAmelCase	)
     lowerCamelCase__    :      List[str]					 =		batch_size
     lowerCamelCase__    :      List[str]					 =		embedding_dim
     lowerCamelCase__    :      Union[str, Any]					 =		num_embeddings

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

     lowerCamelCase__    :      Any					 =		torch.randn((batch_size, embedding_dim)	).to(UpperCAmelCase	)
     lowerCamelCase__    :      Dict					 =		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      A_	(      self  :      Optional[int]	)			->	List[str]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







 @parameterized.expand(
     [
         # fmt: off
         [13, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
         [37, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
         # fmt: on
     ]	)
 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Tuple	)			->	int:
     lowerCamelCase__    :      Any					 =		PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'			,     subfolder='prior'	)
     model.to(UpperCAmelCase	)
     lowerCamelCase__    :      Union[str, Any]					 =		self.get_dummy_seed_input(seed=UpperCAmelCase	)

     with torch.no_grad():
         lowerCamelCase__    :      List[str]					 =		model(**UpperCAmelCase	)[0]

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

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

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












# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase=None )			->     Tuple:
    if subparsers is not None:
        lowerCamelCase__    :      Any					 =		subparsers.add_parser('test' )
    else:
        lowerCamelCase__    :      int					 =		argparse.ArgumentParser('Accelerate test command' )

    parser.add_argument(
        '--config_file'						,	default=_UpperCAmelCase						,	help=(
            'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
            'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
            'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
            'with \'huggingface\'.'
        )						,	)

    if subparsers is not None:
        parser.set_defaults(func=_UpperCAmelCase )
    return parser


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Union[str, Any]:
    lowerCamelCase__    :      Tuple					 =		os.path.sep.join(__file__.split(os.path.sep )[:-2] + ['test_utils', 'scripts', 'test_script.py'] )

    if args.config_file is None:
        lowerCamelCase__    :      List[str]					 =		script_name
    else:
        lowerCamelCase__    :      List[Any]					 =		F"""--config_file={args.config_file} {script_name}"""

    lowerCamelCase__    :      str					 =		['accelerate-launch'] + test_args.split()
    lowerCamelCase__    :      Dict					 =		execute_subprocess_async(_UpperCAmelCase						,	env=os.environ.copy() )
    if result.returncode == 0:
        print('Test is a success! You are ready for your distributed training!' )


def        SCREAMING_SNAKE_CASE      (       )			->     Any:
    lowerCamelCase__    :      Any					 =		test_command_parser()
    lowerCamelCase__    :      List[Any]					 =		parser.parse_args()
    test_command(_UpperCAmelCase )


if __name__ == "__main__":
    main()





