




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 							UpperCamelCase_       (					lowercase_						,   unittest.TestCase	):

    '''simple docstring'''






    UpperCAmelCase__				    = PriorTransformer
    UpperCAmelCase__				    = '''hidden_states'''


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      str)		->Tuple:





           '''simple docstring'''

           A__ 					=       4
           A__ 					=       8
           A__ 					=       7

           A__ 					=       floats_tensor((batch_size, embedding_dim)).to(lowerCamelCase_)

           A__ 					=       floats_tensor((batch_size, embedding_dim)).to(lowerCamelCase_)
           A__ 					=       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 			SCREAMING_SNAKE_CASE				(     self :      str   ,					UpperCAmelCase__ :      Union[str, Any]=0)		->List[str]:





           '''simple docstring'''

           torch.manual_seed(lowerCamelCase_)
           A__ 					=       4
           A__ 					=       8
           A__ 					=       7

           A__ 					=       torch.randn((batch_size, embedding_dim)).to(lowerCamelCase_)

           A__ 					=       torch.randn((batch_size, embedding_dim)).to(lowerCamelCase_)
           A__ 					=       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 			SCREAMING_SNAKE_CASE				(     self :      Any)		->Optional[Any]:





           '''simple docstring'''

           return (4, 8)


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      str)		->Dict:





           '''simple docstring'''

           return (4, 8)


    def 			SCREAMING_SNAKE_CASE				(     self :      str)		->Union[str, Any]:





           '''simple docstring'''

           A__ 					=       {
               """num_attention_heads""": 2,
               """attention_head_dim""": 4,
               """num_layers""": 2,
               """embedding_dim""": 8,
               """num_embeddings""": 7,
               """additional_embeddings""": 4,
           }
           A__ 					=       self.dummy_input
           return init_dict, inputs_dict


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Tuple:





           '''simple docstring'''

           A__ 					=       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_)
           A__ 					=       model(**self.dummy_input)[0]

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


    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->Dict:





           '''simple docstring'''

           A__ 					=       self.prepare_init_args_and_inputs_for_common()

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

           A__ 					=       ["""hidden_states""", """timestep"""]
           self.assertListEqual(arg_names[:2]   ,					lowerCamelCase_)





    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->Union[str, Any]:





           '''simple docstring'''

           A__ 					=       PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy''')
           A__ 					=       model.to(lowerCamelCase_)

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

           A__ 					=       self.get_dummy_seed_input()

           with torch.no_grad():
                  A__ 					=       model(**lowerCamelCase_)[0]

           A__ 					=       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.
           A__ 					=       torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239])
           self.assertTrue(torch_all_close(lowerCamelCase_   ,					lowerCamelCase_   ,					rtol=1e-2))


@slow
class 							UpperCamelCase_       (					unittest.TestCase	):

    '''simple docstring'''






    def 			SCREAMING_SNAKE_CASE				(     self :      Any   ,					UpperCAmelCase__ :      List[Any]=1   ,					UpperCAmelCase__ :      Dict=768   ,					UpperCAmelCase__ :      Tuple=77   ,					UpperCAmelCase__ :      List[str]=0)		->List[str]:





           '''simple docstring'''

           torch.manual_seed(lowerCamelCase_)
           A__ 					=       batch_size
           A__ 					=       embedding_dim
           A__ 					=       num_embeddings

           A__ 					=       torch.randn((batch_size, embedding_dim)).to(lowerCamelCase_)

           A__ 					=       torch.randn((batch_size, embedding_dim)).to(lowerCamelCase_)
           A__ 					=       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 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->Optional[Any]:





           '''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 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any]   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      Union[str, Any])		->Any:





           '''simple docstring'''

           A__ 					=       PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''   ,					subfolder='''prior''')
           model.to(lowerCamelCase_)
           A__ 					=       self.get_dummy_seed_input(seed=lowerCamelCase_)

           with torch.no_grad():
                  A__ 					=       model(**lowerCamelCase_)[0]

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

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

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









'''simple docstring'''




from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__UpperCAmelCase            =       {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase            =       ["""GPTNeoXTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase            =       [
							    """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GPTNeoXForCausalLM""",
							    """GPTNeoXForQuestionAnswering""",
							    """GPTNeoXForSequenceClassification""",
							    """GPTNeoXForTokenClassification""",
							    """GPTNeoXLayer""",
							    """GPTNeoXModel""",
							    """GPTNeoXPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_neox import (
														    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTNeoXForCausalLM,
														    GPTNeoXForQuestionAnswering,
														    GPTNeoXForSequenceClassification,
														    GPTNeoXForTokenClassification,
														    GPTNeoXLayer,
														    GPTNeoXModel,
														    GPTNeoXPreTrainedModel,
														)


else:
							import sys

							__UpperCAmelCase            =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



