






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 						lowercase_   (      lowercase					,    unittest.TestCase  ):






      '''simple docstring'''




      __snake_case	    =    PriorTransformer
      __snake_case	    =    '''hidden_states'''







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




       """simple docstring"""


       a						    = 4
       a						    = 8
       a						    = 7

       a						    = floats_tensor((batch_size, embedding_dim)    ).to(__UpperCAmelCase    )

       a						    = floats_tensor((batch_size, embedding_dim)    ).to(__UpperCAmelCase    )
       a						    = 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  __lowerCAmelCase   ( self :		Tuple					,   __UpperCAmelCase :		Union[str, Any]=0    )       ->Tuple:




       """simple docstring"""


       torch.manual_seed(__UpperCAmelCase    )
       a						    = 4
       a						    = 8
       a						    = 7

       a						    = torch.randn((batch_size, embedding_dim)    ).to(__UpperCAmelCase    )

       a						    = torch.randn((batch_size, embedding_dim)    ).to(__UpperCAmelCase    )
       a						    = 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  __lowerCAmelCase   ( self :		List[str]    )       ->Tuple:




       """simple docstring"""


       return (4, 8)







      @property
      def  __lowerCAmelCase   ( self :		List[Any]    )       ->Tuple:




       """simple docstring"""


       return (4, 8)







      def  __lowerCAmelCase   ( self :		Any    )       ->Tuple:




       """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  __lowerCAmelCase   ( self :		int    )       ->int:




       """simple docstring"""


       a    ,	a						    = 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    )
       a						    = model(**self.dummy_input    )[0]

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







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




       """simple docstring"""


       a    ,	a						    = self.prepare_init_args_and_inputs_for_common()

       a						    = self.model_class(**__UpperCAmelCase    )
       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]					,   __UpperCAmelCase    )







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




       """simple docstring"""


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

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

       a						    = self.get_dummy_seed_input()

       with torch.no_grad():
        a						    = model(**__UpperCAmelCase    )[0]

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






@slow
class 						lowercase_   (      unittest.TestCase  ):






      '''simple docstring'''




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




       """simple docstring"""


       torch.manual_seed(__UpperCAmelCase    )
       a						    = batch_size
       a						    = embedding_dim
       a						    = num_embeddings

       a						    = torch.randn((batch_size, embedding_dim)    ).to(__UpperCAmelCase    )

       a						    = torch.randn((batch_size, embedding_dim)    ).to(__UpperCAmelCase    )
       a						    = 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  __lowerCAmelCase   ( self :		List[Any]    )       ->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  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		List[Any]					,   __UpperCAmelCase :		Optional[int]    )       ->Dict:




       """simple docstring"""


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

       with torch.no_grad():
        a						    = model(**__UpperCAmelCase    )[0]

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

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

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










from typing import TYPE_CHECKING

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


UpperCAmelCase__							  =						{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						[
         "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BioGptForCausalLM",
         "BioGptForTokenClassification",
         "BioGptForSequenceClassification",
         "BioGptModel",
         "BioGptPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

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


