




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							(     UpperCAmelCase__,unittest.TestCase	):
     _a											=		PriorTransformer
     _a											=		"hidden_states"

     @property
     def       a__			(						self       )		->							List[Any]:
           _A							:  List[str]    =		4
           _A							:  List[str]    =		8
           _A							:  int    =		7

           _A							:  Dict    =		floats_tensor((batch_size, embedding_dim)       ).to(lowerCamelCase__       )

           _A							:  Union[str, Any]    =		floats_tensor((batch_size, embedding_dim)       ).to(lowerCamelCase__       )
           _A							:  str    =		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       a__			(						self	,  _a=0       )		->							Optional[int]:
           torch.manual_seed(lowerCamelCase__       )
           _A							:  Union[str, Any]    =		4
           _A							:  Any    =		8
           _A							:  int    =		7

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

           _A							:  Union[str, Any]    =		torch.randn((batch_size, embedding_dim)       ).to(lowerCamelCase__       )
           _A							:  str    =		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       a__			(						self       )		->							Union[str, Any]:
           return (4, 8)

     @property
     def       a__			(						self       )		->							int:
           return (4, 8)

     def       a__			(						self       )		->							List[str]:
           _A							:  str    =		{
               "num_attention_heads": 2,
               "attention_head_dim": 4,
               "num_layers": 2,
               "embedding_dim": 8,
               "num_embeddings": 7,
               "additional_embeddings": 4,
           }
           _A							:  Union[str, Any]    =		self.dummy_input
           return init_dict, inputs_dict

     def       a__			(						self       )		->							Union[str, Any]:
           _A							:  Tuple    =		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							:  Dict    =		model(**self.dummy_input       )[0]

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

     def       a__			(						self       )		->							List[Any]:
           _A							:  List[Any]    =		self.prepare_init_args_and_inputs_for_common()

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

           _A							:  List[str]    =		["hidden_states", "timestep"]
           self.assertListEqual(arg_names[:2]	,  lowerCamelCase__       )



     def       a__			(						self       )		->							Union[str, Any]:
           _A							:  Optional[int]    =		PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""       )
           _A							:  Any    =		model.to(lowerCamelCase__       )

           if hasattr(lowerCamelCase__	,  """set_default_attn_processor"""       ):
                 model.set_default_attn_processor()

           _A							:  Any    =		self.get_dummy_seed_input()

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

           _A							:  int    =		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							:  Dict    =		torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]       )
           self.assertTrue(torch_all_close(lowerCamelCase__	,  lowerCamelCase__	,  rtol=1e-2       )       )







@slow
class 		lowercase							(     unittest.TestCase	):

     def       a__			(						self	,  _a=1	,  _a=768	,  _a=77	,  _a=0       )		->							Union[str, Any]:
           torch.manual_seed(lowerCamelCase__       )
           _A							:  Optional[int]    =		batch_size
           _A							:  Any    =		embedding_dim
           _A							:  int    =		num_embeddings

           _A							:  Tuple    =		torch.randn((batch_size, embedding_dim)       ).to(lowerCamelCase__       )

           _A							:  Any    =		torch.randn((batch_size, embedding_dim)       ).to(lowerCamelCase__       )
           _A							:  Dict    =		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       a__			(						self       )		->							Tuple:
           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       a__			(						self	,  _a	,  _a       )		->							int:
           _A							:  Any    =		PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""	,  subfolder="""prior"""       )
           model.to(lowerCamelCase__       )
           _A							:  List[Any]    =		self.get_dummy_seed_input(seed=lowerCamelCase__       )

           with torch.no_grad():
                 _A							:  List[Any]    =		model(**lowerCamelCase__       )[0]

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

           _A							:  Optional[int]    =		sample[0, :8].flatten().cpu()
           print(lowerCamelCase__       )
           _A							:  Tuple    =		torch.tensor(lowerCamelCase__       )

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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_snake_case            = {
    "configuration_efficientformer": [
        "EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientFormerConfig",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = ["EfficientFormerImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = [
      "EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "EfficientFormerForImageClassification",
      "EfficientFormerForImageClassificationWithTeacher",
      "EfficientFormerModel",
      "EfficientFormerPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = [
      "TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFEfficientFormerForImageClassification",
      "TFEfficientFormerForImageClassificationWithTeacher",
      "TFEfficientFormerModel",
      "TFEfficientFormerPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_efficientformer import EfficientFormerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_efficientformer import (
        EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        EfficientFormerForImageClassification,
        EfficientFormerForImageClassificationWithTeacher,
        EfficientFormerModel,
        EfficientFormerPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_efficientformer import (
        TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFEfficientFormerForImageClassification,
        TFEfficientFormerForImageClassificationWithTeacher,
        TFEfficientFormerModel,
        TFEfficientFormerPreTrainedModel,
    )

else:
  import sys

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

