


"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase      		=			{'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			['''FNetTokenizer''']

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			['''FNetTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			[
     '''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''FNetForMaskedLM''',
     '''FNetForMultipleChoice''',
     '''FNetForNextSentencePrediction''',
     '''FNetForPreTraining''',
     '''FNetForQuestionAnswering''',
     '''FNetForSequenceClassification''',
     '''FNetForTokenClassification''',
     '''FNetLayer''',
     '''FNetModel''',
     '''FNetPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_fnet import FNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_fnet_fast import FNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_fnet import (
      FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      FNetForMaskedLM,
      FNetForMultipleChoice,
      FNetForNextSentencePrediction,
      FNetForPreTraining,
      FNetForQuestionAnswering,
      FNetForSequenceClassification,
      FNetForTokenClassification,
      FNetLayer,
      FNetModel,
      FNetPreTrainedModel,
  )


else:
 import sys

 UpperCAmelCase      		=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





"""simple docstring"""



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 					__magic_name__		(			__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      Tuple        =   PriorTransformer
       __A			:      Any        =   "hidden_states"






       @property
       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				4
              lowercase		:List[Any]       							=				8
              lowercase		:Any       							=				7

              lowercase		:Optional[Any]       							=				floats_tensor((batch_size, embedding_dim)				).to(snake_case__				)

              lowercase		:List[Any]       							=				floats_tensor((batch_size, embedding_dim)				).to(snake_case__				)
              lowercase		:Union[str, Any]       							=				floats_tensor((batch_size, num_embeddings, embedding_dim)				).to(snake_case__				)

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






       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    Optional[Any]=0				):

              '''simple docstring'''






              torch.manual_seed(snake_case__				)
              lowercase		:Union[str, Any]       							=				4
              lowercase		:Optional[int]       							=				8
              lowercase		:str       							=				7

              lowercase		:Dict       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)

              lowercase		:Union[str, Any]       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)
              lowercase		:Tuple       							=				torch.randn((batch_size, num_embeddings, embedding_dim)				).to(snake_case__				)

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






       @property
       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              return (4, 8)






       @property
       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              return (4, 8)






       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				{
                  '''num_attention_heads''': 2,
                  '''attention_head_dim''': 4,
                  '''num_layers''': 2,
                  '''embedding_dim''': 8,
                  '''num_embeddings''': 7,
                  '''additional_embeddings''': 4,
              }
              lowercase		:List[str]       							=				self.dummy_input
              return init_dict, inputs_dict






       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






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

              model.to(snake_case__				)
              lowercase		:List[Any]       							=				model(**self.dummy_input				)[0]

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






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase							,  lowercase		:List[Any]       							=				self.prepare_init_args_and_inputs_for_common()

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

              lowercase		:Optional[Any]       							=				['''hidden_states''', '''timestep''']
              self.assertListEqual(arg_names[:2]  ,     snake_case__				)







       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''				)
              lowercase		:List[Any]       							=				model.to(snake_case__				)

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

              lowercase		:List[str]       							=				self.get_dummy_seed_input()

              with torch.no_grad():
                     lowercase		:List[Any]       							=				model(**snake_case__				)[0]

              lowercase		:Dict       							=				output[0, :5].flatten().cpu()
              print(snake_case__				)

              # 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.
              lowercase		:str       							=				torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]				)
              self.assertTrue(torch_all_close(snake_case__  ,     snake_case__  ,     rtol=1e-2				)				)







@slow
class 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    Tuple  ,     snake_case__  :    int=1  ,     snake_case__  :    int=7_6_8  ,     snake_case__  :    Optional[Any]=7_7  ,     snake_case__  :    Union[str, Any]=0				):

              '''simple docstring'''






              torch.manual_seed(snake_case__				)
              lowercase		:Any       							=				batch_size
              lowercase		:Dict       							=				embedding_dim
              lowercase		:int       							=				num_embeddings

              lowercase		:int       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)

              lowercase		:List[str]       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)
              lowercase		:Optional[Any]       							=				torch.randn((batch_size, num_embeddings, embedding_dim)				).to(snake_case__				)

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






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()







       @parameterized.expand(
           [
               # fmt: off
               [1_3, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
               [3_7, [-0.49_13,  0.01_10, -0.04_83,  0.05_41,  0.49_54, -0.01_70,  0.03_54,  0.16_51]],
               # fmt: on
           ]				)
       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    Dict  ,     snake_case__  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:str       							=				PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''  ,     subfolder='''prior'''				)
              model.to(snake_case__				)
              lowercase		:Union[str, Any]       							=				self.get_dummy_seed_input(seed=snake_case__				)

              with torch.no_grad():
                     lowercase		:int       							=				model(**snake_case__				)[0]

              assert list(sample.shape				) == [1, 7_6_8]

              lowercase		:List[Any]       							=				sample[0, :8].flatten().cpu()
              print(snake_case__				)
              lowercase		:Union[str, Any]       							=				torch.tensor(snake_case__				)

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

