

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




  @property
  def   A__     (				self					)  -> List[Any]:
        '''simple docstring'''






        _lowercase	       =4
        _lowercase	       =8
        _lowercase	       =7

        _lowercase	       =floats_tensor((batch_size, embedding_dim)					).to(lowerCAmelCase					)

        _lowercase	       =floats_tensor((batch_size, embedding_dim)					).to(lowerCAmelCase					)
        _lowercase	       =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							,   lowerCAmelCase=0					)  -> List[Any]:
        '''simple docstring'''






        torch.manual_seed(lowerCAmelCase					)
        _lowercase	       =4
        _lowercase	       =8
        _lowercase	       =7

        _lowercase	       =torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase					)

        _lowercase	       =torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase					)
        _lowercase	       =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					)  -> Dict:
        '''simple docstring'''






        return (4, 8)




  @property
  def   A__     (				self					)  -> int:
        '''simple docstring'''






        return (4, 8)




  def   A__     (				self					)  -> Tuple:
        '''simple docstring'''






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




  def   A__     (				self					)  -> Optional[Any]:
        '''simple docstring'''






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

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




  def   A__     (				self					)  -> List[Any]:
        '''simple docstring'''






        _lowercase       ,       _lowercase	       =self.prepare_init_args_and_inputs_for_common()

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

        _lowercase	       =['hidden_states', 'timestep']
        self.assertListEqual(arg_names[:2]							,   lowerCAmelCase					)




  def   A__     (				self					)  -> List[Any]:
        '''simple docstring'''






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

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

        _lowercase	       =self.get_dummy_seed_input()

        with torch.no_grad():
              _lowercase	       =model(**lowerCAmelCase					)[0]

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





@slow
class  __lowerCAmelCase     (					unittest.TestCase							):




  def   A__     (				self							,   lowerCAmelCase=1							,   lowerCAmelCase=768							,   lowerCAmelCase=77							,   lowerCAmelCase=0					)  -> Optional[Any]:
        '''simple docstring'''






        torch.manual_seed(lowerCAmelCase					)
        _lowercase	       =batch_size
        _lowercase	       =embedding_dim
        _lowercase	       =num_embeddings

        _lowercase	       =torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase					)

        _lowercase	       =torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase					)
        _lowercase	       =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					)  -> 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   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase					)  -> Tuple:
        '''simple docstring'''






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

        with torch.no_grad():
              _lowercase	       =model(**lowerCAmelCase					)[0]

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

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

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





from typing import TYPE_CHECKING

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


lowercase_									=   {'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

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

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   [
      'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
      'XLNetForMultipleChoice',
      'XLNetForQuestionAnswering',
      'XLNetForQuestionAnsweringSimple',
      'XLNetForSequenceClassification',
      'XLNetForTokenClassification',
      'XLNetLMHeadModel',
      'XLNetModel',
      'XLNetPreTrainedModel',
      'load_tf_weights_in_xlnet',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   [
      'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFXLNetForMultipleChoice',
      'TFXLNetForQuestionAnsweringSimple',
      'TFXLNetForSequenceClassification',
      'TFXLNetForTokenClassification',
      'TFXLNetLMHeadModel',
      'TFXLNetMainLayer',
      'TFXLNetModel',
      'TFXLNetPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

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


