






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(	UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		int									=PriorTransformer
       a    :		Tuple									='''hidden_states'''





       @property
       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    4
         UpperCamelCase_: str   				=    8
         UpperCamelCase_: Optional[Any]   				=    7

         UpperCamelCase_: List[str]   				=    floats_tensor((batch_size, embedding_dim)							).to(_lowerCamelCase							)

         UpperCamelCase_: Any   				=    floats_tensor((batch_size, embedding_dim)							).to(_lowerCamelCase							)
         UpperCamelCase_: List[Any]   				=    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							):
         torch.manual_seed(_lowerCamelCase							)
         UpperCamelCase_: Optional[Any]   				=    4
         UpperCamelCase_: Dict   				=    8
         UpperCamelCase_: Optional[int]   				=    7

         UpperCamelCase_: str   				=    torch.randn((batch_size, embedding_dim)							).to(_lowerCamelCase							)

         UpperCamelCase_: List[str]   				=    torch.randn((batch_size, embedding_dim)							).to(_lowerCamelCase							)
         UpperCamelCase_: 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,
         }





       @property
       def       _a   ( self							):
         return (4, 8)





       @property
       def       _a   ( self							):
         return (4, 8)





       def       _a   ( self							):
         UpperCamelCase_: Union[str, Any]   				=    {
             'num_attention_heads': 2,
             'attention_head_dim': 4,
             'num_layers': 2,
             'embedding_dim': 8,
             'num_embeddings': 7,
             'additional_embeddings': 4,
         }
         UpperCamelCase_: Tuple   				=    self.dummy_input
         return init_dict, inputs_dict





       def       _a   ( self							):
         UpperCamelCase_				,UpperCamelCase_: Dict   				=    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							)
         UpperCamelCase_: Union[str, Any]   				=    model(**self.dummy_input							)[0]

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





       def       _a   ( self							):
         UpperCamelCase_				,UpperCamelCase_: Any   				=    self.prepare_init_args_and_inputs_for_common()

         UpperCamelCase_: Dict   				=    self.model_class(**_lowerCamelCase							)
         UpperCamelCase_: str   				=    inspect.signature(model.forward							)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         UpperCamelCase_: Tuple   				=    [*signature.parameters.keys()]

         UpperCamelCase_: int   				=    ['hidden_states', 'timestep']
         self.assertListEqual(arg_names[:2]  ,      _lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Dict   				=    PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'							)
         UpperCamelCase_: Tuple   				=    model.to(_lowerCamelCase							)

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

         UpperCamelCase_: Optional[Any]   				=    self.get_dummy_seed_input()

         with torch.no_grad():
           UpperCamelCase_: Dict   				=    model(**_lowerCamelCase							)[0]

         UpperCamelCase_: Optional[Any]   				=    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.
         UpperCamelCase_: Optional[int]   				=    torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9]							)
         self.assertTrue(torch_all_close(_lowerCamelCase  ,      _lowerCamelCase  ,      rtol=1e-2							)							)







@slow
class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       def       _a   ( self  ,      _lowerCamelCase=1  ,      _lowerCamelCase=7_6_8  ,      _lowerCamelCase=7_7  ,      _lowerCamelCase=0							):
         torch.manual_seed(_lowerCamelCase							)
         UpperCamelCase_: int   				=    batch_size
         UpperCamelCase_: Any   				=    embedding_dim
         UpperCamelCase_: str   				=    num_embeddings

         UpperCamelCase_: Optional[Any]   				=    torch.randn((batch_size, embedding_dim)							).to(_lowerCamelCase							)

         UpperCamelCase_: str   				=    torch.randn((batch_size, embedding_dim)							).to(_lowerCamelCase							)
         UpperCamelCase_: 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,
         }





       def       _a   ( self							):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





       @parameterized.expand(
           [
               # fmt: off
               [1_3, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
               [3_7, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
               # fmt: on
           ]							)
       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: List[str]   				=    PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'  ,      subfolder='prior'							)
         model.to(_lowerCamelCase							)
         UpperCamelCase_: Union[str, Any]   				=    self.get_dummy_seed_input(seed=_lowerCamelCase							)

         with torch.no_grad():
           UpperCamelCase_: Tuple   				=    model(**_lowerCamelCase							)[0]

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

         UpperCamelCase_: Dict   				=    sample[0, :8].flatten().cpu()
         print(_lowerCamelCase							)
         UpperCamelCase_: Dict   				=    torch.tensor(_lowerCamelCase							)

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







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_		:		Tuple		     =			logging.get_logger(__name__)

A_		:		Dict		     =			{
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Tuple									='''xglm'''
       a    :		List[Any]									=['''past_key_values''']

       a    :		Union[str, Any]									={
           '''num_attention_heads''': '''attention_heads''',
           '''hidden_size''': '''d_model''',
           '''num_hidden_layers''': '''num_layers''',
       }





       def __init__( self  ,      _lowerCamelCase=2_5_6_0_0_8  ,      _lowerCamelCase=2_0_4_8  ,      _lowerCamelCase=1_0_2_4  ,      _lowerCamelCase=4_0_9_6  ,      _lowerCamelCase=2_4  ,      _lowerCamelCase=1_6  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=2  ,      _lowerCamelCase=1  ,      _lowerCamelCase=0  ,      _lowerCamelCase=2  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[Any]   				=    vocab_size
         UpperCamelCase_: Optional[int]   				=    max_position_embeddings
         UpperCamelCase_: List[str]   				=    d_model
         UpperCamelCase_: List[Any]   				=    ffn_dim
         UpperCamelCase_: List[Any]   				=    num_layers
         UpperCamelCase_: List[Any]   				=    attention_heads
         UpperCamelCase_: Tuple   				=    activation_function
         UpperCamelCase_: Tuple   				=    dropout
         UpperCamelCase_: Tuple   				=    attention_dropout
         UpperCamelCase_: Optional[Any]   				=    activation_dropout
         UpperCamelCase_: List[str]   				=    layerdrop
         UpperCamelCase_: Any   				=    init_std
         UpperCamelCase_: Any   				=    scale_embedding  # scale factor will be sqrt(d_model) if True
         UpperCamelCase_: Union[str, Any]   				=    use_cache

         super().__init__(
             pad_token_id=_lowerCamelCase  ,      bos_token_id=_lowerCamelCase  ,      eos_token_id=_lowerCamelCase  ,      decoder_start_token_id=_lowerCamelCase  ,      **_lowerCamelCase  ,      )