'''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 			__lowercase		(						_UpperCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						PriorTransformer
     SCREAMING_SNAKE_CASE__		   =						'''hidden_states'''







     @property
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  4
         __UpperCamelCase	:	List[Any]		    =  8
         __UpperCamelCase	:	int		    =  7

         __UpperCamelCase	:	Dict		    =  floats_tensor((batch_size, embedding_dim)    ).to(_UpperCAmelCase    )

         __UpperCamelCase	:	Dict		    =  floats_tensor((batch_size, embedding_dim)    ).to(_UpperCAmelCase    )
         __UpperCamelCase	:	List[str]		    =  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     ,			_lowerCamelCase=0    ):
         torch.manual_seed(_UpperCAmelCase    )
         __UpperCamelCase	:	List[str]		    =  4
         __UpperCamelCase	:	Any		    =  8
         __UpperCamelCase	:	List[str]		    =  7

         __UpperCamelCase	:	str		    =  torch.randn((batch_size, embedding_dim)    ).to(_UpperCAmelCase    )

         __UpperCamelCase	:	List[str]		    =  torch.randn((batch_size, embedding_dim)    ).to(_UpperCAmelCase    )
         __UpperCamelCase	:	Optional[int]		    =  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    ):
         return (4, 8)







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







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  {
             '''num_attention_heads''': 2,
             '''attention_head_dim''': 4,
             '''num_layers''': 2,
             '''embedding_dim''': 8,
             '''num_embeddings''': 7,
             '''additional_embeddings''': 4,
         }
         __UpperCamelCase	:	Dict		    =  self.dummy_input
         return init_dict, inputs_dict







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  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    )
         __UpperCamelCase	:	List[str]		    =  model(**self.dummy_input    )[0]

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







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[int]		    =  self.prepare_init_args_and_inputs_for_common()

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

         __UpperCamelCase	:	Tuple		    =  ['''hidden_states''', '''timestep''']
         self.assertListEqual(arg_names[:2]     ,			_UpperCAmelCase    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	str		    =  PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'    )
         __UpperCamelCase	:	Optional[Any]		    =  model.to(_UpperCAmelCase    )

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

         __UpperCamelCase	:	Union[str, Any]		    =  self.get_dummy_seed_input()

         with torch.no_grad():
             __UpperCamelCase	:	List[Any]		    =  model(**_UpperCAmelCase    )[0]

         __UpperCamelCase	:	Any		    =  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.
         __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(_UpperCAmelCase     ,			_UpperCAmelCase     ,			rtol=1E-2    )    )






@slow
class 			__lowercase		(						unittest.TestCase						):







     """simple docstring"""






     def lowerCAmelCase     (		self     ,			_lowerCamelCase=1     ,			_lowerCamelCase=7_6_8     ,			_lowerCamelCase=7_7     ,			_lowerCamelCase=0    ):
         torch.manual_seed(_UpperCAmelCase    )
         __UpperCamelCase	:	List[Any]		    =  batch_size
         __UpperCamelCase	:	Optional[int]		    =  embedding_dim
         __UpperCamelCase	:	Optional[int]		    =  num_embeddings

         __UpperCamelCase	:	Dict		    =  torch.randn((batch_size, embedding_dim)    ).to(_UpperCAmelCase    )

         __UpperCamelCase	:	Dict		    =  torch.randn((batch_size, embedding_dim)    ).to(_UpperCAmelCase    )
         __UpperCamelCase	:	str		    =  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    ):
         # 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 lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[int]		    =  PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'     ,			subfolder='prior'    )
         model.to(_UpperCAmelCase    )
         __UpperCamelCase	:	int		    =  self.get_dummy_seed_input(seed=_UpperCAmelCase    )

         with torch.no_grad():
             __UpperCamelCase	:	Optional[Any]		    =  model(**_UpperCAmelCase    )[0]

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

         __UpperCamelCase	:	Tuple		    =  sample[0, :8].flatten().cpu()
         print(_UpperCAmelCase    )
         __UpperCamelCase	:	Dict		    =  torch.tensor(_UpperCAmelCase    )

         assert torch_all_close(_UpperCAmelCase     ,			_UpperCAmelCase     ,			atol=1E-3    )




'''simple docstring'''
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class 			__lowercase		(						_lowerCamelCase			,							_lowerCamelCase						):







     """simple docstring"""






     @register_to_config
     def __init__(		self     ,			*,
         _lowerCamelCase = 4     ,			_lowerCamelCase = 7_6_8     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
         super().__init__()

         __UpperCamelCase	:	Optional[int]		    =  nn.Parameter(torch.zeros(_lowerCamelCase    )    )

         # parameters for additional clip time embeddings
         __UpperCamelCase	:	Dict		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )

         # parameters for encoder hidden states
         __UpperCamelCase	:	Optional[Any]		    =  clip_extra_context_tokens
         __UpperCamelCase	:	Dict		    =  nn.Linear(
             _lowerCamelCase     ,			self.clip_extra_context_tokens * cross_attention_dim    )
         __UpperCamelCase	:	str		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  nn.LayerNorm(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			*, _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         if do_classifier_free_guidance:
             # Add the classifier free guidance embeddings to the image embeddings
             __UpperCamelCase	:	int		    =  image_embeddings.shape[0]
             __UpperCamelCase	:	Optional[Any]		    =  self.learned_classifier_free_guidance_embeddings.unsqueeze(0    )
             __UpperCamelCase	:	List[Any]		    =  classifier_free_guidance_embeddings.expand(
                 _lowerCamelCase     ,			-1    )
             __UpperCamelCase	:	Optional[Any]		    =  torch.cat([classifier_free_guidance_embeddings, image_embeddings]     ,			dim=0    )

         # The image embeddings batch size and the text embeddings batch size are equal
         assert image_embeddings.shape[0] == prompt_embeds.shape[0]

         __UpperCamelCase	:	Optional[Any]		    =  prompt_embeds.shape[0]

         # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
         # adding CLIP embeddings to the existing timestep embedding, ...
         __UpperCamelCase	:	Optional[int]		    =  self.embedding_proj(_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  self.clip_image_embeddings_project_to_time_embeddings(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[int]		    =  time_projected_image_embeddings + time_projected_prompt_embeds

         # ... and by projecting CLIP embeddings into four
         # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
         __UpperCamelCase	:	Optional[int]		    =  self.clip_extra_context_tokens_proj(_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  clip_extra_context_tokens.reshape(_lowerCamelCase     ,			-1     ,			self.clip_extra_context_tokens    )
         __UpperCamelCase	:	Dict		    =  clip_extra_context_tokens.permute(0     ,			2     ,			1    )

         __UpperCamelCase	:	Union[str, Any]		    =  self.encoder_hidden_states_proj(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[Any]		    =  self.text_encoder_hidden_states_norm(_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]     ,			dim=1    )

         return text_encoder_hidden_states, additive_clip_time_embeddings



