





'''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      UpperCamelCase__					(					lowercase_						,					unittest.TestCase			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	PriorTransformer
					SCREAMING_SNAKE_CASE__				=	'''hidden_states'''


					@property
					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			4
							SCREAMING_SNAKE_CASE :				Dict		=			8
							SCREAMING_SNAKE_CASE :				List[str]		=			7

							SCREAMING_SNAKE_CASE :				Any		=			floats_tensor((batch_size, embedding_dim)						).to(lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				List[str]		=			floats_tensor((batch_size, embedding_dim)						).to(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				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 lowerCamelCase_			(						self				:			str      ,						lowerCamelCase_				:			Union[str, Any]=0						):

							'''simple docstring'''



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

							SCREAMING_SNAKE_CASE :				str		=			torch.randn((batch_size, embedding_dim)						).to(lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.randn((batch_size, embedding_dim)						).to(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				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 lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							return (4, 8)


					@property
					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							return (4, 8)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			Optional[int]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				List[str]		=			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_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			model(**self.dummy_input						)[0]

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


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Tuple		=			self.prepare_init_args_and_inputs_for_common()

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

							SCREAMING_SNAKE_CASE :				Optional[int]		=			["""hidden_states""", """timestep"""]
							self.assertListEqual(arg_names[:2]      ,						lowerCamelCase_						)



					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""						)
							SCREAMING_SNAKE_CASE :				Tuple		=			model.to(lowerCamelCase_						)

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

							SCREAMING_SNAKE_CASE :				int		=			self.get_dummy_seed_input()

							with torch.no_grad():
									SCREAMING_SNAKE_CASE :				Tuple		=			model(**lowerCamelCase_						)[0]

							SCREAMING_SNAKE_CASE :				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.
							SCREAMING_SNAKE_CASE :				Dict		=			torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]						)
							self.assertTrue(torch_all_close(lowerCamelCase_      ,						lowerCamelCase_      ,						rtol=1e-2						)						)







@slow
class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			Any      ,						lowerCamelCase_				:			List[Any]=1      ,						lowerCamelCase_				:			Dict=7_68      ,						lowerCamelCase_				:			Tuple=77      ,						lowerCamelCase_				:			List[str]=0						):

							'''simple docstring'''



							torch.manual_seed(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			batch_size
							SCREAMING_SNAKE_CASE :				Any		=			embedding_dim
							SCREAMING_SNAKE_CASE :				int		=			num_embeddings

							SCREAMING_SNAKE_CASE :				int		=			torch.randn((batch_size, embedding_dim)						).to(lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.randn((batch_size, embedding_dim)						).to(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			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 lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



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



					@parameterized.expand(
					    [
					        # fmt: off
					        [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
					        [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
					        # fmt: on
					    ]						)
					def lowerCamelCase_			(						self				:			Union[str, Any]      ,						lowerCamelCase_				:			int      ,						lowerCamelCase_				:			Union[str, Any]						):

							'''simple docstring'''



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

							with torch.no_grad():
									SCREAMING_SNAKE_CASE :				str		=			model(**lowerCamelCase_						)[0]

							assert list(sample.shape						) == [1, 7_68]

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			sample[0, :8].flatten().cpu()
							print(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				str		=			torch.tensor(lowerCamelCase_						)

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










'''simple docstring'''




from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__UpperCAmelCase            =       {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase            =       ["""GPTNeoXTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase            =       [
							    """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GPTNeoXForCausalLM""",
							    """GPTNeoXForQuestionAnswering""",
							    """GPTNeoXForSequenceClassification""",
							    """GPTNeoXForTokenClassification""",
							    """GPTNeoXLayer""",
							    """GPTNeoXModel""",
							    """GPTNeoXPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_neox import (
														    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTNeoXForCausalLM,
														    GPTNeoXForQuestionAnswering,
														    GPTNeoXForSequenceClassification,
														    GPTNeoXForTokenClassification,
														    GPTNeoXLayer,
														    GPTNeoXModel,
														    GPTNeoXPreTrainedModel,
														)


else:
							import sys

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



