


'''simple docstring'''
from typing import List, Optional, Union

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


__magic_name__ : Any					=logging.get_logger(__name__)

__magic_name__ : Union[str, Any]					={
    'huggingface/informer-tourism-monthly': (
        'https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}






class 		UpperCamelCase_		(       __lowercase ):





						"""simple docstring"""




						UpperCAmelCase__						:							int       =  "informer"
						UpperCAmelCase__						:							Any       =  {
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						    "num_hidden_layers": "encoder_layers",
						}
						def __init__(			self					:       str						,					_lowerCamelCase					:       Optional[Any] = None						,					_lowerCamelCase					:       List[Any] = None						,					_lowerCamelCase					:       List[Any] = "student_t"						,					_lowerCamelCase					:       List[str] = "nll"						,					_lowerCamelCase					:       int = 1						,					_lowerCamelCase					:       str = None						,					_lowerCamelCase					:       List[str] = "mean"						,					_lowerCamelCase					:       Union[str, Any] = 0						,					_lowerCamelCase					:       List[Any] = 0						,					_lowerCamelCase					:       Any = 0						,					_lowerCamelCase					:       Optional[int] = 0						,					_lowerCamelCase					:       Any = None						,					_lowerCamelCase					:       Union[str, Any] = None						,					_lowerCamelCase					:       Dict = 64						,					_lowerCamelCase					:       int = 32						,					_lowerCamelCase					:       List[str] = 32						,					_lowerCamelCase					:       Any = 2						,					_lowerCamelCase					:       List[str] = 2						,					_lowerCamelCase					:       str = 2						,					_lowerCamelCase					:       List[Any] = 2						,					_lowerCamelCase					:       Any = True						,					_lowerCamelCase					:       int = "gelu"						,					_lowerCamelCase					:       Dict = 0.05						,					_lowerCamelCase					:       Optional[Any] = 0.1						,					_lowerCamelCase					:       Dict = 0.1						,					_lowerCamelCase					:       Tuple = 0.1						,					_lowerCamelCase					:       List[Any] = 0.1						,					_lowerCamelCase					:       int = 1_00						,					_lowerCamelCase					:       Dict = 0.02						,					_lowerCamelCase					:       str=True						,					_lowerCamelCase					:       int = "prob"						,					_lowerCamelCase					:       Dict = 5						,					_lowerCamelCase					:       Any = True						,					**_lowerCamelCase					:       Any						,					)				->       Optional[Any]:
										__magic_name__							=			prediction_length
										__magic_name__							=			context_length or prediction_length
										__magic_name__							=			distribution_output
										__magic_name__							=			loss
										__magic_name__							=			input_size
										__magic_name__							=			num_time_features
										__magic_name__							=			lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
										__magic_name__							=			scaling
										__magic_name__							=			num_dynamic_real_features
										__magic_name__							=			num_static_real_features
										__magic_name__							=			num_static_categorical_features

										# set cardinality
										if cardinality and num_static_categorical_features > 0:
														if len(_lowerCamelCase	) != num_static_categorical_features:
																		raise ValueError(
																		    "The cardinality should be a list of the same length as `num_static_categorical_features`"	)
														__magic_name__							=			cardinality
										else:
														__magic_name__							=			[0]

										# set embedding_dimension
										if embedding_dimension and num_static_categorical_features > 0:
														if len(_lowerCamelCase	) != num_static_categorical_features:
																		raise ValueError(
																		    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"	)
														__magic_name__							=			embedding_dimension
										else:
														__magic_name__							=			[min(50						,					(cat + 1) // 2	) for cat in self.cardinality]

										__magic_name__							=			num_parallel_samples

										# Transformer architecture configuration
										__magic_name__							=			input_size * len(self.lags_sequence	) + self._number_of_features
										__magic_name__							=			d_model
										__magic_name__							=			encoder_attention_heads
										__magic_name__							=			decoder_attention_heads
										__magic_name__							=			encoder_ffn_dim
										__magic_name__							=			decoder_ffn_dim
										__magic_name__							=			encoder_layers
										__magic_name__							=			decoder_layers

										__magic_name__							=			dropout
										__magic_name__							=			attention_dropout
										__magic_name__							=			activation_dropout
										__magic_name__							=			encoder_layerdrop
										__magic_name__							=			decoder_layerdrop

										__magic_name__							=			activation_function
										__magic_name__							=			init_std

										__magic_name__							=			use_cache

										# Informer
										__magic_name__							=			attention_type
										__magic_name__							=			sampling_factor
										__magic_name__							=			distil

										super().__init__(is_encoder_decoder=_lowerCamelCase						,					**_lowerCamelCase	)

						@property
						def        __A       (			self					:       Optional[Any]	)				->       int:
										return (
										    sum(self.embedding_dimension	)
										    + self.num_dynamic_real_features
										    + self.num_time_features
										    + self.num_static_real_features
										    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
										)





import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
		import jax
		import jax.numpy as jnp







@slow
@require_flax
class 							lowerCAmelCase__   ( unittest.TestCase						):

						def 			A_					(      self							,				a							,				a					)       ->   Optional[int]:


								'''simple docstring'''

								return F'gaussian_noise_s={seed}_shape={"_".join([str(a					) for s in shape]					)}.npy'

						def 			A_					(      self					)       ->   List[Any]:


								'''simple docstring'''

								super().tearDown()
								gc.collect()

						def 			A_					(      self							,				a=0							,				a=(4, 4, 64, 64)							,				a=False					)       ->   Optional[int]:


								'''simple docstring'''

								_UpperCamelCase         =						jnp.bfloataa if fpaa else jnp.floataa
								_UpperCamelCase         =						jnp.array(load_hf_numpy(self.get_file_format(a							,				a					)					)							,				dtype=a					)
								return image

						def 			A_					(      self							,				a=False							,				a="CompVis/stable-diffusion-v1-4"					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase         =						jnp.bfloataa if fpaa else jnp.floataa
								_UpperCamelCase         =						"""bf16""" if fpaa else None

								_UpperCamelCase     ,						_UpperCamelCase         =						FlaxUNetaDConditionModel.from_pretrained(
								    a							,				subfolder="""unet"""							,				dtype=a							,				revision=a					)
								return model, params

						def 			A_					(      self							,				a=0							,				a=(4, 77, 7_68)							,				a=False					)       ->   Tuple:


								'''simple docstring'''

								_UpperCamelCase         =						jnp.bfloataa if fpaa else jnp.floataa
								_UpperCamelCase         =						jnp.array(load_hf_numpy(self.get_file_format(a							,				a					)					)							,				dtype=a					)
								return hidden_states

						@parameterized.expand(
						    [
						        # fmt: off
						        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
						        [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
						        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
						        [3, 10_00, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
						        # fmt: on
						    ]					)
						def 			A_					(      self							,				a							,				a							,				a					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase     ,						_UpperCamelCase         =						self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""							,				fpaa=a					)
								_UpperCamelCase         =						self.get_latents(a							,				fpaa=a					)
								_UpperCamelCase         =						self.get_encoder_hidden_states(a							,				fpaa=a					)

								_UpperCamelCase         =						model.apply(
								    {"""params""": params}							,				a							,				jnp.array(a							,				dtype=jnp.intaa					)							,				encoder_hidden_states=a							,				).sample

								assert sample.shape == latents.shape

								_UpperCamelCase         =						jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())					)							,				dtype=jnp.floataa					)
								_UpperCamelCase         =						jnp.array(a							,				dtype=jnp.floataa					)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
								assert jnp.allclose(a							,				a							,				atol=1e-2					)







						@parameterized.expand(
						    [
						        # fmt: off
						        [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
						        [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
						        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
						        [3, 10_00, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
						        # fmt: on
						    ]					)
						def 			A_					(      self							,				a							,				a							,				a					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase     ,						_UpperCamelCase         =						self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""							,				fpaa=a					)
								_UpperCamelCase         =						self.get_latents(a							,				shape=(4, 4, 96, 96)							,				fpaa=a					)
								_UpperCamelCase         =						self.get_encoder_hidden_states(a							,				shape=(4, 77, 10_24)							,				fpaa=a					)

								_UpperCamelCase         =						model.apply(
								    {"""params""": params}							,				a							,				jnp.array(a							,				dtype=jnp.intaa					)							,				encoder_hidden_states=a							,				).sample

								assert sample.shape == latents.shape

								_UpperCamelCase         =						jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())					)							,				dtype=jnp.floataa					)
								_UpperCamelCase         =						jnp.array(a							,				dtype=jnp.floataa					)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
								assert jnp.allclose(a							,				a							,				atol=1e-2					)



