

"""simple docstring"""




# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)





@flax.struct.dataclass
class 			__UpperCamelCase :
		SCREAMING_SNAKE_CASE								=    42
		SCREAMING_SNAKE_CASE								=    42

		# setable values
		SCREAMING_SNAKE_CASE								=    42
		SCREAMING_SNAKE_CASE								=    42
		SCREAMING_SNAKE_CASE								=    None


		@classmethod
		def 					SCREAMING_SNAKE_CASE__   (cls :		Optional[int]     ,     __SCREAMING_SNAKE_CASE :		CommonSchedulerState     ,     __SCREAMING_SNAKE_CASE :		jnp.ndarray     ,     __SCREAMING_SNAKE_CASE :		jnp.ndarray     ,     __SCREAMING_SNAKE_CASE :		jnp.ndarray     ,     ):
							return cls(
							    common=A__     ,     final_alpha_cumprod=A__     ,     init_noise_sigma=A__     ,     timesteps=A__     ,     )





@dataclass
class 			__UpperCamelCase (   _lowerCAmelCase						):
		SCREAMING_SNAKE_CASE								=    42





class 			__UpperCamelCase (   _lowerCAmelCase     ,		_lowerCAmelCase						):
		SCREAMING_SNAKE_CASE								=    [e.name for e in FlaxKarrasDiffusionSchedulers]

		SCREAMING_SNAKE_CASE								=    42


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]):
							return True


		@register_to_config
		def __init__(self :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		int = 1_0_0_0     ,     __SCREAMING_SNAKE_CASE :		float = 0.0_0_0_1     ,     __SCREAMING_SNAKE_CASE :		float = 0.0_2     ,     __SCREAMING_SNAKE_CASE :		str = "linear"     ,     __SCREAMING_SNAKE_CASE :		Optional[jnp.ndarray] = None     ,     __SCREAMING_SNAKE_CASE :		bool = True     ,     __SCREAMING_SNAKE_CASE :		int = 0     ,     __SCREAMING_SNAKE_CASE :		str = "epsilon"     ,     __SCREAMING_SNAKE_CASE :		jnp.dtype = jnp.floataa     ,     ):
							A			=					dtype


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		Optional[CommonSchedulerState] = None):
							if common is None:
												A			=					CommonSchedulerState.create(self)

							# At every step in ddim, we are looking into the previous alphas_cumprod
							# For the final step, there is no previous alphas_cumprod because we are already at 0
							# `set_alpha_to_one` decides whether we set this parameter simply to one or
							# whether we use the final alpha of the "non-previous" one.
							A			=					(
							    jnp.array(1.0     ,     dtype=self.dtype) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
							)

							# standard deviation of the initial noise distribution
							A			=					jnp.array(1.0     ,     dtype=self.dtype)

							A			=					jnp.arange(0     ,     self.config.num_train_timesteps).round()[::-1]

							return DDIMSchedulerState.create(
							    common=A__     ,     final_alpha_cumprod=A__     ,     init_noise_sigma=A__     ,     timesteps=A__     ,     )


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		DDIMSchedulerState     ,     __SCREAMING_SNAKE_CASE :		jnp.ndarray     ,     __SCREAMING_SNAKE_CASE :		Optional[int] = None):
							return sample


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]     ,     __SCREAMING_SNAKE_CASE :		DDIMSchedulerState     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		Tuple = ()):
							A			=					self.config.num_train_timesteps // num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# rounding to avoid issues when num_inference_step is power of 3
							A			=					(jnp.arange(0     ,     A__) * step_ratio).round()[::-1] + self.config.steps_offset

							return state.replace(
							    num_inference_steps=A__     ,     timesteps=A__     ,     )


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]     ,     __SCREAMING_SNAKE_CASE :		DDIMSchedulerState     ,     __SCREAMING_SNAKE_CASE :		List[str]     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]):
							A			=					state.common.alphas_cumprod[timestep]
							A			=					jnp.where(
							    prev_timestep >= 0     ,     state.common.alphas_cumprod[prev_timestep]     ,     state.final_alpha_cumprod)
							A			=					1 - alpha_prod_t
							A			=					1 - alpha_prod_t_prev

							A			=					(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

							return variance


		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]     ,     __SCREAMING_SNAKE_CASE :		DDIMSchedulerState     ,     __SCREAMING_SNAKE_CASE :		jnp.ndarray     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		jnp.ndarray     ,     __SCREAMING_SNAKE_CASE :		float = 0.0     ,     __SCREAMING_SNAKE_CASE :		bool = True     ,     ):
							if state.num_inference_steps is None:
												raise ValueError(
												    "Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler")

							# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
							# Ideally, read DDIM paper in-detail understanding

							# Notation (<variable name> -> <name in paper>
							# - pred_noise_t -> e_theta(x_t, t)
							# - pred_original_sample -> f_theta(x_t, t) or x_0
							# - std_dev_t -> sigma_t
							# - eta -> η
							# - pred_sample_direction -> "direction pointing to x_t"
							# - pred_prev_sample -> "x_t-1"

							# 1. get previous step value (=t-1)
							A			=					timestep - self.config.num_train_timesteps // state.num_inference_steps

							A			=					state.common.alphas_cumprod
							A			=					state.final_alpha_cumprod

							# 2. compute alphas, betas
							A			=					alphas_cumprod[timestep]
							A			=					jnp.where(prev_timestep >= 0     ,     alphas_cumprod[prev_timestep]     ,     A__)

							A			=					1 - alpha_prod_t

							# 3. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							if self.config.prediction_type == "epsilon":
												A			=					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
												A			=					model_output
							elif self.config.prediction_type == "sample":
												A			=					model_output
												A			=					(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
							elif self.config.prediction_type == "v_prediction":
												A			=					(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
												A			=					(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
							else:
												raise ValueError(
												    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
												    " `v_prediction`")

							# 4. compute variance: "sigma_t(η)" -> see formula (16)
							# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
							A			=					self._get_variance(A__     ,     A__     ,     A__)
							A			=					eta * variance ** 0.5

							# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							A			=					(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

							# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							A			=					alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

							if not return_dict:
												return (prev_sample, state)

							return FlaxDDIMSchedulerOutput(prev_sample=A__     ,     state=A__)


		def 					SCREAMING_SNAKE_CASE__   (self :		Tuple     ,     __SCREAMING_SNAKE_CASE :		DDIMSchedulerState     ,     __SCREAMING_SNAKE_CASE :		jnp.ndarray     ,     __SCREAMING_SNAKE_CASE :		jnp.ndarray     ,     __SCREAMING_SNAKE_CASE :		jnp.ndarray     ,     ):
							return add_noise_common(state.common     ,     A__     ,     A__     ,     A__)


		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]     ,     __SCREAMING_SNAKE_CASE :		DDIMSchedulerState     ,     __SCREAMING_SNAKE_CASE :		jnp.ndarray     ,     __SCREAMING_SNAKE_CASE :		jnp.ndarray     ,     __SCREAMING_SNAKE_CASE :		jnp.ndarray     ,     ):
							return get_velocity_common(state.common     ,     A__     ,     A__     ,     A__)


		def __len__(self :		Any):
							return self.config.num_train_timesteps











import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
      from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import PoolFormerImageProcessor






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     def        __snake_case      (			self     :      Optional[Any]	) ->				List[Any]:
           lowerCamelCase__					     = self.config_class(**self.inputs_dict	)
           self.parent.assertTrue(hasattr(A__		,     'hidden_sizes'	)	)
           self.parent.assertTrue(hasattr(A__		,     'num_encoder_blocks'	)	)






class 	A	:
     """simple docstring"""

     def __init__(			self     :      Union[str, Any]		,     A__     :      Dict		,     A__     :      int=1_3		,     A__     :      Optional[Any]=6_4		,     A__     :      List[Any]=3		,     A__     :      Dict=4		,     A__     :      int=[2, 2, 2, 2]		,     A__     :      int=[8, 4, 2, 1]		,     A__     :      Any=[1_6, 3_2, 6_4, 1_2_8]		,     A__     :      List[str]=[1, 4, 8, 1_6]		,     A__     :      List[str]=False		,     A__     :      Optional[int]=True		,     A__     :      Any="gelu"		,     A__     :      List[str]=0.1		,     A__     :      Union[str, Any]=0.02		,     A__     :      Any=3		,     A__     :      Dict=None		,     ) ->				List[str]:
           lowerCamelCase__					     = parent
           lowerCamelCase__					     = batch_size
           lowerCamelCase__					     = image_size
           lowerCamelCase__					     = num_channels
           lowerCamelCase__					     = num_encoder_blocks
           lowerCamelCase__					     = sr_ratios
           lowerCamelCase__					     = depths
           lowerCamelCase__					     = hidden_sizes
           lowerCamelCase__					     = downsampling_rates
           lowerCamelCase__					     = is_training
           lowerCamelCase__					     = use_labels
           lowerCamelCase__					     = hidden_act
           lowerCamelCase__					     = hidden_dropout_prob
           lowerCamelCase__					     = initializer_range
           lowerCamelCase__					     = num_labels
           lowerCamelCase__					     = scope




     def        __snake_case      (			self     :      Any	) ->				str:
           lowerCamelCase__					     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

           lowerCamelCase__					     = None
           if self.use_labels:
                 lowerCamelCase__					     = ids_tensor([self.batch_size, self.image_size, self.image_size]		,     self.num_labels	)

           lowerCamelCase__					     = PoolFormerConfig(
               image_size=self.image_size		,     num_channels=self.num_channels		,     num_encoder_blocks=self.num_encoder_blocks		,     depths=self.depths		,     hidden_sizes=self.hidden_sizes		,     hidden_act=self.hidden_act		,     hidden_dropout_prob=self.hidden_dropout_prob		,     initializer_range=self.initializer_range		,     )

           return config, pixel_values, labels




     def        __snake_case      (			self     :      Union[str, Any]		,     A__     :      Optional[int]		,     A__     :      List[Any]		,     A__     :      Optional[Any]	) ->				str:
           lowerCamelCase__					     = PoolFormerModel(config=A__	)
           model.to(A__	)
           model.eval()
           lowerCamelCase__					     = model(A__	)
           lowerCamelCase__					     = lowerCamelCase__					     = self.image_size // 32.0
           self.parent.assertEqual(
               result.last_hidden_state.shape		,     (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)	)




     def        __snake_case      (			self     :      List[str]	) ->				Any:
           lowerCamelCase__					     = self.prepare_config_and_inputs()
           lowerCamelCase__     ,						lowerCamelCase__     ,						lowerCamelCase__					     = config_and_inputs
           lowerCamelCase__					     = {'pixel_values': pixel_values}
           return config, inputs_dict






@require_torch
class 	A	(						_lowerCAmelCase	,  _lowerCAmelCase	,  unittest.TestCase						):
     """simple docstring"""

     _snake_case:		Dict   =(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
     _snake_case:		Dict   =(
         {"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     _snake_case:		str   =False
     _snake_case:		Tuple   =False
     _snake_case:		Optional[int]   =False
     _snake_case:		Dict   =False
     _snake_case:		Tuple   =False




     def        __snake_case      (			self     :      List[Any]	) ->				Optional[Any]:
           lowerCamelCase__					     = PoolFormerModelTester(self	)
           lowerCamelCase__					     = PoolFormerConfigTester(self		,     config_class=A__	)




     def        __snake_case      (			self     :      str	) ->				List[str]:
           self.config_tester.run_common_tests()




     def        __snake_case      (			self     :      int	) ->				List[str]:
           lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A__	)




     @unittest.skip('PoolFormer does not use inputs_embeds'	)
     def        __snake_case      (			self     :      Dict	) ->				Tuple:
           pass




     @unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'	)
     def        __snake_case      (			self     :      List[str]	) ->				str:
           pass




     def        __snake_case      (			self     :      Dict	) ->				Optional[int]:
           def check_hidden_states_output(A__     :      str		,     A__     :      List[Any]		,     A__     :      List[Any]	):
                 lowerCamelCase__					     = model_class(A__	)
                 model.to(A__	)
                 model.eval()

                 with torch.no_grad():
                       lowerCamelCase__					     = model(**self._prepare_for_class(A__		,     A__	)	)

                 lowerCamelCase__					     = outputs.hidden_states

                 lowerCamelCase__					     = self.model_tester.num_encoder_blocks
                 self.assertEqual(len(A__	)		,     A__	)

                 # verify the first hidden states (first block)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-3:]	)		,     [
                         self.model_tester.hidden_sizes[0],
                         self.model_tester.image_size // 4,
                         self.model_tester.image_size // 4,
                     ]		,     )

           lowerCamelCase__     ,						lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCamelCase__					     = True
                 check_hidden_states_output(A__		,     A__		,     A__	)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowerCamelCase__					     = True

                 check_hidden_states_output(A__		,     A__		,     A__	)




     def        __snake_case      (			self     :      Any	) ->				Tuple:
           if not self.model_tester.is_training:
                 return

           lowerCamelCase__     ,						lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()
           lowerCamelCase__					     = True

           for model_class in self.all_model_classes:
                 if model_class in get_values(A__	):
                       continue
                 lowerCamelCase__					     = model_class(A__	)
                 model.to(A__	)
                 model.train()
                 lowerCamelCase__					     = self._prepare_for_class(A__		,     A__		,     return_labels=A__	)
                 lowerCamelCase__					     = model(**A__	).loss
                 loss.backward()




     def        __snake_case      (			self     :      Tuple	) ->				Any:
           lowerCamelCase__     ,						lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCamelCase__					     = model_class(A__	)
                 lowerCamelCase__					     = inspect.signature(model.forward	)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 lowerCamelCase__					     = [*signature.parameters.keys()]

                 lowerCamelCase__					     = ['pixel_values']
                 self.assertListEqual(arg_names[:1]		,     A__	)




     @slow
     def        __snake_case      (			self     :      Dict	) ->				int:
           for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase__					     = PoolFormerModel.from_pretrained(A__	)
                 self.assertIsNotNone(A__	)

def 	lowerCAmelCase_	(     )    ->	Dict:


      '''simple docstring'''




      lowerCamelCase__					     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
      return image






@require_torch
class 	A	(						unittest.TestCase						):
     """simple docstring"""

     @slow
     def        __snake_case      (			self     :      Any	) ->				Dict:
           lowerCamelCase__					     = PoolFormerImageProcessor()
           lowerCamelCase__					     = PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'	).to(A__	)

           lowerCamelCase__					     = image_processor(images=prepare_img()		,     return_tensors='pt'	).to(A__	)

           # forward pass
           with torch.no_grad():
                 lowerCamelCase__					     = model(**A__	)

           # verify the logits
           lowerCamelCase__					     = torch.Size((1, 1_0_0_0)	)
           self.assertEqual(outputs.logits.shape		,     A__	)

           lowerCamelCase__					     = torch.tensor([-0.6113, 0.1685, -0.0492]	).to(A__	)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]		,     A__		,     atol=1E-4	)	)