





import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
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 torch import nn

	from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
	from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image



class  __lowerCAmelCase	:






			def __init__(self ,							__magic_name__ ,							__magic_name__=3 ,							__magic_name__=32 ,							__magic_name__=3 ,							__magic_name__=10 ,							__magic_name__=[8, 16, 32, 64] ,							__magic_name__=[1, 1, 2, 1] ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__="relu" ,							__magic_name__=3 ,							__magic_name__=None ,							__magic_name__=["stage2", "stage3", "stage4"] ,							__magic_name__=[2, 3, 4] ,							__magic_name__=1 ,							)					->       Any:


							'''simple docstring'''



							snake_case_						:   int           =  parent
							snake_case_						:   int           =  batch_size
							snake_case_						:   Optional[int]           =  image_size
							snake_case_						:   str           =  num_channels
							snake_case_						:   Dict           =  embeddings_size
							snake_case_						:   Any           =  hidden_sizes
							snake_case_						:   str           =  depths
							snake_case_						:   Optional[Any]           =  is_training
							snake_case_						:   Tuple           =  use_labels
							snake_case_						:   Optional[Any]           =  hidden_act
							snake_case_						:   Optional[int]           =  num_labels
							snake_case_						:   Optional[Any]           =  scope
							snake_case_						:   int           =  len(__magic_name__   )
							snake_case_						:   Optional[int]           =  out_features
							snake_case_						:   Optional[int]           =  out_indices
							snake_case_						:   List[Any]           =  num_groups






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

							snake_case_						:   Union[str, Any]           =  None
							if self.use_labels:
											snake_case_						:   List[Any]           =  ids_tensor([self.batch_size] ,							self.num_labels   )

							snake_case_						:   Union[str, Any]           =  self.get_config()

							return config, pixel_values, labels






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


							'''simple docstring'''



							return BitConfig(
							    num_channels=self.num_channels ,							embeddings_size=self.embeddings_size ,							hidden_sizes=self.hidden_sizes ,							depths=self.depths ,							hidden_act=self.hidden_act ,							num_labels=self.num_labels ,							out_features=self.out_features ,							out_indices=self.out_indices ,							num_groups=self.num_groups ,							)






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  BitModel(config=__magic_name__   )
							model.to(__magic_name__   )
							model.eval()
							snake_case_						:   List[Any]           =  model(__magic_name__   )
							self.parent.assertEqual(
							    result.last_hidden_state.shape ,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,							)






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   Tuple           =  self.num_labels
							snake_case_						:   str           =  BitForImageClassification(__magic_name__   )
							model.to(__magic_name__   )
							model.eval()
							snake_case_						:   Optional[int]           =  model(__magic_name__ ,							labels=__magic_name__   )
							self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.num_labels)   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       int:


							'''simple docstring'''



							snake_case_						:   Tuple           =  BitBackbone(config=__magic_name__   )
							model.to(__magic_name__   )
							model.eval()
							snake_case_						:   Any           =  model(__magic_name__   )

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps   ) ,							len(config.out_features   )   )
							self.parent.assertListEqual(list(result.feature_maps[0].shape   ) ,							[self.batch_size, self.hidden_sizes[1], 4, 4]   )

							# verify channels
							self.parent.assertEqual(len(model.channels   ) ,							len(config.out_features   )   )
							self.parent.assertListEqual(model.channels ,							config.hidden_sizes[1:]   )

							# verify backbone works with out_features=None
							snake_case_						:   Tuple           =  None
							snake_case_						:   List[str]           =  BitBackbone(config=__magic_name__   )
							model.to(__magic_name__   )
							model.eval()
							snake_case_						:   int           =  model(__magic_name__   )

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps   ) ,							1   )
							self.parent.assertListEqual(list(result.feature_maps[0].shape   ) ,							[self.batch_size, self.hidden_sizes[-1], 1, 1]   )

							# verify channels
							self.parent.assertEqual(len(model.channels   ) ,							1   )
							self.parent.assertListEqual(model.channels ,							[config.hidden_sizes[-1]]   )






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  self.prepare_config_and_inputs()
							snake_case_      ,							snake_case_      ,							snake_case_						:   Optional[Any]           =  config_and_inputs
							snake_case_						:   Dict           =  {'''pixel_values''': pixel_values}
							return config, inputs_dict




@require_torch
class  __lowerCAmelCase	(      _a,					_a,					unittest.TestCase					):
			lowerCamelCase_					:					List[str]	      =				(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
			lowerCamelCase_					:					Optional[int]	      =				(
			    {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
			    if is_torch_available()
			    else {}
			)

			lowerCamelCase_					:					Optional[Any]	      =				False
			lowerCamelCase_					:					Union[str, Any]	      =				False
			lowerCamelCase_					:					Any	      =				False
			lowerCamelCase_					:					Optional[Any]	      =				False
			lowerCamelCase_					:					int	      =				False






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Any           =  BitModelTester(self   )
							snake_case_						:   Optional[Any]           =  ConfigTester(self ,							config_class=__magic_name__ ,							has_text_modality=__magic_name__   )






			def       lowerCamelCase  (self   )					->       Optional[int]:


							'''simple docstring'''



							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()






			def       lowerCamelCase  (self   )					->       Optional[int]:


							'''simple docstring'''



							return






			@unittest.skip(reason='''Bit does not output attentions'''   )
			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							pass






			@unittest.skip(reason='''Bit does not use inputs_embeds'''   )
			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							pass






			@unittest.skip(reason='''Bit does not support input and output embeddings'''   )
			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							pass






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_      ,							snake_case_						:   List[str]           =  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											snake_case_						:   Union[str, Any]           =  model_class(__magic_name__   )
											snake_case_						:   str           =  inspect.signature(model.forward   )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											snake_case_						:   List[str]           =  [*signature.parameters.keys()]

											snake_case_						:   Any           =  ['''pixel_values''']
											self.assertListEqual(arg_names[:1] ,							__magic_name__   )






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


							'''simple docstring'''



							snake_case_						:   Tuple           =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__magic_name__   )






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


							'''simple docstring'''



							snake_case_						:   List[str]           =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*__magic_name__   )






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							snake_case_      ,							snake_case_						:   Dict           =  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											snake_case_						:   Tuple           =  model_class(config=__magic_name__   )
											for name, module in model.named_modules():
															if isinstance(__magic_name__ ,							(nn.BatchNormad, nn.GroupNorm)   ):
																			self.assertTrue(
																			    torch.all(module.weight == 1   ) ,							msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' ,							)
																			self.assertTrue(
																			    torch.all(module.bias == 0   ) ,							msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' ,							)






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							def check_hidden_states_output(__magic_name__ ,							__magic_name__ ,							__magic_name__   ):
											snake_case_						:   Optional[Any]           =  model_class(__magic_name__   )
											model.to(__magic_name__   )
											model.eval()

											with torch.no_grad():
															snake_case_						:   List[Any]           =  model(**self._prepare_for_class(__magic_name__ ,							__magic_name__   )   )

											snake_case_						:   Union[str, Any]           =  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											snake_case_						:   Union[str, Any]           =  self.model_tester.num_stages
											self.assertEqual(len(__magic_name__   ) ,							expected_num_stages + 1   )

											# Bit's feature maps are of shape (batch_size, num_channels, height, width)
											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]   ) ,							[self.model_tester.image_size // 4, self.model_tester.image_size // 4] ,							)

							snake_case_      ,							snake_case_						:   List[Any]           =  self.model_tester.prepare_config_and_inputs_for_common()
							snake_case_						:   Dict           =  ['''preactivation''', '''bottleneck''']
							for model_class in self.all_model_classes:
											for layer_type in layers_type:
															snake_case_						:   Optional[Any]           =  layer_type
															snake_case_						:   Union[str, Any]           =  True
															check_hidden_states_output(__magic_name__ ,							__magic_name__ ,							__magic_name__   )

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															snake_case_						:   List[str]           =  True

															check_hidden_states_output(__magic_name__ ,							__magic_name__ ,							__magic_name__   )






			@unittest.skip(reason='''Bit does not use feedforward chunking'''   )
			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							pass






			def       lowerCamelCase  (self   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__magic_name__   )






			@slow
			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											snake_case_						:   Tuple           =  BitModel.from_pretrained(__magic_name__   )
											self.assertIsNotNone(__magic_name__   )






def 					lowerCamelCase_						(    )		->						Dict:






				"""simple docstring"""


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



@require_torch
@require_vision
class  __lowerCAmelCase	(      unittest.TestCase					):






			@cached_property
			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							return (
							    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ) if is_vision_available() else None
							)






			@slow
			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(__magic_name__   )

							snake_case_						:   Dict           =  self.default_image_processor
							snake_case_						:   Dict           =  prepare_img()
							snake_case_						:   Tuple           =  image_processor(images=__magic_name__ ,							return_tensors='''pt'''   ).to(__magic_name__   )

							# forward pass
							with torch.no_grad():
											snake_case_						:   Union[str, Any]           =  model(**__magic_name__   )

							# verify the logits
							snake_case_						:   Union[str, Any]           =  torch.Size((1, 1000)   )
							self.assertEqual(outputs.logits.shape ,							__magic_name__   )

							snake_case_						:   Optional[int]           =  torch.tensor([[-0.6_526, -0.5_263, -1.4_398]]   ).to(__magic_name__   )

							self.assertTrue(torch.allclose(outputs.logits[0, :3] ,							__magic_name__ ,							atol=1e-4   )   )




@require_torch
class  __lowerCAmelCase	(      _a,					unittest.TestCase					):
			lowerCamelCase_					:					int	      =				(BitBackbone,) if is_torch_available() else ()
			lowerCamelCase_					:					Optional[int]	      =				BitConfig

			lowerCamelCase_					:					Union[str, Any]	      =				False






			def       lowerCamelCase  (self   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  BitModelTester(self   )







# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
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  __lowerCAmelCase	:
			lowerCamelCase_					:					CommonSchedulerState

			# setable values
			lowerCamelCase_					:					jnp.ndarray
			lowerCamelCase_					:					jnp.ndarray
			lowerCamelCase_					:					Optional[int]	      =				None






			@classmethod
			def       lowerCamelCase  (cls ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							return cls(common=__magic_name__ ,							init_noise_sigma=__magic_name__ ,							timesteps=__magic_name__   )




@dataclass
class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					DDPMSchedulerState




class  __lowerCAmelCase	(      _a,					_a					):
			lowerCamelCase_					:					List[Any]	      =				[e.name for e in FlaxKarrasDiffusionSchedulers]

			lowerCamelCase_					:					jnp.dtype






			@property
			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							return True






			@register_to_config
			def __init__(self ,							__magic_name__ = 1000 ,							__magic_name__ = 0.0_001 ,							__magic_name__ = 0.02 ,							__magic_name__ = "linear" ,							__magic_name__ = None ,							__magic_name__ = "fixed_small" ,							__magic_name__ = True ,							__magic_name__ = "epsilon" ,							__magic_name__ = jnp.floataa ,							)					->       List[str]:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  dtype






			def       lowerCamelCase  (self ,							__magic_name__ = None   )					->       DDPMSchedulerState:


							'''simple docstring'''



							if common is None:
											snake_case_						:   Any           =  CommonSchedulerState.create(self   )

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

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

							return DDPMSchedulerState.create(
							    common=__magic_name__ ,							init_noise_sigma=__magic_name__ ,							timesteps=__magic_name__ ,							)






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None   )					->       jnp.ndarray:


							'''simple docstring'''



							return sample






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = ()   )					->       DDPMSchedulerState:


							'''simple docstring'''



							snake_case_						:   str           =  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
							snake_case_						:   List[str]           =  (jnp.arange(0 ,							__magic_name__   ) * step_ratio).round()[::-1]

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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=None ,							__magic_name__=None   )					->       int:


							'''simple docstring'''



							snake_case_						:   Any           =  state.common.alphas_cumprod[t]
							snake_case_						:   Dict           =  jnp.where(t > 0 ,							state.common.alphas_cumprod[t - 1] ,							jnp.array(1.0 ,							dtype=self.dtype   )   )

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
							# and sample from it to get previous sample
							# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
							snake_case_						:   Tuple           =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

							if variance_type is None:
											snake_case_						:   Any           =  self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small":
											snake_case_						:   int           =  jnp.clip(__magic_name__ ,							a_min=1e-20   )
							# for rl-diffuser https://arxiv.org/abs/2205.09991
							elif variance_type == "fixed_small_log":
											snake_case_						:   List[str]           =  jnp.log(jnp.clip(__magic_name__ ,							a_min=1e-20   )   )
							elif variance_type == "fixed_large":
											snake_case_						:   str           =  state.common.betas[t]
							elif variance_type == "fixed_large_log":
											# Glide max_log
											snake_case_						:   int           =  jnp.log(state.common.betas[t]   )
							elif variance_type == "learned":
											return predicted_variance
							elif variance_type == "learned_range":
											snake_case_						:   str           =  variance
											snake_case_						:   Optional[Any]           =  state.common.betas[t]
											snake_case_						:   Optional[Any]           =  (predicted_variance + 1) / 2
											snake_case_						:   Optional[Any]           =  frac * max_log + (1 - frac) * min_log

							return variance






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None ,							__magic_name__ = True ,							)					->       Union[FlaxDDPMSchedulerOutput, Tuple]:


							'''simple docstring'''



							snake_case_						:   Tuple           =  timestep

							if key is None:
											snake_case_						:   List[Any]           =  jax.random.PRNGKey(0   )

							if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
											snake_case_      ,							snake_case_						:   Any           =  jnp.split(__magic_name__ ,							sample.shape[1] ,							axis=1   )
							else:
											snake_case_						:   Optional[Any]           =  None

							# 1. compute alphas, betas
							snake_case_						:   List[Any]           =  state.common.alphas_cumprod[t]
							snake_case_						:   Union[str, Any]           =  jnp.where(t > 0 ,							state.common.alphas_cumprod[t - 1] ,							jnp.array(1.0 ,							dtype=self.dtype   )   )
							snake_case_						:   List[Any]           =  1 - alpha_prod_t
							snake_case_						:   str           =  1 - alpha_prod_t_prev

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if self.config.prediction_type == "epsilon":
											snake_case_						:   int           =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
											snake_case_						:   List[Any]           =  model_output
							elif self.config.prediction_type == "v_prediction":
											snake_case_						:   Optional[Any]           =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
							else:
											raise ValueError(
											    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
											    ''' for the FlaxDDPMScheduler.'''   )

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
											snake_case_						:   Optional[int]           =  jnp.clip(__magic_name__ ,							-1 ,							1   )

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							snake_case_						:   str           =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
							snake_case_						:   Dict           =  state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							snake_case_						:   Optional[Any]           =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							def random_variance():
											snake_case_						:   List[Any]           =  jax.random.split(__magic_name__ ,							num=1   )
											snake_case_						:   Tuple           =  jax.random.normal(__magic_name__ ,							shape=model_output.shape ,							dtype=self.dtype   )
											return (self._get_variance(__magic_name__ ,							__magic_name__ ,							predicted_variance=__magic_name__   ) ** 0.5) * noise

							snake_case_						:   List[str]           =  jnp.where(t > 0 ,							random_variance() ,							jnp.zeros(model_output.shape ,							dtype=self.dtype   )   )

							snake_case_						:   Any           =  pred_prev_sample + variance

							if not return_dict:
											return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=__magic_name__ ,							state=__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							)					->       jnp.ndarray:


							'''simple docstring'''



							return add_noise_common(state.common ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							)					->       jnp.ndarray:


							'''simple docstring'''



							return get_velocity_common(state.common ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )






			def __len__(self   )					->       int:


							'''simple docstring'''



							return self.config.num_train_timesteps
