

import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def 	__a     (      A__			:  List[str]			,  A__			:  str=0.9_9_9			,  A__			:  Tuple="cosine"			,  ):
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(A__			:  Optional[int]       ):
            return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2       ) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(A__			:  Tuple       ):
            return math.exp(t * -1_2.0       )

    else:
        raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"       )

    SCREAMING_SNAKE_CASE										=   []
    for i in range(A__       ):
        SCREAMING_SNAKE_CASE										=   i / num_diffusion_timesteps
        SCREAMING_SNAKE_CASE										=   (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(A__       ) / alpha_bar_fn(A__       )			,  A__       )       )
    return torch.tensor(A__			,  dtype=torch.floataa       )





class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      [e.name for e in KarrasDiffusionSchedulers]
   lowerCamelCase__				=      2





   @register_to_config
   def __init__(       self					:			Tuple     , __lowerCamelCase					:			int = 1000     , __lowerCamelCase					:			float = 0.00_085     , __lowerCamelCase					:			float = 0.012     , __lowerCamelCase					:			str = "linear"     , __lowerCamelCase					:			Optional[Union[np.ndarray, List[float]]] = None     , __lowerCamelCase					:			str = "epsilon"     , __lowerCamelCase					:			str = "linspace"     , __lowerCamelCase					:			int = 0     , ):
       if trained_betas is not None:
           SCREAMING_SNAKE_CASE										=   torch.tensor(__lowerCamelCase     , dtype=torch.floataa			)
       elif beta_schedule == "linear":
           SCREAMING_SNAKE_CASE										=   torch.linspace(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , dtype=torch.floataa			)
       elif beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           SCREAMING_SNAKE_CASE										=   (
               torch.linspace(beta_start**0.5     , beta_end**0.5     , __lowerCamelCase     , dtype=torch.floataa			) ** 2
           )
       elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           SCREAMING_SNAKE_CASE										=   betas_for_alpha_bar(__lowerCamelCase			)
       else:
           raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"			)

       SCREAMING_SNAKE_CASE										=   1.0 - self.betas
       SCREAMING_SNAKE_CASE										=   torch.cumprod(self.alphas     , dim=0			)

       #  set all values
       self.set_timesteps(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			str     , __lowerCamelCase					:			List[str]=None			):
       if schedule_timesteps is None:
           SCREAMING_SNAKE_CASE										=   self.timesteps

       SCREAMING_SNAKE_CASE										=   (schedule_timesteps == timestep).nonzero()

       # The sigma index that is taken for the **very** first `step`
       # is always the second index (or the last index if there is only 1)
       # This way we can ensure we don't accidentally skip a sigma in
       # case we start in the middle of the denoising schedule (e.g. for image-to-image)
       if len(self._index_counter			) == 0:
           SCREAMING_SNAKE_CASE										=   1 if len(__lowerCamelCase			) > 1 else 0
       else:
           SCREAMING_SNAKE_CASE										=   timestep.cpu().item() if torch.is_tensor(__lowerCamelCase			) else timestep
           SCREAMING_SNAKE_CASE										=   self._index_counter[timestep_int]

       return indices[pos].item()





   @property
   def      _snake_case		(       self					:			str			):
       # standard deviation of the initial noise distribution
       if self.config.timestep_spacing in ["linspace", "trailing"]:
           return self.sigmas.max()

       return (self.sigmas.max() ** 2 + 1) ** 0.5





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			torch.FloatTensor     , __lowerCamelCase					:			Union[float, torch.FloatTensor]     , ):
       SCREAMING_SNAKE_CASE										=   self.index_for_timestep(__lowerCamelCase			)

       if self.state_in_first_order:
           SCREAMING_SNAKE_CASE										=   self.sigmas[step_index]
       else:
           SCREAMING_SNAKE_CASE										=   self.sigmas_interpol[step_index]

       SCREAMING_SNAKE_CASE										=   sample / ((sigma**2 + 1) ** 0.5)
       return sample





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			int     , __lowerCamelCase					:			Union[str, torch.device] = None     , __lowerCamelCase					:			Optional[int] = None     , ):
       SCREAMING_SNAKE_CASE										=   num_inference_steps

       SCREAMING_SNAKE_CASE										=   num_train_timesteps or self.config.num_train_timesteps

       # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
       if self.config.timestep_spacing == "linspace":
           SCREAMING_SNAKE_CASE										=   np.linspace(0     , num_train_timesteps - 1     , __lowerCamelCase     , dtype=__lowerCamelCase			)[::-1].copy()
       elif self.config.timestep_spacing == "leading":
           SCREAMING_SNAKE_CASE										=   num_train_timesteps // self.num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # casting to int to avoid issues when num_inference_step is power of 3
           SCREAMING_SNAKE_CASE										=   (np.arange(0     , __lowerCamelCase			) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase			)
           timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
           SCREAMING_SNAKE_CASE										=   num_train_timesteps / self.num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # casting to int to avoid issues when num_inference_step is power of 3
           SCREAMING_SNAKE_CASE										=   (np.arange(__lowerCamelCase     , 0     , -step_ratio			)).round().copy().astype(__lowerCamelCase			)
           timesteps -= 1
       else:
           raise ValueError(
               f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."			)

       SCREAMING_SNAKE_CASE										=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
       SCREAMING_SNAKE_CASE										=   torch.from_numpy(np.log(__lowerCamelCase			)			).to(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   np.interp(__lowerCamelCase     , np.arange(0     , len(__lowerCamelCase			)			)     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
       SCREAMING_SNAKE_CASE										=   torch.from_numpy(__lowerCamelCase			).to(device=__lowerCamelCase			)

       # interpolate sigmas
       SCREAMING_SNAKE_CASE										=   sigmas.log().lerp(sigmas.roll(1			).log()     , 0.5			).exp()

       SCREAMING_SNAKE_CASE										=   torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2			), sigmas[-1:]]			)
       SCREAMING_SNAKE_CASE										=   torch.cat(
           [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2			), sigmas_interpol[-1:]]			)

       if str(__lowerCamelCase			).startswith("mps"			):
           # mps does not support float64
           SCREAMING_SNAKE_CASE										=   torch.from_numpy(__lowerCamelCase			).to(__lowerCamelCase     , dtype=torch.floataa			)
       else:
           SCREAMING_SNAKE_CASE										=   torch.from_numpy(__lowerCamelCase			).to(__lowerCamelCase			)

       # interpolate timesteps
       SCREAMING_SNAKE_CASE										=   self.sigma_to_t(__lowerCamelCase			).to(__lowerCamelCase     , dtype=timesteps.dtype			)
       SCREAMING_SNAKE_CASE										=   torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])     , dim=-1			).flatten()

       SCREAMING_SNAKE_CASE										=   torch.cat([timesteps[:1], interleaved_timesteps]			)

       SCREAMING_SNAKE_CASE										=   None

       # for exp beta schedules, such as the one for `pipeline_shap_e.py`
       # we need an index counter
       SCREAMING_SNAKE_CASE										=   defaultdict(__lowerCamelCase			)





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			List[str]			):
       # get log sigma
       SCREAMING_SNAKE_CASE										=   sigma.log()

       # get distribution
       SCREAMING_SNAKE_CASE										=   log_sigma - self.log_sigmas[:, None]

       # get sigmas range
       SCREAMING_SNAKE_CASE										=   dists.ge(0			).cumsum(dim=0			).argmax(dim=0			).clamp(max=self.log_sigmas.shape[0] - 2			)
       SCREAMING_SNAKE_CASE										=   low_idx + 1

       SCREAMING_SNAKE_CASE										=   self.log_sigmas[low_idx]
       SCREAMING_SNAKE_CASE										=   self.log_sigmas[high_idx]

       # interpolate sigmas
       SCREAMING_SNAKE_CASE										=   (low - log_sigma) / (low - high)
       SCREAMING_SNAKE_CASE										=   w.clamp(0     , 1			)

       # transform interpolation to time range
       SCREAMING_SNAKE_CASE										=   (1 - w) * low_idx + w * high_idx
       SCREAMING_SNAKE_CASE										=   t.view(sigma.shape			)
       return t





   @property
   def      _snake_case		(       self					:			str			):
       return self.sample is None





   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			Union[torch.FloatTensor, np.ndarray]     , __lowerCamelCase					:			Union[float, torch.FloatTensor]     , __lowerCamelCase					:			Union[torch.FloatTensor, np.ndarray]     , __lowerCamelCase					:			bool = True     , ):
       SCREAMING_SNAKE_CASE										=   self.index_for_timestep(__lowerCamelCase			)

       # advance index counter by 1
       SCREAMING_SNAKE_CASE										=   timestep.cpu().item() if torch.is_tensor(__lowerCamelCase			) else timestep
       self._index_counter[timestep_int] += 1

       if self.state_in_first_order:
           SCREAMING_SNAKE_CASE										=   self.sigmas[step_index]
           SCREAMING_SNAKE_CASE										=   self.sigmas_interpol[step_index + 1]
           SCREAMING_SNAKE_CASE										=   self.sigmas[step_index + 1]
       else:
           # 2nd order / KDPM2's method
           SCREAMING_SNAKE_CASE										=   self.sigmas[step_index - 1]
           SCREAMING_SNAKE_CASE										=   self.sigmas_interpol[step_index]
           SCREAMING_SNAKE_CASE										=   self.sigmas[step_index]

       # currently only gamma=0 is supported. This usually works best anyways.
       # We can support gamma in the future but then need to scale the timestep before
       # passing it to the model which requires a change in API
       SCREAMING_SNAKE_CASE										=   0
       SCREAMING_SNAKE_CASE										=   sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
           SCREAMING_SNAKE_CASE										=   sigma_hat if self.state_in_first_order else sigma_interpol
           SCREAMING_SNAKE_CASE										=   sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
           SCREAMING_SNAKE_CASE										=   sigma_hat if self.state_in_first_order else sigma_interpol
           SCREAMING_SNAKE_CASE										=   model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
               sample / (sigma_input**2 + 1)
           )
       elif self.config.prediction_type == "sample":
           raise NotImplementedError("prediction_type not implemented yet: sample"			)
       else:
           raise ValueError(
               f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"			)

       if self.state_in_first_order:
           # 2. Convert to an ODE derivative for 1st order
           SCREAMING_SNAKE_CASE										=   (sample - pred_original_sample) / sigma_hat
           # 3. delta timestep
           SCREAMING_SNAKE_CASE										=   sigma_interpol - sigma_hat

           # store for 2nd order step
           SCREAMING_SNAKE_CASE										=   sample
       else:
           # DPM-Solver-2
           # 2. Convert to an ODE derivative for 2nd order
           SCREAMING_SNAKE_CASE										=   (sample - pred_original_sample) / sigma_interpol

           # 3. delta timestep
           SCREAMING_SNAKE_CASE										=   sigma_next - sigma_hat

           SCREAMING_SNAKE_CASE										=   self.sample
           SCREAMING_SNAKE_CASE										=   None

       SCREAMING_SNAKE_CASE										=   sample + derivative * dt

       if not return_dict:
           return (prev_sample,)

       return SchedulerOutput(prev_sample=__lowerCamelCase			)





   def      _snake_case		(       self					:			List[str]     , __lowerCamelCase					:			torch.FloatTensor     , __lowerCamelCase					:			torch.FloatTensor     , __lowerCamelCase					:			torch.FloatTensor     , ):
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       SCREAMING_SNAKE_CASE										=   self.sigmas.to(device=original_samples.device     , dtype=original_samples.dtype			)
       if original_samples.device.type == "mps" and torch.is_floating_point(__lowerCamelCase			):
           # mps does not support float64
           SCREAMING_SNAKE_CASE										=   self.timesteps.to(original_samples.device     , dtype=torch.floataa			)
           SCREAMING_SNAKE_CASE										=   timesteps.to(original_samples.device     , dtype=torch.floataa			)
       else:
           SCREAMING_SNAKE_CASE										=   self.timesteps.to(original_samples.device			)
           SCREAMING_SNAKE_CASE										=   timesteps.to(original_samples.device			)

       SCREAMING_SNAKE_CASE										=   [self.index_for_timestep(__lowerCamelCase     , __lowerCamelCase			) for t in timesteps]

       SCREAMING_SNAKE_CASE										=   sigmas[step_indices].flatten()
       while len(sigma.shape			) < len(original_samples.shape			):
           SCREAMING_SNAKE_CASE										=   sigma.unsqueeze(-1			)

       SCREAMING_SNAKE_CASE										=   original_samples + noise * sigma
       return noisy_samples





   def __len__(       self					:			Optional[Any]			):
       return self.config.num_train_timesteps


import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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_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 SwiftFormerForImageClassification, SwiftFormerModel
    from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			List[str]     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int=13     , __lowerCamelCase					:			Optional[int]=3     , __lowerCamelCase					:			str=True     , __lowerCamelCase					:			Any=True     , __lowerCamelCase					:			Optional[Any]=0.1     , __lowerCamelCase					:			Any=0.1     , __lowerCamelCase					:			Optional[int]=224     , __lowerCamelCase					:			Any=1000     , __lowerCamelCase					:			Optional[Any]=[3, 3, 6, 4]     , __lowerCamelCase					:			List[Any]=[48, 56, 112, 220]     , ):
       SCREAMING_SNAKE_CASE										=   parent
       SCREAMING_SNAKE_CASE										=   batch_size
       SCREAMING_SNAKE_CASE										=   num_channels
       SCREAMING_SNAKE_CASE										=   is_training
       SCREAMING_SNAKE_CASE										=   use_labels
       SCREAMING_SNAKE_CASE										=   hidden_dropout_prob
       SCREAMING_SNAKE_CASE										=   attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE										=   num_labels
       SCREAMING_SNAKE_CASE										=   image_size
       SCREAMING_SNAKE_CASE										=   layer_depths
       SCREAMING_SNAKE_CASE										=   embed_dims





   def      _snake_case		(       self					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

       SCREAMING_SNAKE_CASE										=   self.get_config()

       return config, pixel_values, labels





   def      _snake_case		(       self					:			Dict			):
       return SwiftFormerConfig(
           depths=self.layer_depths     , embed_dims=self.embed_dims     , mlp_ratio=4     , downsamples=[True, True, True, True]     , hidden_act="gelu"     , num_labels=self.num_labels     , down_patch_size=3     , down_stride=2     , down_pad=1     , drop_rate=0.0     , drop_path_rate=0.0     , use_layer_scale=__lowerCamelCase     , layer_scale_init_value=1e-5     , )





   def      _snake_case		(       self					:			List[str]     , __lowerCamelCase					:			Union[str, Any]     , __lowerCamelCase					:			Tuple     , __lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   SwiftFormerModel(config=__lowerCamelCase			)
       model.to(__lowerCamelCase			)
       model.eval()
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase			)
       self.parent.assertEqual(result.last_hidden_state.shape     , (self.batch_size, self.embed_dims[-1], 7, 7)			)





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			int     , __lowerCamelCase					:			Any     , __lowerCamelCase					:			Optional[int]			):
       SCREAMING_SNAKE_CASE										=   self.num_labels
       SCREAMING_SNAKE_CASE										=   SwiftFormerForImageClassification(__lowerCamelCase			)
       model.to(__lowerCamelCase			)
       model.eval()
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase     , labels=__lowerCamelCase			)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)			)

       SCREAMING_SNAKE_CASE										=   SwiftFormerForImageClassification(__lowerCamelCase			)
       model.to(__lowerCamelCase			)
       model.eval()

       SCREAMING_SNAKE_CASE										=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase			)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)			)





   def      _snake_case		(       self					:			int			):
       ((SCREAMING_SNAKE_CASE)							,	(SCREAMING_SNAKE_CASE)							,	(SCREAMING_SNAKE_CASE))										=   self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE										=   {"pixel_values": pixel_values}
       return config, inputs_dict





@require_torch
class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       __snake_case					,       unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
   lowerCamelCase__				=      (
       {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
       if is_torch_available()
       else {}
   )

   lowerCamelCase__				=      False
   lowerCamelCase__				=      False
   lowerCamelCase__				=      False
   lowerCamelCase__				=      False
   lowerCamelCase__				=      False





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   SwiftFormerModelTester(self			)
       SCREAMING_SNAKE_CASE										=   ConfigTester(
           self     , config_class=__lowerCamelCase     , has_text_modality=__lowerCamelCase     , hidden_size=37     , num_attention_heads=12     , num_hidden_layers=12     , )





   def      _snake_case		(       self					:			List[Any]			):
       self.config_tester.run_common_tests()





   @unittest.skip(reason="SwiftFormer does not use inputs_embeds"			)
   def      _snake_case		(       self					:			Optional[int]			):
       pass





   def      _snake_case		(       self					:			Optional[int]			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(__lowerCamelCase     , nn.Linear			)			)





   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs_for_common()

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

           SCREAMING_SNAKE_CASE										=   ["pixel_values"]
           self.assertListEqual(arg_names[:1]     , __lowerCamelCase			)





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCamelCase			)





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase			)





   @slow
   def      _snake_case		(       self					:			Tuple			):
       for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           SCREAMING_SNAKE_CASE										=   SwiftFormerModel.from_pretrained(__lowerCamelCase			)
           self.assertIsNotNone(__lowerCamelCase			)





   @unittest.skip(reason="SwiftFormer does not output attentions"			)
   def      _snake_case		(       self					:			Union[str, Any]			):
       pass





   def      _snake_case		(       self					:			Optional[Any]			):
       def check_hidden_states_output(__lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Tuple			):
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()

           with torch.no_grad():
               SCREAMING_SNAKE_CASE										=   model(**self._prepare_for_class(__lowerCamelCase     , __lowerCamelCase			)			)

           SCREAMING_SNAKE_CASE										=   outputs.hidden_states

           SCREAMING_SNAKE_CASE										=   8
           self.assertEqual(len(__lowerCamelCase			)     , __lowerCamelCase			)  # TODO

           # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
           # with the width and height being successively divided by 2, after every 2 blocks
           for i in range(len(__lowerCamelCase			)			):
               self.assertEqual(
                   hidden_states[i].shape     , torch.Size(
                       [
                           self.model_tester.batch_size,
                           self.model_tester.embed_dims[i // 2],
                           (self.model_tester.image_size // 4) // 2 ** (i // 2),
                           (self.model_tester.image_size // 4) // 2 ** (i // 2),
                       ]			)     , )

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           SCREAMING_SNAKE_CASE										=   True
           check_hidden_states_output(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)

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

           check_hidden_states_output(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)





   def      _snake_case		(       self					:			List[Any]			):
       def _config_zero_init(__lowerCamelCase					:			List[Any]			):
           SCREAMING_SNAKE_CASE										=   copy.deepcopy(__lowerCamelCase			)
           for key in configs_no_init.__dict__.keys():
               if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                   setattr(__lowerCamelCase     , __lowerCamelCase     , 1e-10			)
               if isinstance(getattr(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)     , __lowerCamelCase			):
                   SCREAMING_SNAKE_CASE										=   _config_zero_init(getattr(__lowerCamelCase     , __lowerCamelCase			)			)
                   setattr(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
           return configs_no_init

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs_for_common()

       SCREAMING_SNAKE_CASE										=   _config_zero_init(__lowerCamelCase			)
       for model_class in self.all_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(config=__lowerCamelCase			)
           for name, param in model.named_parameters():
               if param.requires_grad:
                   self.assertIn(
                       ((param.data.mean() * 1e9) / 1e9).round().item()     , [0.0, 1.0]     , msg=f"Parameter {name} of model {model_class} seems not properly initialized"     , )





   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."			)
   def      _snake_case		(       self					:			str			):
       pass





def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
    return image





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







   '''simple docstring'''




   @cached_property
   def      _snake_case		(       self					:			List[str]			):
       return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs"			) if is_vision_available() else None





   @slow
   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"			).to(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   self.default_image_processor
       SCREAMING_SNAKE_CASE										=   prepare_img()
       SCREAMING_SNAKE_CASE										=   image_processor(images=__lowerCamelCase     , return_tensors="pt"			).to(__lowerCamelCase			)

       # forward pass
       with torch.no_grad():
           SCREAMING_SNAKE_CASE										=   model(**__lowerCamelCase			)

       # verify the logits
       SCREAMING_SNAKE_CASE										=   torch.Size((1, 1000)			)
       self.assertEqual(outputs.logits.shape     , __lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   torch.tensor([[-2.17_03e00, 2.11_07e00, -2.08_11e00]]			).to(__lowerCamelCase			)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]     , __lowerCamelCase     , atol=1e-4			)			)