

import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


__A			:					List[str]						=			logging.get_logger(__name__)  # pylint: disable=invalid-name





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def __init__(       self					:			Tuple     , __lowerCamelCase					:			WhisperForConditionalGeneration     , __lowerCamelCase					:			WhisperProcessor     , __lowerCamelCase					:			AutoencoderKL     , __lowerCamelCase					:			CLIPTextModel     , __lowerCamelCase					:			CLIPTokenizer     , __lowerCamelCase					:			UNetaDConditionModel     , __lowerCamelCase					:			Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]     , __lowerCamelCase					:			StableDiffusionSafetyChecker     , __lowerCamelCase					:			CLIPImageProcessor     , ):
       super().__init__()

       if safety_checker is None:
           logger.warning(
               f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
               " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
               " results in services or applications open to the public. Both the diffusers team and Hugging Face"
               " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
               " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
               " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."			)

       self.register_modules(
           speech_model=__lowerCamelCase     , speech_processor=__lowerCamelCase     , vae=__lowerCamelCase     , text_encoder=__lowerCamelCase     , tokenizer=__lowerCamelCase     , unet=__lowerCamelCase     , scheduler=__lowerCamelCase     , feature_extractor=__lowerCamelCase     , )





   def      _snake_case		(       self					:			Tuple     , __lowerCamelCase					:			Optional[Union[str, int]] = "auto"			):
       if slice_size == "auto":
           SCREAMING_SNAKE_CASE										=   self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(__lowerCamelCase			)





   def      _snake_case		(       self					:			Any			):
       self.enable_attention_slicing(__lowerCamelCase			)





   @torch.no_grad()
   def __call__(       self					:			Optional[int]     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			List[str]=16000     , __lowerCamelCase					:			int = 512     , __lowerCamelCase					:			int = 512     , __lowerCamelCase					:			int = 50     , __lowerCamelCase					:			float = 7.5     , __lowerCamelCase					:			Optional[Union[str, List[str]]] = None     , __lowerCamelCase					:			Optional[int] = 1     , __lowerCamelCase					:			float = 0.0     , __lowerCamelCase					:			Optional[torch.Generator] = None     , __lowerCamelCase					:			Optional[torch.FloatTensor] = None     , __lowerCamelCase					:			Optional[str] = "pil"     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None     , __lowerCamelCase					:			int = 1     , **__lowerCamelCase					:			Dict     , ):
       SCREAMING_SNAKE_CASE										=   self.speech_processor.feature_extractor(
           __lowerCamelCase     , return_tensors="pt"     , sampling_rate=__lowerCamelCase			).input_features.to(self.device			)
       SCREAMING_SNAKE_CASE										=   self.speech_model.generate(__lowerCamelCase     , max_length=480000			)

       SCREAMING_SNAKE_CASE										=   self.speech_processor.tokenizer.batch_decode(__lowerCamelCase     , skip_special_tokens=__lowerCamelCase     , normalize=__lowerCamelCase			)[
           0
       ]

       if isinstance(__lowerCamelCase     , __lowerCamelCase			):
           SCREAMING_SNAKE_CASE										=   1
       elif isinstance(__lowerCamelCase     , __lowerCamelCase			):
           SCREAMING_SNAKE_CASE										=   len(__lowerCamelCase			)
       else:
           raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(__lowerCamelCase			)}"			)

       if height % 8 != 0 or width % 8 != 0:
           raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}."			)

       if (callback_steps is None) or (
           callback_steps is not None and (not isinstance(__lowerCamelCase     , __lowerCamelCase			) or callback_steps <= 0)
       ):
           raise ValueError(
               f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
               f" {type(__lowerCamelCase			)}."			)

       # get prompt text embeddings
       SCREAMING_SNAKE_CASE										=   self.tokenizer(
           __lowerCamelCase     , padding="max_length"     , max_length=self.tokenizer.model_max_length     , return_tensors="pt"     , )
       SCREAMING_SNAKE_CASE										=   text_inputs.input_ids

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
           SCREAMING_SNAKE_CASE										=   self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]			)
           logger.warning(
               "The following part of your input was truncated because CLIP can only handle sequences up to"
               f" {self.tokenizer.model_max_length} tokens: {removed_text}"			)
           SCREAMING_SNAKE_CASE										=   text_input_ids[:, : self.tokenizer.model_max_length]
       SCREAMING_SNAKE_CASE										=   self.text_encoder(text_input_ids.to(self.device			)			)[0]

       # duplicate text embeddings for each generation per prompt, using mps friendly method
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   text_embeddings.shape
       SCREAMING_SNAKE_CASE										=   text_embeddings.repeat(1     , __lowerCamelCase     , 1			)
       SCREAMING_SNAKE_CASE										=   text_embeddings.view(bs_embed * num_images_per_prompt     , __lowerCamelCase     , -1			)

       # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
       # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
       # corresponds to doing no classifier free guidance.
       SCREAMING_SNAKE_CASE										=   guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
           SCREAMING_SNAKE_CASE										=   42
           if negative_prompt is None:
               SCREAMING_SNAKE_CASE										=   [""] * batch_size
           elif type(__lowerCamelCase			) is not type(__lowerCamelCase			):
               raise TypeError(
                   f"`negative_prompt` should be the same type to `prompt`, but got {type(__lowerCamelCase			)} !="
                   f" {type(__lowerCamelCase			)}."			)
           elif isinstance(__lowerCamelCase     , __lowerCamelCase			):
               SCREAMING_SNAKE_CASE										=   [negative_prompt]
           elif batch_size != len(__lowerCamelCase			):
               raise ValueError(
                   f"`negative_prompt`: {negative_prompt} has batch size {len(__lowerCamelCase			)}, but `prompt`:"
                   f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                   " the batch size of `prompt`."			)
           else:
               SCREAMING_SNAKE_CASE										=   negative_prompt

           SCREAMING_SNAKE_CASE										=   text_input_ids.shape[-1]
           SCREAMING_SNAKE_CASE										=   self.tokenizer(
               __lowerCamelCase     , padding="max_length"     , max_length=__lowerCamelCase     , truncation=__lowerCamelCase     , return_tensors="pt"     , )
           SCREAMING_SNAKE_CASE										=   self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]

           # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
           SCREAMING_SNAKE_CASE										=   uncond_embeddings.shape[1]
           SCREAMING_SNAKE_CASE										=   uncond_embeddings.repeat(1     , __lowerCamelCase     , 1			)
           SCREAMING_SNAKE_CASE										=   uncond_embeddings.view(batch_size * num_images_per_prompt     , __lowerCamelCase     , -1			)

           # For classifier free guidance, we need to do two forward passes.
           # Here we concatenate the unconditional and text embeddings into a single batch
           # to avoid doing two forward passes
           SCREAMING_SNAKE_CASE										=   torch.cat([uncond_embeddings, text_embeddings]			)

       # get the initial random noise unless the user supplied it

       # Unlike in other pipelines, latents need to be generated in the target device
       # for 1-to-1 results reproducibility with the CompVis implementation.
       # However this currently doesn't work in `mps`.
       SCREAMING_SNAKE_CASE										=   (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
       SCREAMING_SNAKE_CASE										=   text_embeddings.dtype
       if latents is None:
           if self.device.type == "mps":
               # randn does not exist on mps
               SCREAMING_SNAKE_CASE										=   torch.randn(__lowerCamelCase     , generator=__lowerCamelCase     , device="cpu"     , dtype=__lowerCamelCase			).to(
                   self.device			)
           else:
               SCREAMING_SNAKE_CASE										=   torch.randn(__lowerCamelCase     , generator=__lowerCamelCase     , device=self.device     , dtype=__lowerCamelCase			)
       else:
           if latents.shape != latents_shape:
               raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"			)
           SCREAMING_SNAKE_CASE										=   latents.to(self.device			)

       # set timesteps
       self.scheduler.set_timesteps(__lowerCamelCase			)

       # Some schedulers like PNDM have timesteps as arrays
       # It's more optimized to move all timesteps to correct device beforehand
       SCREAMING_SNAKE_CASE										=   self.scheduler.timesteps.to(self.device			)

       # scale the initial noise by the standard deviation required by the scheduler
       SCREAMING_SNAKE_CASE										=   latents * self.scheduler.init_noise_sigma

       # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
       # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
       # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
       # and should be between [0, 1]
       SCREAMING_SNAKE_CASE										=   "eta" in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
       SCREAMING_SNAKE_CASE										=   {}
       if accepts_eta:
           SCREAMING_SNAKE_CASE										=   eta

       for i, t in enumerate(self.progress_bar(__lowerCamelCase			)			):
           # expand the latents if we are doing classifier free guidance
           SCREAMING_SNAKE_CASE										=   torch.cat([latents] * 2			) if do_classifier_free_guidance else latents
           SCREAMING_SNAKE_CASE										=   self.scheduler.scale_model_input(__lowerCamelCase     , __lowerCamelCase			)

           # predict the noise residual
           SCREAMING_SNAKE_CASE										=   self.unet(__lowerCamelCase     , __lowerCamelCase     , encoder_hidden_states=__lowerCamelCase			).sample

           # perform guidance
           if do_classifier_free_guidance:
               SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   noise_pred.chunk(2			)
               SCREAMING_SNAKE_CASE										=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

           # compute the previous noisy sample x_t -> x_t-1
           SCREAMING_SNAKE_CASE										=   self.scheduler.step(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , **__lowerCamelCase			).prev_sample

           # call the callback, if provided
           if callback is not None and i % callback_steps == 0:
               callback(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   1 / 0.18_215 * latents
       SCREAMING_SNAKE_CASE										=   self.vae.decode(__lowerCamelCase			).sample

       SCREAMING_SNAKE_CASE										=   (image / 2 + 0.5).clamp(0     , 1			)

       # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
       SCREAMING_SNAKE_CASE										=   image.cpu().permute(0     , 2     , 3     , 1			).float().numpy()

       if output_type == "pil":
           SCREAMING_SNAKE_CASE										=   self.numpy_to_pil(__lowerCamelCase			)

       if not return_dict:
           return image

       return StableDiffusionPipelineOutput(images=__lowerCamelCase     , nsfw_content_detected=__lowerCamelCase			)


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			)			)