

'''simple docstring'''






from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFResNetForImageClassification, TFResNetModel
 from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class 			lowercase_	:




  """simple docstring"""




  def __init__(      self   :     List[str]					,lowercase__   :     Tuple					,lowercase__   :     List[str]=3					,lowercase__   :     Optional[int]=3_2					,lowercase__   :     Dict=3					,lowercase__   :     List[str]=1_0					,lowercase__   :     Dict=[1_0, 2_0, 3_0, 4_0]					,lowercase__   :     Optional[Any]=[1, 1, 2, 1]					,lowercase__   :     Tuple=True					,lowercase__   :     int=True					,lowercase__   :     Tuple="relu"					,lowercase__   :     List[Any]=3					,lowercase__   :     str=None					,):
   __lowercase							  = parent
   __lowercase							  = batch_size
   __lowercase							  = image_size
   __lowercase							  = num_channels
   __lowercase							  = embeddings_size
   __lowercase							  = hidden_sizes
   __lowercase							  = depths
   __lowercase							  = is_training
   __lowercase							  = use_labels
   __lowercase							  = hidden_act
   __lowercase							  = num_labels
   __lowercase							  = scope
   __lowercase							  = len(lowercase__			)






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

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

   __lowercase							  = self.get_config()

   return config, pixel_values, labels






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   return ResNetConfig(
       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					,image_size=self.image_size					,)






  def 					SCREAMING_SNAKE_CASE      (      self   :     int					,lowercase__   :     Any					,lowercase__   :     List[str]					,lowercase__   :     Optional[Any]			):
   __lowercase							  = TFResNetModel(config=lowercase__			)
   __lowercase							  = model(lowercase__			)
   # expected last hidden states: B, C, H // 32, W // 32
   self.parent.assertEqual(
       result.last_hidden_state.shape					,(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)					,)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     Dict					,lowercase__   :     List[Any]					,lowercase__   :     Optional[Any]			):
   __lowercase							  = self.num_labels
   __lowercase							  = TFResNetForImageClassification(lowercase__			)
   __lowercase							  = model(lowercase__					,labels=lowercase__			)
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_labels)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.prepare_config_and_inputs()
   __lowercase  ,	__lowercase  ,	__lowercase							  = config_and_inputs
   __lowercase							  = {'''pixel_values''': pixel_values}
   return config, inputs_dict






@require_tf
class 			lowercase_	(lowerCamelCase__				,       lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Any							=			(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE    :  int							=			(
      {'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification}
      if is_tf_available()
      else {}
  )

  SCREAMING_SNAKE_CASE    :  int							=			False
  SCREAMING_SNAKE_CASE    :  Dict							=			False
  SCREAMING_SNAKE_CASE    :  str							=			False
  SCREAMING_SNAKE_CASE    :  Any							=			False
  SCREAMING_SNAKE_CASE    :  List[Any]							=			False






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = TFResNetModelTester(self			)
   __lowercase							  = ConfigTester(self					,config_class=lowercase__					,has_text_modality=lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   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 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   return






  @unittest.skip(reason='''ResNet does not use inputs_embeds'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   pass






  @unittest.skip(reason='''ResNet does not support input and output embeddings'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   pass






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

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

    __lowercase							  = ['''pixel_values''']
    self.assertListEqual(arg_names[:1]					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   def check_hidden_states_output(lowercase__   :     Optional[int]					,lowercase__   :     Any					,lowercase__   :     str			):
    __lowercase							  = model_class(lowercase__			)
    __lowercase							  = model(**self._prepare_for_class(lowercase__					,lowercase__			)			)

    __lowercase							  = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

    __lowercase							  = self.model_tester.num_stages
    self.assertEqual(len(lowercase__			)					,expected_num_stages + 1			)

    # ResNet'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]					,)

   __lowercase  ,	__lowercase							  = self.model_tester.prepare_config_and_inputs_for_common()
   __lowercase							  = ['''basic''', '''bottleneck''']
   for model_class in self.all_model_classes:
    for layer_type in layers_type:
     __lowercase							  = layer_type
     __lowercase							  = True
     check_hidden_states_output(lowercase__					,lowercase__					,lowercase__			)

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

     check_hidden_states_output(lowercase__					,lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*lowercase__			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowercase							  = TFResNetModel.from_pretrained(lowercase__			)
    self.assertIsNotNone(lowercase__			)


def 		_A     (    ):






 """simple docstring"""



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






@require_tf
@require_vision
class 			lowercase_	(unittest.TestCase      ):




  """simple docstring"""




  @cached_property
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   return (
       AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
       if is_vision_available()
       else None
   )






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   __lowercase							  = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)

   __lowercase							  = self.default_image_processor
   __lowercase							  = prepare_img()
   __lowercase							  = image_processor(images=lowercase__					,return_tensors='''tf'''			)

   # forward pass
   __lowercase							  = model(**lowercase__			)

   # verify the logits
   __lowercase							  = tf.TensorShape((1, 1_0_0_0)			)
   self.assertEqual(outputs.logits.shape					,lowercase__			)

   __lowercase							  = tf.constant([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]			)

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








from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


snake_case__					:							int													=     transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)



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







      '''simple docstring'''






      if isinstance(lowerCamelCase					,				torch.Tensor ):
            return image
      elif isinstance(lowerCamelCase					,				PIL.Image.Image ):
            __A          =				[image]

      __A          =				[trans(img.convert('''RGB''' ) ) for img in image]
      __A          =				torch.stack(lowerCamelCase )
      return image







class A_					(			_lowerCamelCase   ):




   def __init__(self	:List[str]    ,   _UpperCamelCase	:List[Any]    ,   _UpperCamelCase	:List[str] )->					List[Any]:
         super().__init__()

         # make sure scheduler can always be converted to DDIM
         __A          =				DDIMScheduler.from_config(scheduler.config )

         self.register_modules(unet=_UpperCamelCase    ,   scheduler=_UpperCamelCase )




   def _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:Optional[Any] )->					Union[str, Any]:
         if strength < 0 or strength > 1:
               raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}""" )




   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:List[str] )->					Union[str, Any]:
         # get the original timestep using init_timestep
         __A          =				min(int(num_inference_steps * strength )    ,   _UpperCamelCase )

         __A          =				max(num_inference_steps - init_timestep    ,   0 )
         __A          =				self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start




   def _lowerCAmelCase      (self	:str    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:Dict=None )->					List[str]:
         if not isinstance(_UpperCamelCase    ,   (torch.Tensor, PIL.Image.Image, list) ):
               raise ValueError(
                   f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_UpperCamelCase )}""" )

         __A          =				image.to(device=_UpperCamelCase    ,   dtype=_UpperCamelCase )

         if isinstance(_UpperCamelCase    ,   _UpperCamelCase ) and len(_UpperCamelCase ) != batch_size:
               raise ValueError(
                   f"""You have passed a list of generators of length {len(_UpperCamelCase )}, but requested an effective batch"""
                   f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )

         __A          =				init_latents.shape
         __A          =				randn_tensor(_UpperCamelCase    ,   generator=_UpperCamelCase    ,   device=_UpperCamelCase    ,   dtype=_UpperCamelCase )

         # get latents
         print('''add noise to latents at timestep'''    ,   _UpperCamelCase )
         __A          =				self.scheduler.add_noise(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )
         __A          =				init_latents

         return latents






   @torch.no_grad()
   def __call__(self	:List[str]    ,   _UpperCamelCase	:Union[torch.FloatTensor, PIL.Image.Image] = None    ,   _UpperCamelCase	:float = 0.8    ,   _UpperCamelCase	:int = 1    ,   _UpperCamelCase	:Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,   _UpperCamelCase	:float = 0.0    ,   _UpperCamelCase	:int = 50    ,   _UpperCamelCase	:Optional[bool] = None    ,   _UpperCamelCase	:Optional[str] = "pil"    ,   _UpperCamelCase	:bool = True    ,   )->					Union[ImagePipelineOutput, Tuple]:
         self.check_inputs(_UpperCamelCase )

         # 2. Preprocess image
         __A          =				preprocess(_UpperCamelCase )

         # 3. set timesteps
         self.scheduler.set_timesteps(_UpperCamelCase    ,   device=self.device )
         __A   ,						__A          =				self.get_timesteps(_UpperCamelCase    ,   _UpperCamelCase    ,   self.device )
         __A          =				timesteps[:1].repeat(_UpperCamelCase )

         # 4. Prepare latent variables
         __A          =				self.prepare_latents(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   self.unet.dtype    ,   self.device    ,   _UpperCamelCase )
         __A          =				latents

         # 5. Denoising loop
         for t in self.progress_bar(_UpperCamelCase ):
               # 1. predict noise model_output
               __A          =				self.unet(_UpperCamelCase    ,   _UpperCamelCase ).sample

               # 2. predict previous mean of image x_t-1 and add variance depending on eta
               # eta corresponds to η in paper and should be between [0, 1]
               # do x_t -> x_t-1
               __A          =				self.scheduler.step(
                   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   eta=_UpperCamelCase    ,   use_clipped_model_output=_UpperCamelCase    ,   generator=_UpperCamelCase    ,   ).prev_sample

         __A          =				(image / 2 + 0.5).clamp(0    ,   1 )
         __A          =				image.cpu().permute(0    ,   2    ,   3    ,   1 ).numpy()
         if output_type == "pil":
               __A          =				self.numpy_to_pil(_UpperCamelCase )

         if not return_dict:
               return (image, latent_timestep.item())

         return ImagePipelineOutput(images=_UpperCamelCase )





