
"""simple docstring"""
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


lowercase_							:    Optional[int]         =						transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)





def  _lowerCAmelCase     (  lowerCamelCase__				:				Any					)	->     Optional[Any]:
  if isinstance(lowerCamelCase__,    torch.Tensor					):
    return image
  elif isinstance(lowerCamelCase__,    PIL.Image.Image					):
    _SCREAMING_SNAKE_CASE :					Any				     =			[image]

  _SCREAMING_SNAKE_CASE :					Tuple				     =			[trans(img.convert("RGB"					)					) for img in image]
  _SCREAMING_SNAKE_CASE :					List[Any]				     =			torch.stack(lowerCamelCase__					)
  return image





class       UpperCamelCase   (				__a	):
     def __init__(  self	,  snake_case__	,  snake_case__					):




       """simple docstring"""


       super().__init__()

       # make sure scheduler can always be converted to DDIM
       _SCREAMING_SNAKE_CASE :					List[Any]				     =			DDIMScheduler.from_config(scheduler.config					)

       self.register_modules(unet=_lowercase	,  scheduler=_lowercase					)
     def 					__SCREAMING_SNAKE_CASE				(  self	,  snake_case__					):




       """simple docstring"""


       if strength < 0 or strength > 1:
         raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}'''					)
     def 					__SCREAMING_SNAKE_CASE				(  self	,  snake_case__	,  snake_case__	,  snake_case__					):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE :					Tuple				     =			min(int(num_inference_steps * strength					)	,  _lowercase					)

       _SCREAMING_SNAKE_CASE :					Union[str, Any]				     =			max(num_inference_steps - init_timestep	,  0					)
       _SCREAMING_SNAKE_CASE :					List[str]				     =			self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start
     def 					__SCREAMING_SNAKE_CASE				(  self	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__=None					):




       """simple docstring"""


       if not isinstance(_lowercase	,  (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(_lowercase					)}'''					)

       _SCREAMING_SNAKE_CASE :					Dict				     =			image.to(device=_lowercase	,  dtype=_lowercase					)

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

       _SCREAMING_SNAKE_CASE :					Optional[int]				     =			init_latents.shape
       _SCREAMING_SNAKE_CASE :					Dict				     =			randn_tensor(_lowercase	,  generator=_lowercase	,  device=_lowercase	,  dtype=_lowercase					)

       # get latents
       print("add noise to latents at timestep"	,  _lowercase					)
       _SCREAMING_SNAKE_CASE :					Any				     =			self.scheduler.add_noise(_lowercase	,  _lowercase	,  _lowercase					)
       _SCREAMING_SNAKE_CASE :					Tuple				     =			init_latents

       return latents




     @torch.no_grad()
     def __call__(  self	,  snake_case__ = None	,  snake_case__ = 0.8	,  snake_case__ = 1	,  snake_case__ = None	,  snake_case__ = 0.0	,  snake_case__ = 50	,  snake_case__ = None	,  snake_case__ = "pil"	,  snake_case__ = True	,  ):




       """simple docstring"""


       self.check_inputs(_lowercase					)

       # 2. Preprocess image
       _SCREAMING_SNAKE_CASE :					Optional[int]				     =			preprocess(_lowercase					)

       # 3. set timesteps
       self.scheduler.set_timesteps(_lowercase	,  device=self.device					)
       _SCREAMING_SNAKE_CASE :					int				     =			self.get_timesteps(_lowercase	,  _lowercase	,  self.device					)
       _SCREAMING_SNAKE_CASE :					int				     =			timesteps[:1].repeat(_lowercase					)

       # 4. Prepare latent variables
       _SCREAMING_SNAKE_CASE :					str				     =			self.prepare_latents(_lowercase	,  _lowercase	,  _lowercase	,  self.unet.dtype	,  self.device	,  _lowercase					)
       _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			latents

       # 5. Denoising loop
       for t in self.progress_bar(_lowercase					):
         # 1. predict noise model_output
         _SCREAMING_SNAKE_CASE :					str				     =			self.unet(_lowercase	,  _lowercase					).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
         _SCREAMING_SNAKE_CASE :					Tuple				     =			self.scheduler.step(
             _lowercase	,  _lowercase	,  _lowercase	,  eta=_lowercase	,  use_clipped_model_output=_lowercase	,  generator=_lowercase	,  ).prev_sample

       _SCREAMING_SNAKE_CASE :					str				     =			(image / 2 + 0.5).clamp(0	,  1					)
       _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			image.cpu().permute(0	,  2	,  3	,  1					).numpy()
       if output_type == "pil":
         _SCREAMING_SNAKE_CASE :					Optional[int]				     =			self.numpy_to_pil(_lowercase					)

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

       return ImagePipelineOutput(images=_lowercase					)




import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  from transformers.models.bert.modeling_flax_bert import (
      FlaxBertForMaskedLM,
      FlaxBertForMultipleChoice,
      FlaxBertForNextSentencePrediction,
      FlaxBertForPreTraining,
      FlaxBertForQuestionAnswering,
      FlaxBertForSequenceClassification,
      FlaxBertForTokenClassification,
      FlaxBertModel,
  )




class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   def __init__(						self       :	Tuple		,	_lowercase       :	List[str]		,	_lowercase       :	Dict=13		,	_lowercase       :	Any=7		,	_lowercase       :	List[str]=True		,	_lowercase       :	Any=True		,	_lowercase       :	Optional[int]=True		,	_lowercase       :	int=True		,	_lowercase       :	str=99		,	_lowercase       :	Union[str, Any]=32		,	_lowercase       :	Any=5		,	_lowercase       :	Dict=4		,	_lowercase       :	Tuple=37		,	_lowercase       :	Optional[Any]="gelu"		,	_lowercase       :	Optional[int]=0.1		,	_lowercase       :	Any=0.1		,	_lowercase       :	Union[str, Any]=512		,	_lowercase       :	Dict=16		,	_lowercase       :	int=2		,	_lowercase       :	Union[str, Any]=0.02		,	_lowercase       :	Dict=4		,	):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			parent
    _UpperCamelCase:  Optional[int]    						=			batch_size
    _UpperCamelCase:  Any    						=			seq_length
    _UpperCamelCase:  List[str]    						=			is_training
    _UpperCamelCase:  Union[str, Any]    						=			use_attention_mask
    _UpperCamelCase:  Union[str, Any]    						=			use_token_type_ids
    _UpperCamelCase:  str    						=			use_labels
    _UpperCamelCase:  List[str]    						=			vocab_size
    _UpperCamelCase:  Dict    						=			hidden_size
    _UpperCamelCase:  List[Any]    						=			num_hidden_layers
    _UpperCamelCase:  List[Any]    						=			num_attention_heads
    _UpperCamelCase:  Tuple    						=			intermediate_size
    _UpperCamelCase:  Optional[Any]    						=			hidden_act
    _UpperCamelCase:  Any    						=			hidden_dropout_prob
    _UpperCamelCase:  int    						=			attention_probs_dropout_prob
    _UpperCamelCase:  Optional[int]    						=			max_position_embeddings
    _UpperCamelCase:  List[Any]    						=			type_vocab_size
    _UpperCamelCase:  List[Any]    						=			type_sequence_label_size
    _UpperCamelCase:  List[Any]    						=			initializer_range
    _UpperCamelCase:  Optional[int]    						=			num_choices



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  int    						=			ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size				)

    _UpperCamelCase:  Optional[int]    						=			None
    if self.use_attention_mask:
     _UpperCamelCase:  List[str]    						=			random_attention_mask([self.batch_size, self.seq_length]				)

    _UpperCamelCase:  Optional[int]    						=			None
    if self.use_token_type_ids:
     _UpperCamelCase:  Tuple    						=			ids_tensor([self.batch_size, self.seq_length]		,	self.type_vocab_size				)

    _UpperCamelCase:  str    						=			BertConfig(
        vocab_size=self.vocab_size		,	hidden_size=self.hidden_size		,	num_hidden_layers=self.num_hidden_layers		,	num_attention_heads=self.num_attention_heads		,	intermediate_size=self.intermediate_size		,	hidden_act=self.hidden_act		,	hidden_dropout_prob=self.hidden_dropout_prob		,	attention_probs_dropout_prob=self.attention_probs_dropout_prob		,	max_position_embeddings=self.max_position_embeddings		,	type_vocab_size=self.type_vocab_size		,	is_decoder=_lowercase		,	initializer_range=self.initializer_range		,	)

    return config, input_ids, token_type_ids, attention_mask



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			self.prepare_config_and_inputs()
    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Any    						=			config_and_inputs
    _UpperCamelCase:  List[Any]    						=			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
    return config, inputs_dict



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			self.prepare_config_and_inputs()
    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  str    						=			config_and_inputs

    _UpperCamelCase:  int    						=			True
    _UpperCamelCase:  Optional[Any]    						=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
    _UpperCamelCase:  Optional[Any]    						=			ids_tensor([self.batch_size, self.seq_length]		,	vocab_size=2				)

    return (
        config,
        input_ids,
        attention_mask,
        encoder_hidden_states,
        encoder_attention_mask,
    )




@require_flax
class 			__magic_name__	(		__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Dict	      = True

   lowerCAmelCase   :     List[Any]	      = (
       (
           FlaxBertModel,
           FlaxBertForPreTraining,
           FlaxBertForMaskedLM,
           FlaxBertForMultipleChoice,
           FlaxBertForQuestionAnswering,
           FlaxBertForNextSentencePrediction,
           FlaxBertForSequenceClassification,
           FlaxBertForTokenClassification,
           FlaxBertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			FlaxBertModelTester(self				)



   @slow
   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			FlaxBertModel.from_pretrained('''bert-base-cased'''				)
    _UpperCamelCase:  int    						=			model(np.ones((1, 1)				)				)
    self.assertIsNotNone(_lowercase				)