



"""simple docstring"""







import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class   UpperCamelCase	(     SCREAMING_SNAKE_CASE__					,   unittest.TestCase       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Any						       =  KandinskyVaaPriorEmbaEmbPipeline
	SCREAMING_SNAKE_CASE_							:       int						       =  ['''prompt''', '''image''']
	SCREAMING_SNAKE_CASE_							:       Optional[int]						       =  ['''prompt''', '''image''']
	SCREAMING_SNAKE_CASE_							:       Optional[Any]						       =  [
	    '''num_images_per_prompt''',
	    '''strength''',
	    '''generator''',
	    '''num_inference_steps''',
	    '''latents''',
	    '''negative_prompt''',
	    '''guidance_scale''',
	    '''output_type''',
	    '''return_dict''',
	]
	SCREAMING_SNAKE_CASE_							:       Dict						       =  False






	@property
	def    lowerCamelCase__					(     self   ):
			return 32






	@property
	def    lowerCamelCase__					(     self   ):
			return 32






	@property
	def    lowerCamelCase__					(     self   ):
			return self.time_input_dim






	@property
	def    lowerCamelCase__					(     self   ):
			return self.time_input_dim * 4






	@property
	def    lowerCamelCase__					(     self   ):
			return 1_00






	@property
	def    lowerCamelCase__					(     self   ):
			_lowercase				: int     =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )
			return tokenizer






	@property
	def    lowerCamelCase__					(     self   ):
			torch.manual_seed(0   )
			_lowercase				: Optional[Any]     =   CLIPTextConfig(
			    bos_token_id=0				,eos_token_id=2				,hidden_size=self.text_embedder_hidden_size				,projection_dim=self.text_embedder_hidden_size				,intermediate_size=37				,layer_norm_eps=1E-05				,num_attention_heads=4				,num_hidden_layers=5				,pad_token_id=1				,vocab_size=10_00				,)
			return CLIPTextModelWithProjection(lowerCAmelCase_   )






	@property
	def    lowerCamelCase__					(     self   ):
			torch.manual_seed(0   )

			_lowercase				: int     =   {
			    '''num_attention_heads''': 2,
			    '''attention_head_dim''': 12,
			    '''embedding_dim''': self.text_embedder_hidden_size,
			    '''num_layers''': 1,
			}

			_lowercase				: List[str]     =   PriorTransformer(**lowerCAmelCase_   )
			# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
			_lowercase				: Optional[int]     =   nn.Parameter(torch.ones(model.clip_std.shape   )   )
			return model






	@property
	def    lowerCamelCase__					(     self   ):
			torch.manual_seed(0   )
			_lowercase				: List[str]     =   CLIPVisionConfig(
			    hidden_size=self.text_embedder_hidden_size				,image_size=2_24				,projection_dim=self.text_embedder_hidden_size				,intermediate_size=37				,num_attention_heads=4				,num_channels=3				,num_hidden_layers=5				,patch_size=14				,)

			_lowercase				: int     =   CLIPVisionModelWithProjection(lowerCAmelCase_   )
			return model






	@property
	def    lowerCamelCase__					(     self   ):
			_lowercase				: int     =   CLIPImageProcessor(
			    crop_size=2_24				,do_center_crop=lowerCAmelCase_				,do_normalize=lowerCAmelCase_				,do_resize=lowerCAmelCase_				,image_mean=[0.48145466, 0.4578275, 0.40821073]				,image_std=[0.26862954, 0.26130258, 0.27577711]				,resample=3				,size=2_24				,)

			return image_processor






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Tuple     =   self.dummy_prior
			_lowercase				: str     =   self.dummy_image_encoder
			_lowercase				: Optional[Any]     =   self.dummy_text_encoder
			_lowercase				: Optional[int]     =   self.dummy_tokenizer
			_lowercase				: List[str]     =   self.dummy_image_processor

			_lowercase				: List[str]     =   UnCLIPScheduler(
			    variance_type="""fixed_small_log"""				,prediction_type="""sample"""				,num_train_timesteps=10_00				,clip_sample=lowerCAmelCase_				,clip_sample_range=10.0				,)

			_lowercase				: Optional[Any]     =   {
			    '''prior''': prior,
			    '''image_encoder''': image_encoder,
			    '''text_encoder''': text_encoder,
			    '''tokenizer''': tokenizer,
			    '''scheduler''': scheduler,
			    '''image_processor''': image_processor,
			}

			return components






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=0   ):
			if str(lowerCAmelCase_   ).startswith("""mps"""   ):
					_lowercase				: Optional[Any]     =   torch.manual_seed(lowerCAmelCase_   )
			else:
					_lowercase				: Union[str, Any]     =   torch.Generator(device=lowerCAmelCase_   ).manual_seed(lowerCAmelCase_   )

			_lowercase				: Tuple     =   floats_tensor((1, 3, 64, 64)				,rng=random.Random(lowerCAmelCase_   )   ).to(lowerCAmelCase_   )
			_lowercase				: Optional[Any]     =   image.cpu().permute(0				,2				,3				,1   )[0]
			_lowercase				: Optional[int]     =   Image.fromarray(np.uinta(lowerCAmelCase_   )   ).convert("""RGB"""   ).resize((2_56, 2_56)   )

			_lowercase				: Union[str, Any]     =   {
			    '''prompt''': '''horse''',
			    '''image''': init_image,
			    '''strength''': 0.5,
			    '''generator''': generator,
			    '''guidance_scale''': 4.0,
			    '''num_inference_steps''': 2,
			    '''output_type''': '''np''',
			}
			return inputs






	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   '''cpu'''

			_lowercase				: Optional[Any]     =   self.get_dummy_components()

			_lowercase				: str     =   self.pipeline_class(**lowerCAmelCase_   )
			_lowercase				: Dict     =   pipe.to(lowerCAmelCase_   )

			pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

			_lowercase				: Tuple     =   pipe(**self.get_dummy_inputs(lowerCAmelCase_   )   )
			_lowercase				: Tuple     =   output.image_embeds

			_lowercase				: List[Any]     =   pipe(
			    **self.get_dummy_inputs(lowerCAmelCase_   )				,return_dict=lowerCAmelCase_				,)[0]

			_lowercase				: Any     =   image[0, -10:]
			_lowercase				: int     =   image_from_tuple[0, -10:]

			assert image.shape == (1, 32)

			_lowercase				: Union[str, Any]     =   np.array(
			    [
			        0.1071284,
			        1.3330271,
			        0.61260223,
			        -0.6691065,
			        -0.3846852,
			        -1.0303661,
			        0.22716111,
			        0.03348901,
			        0.30040675,
			        -0.24805029,
			    ]   )

			assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2






	@skip_mps
	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   torch_device == '''cpu'''
			_lowercase				: str     =   True
			_lowercase				: List[Any]     =   False

			self._test_inference_batch_single_identical(
			    test_max_difference=lowerCAmelCase_				,relax_max_difference=lowerCAmelCase_				,test_mean_pixel_difference=lowerCAmelCase_				,)






	@skip_mps
	def    lowerCamelCase__					(     self   ):
			_lowercase				: Union[str, Any]     =   torch_device == '''cpu'''
			_lowercase				: Tuple     =   False

			self._test_attention_slicing_forward_pass(
			    test_max_difference=lowerCAmelCase_				,test_mean_pixel_difference=lowerCAmelCase_				,)





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__						:	List[str]	     = logging.get_logger(__name__)

UpperCAmelCase__						:	str	     = {
    'flaubert/flaubert_small_cased': 'https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json',
    'flaubert/flaubert_base_uncased': 'https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json',
    'flaubert/flaubert_base_cased': 'https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json',
    'flaubert/flaubert_large_cased': 'https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json',
}






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Tuple												=       '''flaubert'''
   __UpperCamelCase						:       str												=       {
       '''hidden_size''': '''emb_dim''',
       '''num_attention_heads''': '''n_heads''',
       '''num_hidden_layers''': '''n_layers''',
       '''n_words''': '''vocab_size''',  # For backward compatibility
   }


   def __init__(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				Union[str, Any]=False     ,     lowerCAmelCase_	:				List[str]=0.0     ,     lowerCAmelCase_	:				Dict=3_0_1_4_5     ,     lowerCAmelCase_	:				Tuple=2_0_4_8     ,     lowerCAmelCase_	:				List[Any]=1_2     ,     lowerCAmelCase_	:				str=1_6     ,     lowerCAmelCase_	:				Union[str, Any]=0.1     ,     lowerCAmelCase_	:				List[str]=0.1     ,     lowerCAmelCase_	:				str=True     ,     lowerCAmelCase_	:				Optional[int]=False     ,     lowerCAmelCase_	:				Any=False     ,     lowerCAmelCase_	:				int=False     ,     lowerCAmelCase_	:				int=1     ,     lowerCAmelCase_	:				List[str]=True     ,     lowerCAmelCase_	:				Dict=5_1_2     ,     lowerCAmelCase_	:				Any=2_0_4_8**-0.5     ,     lowerCAmelCase_	:				List[str]=1e-12     ,     lowerCAmelCase_	:				Any=0.02     ,     lowerCAmelCase_	:				Optional[Any]=0     ,     lowerCAmelCase_	:				Union[str, Any]=1     ,     lowerCAmelCase_	:				List[Any]=2     ,     lowerCAmelCase_	:				Dict=3     ,     lowerCAmelCase_	:				Optional[Any]=5     ,     lowerCAmelCase_	:				Any=True     ,     lowerCAmelCase_	:				Union[str, Any]="first"     ,     lowerCAmelCase_	:				List[Any]=True     ,     lowerCAmelCase_	:				Tuple=None     ,     lowerCAmelCase_	:				Optional[int]=True     ,     lowerCAmelCase_	:				int=0.1     ,     lowerCAmelCase_	:				Optional[int]=5     ,     lowerCAmelCase_	:				Union[str, Any]=5     ,     lowerCAmelCase_	:				List[Any]=0     ,     lowerCAmelCase_	:				Dict=0     ,     lowerCAmelCase_	:				Union[str, Any]=2     ,     lowerCAmelCase_	:				Optional[int]=0     ,     **lowerCAmelCase_	:				Any     ,     ):

      """simple docstring"""
      _A:    List[str]						     =			pre_norm
      _A:    List[Any]						     =			layerdrop
      _A:    Dict						     =			vocab_size
      _A:    List[Any]						     =			emb_dim
      _A:    Optional[Any]						     =			n_layers
      _A:    Union[str, Any]						     =			n_heads
      _A:    Optional[Any]						     =			dropout
      _A:    Optional[Any]						     =			attention_dropout
      _A:    Dict						     =			gelu_activation
      _A:    Tuple						     =			sinusoidal_embeddings
      _A:    Tuple						     =			causal
      _A:    List[Any]						     =			asm
      _A:    Dict						     =			n_langs
      _A:    Optional[int]						     =			use_lang_emb
      _A:    Any						     =			layer_norm_eps
      _A:    str						     =			bos_index
      _A:    Optional[int]						     =			eos_index
      _A:    int						     =			pad_index
      _A:    Tuple						     =			unk_index
      _A:    int						     =			mask_index
      _A:    int						     =			is_encoder
      _A:    Union[str, Any]						     =			max_position_embeddings
      _A:    List[str]						     =			embed_init_std
      _A:    List[str]						     =			init_std
      _A:    Tuple						     =			summary_type
      _A:    List[str]						     =			summary_use_proj
      _A:    Union[str, Any]						     =			summary_activation
      _A:    Union[str, Any]						     =			summary_proj_to_labels
      _A:    int						     =			summary_first_dropout
      _A:    Optional[Any]						     =			start_n_top
      _A:    Optional[Any]						     =			end_n_top
      _A:    Dict						     =			mask_token_id
      _A:    str						     =			lang_id

      if "n_words" in kwargs:
         _A:    int						     =			kwargs['''n_words''']

      super().__init__(pad_token_id=lowerCAmelCase_     ,     bos_token_id=lowerCAmelCase_     ,     **lowerCAmelCase_	)






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''


   @property
   def 					__magic_name__							(   self	:				Tuple	):

      """simple docstring"""
      if self.task == "multiple-choice":
         _A:    int						     =			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
         _A:    Any						     =			{0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
          ]	)



