


import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
     import torch_xla.debug.metrics as met







class 						_snake_case    ( _snake_case				):




 def __init__(						self   ,    *_lowerCamelCase   ,    _lowerCamelCase=None   ,    _lowerCamelCase=None   ,    **_lowerCamelCase       ):
       super().__init__(*lowerCamelCase_   ,    **lowerCamelCase_       )
       a		:Tuple									=			eval_examples
       a		:Optional[Any]									=			post_process_function




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase = None   ,    _lowerCamelCase=None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = "eval"   ,    **_lowerCamelCase   ,    ):
       a		:Optional[Any]									=			gen_kwargs.copy()
       a		:str									=			(
           gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length'''       ) is not None else self.args.generation_max_length
       )
       a		:Union[str, Any]									=			(
           gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''       ) is not None else self.args.generation_num_beams
       )
       a		:Union[str, Any]									=			gen_kwargs

       a		:int									=			self.eval_dataset if eval_dataset is None else eval_dataset
       a		:str									=			self.get_eval_dataloader(lowerCamelCase_       )
       a		:Optional[Any]									=			self.eval_examples if eval_examples is None else eval_examples

       # Temporarily disable metric computation, we will do it in the loop here.
       a		:Optional[int]									=			self.compute_metrics
       a		:Tuple									=			None
       a		:Optional[int]									=			time.time()
       a		:int									=			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
             a		:List[str]									=			eval_loop(
                 lowerCamelCase_   ,    description='''Evaluation'''   ,    prediction_loss_only=True if compute_metrics is None else None   ,    ignore_keys=lowerCamelCase_   ,    metric_key_prefix=lowerCamelCase_   ,    )
       finally:
             a		:Optional[Any]									=			compute_metrics
       a		:List[str]									=			self.args.eval_batch_size * self.args.world_size
       if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
             start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
       output.metrics.update(
           speed_metrics(
               lowerCamelCase_   ,    lowerCamelCase_   ,    num_samples=output.num_samples   ,    num_steps=math.ceil(output.num_samples / total_batch_size       )   ,    )       )

       if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
             # Only the main node write the results by default
             a		:Optional[Any]									=			self.post_process_function(lowerCamelCase_   ,    lowerCamelCase_   ,    lowerCamelCase_       )
             a		:Union[str, Any]									=			self.compute_metrics(lowerCamelCase_       )

             # Prefix all keys with metric_key_prefix + '_'
             for key in list(metrics.keys()       ):
                   if not key.startswith(F'''{metric_key_prefix}_'''       ):
                         a		:List[str]									=			metrics.pop(lowerCamelCase_       )

             metrics.update(output.metrics       )
       else:
             a		:List[Any]									=			output.metrics

       if self.args.should_log:
             # Only the main node log the results by default
             self.log(lowerCamelCase_       )

       if self.args.tpu_metrics_debug or self.args.debug:
             # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
             xm.master_print(met.metrics_report()       )

       a		:Union[str, Any]									=			self.callback_handler.on_evaluate(self.args   ,    self.state   ,    self.control   ,    lowerCamelCase_       )
       return metrics




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase=None   ,    _lowerCamelCase = "test"   ,    **_lowerCamelCase       ):
       a		:Tuple									=			gen_kwargs.copy()

       a		:Union[str, Any]									=			self.get_test_dataloader(lowerCamelCase_       )

       # Temporarily disable metric computation, we will do it in the loop here.
       a		:List[Any]									=			self.compute_metrics
       a		:int									=			None
       a		:List[str]									=			time.time()
       a		:Optional[Any]									=			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
             a		:str									=			eval_loop(
                 lowerCamelCase_   ,    description='''Prediction'''   ,    prediction_loss_only=True if compute_metrics is None else None   ,    ignore_keys=lowerCamelCase_   ,    metric_key_prefix=lowerCamelCase_   ,    )
       finally:
             a		:str									=			compute_metrics

       a		:List[str]									=			self.args.eval_batch_size * self.args.world_size
       if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
             start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
       output.metrics.update(
           speed_metrics(
               lowerCamelCase_   ,    lowerCamelCase_   ,    num_samples=output.num_samples   ,    num_steps=math.ceil(output.num_samples / total_batch_size       )   ,    )       )
       if self.post_process_function is None or self.compute_metrics is None:
             return output

       a		:Optional[int]									=			self.post_process_function(lowerCamelCase_   ,    lowerCamelCase_   ,    lowerCamelCase_   ,    '''predict'''       )
       a		:str									=			self.compute_metrics(lowerCamelCase_       )

       # Prefix all keys with metric_key_prefix + '_'
       for key in list(metrics.keys()       ):
             if not key.startswith(F'''{metric_key_prefix}_'''       ):
                   a		:Any									=			metrics.pop(lowerCamelCase_       )
       metrics.update(output.metrics       )
       return PredictionOutput(predictions=predictions.predictions   ,    label_ids=predictions.label_ids   ,    metrics=lowerCamelCase_       )






'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 					_lowercase						(   lowerCAmelCase		,unittest.TestCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Any   				= KandinskyInpaintPipeline
      UpperCAmelCase_   :   str   				= ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
      UpperCAmelCase_   :   Union[str, Any]   				= [
          '''prompt''',
          '''negative_prompt''',
          '''image_embeds''',
          '''negative_image_embeds''',
          '''image''',
          '''mask_image''',
      ]
      UpperCAmelCase_   :   Union[str, Any]   				= [
          '''generator''',
          '''height''',
          '''width''',
          '''latents''',
          '''guidance_scale''',
          '''negative_prompt''',
          '''num_inference_steps''',
          '''return_dict''',
          '''guidance_scale''',
          '''num_images_per_prompt''',
          '''output_type''',
          '''return_dict''',
      ]
      UpperCAmelCase_   :   Optional[int]   				= False




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         return 32




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Optional[Any]:



         '''simple docstring'''



         return 32




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         return self.time_input_dim




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Optional[int]:



         '''simple docstring'''



         return self.time_input_dim * 4




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         return 100




      @property
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''				)
         return tokenizer




      @property
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         torch.manual_seed(0				)
         UpperCAmelCase__				:					Any 					=	MCLIPConfig(
             numDims=self.cross_attention_dim       ,transformerDimensions=self.text_embedder_hidden_size       ,hidden_size=self.text_embedder_hidden_size       ,intermediate_size=37       ,num_attention_heads=4       ,num_hidden_layers=5       ,vocab_size=1005       ,)

         UpperCAmelCase__				:					Any 					=	MultilingualCLIP(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	text_encoder.eval()

         return text_encoder




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         torch.manual_seed(0				)

         UpperCAmelCase__				:					Tuple 					=	{
             '''in_channels''': 9,
             # Out channels is double in channels because predicts mean and variance
             '''out_channels''': 8,
             '''addition_embed_type''': '''text_image''',
             '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
             '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
             '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
             '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
             '''layers_per_block''': 1,
             '''encoder_hid_dim''': self.text_embedder_hidden_size,
             '''encoder_hid_dim_type''': '''text_image_proj''',
             '''cross_attention_dim''': self.cross_attention_dim,
             '''attention_head_dim''': 4,
             '''resnet_time_scale_shift''': '''scale_shift''',
             '''class_embed_type''': None,
         }

         UpperCAmelCase__				:					List[str] 					=	UNetaDConditionModel(**lowerCamelCase_				)
         return model




      @property
      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         return {
             "block_out_channels": [32, 64],
             "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 12,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         torch.manual_seed(0				)
         UpperCAmelCase__				:					Union[str, Any] 					=	VQModel(**self.dummy_movq_kwargs				)
         return model




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.dummy_text_encoder
         UpperCAmelCase__				:					Optional[int] 					=	self.dummy_tokenizer
         UpperCAmelCase__				:					Optional[Any] 					=	self.dummy_unet
         UpperCAmelCase__				:					Any 					=	self.dummy_movq

         UpperCAmelCase__				:					Tuple 					=	DDIMScheduler(
             num_train_timesteps=1000       ,beta_schedule='''linear'''       ,beta_start=0.0_0085       ,beta_end=0.012       ,clip_sample=lowerCamelCase_       ,set_alpha_to_one=lowerCamelCase_       ,steps_offset=1       ,prediction_type='''epsilon'''       ,thresholding=lowerCamelCase_       ,)

         UpperCAmelCase__				:					Dict 					=	{
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }

         return components




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=0				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	floats_tensor((1, self.cross_attention_dim)       ,rng=random.Random(lowerCamelCase_				)				).to(lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	floats_tensor((1, self.cross_attention_dim)       ,rng=random.Random(seed + 1				)				).to(lowerCamelCase_				)
         # create init_image
         UpperCAmelCase__				:					str 					=	floats_tensor((1, 3, 64, 64)       ,rng=random.Random(lowerCamelCase_				)				).to(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	image.cpu().permute(0       ,2       ,3       ,1				)[0]
         UpperCAmelCase__				:					Union[str, Any] 					=	Image.fromarray(np.uinta(lowerCamelCase_				)				).convert('''RGB'''				).resize((256, 256)				)
         # create mask
         UpperCAmelCase__				:					List[str] 					=	np.ones((64, 64)       ,dtype=np.floataa				)
         UpperCAmelCase__				:					Optional[Any] 					=	0

         if str(lowerCamelCase_				).startswith('''mps'''				):
            UpperCAmelCase__				:					Any 					=	torch.manual_seed(lowerCamelCase_				)
         else:
            UpperCAmelCase__				:					Dict 					=	torch.Generator(device=lowerCamelCase_				).manual_seed(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	{
             '''prompt''': '''horse''',
             '''image''': init_image,
             '''mask_image''': mask,
             '''image_embeds''': image_embeds,
             '''negative_image_embeds''': negative_image_embeds,
             '''generator''': generator,
             '''height''': 64,
             '''width''': 64,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 4.0,
             '''output_type''': '''np''',
         }
         return inputs




      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	'''cpu'''

         UpperCAmelCase__				:					List[str] 					=	self.get_dummy_components()

         UpperCAmelCase__				:					Tuple 					=	self.pipeline_class(**lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	pipe.to(lowerCamelCase_				)

         pipe.set_progress_bar_config(disable=lowerCamelCase_				)

         UpperCAmelCase__				:					Tuple 					=	pipe(**self.get_dummy_inputs(lowerCamelCase_				)				)
         UpperCAmelCase__				:					List[Any] 					=	output.images

         UpperCAmelCase__				:					Union[str, Any] 					=	pipe(
             **self.get_dummy_inputs(lowerCamelCase_				)       ,return_dict=lowerCamelCase_       ,)[0]

         UpperCAmelCase__				:					Tuple 					=	image[0, -3:, -3:, -1]
         UpperCAmelCase__				:					int 					=	image_from_tuple[0, -3:, -3:, -1]

         print(f'''image.shape {image.shape}'''				)

         assert image.shape == (1, 64, 64, 3)

         UpperCAmelCase__				:					List[Any] 					=	np.array(
             [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]				)

         assert (
             np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
         ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2
         ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''




      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         super().test_inference_batch_single_identical(expected_max_diff=3e-3				)




@slow
@require_torch_gpu
class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''				)

         UpperCAmelCase__				:					str 					=	load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''				)
         UpperCAmelCase__				:					Union[str, Any] 					=	np.ones((768, 768)       ,dtype=np.floataa				)
         UpperCAmelCase__				:					int 					=	0

         UpperCAmelCase__				:					List[Any] 					=	'''a hat'''

         UpperCAmelCase__				:					str 					=	KandinskyPriorPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-prior'''       ,torch_dtype=torch.floataa				)
         pipe_prior.to(lowerCamelCase_				)

         UpperCAmelCase__				:					List[str] 					=	KandinskyInpaintPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-inpaint'''       ,torch_dtype=torch.floataa				)
         UpperCAmelCase__				:					Tuple 					=	pipeline.to(lowerCamelCase_				)
         pipeline.set_progress_bar_config(disable=lowerCamelCase_				)

         UpperCAmelCase__				:					List[Any] 					=	torch.Generator(device='''cpu'''				).manual_seed(0				)
         UpperCAmelCase__					,   UpperCAmelCase__				:					int 					=	pipe_prior(
             lowerCamelCase_       ,generator=lowerCamelCase_       ,num_inference_steps=5       ,negative_prompt=''''''       ,).to_tuple()

         UpperCAmelCase__				:					Optional[Any] 					=	pipeline(
             lowerCamelCase_       ,image=lowerCamelCase_       ,mask_image=lowerCamelCase_       ,image_embeds=lowerCamelCase_       ,negative_image_embeds=lowerCamelCase_       ,generator=lowerCamelCase_       ,num_inference_steps=100       ,height=768       ,width=768       ,output_type='''np'''       ,)

         UpperCAmelCase__				:					str 					=	output.images[0]

         assert image.shape == (768, 768, 3)

         assert_mean_pixel_difference(lowerCamelCase_       ,lowerCamelCase_				)




