






'''simple docstring'''



import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[Any],					SCREAMING_SNAKE_CASE__:	List[Any],					SCREAMING_SNAKE_CASE__:	Optional[int],					SCREAMING_SNAKE_CASE__:	Tuple    )  ->			List[Any]:

   """simple docstring"""
   if isinstance(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    ):
      __a				     =    np.full((len(SCREAMING_SNAKE_CASE__    ), sequence_length, 2),					SCREAMING_SNAKE_CASE__    )
   else:
      __a				     =    np.full((len(SCREAMING_SNAKE_CASE__    ), sequence_length),					SCREAMING_SNAKE_CASE__    )

   for i, tensor in enumerate(SCREAMING_SNAKE_CASE__    ):
      if padding_side == "right":
         if isinstance(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    ):
            __a				     =    tensor[:sequence_length]
         else:
            __a				     =    tensor[:sequence_length]
      else:
         if isinstance(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    ):
            __a				     =    tensor[:sequence_length]
         else:
            __a				     =    tensor[:sequence_length]

   return out_tensor.tolist()



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[Any]    )  ->			Optional[Any]:

   """simple docstring"""
   __a				     =    ord(SCREAMING_SNAKE_CASE__    )
   if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
      return True
   __a				     =    unicodedata.category(SCREAMING_SNAKE_CASE__    )
   if cat.startswith('P'    ):
      return True
   return False



@dataclass
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					=42
      __a  					=True
      __a  					=None
      __a  					=None
      __a  					=-100
      __a  					="pt"




      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->str:



         '''simple docstring'''
         import torch

         __a				     =    'label' if 'label' in features[0].keys() else 'labels'
         __a				     =    [feature[label_name] for feature in features] if label_name in features[0].keys() else None
         __a				     =    self.tokenizer.pad(
             lowerCamelCase ,   padding=self.padding ,   max_length=self.max_length ,   pad_to_multiple_of=self.pad_to_multiple_of ,   return_tensors='pt' if labels is None else None ,   )

         if labels is None:
            return batch

         __a				     =    torch.tensor(batch['entity_ids']      ).shape[1]
         __a				     =    self.tokenizer.padding_side
         if padding_side == "right":
            __a				     =    [
                list(lowerCamelCase      ) + [self.label_pad_token_id] * (sequence_length - len(lowerCamelCase      )) for label in labels
            ]
         else:
            __a				     =    [
                [self.label_pad_token_id] * (sequence_length - len(lowerCamelCase      )) + list(lowerCamelCase      ) for label in labels
            ]

         __a				     =    [feature['ner_tags'] for feature in features]
         __a				     =    padding_tensor(lowerCamelCase ,   -1 ,   lowerCamelCase ,   lowerCamelCase      )
         __a				     =    [feature['original_entity_spans'] for feature in features]
         __a				     =    padding_tensor(lowerCamelCase ,   (-1, -1) ,   lowerCamelCase ,   lowerCamelCase      )
         __a				     =    {k: torch.tensor(lowerCamelCase ,   dtype=torch.intaa      ) for k, v in batch.items()}

         return batch







'''simple docstring'''



import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase      ,      _lowerCAmelCase      ,      unittest.TestCase     ):
      __a  					=IFPipeline
      __a  					=TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
      __a  					=TEXT_TO_IMAGE_BATCH_PARAMS
      __a  					=PipelineTesterMixin.required_optional_params - {"latents"}




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         return self._get_dummy_components()




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase=0      )							->Tuple:



         '''simple docstring'''
         if str(lowerCamelCase      ).startswith('mps'      ):
            __a				     =    torch.manual_seed(lowerCamelCase      )
         else:
            __a				     =    torch.Generator(device=lowerCamelCase      ).manual_seed(lowerCamelCase      )

         __a				     =    {
             'prompt': 'A painting of a squirrel eating a burger',
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         self._test_save_load_optional_components()




      @unittest.skipIf(torch_device != 'cuda' ,   reason='float16 requires CUDA'      )
      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
         super().test_save_load_floataa(expected_max_diff=1e-1      )




      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2      )




      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         self._test_save_load_local()




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2 ,   )






      @unittest.skipIf(
          torch_device != 'cuda' or not is_xformers_available() ,   reason='XFormers attention is only available with CUDA and `xformers` installed' ,   )
      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )







@slow
@require_torch_gpu
class 							__SCREAMING_SNAKE_CASE       (							unittest.TestCase     ):




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         # if

         __a				     =    IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0' ,   variant='fp16' ,   torch_dtype=torch.floataa      )

         __a				     =    IFSuperResolutionPipeline.from_pretrained(
             'DeepFloyd/IF-II-L-v1.0' ,   variant='fp16' ,   torch_dtype=torch.floataa ,   text_encoder=lowerCamelCase ,   tokenizer=lowerCamelCase      )

         # pre compute text embeddings and remove T5 to save memory

         pipe_a.text_encoder.to('cuda'      )

         __a     ,			__a				     =    pipe_a.encode_prompt('anime turtle' ,   device='cuda'      )

         del pipe_a.tokenizer
         del pipe_a.text_encoder
         gc.collect()

         __a				     =    None
         __a				     =    None

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )

         self._test_if(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # img2img

         __a				     =    IFImgaImgPipeline(**pipe_a.components      )
         __a				     =    IFImgaImgSuperResolutionPipeline(**pipe_a.components      )

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )

         self._test_if_imgaimg(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # inpainting

         __a				     =    IFInpaintingPipeline(**pipe_a.components      )
         __a				     =    IFInpaintingSuperResolutionPipeline(**pipe_a.components      )

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )

         self._test_if_inpainting(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         # pipeline 1

         _start_torch_memory_measurement()

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )
         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   num_inference_steps=2 ,   generator=lowerCamelCase ,   output_type='np' ,   )

         __a				     =    output.images[0]

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

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 13 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )

         # pipeline 2

         _start_torch_memory_measurement()

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )

         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )

         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   generator=lowerCamelCase ,   num_inference_steps=2 ,   output_type='np' ,   )

         __a				     =    output.images[0]

         assert image.shape == (256, 256, 3)

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Any:



         '''simple docstring'''
         # pipeline 1

         _start_torch_memory_measurement()

         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )

         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   num_inference_steps=2 ,   generator=lowerCamelCase ,   output_type='np' ,   )

         __a				     =    output.images[0]

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

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )

         # pipeline 2

         _start_torch_memory_measurement()

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )

         __a				     =    floats_tensor((1, 3, 256, 256) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )

         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   original_image=lowerCamelCase ,   generator=lowerCamelCase ,   num_inference_steps=2 ,   output_type='np' ,   )

         __a				     =    output.images[0]

         assert image.shape == (256, 256, 3)

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Optional[int]:



         '''simple docstring'''
         # pipeline 1

         _start_torch_memory_measurement()

         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(1      )      ).to(lowerCamelCase      )

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )
         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   mask_image=lowerCamelCase ,   num_inference_steps=2 ,   generator=lowerCamelCase ,   output_type='np' ,   )

         __a				     =    output.images[0]

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

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )

         # pipeline 2

         _start_torch_memory_measurement()

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )

         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 256, 256) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 256, 256) ,   rng=random.Random(1      )      ).to(lowerCamelCase      )

         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   mask_image=lowerCamelCase ,   original_image=lowerCamelCase ,   generator=lowerCamelCase ,   num_inference_steps=2 ,   output_type='np' ,   )

         __a				     =    output.images[0]

         assert image.shape == (256, 256, 3)

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )







def    __UpperCAmelCase	(				)  ->			Dict:

   """simple docstring"""
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()